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Chronic kidney disease (CKD) in children presents multifaceted challenges, impacting various aspects of health, including oral health. This narrative review provides a comprehensive synthesis of literature focusing on the oral health status of pediatric CKD patients, encompassing oral manifestations, dental considerations, and management challenges associated with hemodialysis and kidney transplantation. A comprehensive search strategy was employed, utilizing databases such as PubMed, Scopus, Web of Science, and Google Scholar, to identify relevant literature on oral manifestations in children with CKD, including those undergoing hemodialysis or renal transplantation. Search terms were carefully selected to capture studies examining enamel hypoplasia, dental caries, delayed tooth eruption, gingival diseases, periodontal diseases, radiographic alterations, craniofacial development, dry mouth, and changes in the oral mucosa. Our narrative review meticulously selected articles through a systematic process. Ultimately, 12 studies meeting the inclusion criteria were included in the review. Relevant data from each included study were independently extracted and synthesized, focusing on oral manifestations and their implications in pediatric CKD patients. The synthesized findings were organized and presented in a structured manner within the review article, considering their clinical implications and informing recommendations for dental management of children with CKD. This article highlights the importance of a coordinated effort between nephrologists, dentists, and other healthcare professionals in providing holistic care for pediatric CKD patients. A comprehensive understanding of the oral health status of these children, along with proactive dental management strategies, contributes to improved overall health outcomes and a better quality of life. This review aims to serve as a valuable resource for the oral healthcare providers involved in the care of pediatric CKD patients. [ABSTRACT FROM AUTHOR]

Objective: This systematic review evaluates the efficacy of buccal pad fat (BPF) as an autologous graft in the treatment of gingival recession (GR). Thus, the research question explores if the BPF can serve as a viable alternative to the gold standard connective tissue graft. Materials and methods: Only seven studies met the inclusion criteria were critically appraised including the randomized controlled clinical trials, and case series. The inclusion criteria were systemically healthy individuals in the age range (18–65 years old) with Miller's classification of GR either class I, II, III, or IV while exclusion criteria were patients with poor oral hygiene, pregnant and lactating patients, teeth with caries, any prior surgery in the relevant regions, and use of medications. Results: The review included 117 patients with 136 GR defects. The age of participants ranges from 20 to 65 years old with the higher percentage of root coverage (%RC) at 6 months in the pedicled BPF group which was 89.30%while the lowest (%RC) at 6 months in the same group was 46.78%. The BPF group's width of keratinized gingiva (WKG) values indicate a notable improvement, suggesting a positive impact on WKG compared to the control group. Conclusions: BPF can be considered as a promising graft to augment gingival tissues at different sites in the oral cavity with different Miller's classes of GR providing a new era in GR treatment. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Lichen planus is one of the common adverse reactions after COVID‐19 infection and vaccination. Despite it being reported in several case reports, the literature including a large sample of the studied population is lacking. The current study was performed to assess the risk of LP after COVID‐19 infection as well as COVID‐19 vaccination.The current study was designed as a retrospective cross‐sectional hospital‐based study of registered patients at the University of Florida (UF) health centers. The diagnoses of LP, COVID‐19 infection, and COVID‐19 vaccines were detected. The logistic regression model was used to assess the risk of developing LP after COVID‐19 infection and vaccination.A total hospital patient of 684,110 attended UF Health centers were included in this study. 181 patients reported LP after COVID‐19 vaccination and 24 patients developed LP after COVID‐19 infection. The risk of developing LP after COVID‐19 vaccination was 1.573 while the risk of developing LP after COVID‐19 infection was 1.143.The odds of getting LP after COVID‐19 vaccination are significantly developed. The current study showed that COVID‐19 infection and vaccination are associated with LP. So, healthcare practitioners should be aware of this reaction for rapid recognition and treatment. [ABSTRACT FROM AUTHOR]

Objective: This study aims to determine the prevalence and risk factors associated with lymphoma in primary Sjögren's syndrome (pSS). Methods: We conducted a cross‐sectional study on pSS patients who were registered into the Integrated Data Repository (IDR) at the University of Florida (UF) Health Shands Hospital. The parameters, such as age, sex, race, and smoking status, were included. Lymphoma types in pSS were categorized. The clinical and laboratory features were compared between pSS patients with and those without lymphoma by utilizing the items in the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI). Results: Among 1,211,343 patients, we found 6799 patients (0.56%) with lymphomas and 2562 patients (0.21%) with pSS. Out of the 2562 pSS patients, 67 patients (2.6%) were diagnosed with lymphoma. The difference in the clinical and laboratory features listed under the ESSDAI domains between pSS patients with lymphomas and pSS without it was significant (p < 0.05 or 0.01): fever, weight loss, lymphadenopathy, splenomegaly, lacrimal gland diseases, cough, shortness of breath, hematuria, cerebrovascular accident diseases, peripheral nerve involvement due to vasculitis, neutropenia, and thrombocytopenia. Conclusion: We report 2.6% of lymphoma prevalence in pSS, lower than previously reported in the literature. [ABSTRACT FROM AUTHOR]

Nanomaterials have been the focus of intense study and growth in the modern era across the globe because of their outstanding qualities, which are brought about by their nanoscale size; for instance, increased adsorption and catalysis capabilities plus significant reactivity. Multiple investigations have verified the fact that nanoparticles may successfully remove a variety of pollutants from water, and, as a result, they have been utilized in the treatment of both water and wastewater. Therefore, the current research intent is to examine the nonlinear heat source/sink influence on the 3D flow of water-based silver nanoparticles incorporated in an Eyring–Powell fluid across a deformable sheet with concentration pollutants. Silver particles have been used intensively to filter water, due to their potent antibacterial properties. The leading equations involving partial differential equations are renewed into the form of ordinary ordinary differential equations through utilizing the appropriate similarity technique. Then, these converted equations are solved by utilizing an efficient solver bvp4c. Visual displays and extensive exploration of the different impacts of the non-dimensional parameters on the concentration, temperature, and velocity profiles are provided. Also, the important engineering variables including skin friction, the rate of heat, and mass transfer are examined. The findings suggest that the mass transfer rate declines due to pollutant parameters. Also, the results suggest that the friction factor is uplifted by about 15% and that the heat transfer rate, as well as the mass transfer rate, declines by about 21%, due to the presence of the nanoparticle volume fraction. We believe that these results may improve the flow rate of nanofluid systems, improve heat transfer, and reduce pollutant dispersal. [ABSTRACT FROM AUTHOR]

Climate control in a pixel non-uniformity metro system includes regulating the air, humidity, and temperature quality within metro trains and stations to ensure passenger comfort and safety. The climate control system in a PNU metro system combines intelligent algorithms, energy-efficient practices, and advanced technologies to make a healthy and comfortable environment for passengers while reducing energy consumption. The proposed an automated climate control using an improved salp swarm algorithm with an optimal ensemble learning technique examines the underlying factors, including indoor air temperature, wind direction, indoor air relative humidity, light sensor 1 (wavelength), return air relative humidity, supply air temperature, wind speed, supply air relative humidity, airflow rate, and return air temperature. Moreover, this new proposed technique applies ISSA to elect an optimal set of features. Then, the climate control process takes place using an ensemble learning approach comprising long short-term memory, gated recurrent unit, and recurrent neural network. Lastly, the Harris hawks optimization algorithm can be employed to adjust the hyperparameters related to the ensemble learning models. The extensive results demonstrated the supremacy of the proposed algorithms over other approaches to the climate control process on PNU metro systems. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia (KSA) is known for its high car ownership and usage and its high GDP per capita. This, combined with a low provision of public transportation (PT) systems, has resulted in perceptual attitudes of high dependency on private car travel. The level of awareness of the benefits of reducing car use and increasing travel by more sustainable options has a great impact on social change and behaviour. The Kingdom is currently progressing towards a new phase of "national reform" through implementation of strategic sustainable programs. Riyadh city is constructing a massive metro-system, which is nearing completion and operation. The public is aware of the national agenda, the construction of transportation projects, and of the social changes needed to realize the new vision of the country. This paper aims to assess travel behaviours and attitudes of Saudi nationals towards public transportation. A discrete choice model of the intent to use public transportation is calibrated as a function of social and attitudinal factors, using online survey data. The analysis was carried out using an ordered logit model (OLR) which is derived from the theory of random utility. The results show that, overall, a higher support for public transportation modes was stated by young females, lower income groups, and university graduates. The level of awareness of the national agenda appears to be increasing the level of support for public transportation. The paper is the first investigation of willingness to use public transportation in Riyadh, at a crucial time of national reform; the choice of mode of travel appears to be determined by the travellers' social and economic characteristics and the level of awareness of the country's agenda. [ABSTRACT FROM AUTHOR]

Renewable energy has become a solution to the world's energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key tomanaging storage, delivery, and smart grids. With unparalleled data granularity, a data-driven system could better anticipate solar generation. Deep learning (DL) models have gained popularity due to their capacity to handle complex datasets and increase computing power. This article introduces theGalactic SwarmOptimization with Deep BeliefNetwork (GSODBN-PPGF) model. The GSODBN-PPGF model predicts PV power production. The GSODBN-PPGF model normalises data using data scaling. DBN is used to forecast PV power output. The GSO algorithm boosts the DBN model's predicted output. GSODBN-PPGF projected 0.002 after 40 h but observed 0.063. The GSODBN-PPGF model validation is compared to existing approaches. Simulations showed that the GSODBN-PPGF model outperformed recent techniques. It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day. [ABSTRACT FROM AUTHOR]

Car ownership and use in the Kingdom of Saudi Arabia (KSA) are very high due to the high income, the low fuel prices and the near absence of public transportation in the Kingdom. Currently, the Kingdom is going through a massive transformation and entering a new era of national reforms. One of the main aims of the national reforms is to reduce car dependency and enhance more sustainable options of travel such as public transportation in the KSA. In order to achieve such an aim, there are two hurdles: Firstly, there must be a provision of decent public transportation options, and secondly, there is a need to influence travel behaviour and encourage the shift from private cars to public transportation. For example, in Riyadh city, an impressive metro system is being constructed and will start operation in 2023. To influence travel behaviour, travel demand management measures (TDM), in particular pricing measures, can be adopted and implemented, in order to help and support achieving the target. The main aim of this paper, therefore, is to assess the attitudes of Saudi nationals towards—and willingness to accept—pricing measures and their possible impacts on their travel behaviour in the city of Riyadh. The methodology includes collecting data using an online survey on travel behaviour and attitudes in Riyadh and calibrating multinomial logit modal choice models. The participants in the survey were asked to report their support of the pricing measures for the objective of reducing congestion in the city, improving road safety or reducing travel time. The results show the highest support towards pricing measures for improving road safety, reducing travel times and, lastly, reducing congestion in the city. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies. [ABSTRACT FROM AUTHOR]

Objectives: The role of Human papillomavirus (HPV) in the oral squamous cell carcinoma (OSCC) has not been completely elucidated. The purpose of the present study was to investigate the prevalence and localization of HPV‐16 virus in OSCC and to correlate HPV‐16 positivity and p16INK4A expression with the clinical and pathological features of OSCC. Methods: The archives of Oral Pathology at the University of Florida, College of Dentistry were accessed for demographic, clinical, histopathological data, and slides of 114 OSCC patients. HPV‐16 positivity of OSCC was evaluated by p16INK4A immunohistochemistry (IHC) and HPV‐16 E6/E7mRNA by in situ hybridization (ISH). Results: Out of 114 consecutive pathological slides of OSCC, 16 samples (14%) showed positivity for p16INK4A by IHC and 14 samples (12%) were positive for HPV‐16 E6/E7mRNA ISH and the Positivity showed a significant correlation with the patients' age, alcohol consumption, and the degree of OSSC differentiation. The hard palate showed the highest positivity of p16INK4A IHC and HPV‐16 mRNA ISH (38%, 36% respectively). Conclusion: HPV‐16 is a significant factor in oral carcinogenesis. We recommend using p16INK4A as a surrogate marker for HPV detection in OSCC, which can be complemented by RNA ISH for the identification of HPV subtypes. [ABSTRACT FROM AUTHOR]

The background of this study is relevant to parking facilities at shopping malls in the Kingdom of Saudi Arabia (KSA), where all parking services are mostly free, with very few exceptions. Demand on parking places at shopping malls and recreational areas in Saudi cities are generally very high. This is partly because most of the entertaining, shopping, and recreational areas are indoors and are typically located at shopping malls with huge parking provision. With the vast increase in car ownership and use in the country over the past few decades, a consequent increase in demand on roads and parking facilities has been observed, which has no doubt resulted in further congestion. This study aims to investigate the willingness to pay to save time searching for a parking place at shopping malls in Riyadh city in the KSA. The methodology includes interviewing shoppers in shopping mall car parks and asking about their search time for a parking space and their willingness to pay to save search time, as well as to report on their socio-economic characteristics including age, gender, employment, education, car ownership, and income. The outcome of this study shows that on average, shoppers spend about 9 min searching for a parking place for their shopping and recreational trips. The results also show that, on average, shoppers reported that they go shopping, eating out, etc., about three times per week, and the average willingness to pay to save time searching for parking space is about 10 Saudi Riyal (about 2.7 USD) per visit. The modelling results show that there is a higher willingness to pay from the middle income and car-ownership categories of the population than from the highest income and car ownership groups. This might reflect the fact that the middle-income groups are much more dependent on themselves to drive their cars and search for a parking space every time, while those of higher incomes could possibly rely on their private drivers most of the times to drive them and park their cars. This paper contributes to the literature by providing a first understanding on how demand for parking facilities is affected by various socio-economic factors and the willingness to pay to save search time at these parking locations. The implications of the outcome of this research will be of use for decision makers and city authorities in planning travel demand management policies and programs that aim at reducing demand on private cars and achieving sustainability. [ABSTRACT FROM AUTHOR]

Attitudes and feelings towards food waste and positions on management policies have been vastly increased over the past few decades. Most of the available research on the analysis of attitudes towards food waste have been carried out using conventional statistical methods. This paper aims to assess and analyse attitudes and preferences of young Saudi females towards a number of policies and plans that are designed to meeting sustainable targets, using fuzzy logic analysis. This is a very important aim, especially since Vision 2030 in Saudi Arabia puts a major emphasis on sustainability, setting many resources to tackle environmental problems and achieving better social standards. The Methodology includes designing and collecting data from 199 participants using a questionnaire that includes 23 questions. Data were obtained from students at Princess Nourah bint Abdulrahman University (PNU). The analysis includes utilising artificial intelligence (AI) techniques. Fuzzy logic analysis has been widely used in many fields, but has not seen many applications on food waste analysis and attitudes. Fuzzy logic analysis has the advantage of producing efficient results from smaller sample sizes and, in particular, with qualitative characteristics of the used indicators. The participants expressed positive preferences and attitudes towards the programs and policies that are designed to achieve sustainability and manage food waste. The results show that over 25% of them prefer the option of "storage for reuse" of food waste, over 35% prefer the option of distribute it to needed families and over 30% opted to the option of recycling to fertilisers. The study also reveals a very good level of awareness and appreciation of food waste and plans associated with it. The implications from this study suggest that despite the positive attitudes, there still is more research needed to obtain full understanding of attitudes towards food waste from the whole range of the population in order to gain knowledge and build specific programs to reduce food waste and achieve sustainability in the country. [ABSTRACT FROM AUTHOR]

In this study, we investigated the current Saudi traffic safety policies and how they are perceived by Saudi women in the Kingdom of Saudi Arabia. The study was based on using a survey to define and calculate two sets of specific indicators, namely the Perceived Effectiveness Index (PEI) and the Commendation Index (CI), representing, respectively, the perception and acceptance of road traffic safety. The survey, which was conducted on a sample of Saudi women in Riyadh, demonstrated a high level of support for the ongoing road safety policies amongst the respondents, bringing to the fore some country-specific indications such as the low relevance of pedestrian crossing policies. The study aimed to contribute to an understanding of the specific gender aspects of transportation policies and their perception amongst the female population in Saudi Arabia to enable an understanding of the entire context of ongoing reforms in the Kingdom at various political, cultural, and societal levels. Our study, therefore, reveals potential strengths and provides an interdisciplinary contribution by drawing on the relevant literature in the field of travel behaviour and traffic safety policies and their perception amongst women. Saudi women's attitudes towards 17 safety policies, including engineering, enforcement, education, and legislation policies, were examined. The policies were carefully designated to be practical and possible to implement. The analysis and assessment of the results obtained by the two indicators shed light on the participants' acceptance, and hence their willingness, to cope with road safety policies, should they be implemented. The findings of this study showed that Saudi women's attitudes to road safety policies were very positive; in particular, they showed sound support for enforcement policies. [ABSTRACT FROM AUTHOR]

The introduction of autonomous vehicles has been considered as a possible option for reducing traffic congestion in many transport studies. Many types of models, in particular car-following microsimulation models have been adopted in most studies. The impacts of autonomous vehicles (AVs) on congestion, however, have not yet been concluded. This could be because different researchers use different forms of car-following models to assess these impacts, or because the utilised modelling approaches and their parameters are different in different studies. In particular, two of the important parameters that are associated with car-following models are the used values for maximum acceleration and the average desired time gaps. While the values of these parameters can be adjusted and controlled by the ACC controllers in the AV, they can also be controlled by the users. Therefore, assigning unrealistic values to these parameters could well result in unrealistic conclusions. This paper investigated the impacts of the maximum acceleration and the average desired time gaps on congestion levels using the loss-time indicator. The analysis was carried out on the Hanshin expressway in Japan and was tested and assessed using the Helly (FACC) car-following microsimulation model. This includes estimating the values of the desired time gap from real traffic time-gap distributions. The Hanshin expressway is an urban toll highway of 273 km that extends from Osaka to Kobe, representing the Hanshin area in Japan. The Hanshin highway serves a huge traffic volume that consists of private and freight vehicles that operate within the Hanshin area. This area represents one of three major municipal areas in Japan including Tokyo and Nagoya. A total of 740,000 vehicles per day travel on the expressway. As a result, there is significant congestion on the Hanshin expressway. There have been various plans put in place to ease congestion ranging from building new roads to the implementation of traffic-demand-management measures. However, the predictions of the impacts of such measures do not provide any evidence that they would ease traffic congestion. Other possible measures that could be investigated for easing traffic congestion include technology-based solutions such as autonomous vehicles. The modelling results recommend that the results obtained from microsimulation models should be taken with care, and good attention should be paid to the parameters used and their values in the model. The values assigned to driving-behaviour parameters, the maximum values of acceleration, and the time-gap settings, for example, control the final outcomes of the models. [ABSTRACT FROM AUTHOR]

Perceptions, attitudes and anticipated behavior represent very useful information for decision and policy makers before they implement proposed new policies. Road safety is one of the most critical fields in the built environment. In the Kingdom of Saudi Arabia, women started driving in 2019. There is a huge shortage of studies that investigate users' attitudes and behavior, particularly women, in Saudi Arabia. It is very timely therefore, to assess their perceptions, attitudes and raise their awareness towards road safety. In this paper, the perceptions and attitudes towards road safety and relevant policies and programs were assessed and compared using two approaches: analytical and a fuzzy logic system. By means of a questionnaire, a total of 17 road safety policies, including engineering, enforcement programs, education programs and legislation programs, were presented to the participants and assessed. These policies were assessed from the viewpoint of Saudi women. The engineering policies included the use of road signs and marking, improving road network alignment, monitoring driver speed and engineering speed reduction measures. The assessment was in terms of the usefulness of the policies to reduce traffic accidents and improve road safety in Riyadh. A total of 375 participants were surveyed. Using a Likert scale response system to report the Saudi female participants' opinions, two sets of indices were calibrated to evaluate the perceived usefulness and acceptability of the safety policies. A MATLAB simulation of a fuzzy logic system was utilized to assess, investigate and compare travel attitudes and perceptions of road safety. The results indicate overall encouragement for the safety programs and policies by the participants. Most of the support seems to be for the engineering program. The physical presence of traffic police was observed to have scored the highest. This is an interesting result that shows that enforcement policies are still perceived to be the most effective. In this category, the policy of enforcing speed limits showed high support, amongst the other policies. While a fuzzy logic analysis is typically used in hard engineering assessments and investigations, such as mechanical engineering and system design applications, the results obtained in this investigation show that the efficacy and adequacy of the proposed fuzzy logic control system in assessing attitudinal data was sufficiently good to be recommended for future research. The outcomes from both sets of approaches, the analytical and the fuzzy logic, showed comparable results, which also demonstrates the potential benefits of using the fuzzy system in many attitudinal and behavioral studies. [ABSTRACT FROM AUTHOR]