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Abstract This research work proposes unique MLSTM-ZOA to quickly measure SAs for an MLI. Within a certain MI range, the suggested method may calculate a greater count of SAs with various solutions. Here, the main objective lies in minimizing the THD with consideration of three parameters such as MI, number of pulses per quarter cycle, and duty cycle. Furthermore, by achieving a lower fitness value in less iteration, the suggested method has definitely outperformed other methods with respect to convergence behavior, according to the data. Lastly, analysis and performance associated with the experimental validation of SHE in multi-level inverter are also carried out. The proposed MLSTM-ZOA model in terms of THD is 56.25%, 81.58%, 53.33%, and 41.67% better than MPA, HHO, MGMPA, and SF-BOA respectively. Similarly, the proposed MLSTM-ZOA model with respect to HDP is 64.03%, 47.16%, 84.01%, and 27.62% advanced than MPA, HHO, MGMPA, and SF-BOA respectively.

In recent scenarios, induction motors are used in many different fields due to their small size, advanced technology, and capacity to handle large amounts of power. One of the annoying downsides of conventional AC traction drive systems is the engine's speed and the uncertainty of the ripple content. The goal of the proposed work is to use various control techniques to analyze and enhance the performance of two five-phase induction motors that are supplied in parallel via an inverter. The control techniques are scalar control, six-step voltage type source inverter (VSI), space vector modulation (SVM) based VSI inverter, field-oriented control, model reference adaptive system-based field-oriented control (FOC), direct torque control (DTC), direct torque control -SVM, model reference adaptive system based direct torque control-SVM for traction application. The modified space vector modulation-based induction motor (IM) sensorless drive is realized together with a comparison of several control strategies. It makes use of direct torque control approaches with a model reference adaptive system (MRAS). An IM is designed, and performances are analyzed in high-speed and low-speed regions. Two bogies sets were considered in the closed-loop scheme, tested under parameter variations and in low-speed areas. The simulation result examined the most significant control methods for an IM fed by an inverter for electric traction applications. The performance of the torque in terms of reduced torque ripple in the IM drive is analyzed. The proposed direct torque control technique will reduce torque harmonic ripple by 100 % under the steady-state condition. Under a change in a parameter, the torque harmonic ripple is decreased by 99 %. For the IM drive, the hardware implementation of the advanced control approaches is examined.

Abstract The importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.

Abstract Objective This scoping review aims to explore published literature testing Virtual Reality (VR) interventions for improving upper limb motor performance in children and adolescents with Developmental Coordination Disorder (DCD). Our primary focus was on the types of VR systems used and the measurement tools employed within the International Classification of Functioning, Disability and Health Children and Youth Version (ICF-CY) domains in these studies. Methods A comprehensive search of six electronic databases up to 11th January 2024 was conducted using predefined terms. Inclusion and exclusion criteria were applied to determine study eligibility, with two authors independently assessing titles, abstracts, and full-text articles. Results Out of 788 potential studies, 14 met the eligibility criteria. Studies predominantly utilized non-immersive VR (nVR) systems, for example, commercial platforms such as Nintendo Wii. Most interventions targeted general motor coordination or balance, with only four studies specifically focusing on upper limb motor performance. The Movement Assessment Battery for Children-2 was the predominant assessment tool. However, the use of game scores and trial durations raised concerns about the accuracy of assessments. The majority of studies reported no significant improvement in upper limb motor performance following VR interventions, though some noted improvements in specific tasks or overall outcomes. Conclusion The findings suggest that, while nVR interventions are being explored for paediatric motor rehabilitation, their impact on enhancing upper limb motor performance in children with DCD is unclear. The variability in intervention designs, outcome measures, and the predominant focus on general motor skills rather than specific upper limb improvements highlight the need for more targeted research in this area. Impact This review underscores the importance of developing precise and clinically relevant measurement tools in a broader range of VR technologies to optimize the use of VR in therapy for children with DCD. Future research should aim for more rigorous study designs and emerging immersive technologies to maximize therapeutic benefits.

IntroductionThe primary objective of this study is to report the results of an online questionnaire and the in-person discussion sessions of physicians specializing in diabetes care in which their opinions about current diabetes management was obtained.MethodsThe Diabetes Innovation Summit 2023 drew attendance from a diverse group of specialized physicians from multiple countries. A comprehensive literature review was conducted to examine the technologies and medical needs associated with diabetes management. Using the results of the review, a questionnaire was developed by three experts from the steering committee to solicit feedback from specialized physicians. The online survey was made accessible between 10th December 2022 and 10th January 2023. Following the online survey, six structured in-person discussion sessions were conducted with specialized physicians from the Middle East, Central-Eastern Europe, and North Africa regions.ResultsThe study revealed that about 59% of survey requests were answered, with many participants being pediatric endocrinologists from North Africa. Around 60% of diabetes patients followed Multiple Daily Injections (MDI) according to specialized physicians. Among MDI users, 62% employed Blood Glucose Monitors (BGM), 31% used intermittent-scanning Continuous Glucose Monitors (isCGM), and 23% used CGM. In North Africa, nearly 90% of patients used MDI due to financial constraints. While physicians focused on both Time in Range (TIR) and HbA1c for MDI-treated patients, satisfaction with TIR achieved was expressed by 31%, while 74·1% believed Real-Time CGM (rtCGM) was effective. Concerns arose about potentially misleading HbA1c results and the relatively low patient achievement of target TIR despite CGM usage. The Smart MDI System was seen favorably compared to other applications. The system’s affordability was a significant barrier, particularly in the Middle East and Africa.ConclusionThe present study highlights that physicians are generally supportive of utilizing new technology. The questionnaires and the open discussion revealed the expectation that the Smart MDI technology provides better control, primarily by identifying missed boluses, while expressing concerns on the use of the technology by teenagers and children, who might forget the device and be reluctant to use in public, and by the older population, who might be challenged by the technology.

The need to incorporate renewable energy generators (REGs) into the electrical grid has become increasingly crucial due to the push for a more sustainable environment. This study advocates an innovative strategy for optimizing inertia-integrated generation and transmission expansion planning (GTEP) to implement feed-in tariffs (FiT). The application of the GAMS CPLEX solver to the model, which tested on an IEEE 6/IEEE 16 system, reveals that using FiT results in a 12.1 % drop in system cost ($599 million to $526 million) and a 7.91 % rise in total system inertia. Sensitivity analysis highlights the correlation between increased REG integration and FiT payment reduction at 50 % penetration. The model outperforms soft computing optimization techniques, showcasing rapid convergence and computational efficiency. The proposed model's validated superiority in rapid convergence and computational efficiency is demonstrated by comparing its results with those obtained from other soft computing optimization techniques.

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

This paper presents a novel application of the Electric Eel Foraging Optimization (EEFO) algorithm. An optimal control technique based on EEFO is used to improve grid-connected photovoltaic (PV) power plants’ low-voltage ride-through (LVRT) capabilities. The PV arrays are connected to the grid at the point of common coupling (PCC) through a DC-DC boost converter, DC-link capacitor, a grid-side inverter and a three-phase step-up transformer. The DC-DC converter circuit is implemented to obtain the maximum power point tracking operation based on the incremental conductance method. The grid-side inverter uses the cascaded control loop to regulate the DC-link voltage and the terminal voltage at the PCC. Because of its highly rapid convergence, the optimized proportional-integral (PI) controller based on the EEFO algorithm is utilized to regulate the power of electronic circuits. This paper also presents a comparative analysis among the results using the EEFO, genetic, and Particle swarm optimization algorithms. This comparison verifies the effectiveness of the control strategy based on the EEFO algorithm than other techniques. It is studied also by subject the system to symmetrical and unsymmetrical faults as well as unsuccessful circuit breaker reclosing caused by the presence of a permanent fault. MATLAB/Simulink software package is used to provide substantial validations of the proposed control technique. The LVRT capability of such a system can be improved using the proposed methodology.

This paper introduces an adaptive filtering algorithm based on the Laplacian continuous mixed-norm (LCMN) as a control methodology to improve both transient and dynamic wind energy systems (WESs) performances. The foundation of this wind system is a variable-speed wind turbine that powers a permanent magnet synchronous generator. The proposed LCMN algorithm automatically updates all interface circuits’ proportional-integral (PI) controllers gains. It has several advantages compared with other algorithms such as higher algorithm stability, lower fluctuations and steady-state errors. The efficacy of the LCMN-based PI control approach is validated by a fair comparison with other control methods such as the least mean square, robust mixed norm and continuous mixed p-norm when the WES is subjected to severe symmetrical and various unbalanced conditions. Furthermore, the applicability of the suggested technique is examined in typical operational circumstances utilizing actual wind speed measurements obtained from Hokkaido Island. The results indicate preferable performance of dynamic analyses even though the wind speed profile is intermittent.. The dynamic responses exhibit an actuating error of less than 2% across numerous profiles. Therefore, the LCMN-based PI control methodology is considered as an effective solution for online adjustment of controller gains in the course of system nonlinearities and uncertainties. A key advantage of this method is its independence from constructing system transfer functions and the avoidance of optimization methods. This can save substantial time and effort typically required for optimization processes.

The adoption of wireless charging for Electric Vehicles (EVs) is on the rise, promising enhanced user convenience. Concurrently, there is a pressing need for increased integration of renewable energy into the transportation sector to mitigate greenhouse gas emissions. However, wireless charging systems face challenges such as power transfer fluctuations under load and variations in coupling coefficients. This research introduces a novel solution: a Photovoltaic (PV)-integrated hybrid-compensated wireless charging system tailored for EV applications. The study addresses critical hurdles in achieving optimal efficiency and power delivery while accommodating significant misalignments inherent in Wireless Power Transfer (WPT) systems, particularly beneficial for EV charging. Compared to conventional compensation WPT systems, the proposed hybrid compensation approach significantly enhances transfer efficiency and reduces system current stress. The paper proposes the utilization of S-LCC/SP-compensated constant current (CC) and constant voltage (CV) system for PV-integrated static WPT systems. Experimental validation of the proposed system is conducted on a 3.3-kW laboratory-scale prototype. The findings demonstrate consistent power transfer across a 100–200 mm air gap, maintaining an efficiency of 91.3%. These results confirm the effectiveness of the proposed system in real-world applications.

Proton Exchange Membrane Fuel Cells (PEMFCs) play a crucial role in the advancement of clean hydrogen vehicles. Their ability to convert hydrogen into electricity makes them promising candidates to replace conventional engines. However, optimizing their performance and efficiency necessitates accurate modeling techniques capable of simulating their behavior. In this context, this paper proposes an advanced approach for precise parameter estimation in PEMFC models. Employing an Enhanced Walrus Optimization (EWO) algorithm integrated with Lévy flight exploration, the approach tackles the inherent nonlinearity of PEMFC systems. The technique aims to minimize the squared error between measured and simulated terminal voltage, thereby ensuring superior accuracy and robustness compared to established algorithms. The effectiveness of the proposed model is validated through comparisons between theoretical simulations and experimental measurements. The findings demonstrate the efficacy of the EWO algorithm, consistently outperforming previously published algorithms and achieving notably lower errors. Moreover, the incorporation of Lévy flights enhances the algorithm’s capabilities, leading to expedited convergence and more accurate parameter estimations. Beyond facilitating precise parameter estimation, this enhanced modeling strategy opens avenues for refining design and optimization strategies in fuel cell research and development. The major contributions of this paper include the enhancement of the WO algorithm, evaluation of theoretical model accuracy, and robustness assessment of the EWO in optimizing the PEMFC model. By furnishing accurate models validated through experimental evidence, this enhanced modeling strategy paves the way for refining design and optimization strategies in fuel cell research and development.

Energy efficiency considerations in terms of reduced power dissipation are a significant issue in the design of digital circuits for very large-scale integration (VLSI) systems. Quantum-dot cellular automata (QCA) is an emerging ultralow power dissipation approach, distinct from traditional, complementary metal-oxide semiconductor (CMOS) technology, for building digital computing circuits. Developing fully reversible QCA circuits has the potential to significantly reduce energy dissipation. Multiplexers are fundamental elements in the construction of useful digital circuits. In this paper, a novel, multilayer, fully reversible QCA 8:1 multiplexer circuit with ultralow energy dissipation is introduced. The power dissipation of the proposed multiplexer is simulated using the QCADesigner-E version 2.2 tool, describing the microscopic physical mechanisms underlying the QCA operation. The results show that the proposed reversible QCA 8:1 multiplexer consumes 89% less energy than the most energy-efficient 8:1 multiplexer circuit previously presented in the literature.

DC power systems have emerged as a cost-effective solution for electric power generation and transmission, challenging the dominance of AC distribution systems. However, a comprehensive efficiency comparison between DC and AC microgrids remains understudied. This study seeks to explore and conduct a thorough survey on development and designing of DC microgrids to address this gap. Firstly, a comprehensive literature review comparing the efficiencies of AC and DC microgrids has been presented. The analysis highlights the superior efficiency of DC distribution systems over AC systems, supported by detailed advantages. Secondly, hardware implementation has been performed to directly compare the efficiency of DC versus AC systems. Research validity and application are further improved by the hardware prototype’s scalability, which in simulation allows for a thorough assessment of system stability over a range of scenarios from four to six terminals. Test results from the built hardware prototype demonstrate an astounding 15% increase in efficiency using the DC system compared to the AC system, demonstrating its potential for improved performance in real-world scenarios. In simulation results, the designed DC microgrid demonstrates stable voltages of 500V under steady state operation and rapid recovery within 80 ms under both symmetrical and asymmetrical faults has been observed. The research being investigated utilizes hardware implementation and simulation to provide useful insights into the efficiency and stability of DC microgrids in comparison to AC systems. These results are important for developing robust power distribution networks in modern energy environments, promoting sustainability and dependability in infrastructure growth.

Lumpy skin disease is a viral disease that affects cattle and is caused by the lumpy skin disease virus. This work is devoted to presenting and analyzing the nonlinear dynamics of a novel discrete fractional model for lumpy skin disease. The equilibrium points of the proposed discrete fractional model are found. The stability analysis of equilibrium points is carried out. The influences of key parameters in the model are investigated, and then the stability regions of a disease-free steady state in the space of parameters are obtained. A proposed efficient control scheme is implemented to stabilize the disease-free equilibrium point when it is unstable. The influences of fractional-order parameters on the applied control scheme are explored. Finally, numerical simulations are performed to verify the theoretical findings obtained and confirm the effectiveness of the employed control scheme.

A negative attitude toward sleep has greatly affected sleep habits. In addition to contributing to physical and metabolic disorders, poor sleep quality may cause emotional disturbances. This study aimed to measure sleep behavior and factors contributing to poor sleep quality in the Madinah region, Saudi Arabia. We also assessed whether the use of sleeping aids improved peoples sleep. Three hundred and ninety-nine adults in the Madinah region of Saudi Arabia participated in this cross-sectional study. Three data domains were collected using an online questionnaire between 30 January and 26 April 2022. In the first domain, the characteristics of participants were discussed. In the second domain, questions about sleep behavior were asked. In the third domain, we examined the types, frequency, and impact of sleep aid use. Out of the 399 participants, 154 (38.59%) reported sleep problems. A total of 64.94% of the 154 participants blamed stress as the leading cause of their sleep disorders, and 74.68% of those with sleep problems reported reduced productivity. Among those who reported having sleep problems, 46.10% used sleep aids, with Panadol night (antihistamine) being the most used, 49.30%, followed by Melatonin at 39.44%. Sleep quality improved by 67.6% among those who used sleep aids. A total of 71.8% of the participants think it is not safe to use sleep aids in the long term. Our findings suggest that sleep problems are a prevalent concern in Madinah, Saudi Arabia, and even though the use of sleep aids improved sleep quality, it should be considered an emerging and important public health objective in Saudi Arabia. Further studies are needed to evaluate sleep quality and the level of sleep aid usage among other Saudi Arabian regions.

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Lipopolysaccharides (LPS), the lipid component of gram-negative bacterial cell wall, is recognized as the key factor in acute lung inflammation and is found to exhibit severe immunologic reactions. Phosphodiesterase-4 (PDE-4) inhibitor: “apremilast (AP)” is an immune suppressant and anti-inflammatory drug which introduced to treat psoriatic arthritis. The contemporary experiment designed to study the protective influences of AP against LPS induced lung injury in rodents. Twenty-four (24) male experimental Wistar rats selected, acclimatized, and administered with normal saline, LPS, or AP + LPS respectively from 1 to 4 groups. The lung tissues were evaluated for biochemical parameters (MPO), Enzyme Linked Immunosorbent Assay (ELISA), flowcytometry assay, gene expressions, proteins expression and histopathological examination. AP ameliorates the lung injuries by attenuating immunomodulation and inflammation. LPS exposure upregulated IL-6, TNF-α, and MPO while downregulating IL-4 which were restored in AP pretreated rats. The changes in immunomodulation markers by LPS were reduced by AP treatment. Furthermore, results from the qPCR analysis represented an upregulation in IL-1β, MPO, TNF-α, and p38 whereas downregulated in IL-10 and p53 gene expressions in disease control animals while AP pretreated rats exhibited significant reversal in these expressions. Western blot analysis suggested an upregulation of MCP-1, and NOS-2, whereas HO-1, and Nrf-2 expression were suppressed in LPS exposed animals, while pretreatment with AP showed down regulation in the expression MCP-1, NOS-2, and upregulation of HO-1, and Nrf-2 expression of the mentioned intracellular proteins. Histological studies further affirmed the toxic influences of LPS on the pulmonary tissues. It is concluded that, LPS exposure causes pulmonary toxicities via up regulation of oxidative stress, inflammatory cytokines and stimulation of IL-1β, MPO, TNF-α, p38, MCP-1, and NOS-2 while downregulation of IL-4, IL-10, p53, HO-1, and Nrf-2 at different expression level. Pretreatment with AP controlled the toxic influences of LPS by modulating these signaling pathways.

Reinforcement learning has been employed in recent research articles to optimize the energy storage system scheduling in microgrids, aiming to reduce overall system costs. However, applying reinforcement learning in real-time scenarios introduces uncertainties and delays due to the extensive training required to develop the optimal policy for the storage system. This work addresses these challenges and explores potential solutions for real-time dispatch control actions of the battery in a grid-tied microgrid. The study considers different approaches for training the agent, distinguishing between online and offline scheduling of the energy storage system. The limitations of these approaches and their implications on real-time performance are also analyzed. By developing a comprehensive microgrid model and comparing two training approaches, this research contributes to novel insights for efficient real-time scheduling of energy storage systems in grid-tied microgrids. The proposed approach presents a promising path towards addressing uncertainties and achieving optimal operation in grid-tied microgrids. In terms of average cost per year, the difference between the two approaches is 4% if foresight of the real data is perfect, otherwise the real-time approach is more cost-effective.

Background: Various reports described new-onset diabetes during or after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with no previous history of diabetes or glucocorticoid use. Further, SARS-CoV-2 could increase the risk of diabetes, including diabetic ketoacidosis (DKA). However, data on the relationship between new-onset diabetes and COVID-19 are still limited in our region. Thus, we aimed in this study to evaluate the association between new-onset diabetes and DKA in patients with COVID-19. Methods: A retrospective, cross-sectional study was conducted at a diabetic center in Jazan province, Saudi Arabia, between 2020 and 2021. Demographic data, COVID-19 status, and DKA incidence were collected and verified manually from diabetic patients’ medical records. Data were analyzed using a t-test and chi-square test. Results: We included 54 diabetic patients diagnosed during the COVID-19 pandemic, with a median age of 17 years. The majority of patients were females (57.4%). About 38.8% were diagnosed with COVID-19, and 16.6% reported having DKA. About 33.3% of the patients who experienced DKA reported being COVID-19-positive. However, only 6% of patients who denied contracting SARS-CoV-2 developed DKA (p-value = 0.020). Conclusions: Patients with newly diagnosed diabetes due to COVID-19 seem at a higher risk of developing DKA. Further epidemiological and molecular studies are required for a better understanding of the correlation between DKA in patients with diabetes and COVID-19.

Background: Anthropometry facilitates the evaluation of risks associated with reduced macular pigment optical density (MPOD). Objectives: To investigate the predictors and anthropometric indices associated with MPOD in healthy adult in Arab population. Design: This is a cross-sectional study. Methods: The MPOD was measured at 0.5° from fovea using a heterochromatic flicker photometer. Healthy participants aged between 20 and 40 years were recruited. The study evaluated the following data of the participants: height, weight, body mass index, body fat percentage, basal metabolic rate, visceral fat level, muscle mass, bone mineral content, and percentage of protein and body water. The correlation between MPOD with anthropometrics and demographic data was evaluated using Spearman’s correlation test. The differences among genders were investigated using the Mann–Whitney U test. The smoking effect on MPOD was analyzed using the Friedman test. Results: In all, 143 participants were recruited. The median ± interquartile range was calculated for age (23 ± 4 years), visual acuity (0.00 ± 0.00 logMAR), and MPOD (0.41 ± 0.18). The average MPOD was higher in males than in females but it was not statistically significant ( p > 0.05); on the other hand, they were statistically significantly different in most of the anthropometric data. A significant relationship was found between MPOD and percentage of body fat, protein, and body water ( r = 0.30, p

A systems approach to obesity prevention is increasingly urged. However, confusion exists on what a systems approach entails in practice, and the empirical evidence on this new approach is unclear. This scoping review aimed to identify and synthesise studies/programmes that have comprehensively applied a systems approach to obesity prevention in intervention development, delivery/implementation, and evaluation. By searching international databases and grey literature, only three studies (10 publications) met inclusion criteria, which might be explained partially by suboptimal reporting. No conclusion on the effectiveness of this approach can be drawn yet due to the limited evidence base. We identified common features shared by the included studies, such as measuring ongoing changes, in addition to endpoint outcomes, and supporting capacity building. Some facilitators and barriers to applying a comprehensive systems approach in practice were identified. More well-designed and reported studies are needed, especially from low- and middle-income countries.

This research paper introduces the Generalized Continuous Mixed P-Norm Sub-Band Adaptive Filtering (GCMPNSAF) algorithm, designed for efficient online control of Superconducting Magnetic Energy Storage Devices (SMESDs) in Wind Energy Systems (WESs). The primary objective of this algorithm is for minimizing power ripples in WESs. The Wind Energy System (WES) under consideration is tied to the IEEE 39 bus system, with the Superconducting Magnetic Energy Storage Device (SMESD) integrated at the point of common coupling. The GCMPNSAF algorithm is applied to update or adapt proportional-integral (PI) controller gains of SMESD interface circuits. The proposed algorithm is an enhanced version of the CMPN by adding the sub-band filtering algorithm effect. It depends mainly on the actuating error signal, and it has a variable step size of the CMPN. The detailed modeling of the whole system is presented, including measured wind speed data, detailed switching techniques, a drive train model of the turbine, and real SMESD. The efficacy of the proposed SMESD has been validated through a comparative analysis with the Least Mean Square-SMESD approach, under conditions of varying and unpredictable wind speeds. The simulation results produced by the PSCAD software are used to evaluate the study's validity. The utilization of controlled SMESDs has the potential to significantly enhance the power quality of WESs.

Photovoltaic (PV) systems are becoming essential to our energy landscape as renewable energy sources become more widely integrated into power networks. Preserving grid stability, especially during voltage sags, is one of the significant difficulties confronting the implementation of these technologies. This attribute is referred to as low-voltage ride-through (LVRT). To overcome this issue, adopting a Proportional-Integral (PI) controller, a control system standard, is proving to be an efficient solution. This paper provides a unique algorithm-based approach of the Marine Predator Algorithm (MPA) for optimized tuning of the used PI controller, mainly focusing on inverter control, to improve the LVRT of the grid, leading to improvements in the overshoot, undershoot, settling time, and steady-state response of the system. The fitness function is optimized using the MPA to determine the settings of the PI controller. This process helps to optimally design the controllers optimally, thus improving the inverter control and performance and enhancing the system’s LVRT capability. The methodology is tested in case of a 3L-G fault. To test its validity, the proposed approach is compared with rival standard optimization-based PI controllers, namely Grey Wolf Optimization and Particle Swarm Optimization. The comparison shows that the used algorithm provides better results with a higher convergence rate with overshoot ranging from 14% to 40% less in the case of DC-Link Voltage and active power and also settling times in the case of MPA being less than PSO and GWO by 0.76 to 0.95 s.

Modular multilevel converter (MMC) is adopted mainly for high voltage applications with many power blocks per arm. Before commissioning a large-scale MMC application, it is vital to simulate and study internal and system-level dynamics. However, it is challenging to simulate an MMC with many SMs in EMT simulation tools due to simulation time and computation burden. Therefore, several simplified modeling techniques are proposed to reduce the challenges. Even though the existing models reasonably reduce the computation complexity and simulation time, there are still challenges as the internal dynamics of an MMC cannot be fully captured. On the other hand, the detailed equivalent models capture the internal dynamics, but the simulation complexity and the time increase. Therefore, it is still a need for better, faster, and more accurate simulation models to study the system-level and internal dynamics of an MMC. Therefore, this paper proposes a hybrid simulation model for a large-scale MMC application using a scale-up control structure method. The proposed method is verified in the MATLAB/Simulink simulation tool. Besides, the proposed model is tested and verified at the Real-Time Digital Simulator (RTDS) in a Hardware-in-Loop (HIL) environment.

Sensor uncertainty significantly affects the performance of autonomous vehicles (AVs). Sensor uncertainty is predominantly linked to sensor specifications, and because sensor behaviors change dynamically, the machine learning approach is not suitable for learning them. This paper presents a novel learning approach for predicting sensor performance in challenging environments. The design of our approach incorporates both epistemic uncertainties, which are related to the lack of knowledge, and aleatoric uncertainties, which are related to the stochastic nature of the data acquisition process. The proposed approach combines a state-based model with a predictive model, where the former estimates the uncertainty in the current environment and the latter finds the correlations between the source of the uncertainty and its environmental characteristics. The proposed approach has been evaluated on real data to predict the uncertainties associated with global navigation satellite systems (GNSSs), showing that our approach can predict sensor uncertainty with high confidence.

Electric power industry is continually adopting new techniques to improve the reliability and efficiency of the energy system and to cope with the increasing energy demand and the associated technical challenges. In recent years, the maturation of Artificial Intelligence (AI) led researchers to solve various problems in the power system by using AI techniques. Voltage Source Converter is the result of advancements in the field of power electronics and semiconductors technology, which holds a promising future for the realization of smart grid, renewable energy integration, and HVDC transmission system. Usually hit and trial method or the design engineer’s experience is used for the manual tuning of the PI controllers, which cannot yield superior performance. The process becomes more complicated when multiple grids are involved, such as in VSC-based MTDC grids. This research article use a deep learning optimization technique for the tuning of the VSC controllers, which resulted in quick settling time, better slew rate, less undershoot and low overshoot. The deep learning neural network is trained through the Particle Swarm Optimization (PSO) algorithm to produce the best possible tuned or optimally tuned parameters for the controllers. The optimal tuning of the controller will result in an overall better performance of the converter and the grid. A four-layered deep learning neural network and a three-terminal MTDC grid were designed and simulated in MATLAB/SIMULINK environment.

Quantum-dot cellular automata (QCA) are a promising nanoscale computing technology that exploits the quantum mechanical tunneling of electrons between quantum dots in a cell and electrostatic interaction between dots in neighboring cells. QCA can achieve higher speed, lower power, and smaller areas than conventional, complementary metal-oxide semiconductor (CMOS) technology. Developing QCA circuits in a logically and physically reversible manner can provide exceptional reductions in energy dissipation. The main challenge is to maintain reversibility down to the physical level. A crucial component of a computer’s central processing unit (CPU) is the arithmetic logic unit (ALU), which executes multiple logical and arithmetic functions on the data processed by the CPU. Current QCA ALU designs are either irreversible or logically reversible; however, they lack physical reversibility, a crucial requirement to increase energy efficiency. This paper shows a new multilayer design for a QCA ALU that can carry out 16 different operations and is both logically and physically reversible. The design is based on reversible majority gates, which are the key building blocks. We use QCADesigner-E software to simulate and evaluate energy dissipation. The proposed logically and physically reversible QCA ALU offers an improvement of 88.8% in energy efficiency. Compared to the next most efficient 16-operation QCA ALU, this ALU uses 51% fewer QCA cells and 47% less area.

This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.

Background: Visuospatial abilities are an important component of technical skill acquisition. Targeted visuospatial ability training may have positive implications for training programs. The development of such interventions requires an adequate understanding of the visuospatial ability processes necessary for surgical and nonsurgical tasks. This scoping review aims to identify the components of visuospatial ability that have been reported in surgical and nonsurgical trainees and determine if there is consensus regarding the language and psychometric measures used, clarifying the elements that may be required to develop interventions that enhance visuospatial ability. Methods: A scoping review was designed to identify relevant records from EMBASE and Medline until January 13, 2020. Data were extracted on visuospatial ability terminology, dimensions, instruments, and interventions with results stratified by specialty (surgical, nonsurgical, or mixed). Conference abstracts, opinion pieces, and review studies were excluded. Results: Out of 882 total records, 26 were identified that met criteria for inclusion. Surgical specialities were represented in >90% of results. A total of 16 unique terms were used to describe visuospatial ability and were measured using 34 instruments, of which eight were used more than once. Eighteen different dimensions were identified. A single study explored the effects of a targeted visuospatial ability intervention. Conclusion: A wide range of visuospatial ability terms, instruments, and dimensions were identified, suggesting an incomplete understanding of the components most relevant to surgical and nonsurgical tasks. This confusion may be hindering the development of visuospatial ability targeted interventions during residency training. A rigorous methodological model is proposed to help unify the field and guide future research.

BackgroundChildhood obesity and pediatric metabolic syndrome (MetS) have steadily increased during the last decade in Saudi Arabia. Intervention programs to prevent cardiometabolic disorders in Arab youth are needed.ObjectiveIn this multi-school intervention study which was disrupted by COVID-19-imposed lockdowns (September 2019–April 2021), a 12-month lifestyle education program focused on improving the cardiometabolic status of Arab adolescents was investigated.MethodsA total of 2,677 Saudi students aged 12–18 years were recruited from 60 different secondary and preparatory year schools in Riyadh city, Saudi Arabia. The intervention was initially in-person counseling sessions and the subsequent sessions conducted virtually post-pandemic. Baseline anthropometrics and fasting blood samples for glucose, HbA1c, and lipid assessments were collected at baseline and after 12 months (704 participants).ResultsOnly 704 out of 2,677 (73.7% dropout) completed the intervention. At baseline, 19.6% of the participants were overweight and 18.1% were obese. A modest but significant decrease in the prevalence of central obesity [11.2 vs. 6.7% (−4.5% change, p = 0.002)], hypertension [22.3 vs. 11.4% (−10.9% change, p < 0.001)], and low-HDL cholesterol [61.6 vs. 23.3% (−38.3% change, p < 0.001)] was noted. Consequently, the prevalence of hypertriglyceridemia increased from 22.7 to 56.3% (+ 33.6%, p < 0.001) overtime. Also, the proportion of subjects who were able to change their status from MetS to non-MetS was significantly more in overweight/obese at baseline than normal weight (16.9 vs. 3.6%, adjusted OR = 3.42, p < 0.001).ConclusionInterrupted lifestyle education programs secondary to COVID-19-imposed lockdowns still provided modest effects in improving cardiometabolic indices of Arab adolescents. Given the high digital literacy of Arab youth, improving the delivery of virtual lifestyle education programs may prove beneficial.

Abstract Background Inadequate neuromuscular recovery might impair pulmonary function among adult patients who undergo general anaesthesia and might thus contribute to critical respiratory events in the post-anaesthesia care unit (PACU). The pilot study aims to understand the baseline incidence of residual neuromuscular blockade (RNMB) and postoperative critical respiratory events (CREs), which are described in a modified Murphy’s criteria in the PACU. Method This is a prospective cohort study from January to March 2017 from a tertiary hospital in Saudi Arabia with thirty adult patients over 18 years old scheduled for elective surgery under general anaesthesia with neuromuscular blocking drugs (NMBDs) who were enrolled in the study. The Mann-Whitney U tests, chi-square tests and independent-samples T tests were used. The train-of-four (TOF) ratios were measured upon arrival in the PACU by using acceleromyography with TOF-Scan. Subjects’ demographics, perioperative data and the occurrence of postoperative CREs in the PACU were recorded. Results Twenty-six (86.7%) patients out of thirty in the study have received rocuronium as NMBDs whilst neostigmine as a reversal drug with only 23 (76.7%). The incidence of RNMB (TOF ratio

This paper discusses the importance of microgrids in power systems and introduces a new method for enhancing their performance by improving the transient voltage response in the face of disturbances. The method involves using a hybrid optimization approach that combines driving training-based and particle swarm optimization techniques (HDTPS). This hybrid approach is used to fine-tune the system’s cascaded control scheme parameters, based on proportional–integral–accelerator (PIA) and proportional–integral controllers. The optimization problem is formulated using a central composite response surface methodology (CCRSM) to create an objective function. To validate the suggested control methodology, PSCAD/EMTDC software is used to carry out the simulations. The simulations explore various scenarios wherein the microgrid is transformed into an islanded system and is subjected to various types of faults and load changes. A comparison was made between the two proposed optimized controllers. The simulation results demonstrate the effectiveness of using a PIA-optimized controller; it improved the microgrid performance and greatly enhanced the voltage profile. In addition, the two controllers’ gains were optimized using only PSO to ensure that the outcomes of the HDTPS model demonstrated the same results. Finally, a comparison was made between the two optimization techniques (HDTPS and PSO); the results show a better impact when using the HDTPS model for controller optimization.

This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

In this article, the problem of voltage and frequency stability in a hybrid multi-area power system including renewable energy sources (RES) and electric vehicles has been investigated. Fractional order systems have been used to design innovative controllers for both load frequency control (LFC) and automatic voltage regulator (AVR) based on the combination of fractional order proportional-integral and proportional-integral-derivative plus double derivative (FOPI–PIDD2). Here, the dandelion optimizer (DO) algorithm is used to optimize the proposed FOPI–PIDD2 controller to stabilize the voltage and frequency of the system. Finally, the results of simulations performed on MATLAB/Simulink show fast, stable, and robust performance based on sensitivity analysis, as well as the superiority of the proposed optimal control strategy in damping frequency fluctuations and active power, exchanged between areas when faced with step changes in load, the changes in the generation rate of units, and the uncertainties caused by the wide changes of dynamic values.

Background: The Glidescope is a novel, portable, reusable video laryngoscope that has provided superior laryngeal visualization to facilitate tracheal intubation, especially in the management of difficult airways. In this study, we aimed to compare the efficacy of the Glidescope (video-laryngoscope) against the Macintosh direct laryngoscope. Methods: Fifty patients were randomly selected via simple randomization using computer-generated random numbers, and sorted into two groups of 25 patients: the Glidescope group and the Macintosh group. We included pediatric patients undergoing cardiac surgery for the repair of congenital heart disease. Those with suspected difficult intubation, preterm babies with low body weight, and patients at risk of aspiration were all excluded. Results: Patients' baseline demographic and clinical characteristics were found to be comparable in the two groups. The mean intubation time was 24.1 ± 13.6 s in the Glidescope group, as compared to 18.1 ± 5.9 s in the Macintosh group. Blade insertion was easy in 92% and 96% of the patients in the Glidescope and Macintosh groups, respectively. Tracheal intubation was considered easy in 84% of the Glidescope group, compared to 92% of the Macintosh group. There was a statistically significant correlation between the ease of tracheal intubation and the used intubation method (rho = –0.35; P = 0.014). Conclusion: Our findings indicate that the Glidescope can be used as an efficient modality for obtaining successful intubations with no complications. Ease of tracheal intubation was the only outcome that was found to be affected by the used modality. Further investigations with proper sample sizes are needed.

Introduction. Non-convulsive status epilepticus (NCSE) is an epileptic condition lasting more than 30 minutes in which continuous or recurrent seizure activity on the electroencephalogram (EEG) is associated with diverse clinical symptoms including alteration of mental state, abnormal behavior, perceptual disturbances or altered consciousness. Methods. This was a case study conducted at King Khalid Hospital. Results. Our patient presented with catatonia and segmental catalepsy associated with non-convulsive status epilepticus. Conclusions. The simultaneous occurrence of non-convulsive status epilepticus and catatonia/ catalepsy in our patient suggests a common pathophysiological mechanism. Furthermore, our data suggest that dysfunction of the posterior areas of the brain may be responsible for catalepsy

Diabetes mellitus (DM) is a major health issue in Saudi Arabia. Prevention of DM and its complications requires an understanding of the disease and modifiable behaviors (e.g., physical activity—PA). The purpose of this study was to examine the trends in knowledge of the general population regarding DM to better understand the shortcomings in the current awareness programs. This article presents a cross-sectional series study where a survey was distributed to a total of 3493 participants over four years, from 2017 till 2020, to assess general knowledge about DM, including information about PA. The mean percentage of correct responses of DM general knowledge was 63.8 ± 19.0 in 2017, which decreased to 61.3 ± 18.7 in 2020 with a significant beta coefficient of −0.8 ± 0.2 (p < 0.001). Participants’ awareness about PA remained constantly high for four years: the mean percentage of correct responses was 82.1 ± 23.6 in 2017 and 82.0 ± 23.1 in 2020, and the beta coefficient was −0.5 ± 0.3 (p = 0.147). Furthermore, stratification by demographics showed that the majority of the subgroups (age, sex, educational status, marital status, having relative with DM, nationality) reported a significant declining trend in general DM knowledge. In addition, some of the subgroups also showed a declining trend in PA awareness. Future prevention efforts should assess the community’s DM knowledge regularly to tailor awareness efforts to the population segments that need heightened educational interventions.

BACKGROUND: Treatment of glioblastoma (GB), the most common malignant primary brain tumor in adults, can include alkylating chemo-therapeutic agents. Two molecular biomarkers of treatment response are MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation and IDH (isocitrate dehydrogenase) mutations, which prevent repair of tumor cell DNA damage caused by alkylating chemotherapy. The status of MGMT promoter methylation and IDH mutation are associated with longer survival and a better response to chemotherapy. OBJECTIVE: Assess the prognostic value of MGMT methylation status and IDH mutation in adult Saudi glioblastoma patients. DESIGN: Retrospective, comparative survival analysis. SETTING: Tertiary care center. PATIENTS AND METHODS: The status of the MGMT promoter methylation and IDH mutation was assessed in adult patients diagnosed with GB between 2006 and 2019. A PCR-based assay was used to analyze for methylation of the MGMT promoter. A qualitative assay combining PCR clamping and amplification refractory mutation system technology was used to search for any of the 12 most common mutations in IDH1 and IDH2. Differences in survival were compared between those with and without MGMT promoter methylation and IDH mutation and between other subgroups. MAIN OUTCOME MEASURES: Survival of GB patients relative to MGMT promoter methylation and IDH mutation status. SAMPLE SIZE: 146 patients (80 males and 66 females). RESULTS: Of 43 (29.5%) cases tested for MGMT promoter methylation, 14 (32.5%) were positive. Of 65 (44.5%) cases screened for IDH mutation, 6 cases (9.2%) tested positive. The 36-month survival rate was 47% for the MGMT methylated cohort compared to 27% for their unmethylated counterparts. The 18-month survival rate for the IDH-mutant was 75% compared to 48% for their IDH-wildtype counterparts. CONCLUSION: The findings confirm the positive impact of both MGMT promoter methylation and IDH mutation on the overall survival of Saudi GB patients. LIMITATIONS: Single institute study with relatively few tested cases. CONFLICT OF INTEREST: None.

Centralized control algorithm limits the hardware flexibility of a modular multilevel converter (MMC). Therefore, distributed control structure has recently started to be seen in the industry application. Even though distributed controller reduces a single point of failure risk compared to the centralized controller, the failure risk of the entire control systems increases due to the number of local controllers. However, the distributed controller can be programmed in such a way as to replace the faulty local controller and sustain the MMC operation. In this paper, the distributed modular fault-tolerant controller is implemented in a laboratory-scale MMC prototype. The controller is built to control four SMs per phase for the proof-of-concept. Therefore, the MMC prototype is also built by two SMs per arm. The controller capability is validated with experimental and the Opal-RT result-time simulator results in a control-hardware-in-loop (CHIL) environment.

Kidney cancer, with 4% of all malignancies, is one of the most common malignancies occurring among in adults. In Saudi Arabia, kidney cancer comprises 2.3% of all cancers, and its incidence has increased by 33%. Partial nephrectomy (PN) is considered as the gold standard for T1 renal masses. In this retrospective study, we did a chart review for all patients who underwent PNs between April 2013 and February 2019. Data comprised pre-sentation, tumor size, type of procedure (open vs. laparoscopic vs. robotic), and intra- and post-operative complications. Chi-square, ANOVA, and cross-tabulation were done using SPSS software. P > 0.05 was considered significant. Approval was obtained from the institutional review board of King Abdullah International Medical Research Center. In all, 69 patients were identified: 26 (37.7%) males and 43 (62.3%) females, with mean age = 54.53 ± 13.21 years; mean body mass index = 32.36 ± 7.03, and mean tumor size = 3.7 ± 1.72 cm. In terms of presentation, most patients (50, 72.4%) presented incidentally as opposed to symptomatic presentation. Of these patients, 18 (26.1%) underwent open partial nephrectomy (OPN), 29 (42%) laparoscopic partial nephrec-tomy (LPN), and 22 (31.9%) robotic partial nephrectomy (RPN). On comparing minimally invasive surgery (MIS) PN with OPN, we found that OPN had more blood loss and a longer hospital stay but a shorter operating room (OR) time. Results of PN irrespective of the procedure type, whether it was OPN, LPN, or RPN, were similar if performed by experienced surgeons. However, open procedures involved a higher blood loss, more operative time, and longer hospital stay when compared with minimally invasive techniques.

Modular multilevel converters (MMCs) are primarily adopted for high-voltage applications, and are highly desired to be operated even under fault conditions. Researchers focused on improving current controllers to reduce the adverse effects of faults. Vector control in the DQ reference domain is generally adopted to control the MMC applications. Under unstable grid conditions, it is challenging to control double-line frequency oscillations in the DQ reference frame. Therefore, active power fluctuations are observed in the active power due to the uncontrolled AC component’s double line frequency component. This paper proposes removing the active power’s double-line frequency under unbalanced grid conditions during DQ transformation. Feedforward and feedback control methods are proposed to eliminate ripple in active power under fault conditions. An extraction method for AC components is also proposed for the power ripple control to eliminate the phase error occurring with the conventional high-pass filters. The system’s stability with the proposed controller is tested and compared with a traditional MMC controller using the Nyquist stability criterion. A real-time digital simulator (RTDS) and Xilinx Virtex 7-based FPGA were used to verify the proposed control methods under single-line-to-ground (SLG) faults.

This 12-month school-based intervention study investigated the effects of hybrid educational lifestyle modifications on glycemic control among Saudi youth with different glycemic statuses. A total of 2600 Arab adolescents aged 12–18 years were recruited from 60 randomly selected schools. Anthropometrics, blood glucose, and HbA1c were measured pre- and post-intervention. Participants were grouped according to baseline HbA1c into normal, prediabetes, and diabetes groups. All participants received lifestyle education at base line and at every 3-month interval to improve diet and exercise behavior. Diabetic and prediabetic participants received a tailored lifestyle intervention. Post-intervention, 643 participants were analyzed as follows: 20 participants from the diabetes group, 39 from prediabetes, and 584 from the normal group. A modest but significant improvement in the glycemic status of diabetic and prediabetic participants was observed, but not in the normal group. In the diabetes group, 11 (55%) participants achieved normal HbA1c levels, 5 had prediabetes levels, and only 4 remained within diabetes HbA1c levels. In the prediabetes group, 34 (87.2%) participants achieved normal HbA1c levels, while 2 (5.1%) participants remained prediabetic and 3 (7.7%) had diabetes HbA1c levels (p < 0.001). This hybrid lifestyle intervention program modestly reduces the risk of T2DM among youth with elevated HbA1c levels. The challenge of sustaining interest in adopting lifestyle changes for a longer duration should be addressed in further studies in this population.

Abstract Background and purpose Radiation pneumonitis (RP) is the most common subacute side effect after concurrent chemoradiotherapy (CRT) for locally advanced non-small cell lung cancer. Several clinical and dose-volume (DV) parameters are associated with a distinct risk of symptomatic RP. The aim of this study was to assess the spatial dose distribution of the RP volume from first occurence to maximum volume expansion of RP. Material and methods Between 2007 and 2015, 732 patients with lung cancer were treated in an institution. Thirty-three patients met the following inclusion criteria: an RP grade II after CRT and a radiation dose ≥60 Gy and no prior medical history of cardiopulmonary comorbidities. The images of the first chest computed tomography (CT) confirming the diagnosis of RP and the CT images showing the maximum expansion of RP were merged with the treatment plan. The RP volume was delineated within the treatment plan, and a DV analysis was performed to evaluate the lung dose volume areas in which the RP manifested over time and whether dose volume changes within the RP volume occurred. Results A change from clinical diagnosis to maximum expansion of RP was observed as the RP at clinical appearance mainly manifested in the lower dose areas of the lung, whereas the RP volume at maximum expansion manifested in the higher dose areas, resulting in a significant shift of the assessed relative mean dose volume proportions within the RP volume. The mean relative dose volume proportion 0- ≤ 20 Gy decreased from 30.2% (range, 0–100) to 21.9% (range, 0–100; p = 0.04) at the expense of the dose volume > 40 Gy which increased from 39.2% (range, 0–100) to 49.8% (range, 0–100; p = 0.02), whereas the dose relative volume proportion > 20- ≤ 40 Gy showed no relevant change and slightly decreased from 30.6% (range, 0–85.7) to 28.3%, (range, 0–85.7; p = 0.34). Conclusion We observed a considerable increase in the relative dose proportions within the RP volume from diagnosis to maximum volume extent from low dose zones below 20 Gy to zones above 40 Gy. Although the clinical impact on RP remains unknown, a reduction of healthy healthy lung tissue receiving >40 Gy (V40) might be an additional parameter for irradiation planning in lung cancer patients.

Objective: To access the association of dental esthetic perception with oral health-related quality of life (OHRQoL) among adolescents in Dammam the Eastern province of Saudi Arabia. Material and methods: This cross-sectional study was conducted on intermediate and high school students in Dammam the Eastern region of Saudi Arabia in the form of questionnaire during February–March 2018. The schools were randomly selected using cluster sampling and students in each school were invited to participate in the study. The students with physical or mental disability were excluded from the study. The perception of dental esthetic was measured by the Arabic version of the Orofacial Esthetic Scale (OES-Ar) on a 5-point Likert scale with ‘unsatisfactory’(1) to ‘excellent’(5). OHRQoL was measured by the validated Arabic version of the Child Oral Health Impact Profile-Short Form (COHIP-SF 19). Pearson correlation test was performed to investigate an association between esthetic perception and OHRQoL. Results: There were 846 (51.6%) female and 792 (48.8%) male students with mean age14.5 ± 1.6 years in the study. The mean score of perception of esthetic of the sample was 31.44 ± 5.09 which indicates a moderate self-perceived esthetics score (range 8–40). The mean score of OHRQoL was 20.21 ± 11.85 which demonstrates a low quality of life among study participants (range 0–76). The study found a statistically significant correlation between the perception of dental esthetics and oral health related quality of life (Pearson correlation coefficient = −0.573, P.

Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two components: sensor analytics and path finder. The sensor analytics component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.

This 12-month, randomized, controlled lifestyle intervention study was aimed at assessing the effectiveness of a lifestyle intervention in terms of (1) the reduction of at least 5% of body weight compared to baseline and (2) the percentage of participants in which fasting blood glucose (FBG) normalizes (n = 21) of the subjects in the IG arm discontinued, compared to 8% (n = 12) in the GA arm. Data from completers (92% (n = 138) and 86% (n= 129) participants in GA and IG arms, respectively) were considered for the final analysis. Post-intervention, 37.2% (n = 48) of participants in the IG group had ≥5% weight reduction, as compared to 12.3% (n = 17) in the GA group (p < 0.01). Similarly, the percentage of participants who normalized their FBG post-intervention was 46.5% (n = 60) in the IG group compared to 21.7% (n = 30) in the GA group (p < 0.01). A 12-month Diabetes Prevention Program (DPP)-styled intensive lifestyle program translated effectively in decreasing weight and improving fasting glucose compared to the GA group in predominantly overweight/obese Saudi adults with prediabetes, suggesting that in the case of guided intervention programs, people are willing to participate and possibly change a sedentary lifestyle.

This research aimed to develop a unique framework to help architects understand and apply architectural management (AM) in their practices. A comprehensive literature review identified several components belonging to different specialist fields. A pragmatic methodology for developing the framework was adopted by combining the methodology of Japareen for building conceptual frameworks with the Concept Mapping and Qualitative Met-Synthesis techniques. The resulting framework underwent a series of testing stages aimed at refining the framework further. The testing process targeted two groups (researchers and professionals) by adopting a mixed method approach, which included a facilitated workshop, interviews, and a questionnaire survey. The feedback from the testing phase was used to create the final AM Taxonomy Framework (AMTF), and served as an original and practical guide for practitioners, further extending their understanding of AM. Further validation and refinement are planned in the long term by applying the framework to selected architectural practices.