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Energy consumption is considered an important driver of economic growth in recent times due to its positive impacts on all economic activities. However, the Saudi Arabian economy has received very less attention from researchers as far as the energy-growth nexus is concerned. Amid this backdrop, the current paper is interested in assessing the responsiveness of economic growth to increased energy consumption in the presence of control variables. Our analysis of Saudi Arabia is based on date for the period 1971-2021. The recently proposed “Auto Regressive Distributed Lagged (ARDL)” cointegration approach is adopted for the estimation purpose. The estimation results revealed a significant positive short-run and long-run impacts of energy consumption on economic growth. The causality exercise also revealed a one-way causality running from energy consumption towards economic growth. Besides, energy consumption, we found that education and employment level have also a dominant positive role in promoting the economic growth of Saudi Arabia. On the other hand, openness to trade is found to be important for economic growth only in the long run confirming that trade liberalization policies could only be effective in the long run. Our results have important policy implications for the policymakers of Saudi Arabia.

Abstract Cutaneous leishmaniasis (CL), a neglected tropical disease (NTD), is a major public health problem in Yemen with widespread distribution in rural areas. Evaluating the knowledge and understanding of people’s beliefs towards the disease is essential to the implementation of effective control measures. This study aims to assess the knowledge, attitudes, practices (KAP) and treatment-seeking behaviour concerning CL among rural populations in the western highlands of Yemen. A community-based cross-sectional study was conducted among 289 household heads in four rural areas of the Utmah District. Data were collected using a pre-tested questionnaire. All the participants had heard about the disease; however, only 9.3% attributed it to sandflies. Nearly half (48.1%) of the participants could not mention any preventive measures for CL, and nearly two-thirds (65.4%) could not do so for sandflies. The overall ‘good’ knowledge about CL was found to be 51.2%, and it was 33.9% for sandflies. The participants’ attitude and prevention practices towards CL were not satisfactory, as only 38.1% and 16.3% had a positive attitude and good CL-related prevention practices, respectively. Moreover, 45.7% believed CL to be a stigmatising disease, and 50% had used traditional remedies to treat suspected CL lesions. Multivariate analyses showed that age, sex, presence of CL-confirmed cases in the same household, residency, occupation and monthly household income were the significant predictors associated with KAP concerning CL among the participants. The findings support an urgent need for integrated health education and community mobilisation interventions to improve awareness of these vulnerable populations about this devastating disease.

This paper investigates the influence of Renewable Portfolio Standards (RPSs) on the reliability of grids while considering key indicators, including the System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI), both without and with the inclusion of Major Event Days (MEDs). The study adopted the instrumental variable quantile regression (IVQR) method for estimation purposes. The first-stage 2SLS results confirm the robustness and validity of the instruments used, including the Democrats’ Participation Rate, Real Average Household Income, and Renewable Energy Capacity, which show strong relevance and satisfy the conditions of homogeneity. Our findings demonstrated that the adoption of RPS policies has significantly decreased the interruption of power in all of the considered quantiles. The impact is more potent in states facing more severe reliability challenges. The results proved the important role of the policies of RPSs in improving the stability of grids in the context of states having relatively poor reliability. In summary, the obtained findings demonstrate significant insights related to the policies of renewable energy in the resilience of infrastructure. Finally, the findings suggest that the government authorities should promote the RPS as a tool for enhancing the performance of grids in different states.

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.

Emergency incidents can appear anytime and any place, which makes it very challenging for emergency medical services practitioners to predict the location and the time of such emergencies. The dynamic nature of the appearance of emergency incidents can cause delays in emergency medical services, which can sometimes lead to vital injury complications or even death, in some cases. The delay of emergency medical services may occur as a result of a call that was made too late or because no one was present to make the call. With the emergence of smart cities and promising technologies, such as the Internet of Things (IoT) and computer vision techniques, such issues can be tackled. This article proposes a human action recognition-based IoT services architecture for emergency response management. In particular, the architecture exploits IoT devices (e.g., surveillance cameras) that are distributed in public areas to detect emergency incidents, make a request for the nearest emergency medical services, and send emergency location information. Moreover, this article proposes an emergency incidents detection model, based on human action recognition and object tracking, using image processing and classifying the collected images, based on action modeling. The primary notion of the proposed model is to classify human activity, whether it is an emergency incident or other daily activities, using a Convolutional Neural Network (CNN) and Support Vector Machine (SVM). To demonstrate the feasibility of the proposed emergency detection model, several experiments were conducted using the UR fall detection dataset, which consists of emergency and other daily activities footage. The results of the conducted experiments were promising, with the proposed model scoring 0.99, 0.97, 0.97, and 0.98 in terms of sensitivity, specificity, precision, and accuracy, respectively.

BACKGROUND: Diabetic nephropathy (DN) is a complication of chronic hyperglycemia associated with diabetes mellitus (DM). Several studies have demonstrated the positive impact of sodium-glucose cotransporter-2 (SGLT2) inhibitors on kidney outcomes. The objective of the study was to evaluate the effects of dapagliflozin, an SGLT2 inhibitor, on kidney outcomes in Saudi patients with type 2 DM. MATERIALS AND METHODS: Study included all Saudi patients with type 2 DM who visited our center from August 1, 2021, to July 31, 2022, and had been on dapagliflozin for at least 3 months. Data was abstracted through chart review for all patients included in the study. Paired t-test or Wilcoxon signed-rank test were used to compare the results before and after treatment for continuous variables and the McNemar test was used to compare the results for categorical data. RESULTS: Study included 184 Saudi patients with type 2 diabetes with a mean age of 61.32 years (SD=9.37). Dapagliflozin 10 mg/day significantly reduced hemoglobin A1C (HbA1C) from a mean (SD) of 9.00 to 8.40 (P < 0.001). Among a subgroup of patients with significant proteinuria (n = 83), dapagliflozin significantly reduced ACR from a median of 93.1 to 64.9 mg/g (P = 0.001). Following treatment, the estimated glomerular filtration rate improved from a mean of 69.83 to 71.68 mL/min and the mean arterial pressure (MAP) fell from 90.03 to 89.06 mmHg, both were not statistically significant. Despite a statistically insignificant increase in the episodes of urinary tract infections (UTIs), the hospitalization rate declined. No episodes of amputations or ketoacidosis occurred during the study period. CONCLUSION: SGLT2 inhibitors had beneficial effects among Saudi patients with type 2 diabetes by improving diabetic control and lowering proteinuria. Dapagliflozin did not result in significant harm, including UTIs, amputations, and ketoacidosis.

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

In the age of social media, the spread of rumors is becoming easier due to the proliferation of communication and information dissemination platforms. Detecting rumors is a major problem with significant consequences for the economy, democracy, and public safety. Deep learning approaches were used to classify rumors and have yielded state-of-the-art results. Nevertheless, the majority of techniques do not attempt to explain why or how decisions are made. This paper introduces a hybrid attention neural network (HANN) to identify rumors from social media. The advantage of HANN is that it will allow the main user to capture the relative and important features between different classes as well as provide an explanation of the model’s decisions. Two deep neural networks are included in the proposal: CNNs and Bidirectional Long Short Term Memory (Bi-LSTM) networks with attention modules. In this paper, the model is trained using a benchmark dataset containing 3612 distinct tweets crawled from Twitter including several types of rumors related to COVID-19. Each subset of data has a balanced label distribution with 1480 rumors tweets (46.87%) and 1677 non-rumors tweets (53.12%). The experimental results demonstrate that the new approach (HANN model) performs better results in terms of performance and accuracy (about 0.915%) than many contemporary models (AraBERT, MARBEART, PCNN, LSTM, LSTM-PCNN and Attention LSTM). Moreover, a number of software engineering features such as followers, friends, and registration age are used to enhance the model’s accuracy.

Abstract A total of 227 Plasmodium falciparum isolates from Jazan region, southwestern Saudi Arabia were amplified for the P. falciparum multi-drug resistance 1 (pfmdr1) gene to detect point mutations 11 years after the introduction of artemisinin-based combination therapy (ACT) in Saudi Arabia. The pfmdr1 86Y mutation was found in 11.5% (26/227) of the isolates while the N86 wild allele was detected in 88.5%. Moreover, 184F point mutations dominated (86.3%) the instances of pfmdr1 polymorphism while no mutation was observed at codons 1034, 1042 and 1246. Three pfmdr1 haplotypes were identified, NFSND (74.9%), NYSND (13.7%) and YFSND (11.4%). Associations of the prevalence of 86Y mutation and YFSND haplotype with participants’ nationality, residency and parasitaemia level were found to be significant (P

Background: Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged in 2019 and caused a global pandemic in 2020, manifesting in the coronavirus disease 2019 (COVID-19). The majority of patients exhibit a mild form of the disease with no major complications; however, moderate to severe and fatal cases are of public health concerns. Predicting the potential prognosis of COVID-19 could assist healthcare workers in managing cases and controlling the pandemic in an effective way. Therefore, the objectives of the study were to search for biomarkers associated with COVID-19 mortality and predictors of the overall survival (OS). Methods: Here, clinical data of 6026 adult COVID-19 patients admitted to two large centers in Saudi Arabia (Riyadh and Hafar Al-Batin cities) between April and June 2020 were retrospectively analysed. Results: More than 23% of the study subjects with available data have died, enabling the prediction of mortality in our cohort. Markers that were significantly associated with mortality in this study were older age, increased d-dimer in the blood, higher counts of WBCs, higher percentage of neutrophil, and a higher chest X-ray (CXR) score. The CXR scores were also positively associated with age, d-dimer, WBC count, and percentage of neutrophil. This supports the utility of CXR scores in the absence of blood testing. Predicting mortality based on Ct values of RT-PCR was not successful, necessitating a more quantitative RT-PCR to determine virus quantity in samples. Our work has also identified age, d-dimer concentration, leukocyte parameters and CXR score to be prognostic markers of the OS of COVID-19 patients. Conclusion: Overall, this retrospective study on hospitalised cohort of COVID-19 patients presents that age, haematological, and radiological data at the time of diagnosis are of value and could be used to guide better clinical management of COVID-19 patients.

Abstract Background Cutaneous leishmaniasis (CL), a neglected tropical disease, represents a significant public health problem in many endemic countries including Yemen. The ongoing armed conflict that started in March 2015 has had a negative impact on the entire healthcare system as well as on infectious disease control programmes. Therefore, this cross-sectional study aimed to assess knowledge and attitude towards CL among rural endemic communities in southwestern Yemen. Methods Five hundred households in five areas of Shara’b district of Taiz governorate were randomly selected to participate in a quantitative survey. A pretested structured questionnaire was used to collect data on the sociodemographic characteristics of the participants, their knowledge and attitude towards CL and their knowledge on the sand fly vector. Results The analysis was conducted on a final sample of 466 individuals (62.7% males and 37.3% females) aged between 18 and 70 years. Among the participants, 21.5% were non-educated while 39.7 and 20.8% had completed secondary school and tertiary education, respectively. Although the participants were aware of CL, about three quarters (77.7%) of them had poor overall knowledge about disease transmission, clinical presentation, treatment, and prevention. Interestingly, approximately half of the participants (49.1%) were able to differentiate sand flies from other flies and mosquitoes. However, only 14.8% of the participants knew about the role of the phlebotomine sand fly in the transmission of CL. Only 36.6% believed that CL can be prevented and 49.6% had a negative attitude towards the disease. Univariate and multivariate analyses showed that age and gender were the significant determinants of knowledge about CL and the sand fly vector among the studied population. Conclusion A poor level of knowledge about the different epidemiological aspects of CL was found among rural CL-endemic communities in Taiz. This factor, together with the major collapse of the healthcare infrastructure due to the ongoing civil war in Yemen, may be contributing to the continued endemicity of CL in the governorate. It is therefore recommended that health education on CL transmission and prevention should be provided to the targeted communities.

During the COVID-19 epidemic, Twitter has become a vital platform for people to express their impressions and feelings towards the COVID-19 epidemic. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. Based on this study, various public service messages can be disseminated, and necessary steps can be taken to manage the scourge. There has already been a lot of work conducted in several languages, but little has been conducted on Arabic tweets. The primary goal of this study is to analyze Arabic tweets about COVID-19 and extract people’s impressions of different locations. This analysis will provide some insights into understanding public mood variation on Twitter, which could be useful for governments to identify the effect of COVID-19 over space and make decisions based on that understanding. To achieve that, two strategies are used to analyze people’s impressions from Twitter: machine learning approach and the deep learning approach. To conduct this study, we scraped Arabic tweets up with 12,000 tweets that were manually labeled and classify them as positive, neutral or negative feelings. Specialising in Saudi Arabia, the collected dataset consists of 2174 positive tweets and 2879 negative tweets. First, TF-IDF feature vectors are used for feature representation. Then, several models are implemented to identify people’s impression over time using Twitter Geo-tag information. Finally, Geographic Information Systems (GIS) are used to map the spatial distribution of people’s emotions and impressions. Experimental results show that SVC outperforms other methods in terms of performance and accuracy.

Multi-junction concentrator solar cells are a promising technology as they can fulfill the increasing energy demand with renewable sources. Focusing sunlight upon the aperture of multi-junction photovoltaic (PV) cells can generate much greater power densities than conventional PV cells. So, concentrated PV multi-junction solar cells offer a promising way towards achieving minimum cost per kilowatt-hour. However, these cells have many aspects that must be fixed to be feasible for large-scale energy generation. In this work, a model is developed to analyze the impact of various atmospheric factors on concentrator PV performance. A single-diode equivalent circuit model is developed to examine multi-junction cells performance in hot weather conditions, considering the impacts of both temperature and concentration ratio. The impacts of spectral variations of irradiance on annual performance of various high-concentrated photovoltaic (HCPV) panels are examined, adapting spectra simulations using the SMARTS model. Also, the diode shunt resistance neglected in the existing models is considered in the present model. The present results are efficiently validated against measurements from published data to within 2% accuracy. Present predictions show that the single-diode model considering the shunt resistance gives accurate and reliable results. Also, aerosol optical depth (AOD) and air mass are most important atmospheric parameters having a significant impact on HCPV cell performance. In addition, the electrical efficiency (η) is noticed to increase with concentration to a certain concentration degree after which it decreases. Finally, based on the model predictions, let us conclude that the present model could be adapted properly to examine HCPV cells' performance over a broad range of operating conditions.

Aims: To evaluate the efficacy of a newly prepared endodontic irrigant solution, against E. faecalis.Materials and Methods: Sixty human extracted single rooted teeth samples were prepared by using protaper NiTi rotary system. The apical foramen of each sample was sealed by acrylic resin on the apical 3mm. Each root was embedded in silicon impression material block and autoclaved. A ten µl (4X105) of inoculated broth media (nutrient broth) with E. faecalis was injected gently inside the canals of group A,B&C was incubated for 24hours at 37Co. Group D was left without inoculation to serve as a control negative. The samples of group A,B and C were disinfected as fallow: Group A was disinfected with MCP solution(mixture of 20% castor detergent and 4% papain). Group B was disinfected with freshly diluted 2.5% NaOCl solution. Group C not treated with any solution control positive. The disinfected solutions were left inside the canal for 5 min then each canal was dried with sterilized paper point, then bacterial samples were taken from the samples of 4 groups using size 40 sterilized file which was inserted to full canal length and rotated 360 degrees in clock wise direction for dentin engagement. The file was put inside the sterilized plastic tube containing 1ml of normal saline then 200 µl was inoculated and spread on Enterococcus agar media and incubated at 37oC for 18hours, the numbers of bacterial colonies were counted. Results: Both solutions (MCP and NaOCl) produce complete eradication of E Faecalis within 5 minutes. Conclusion: Castor detergent 20% and papain enzyme 4% (MCP) has ability to completely eradicate Enterococcus Faecalis bacteria from the infected root canal in vitro in 5 min. It's antibacterial action is similar to the action of 2.5 % NaOCl.

Introduction: The infantile colic is a difficult experience met by parents in the first few months of an infant’s life. This may lead to increased discomfort for infants as well as psychological distress for mothers. This study aimed at assessing the baseline knowledge of mothers in Saudi Arabia about infantile colic mainly in regard to the etiology and management. Methods: In this cross-sectional study, a questionnaire was distributed among mothers in six primary healthcare centers (PHCC) in Riyadh, Saudi Arabia, during their visit for immunization clinics in 2016. The questionnaire consisted of two domains for determining the sociodemography characteristics and the maternal knowledge of participants about infantile colic. SPSS version 20 and chi-square test were used for data analysis. Results: A total of 230 mothers completed the survey questionnaire. Of these, 208 participants were Saudis. The majority of the participants were in the age group of 18–29 years (42.6%). The average age of the infants in this study was found to be 5.75±4.26 months. Eighty percent replied that they did not have any previous knowledge of infantile colic; 42.6% mothers believed that the causes of infantile colic were unclear and might involve several factors; 36% of the participants perceived milk allergy as the attributing cause for infantile colic. The source of knowledge about infantile colic was mainly through experiences of dealing with previous siblings who have the same issue (34.4%); 27.4% of mothers prefer the use of herbal medicines to treat this condition. Conclusions: It is recommended that health education needs to be provided to mothers at outpatient clinics during their antenatal hospital visits. This reduces the discomfort of infant and distress in mothers.

Aims :evaluate the effect of abutment angulation on fracture strength of pre-fabricated zircon implant abutment supported CADCAM zircon crown. Material and methods: Ten analog with 3.75×14mm were placed into resin blocks divided in to 2 groups: group A (n=5) parallel to long axis and group B (n= 5) fixed at angulation (17°) to long axis. Then ten abutments were tightened to 25 N/cm2. Ten full anatomic zircon (CAD/CAM) crowns constructed as upper right central incisor: 5 on each group. After luting all crowns with dual cure resin-based luting, all samples passed to artificial aging using thermocycling machine, 500 cycles at (5 to 55 C°±2) degree. Then the load was applied by universal testing machine at 30 degree to the long axis until fracture occurred. Results: Statistical analysis showed that the implant abutment angulations significantly (p < 0.05) reduced the fracture resistance of zircon implant abutment. Conclusions: Within the limitation of this in vitro study it may be concluded that: the fracture strength of zircon implant abutment supported CADCAM zircon crown is significantly reduced in respect to the angulation fromstraight to angulated (17°) zircon abutment.

Introduction: TMD sufferers are not able to move their jaw normally. Several theories tried to demonstrate the relation between pain and movement in patients with TMD. The Vicious Cycle Theory (VCT) suggested that a sore muscle caused by prolonged contraction would lead to more muscle activity which would lead to more pain and a continuation of the vicious cycle. The Pain Adaptation Model (PAM) suggested that a decreased activity of a sore muscle responsible for producing movement is more likely in order to reduce speed and the detrimental effect of movement and promote healing. The hypothesis: The Hybrid model, developed in 2006, suggested that both the VCT and the PAM are valid and applicable in orofacial pain patients, depends on the origin of the pain whether from the muscle or from the joint. Evaluation of the hypothesis: The data from a study on pain and movement showed that both increased and decreased activities of masticatory muscles are possible in the presence of pain as the system reacts differently if the pain originates from the muscle or from the joint. In case of muscle pain, the data showed decreased activity of agonistic muscles during movement and this is conformed to the PAM. When the pain is in the joint, both decreased and increased activities were observed throughout the movement. Neurophysiological mechanisms that govern the relationship between pain and movement, as well as clinical manifestation of the pain, are different if the pain arises from the muscle or from the joint. Finding new treatment approaches that deal with the muscle pain and joint pain as two different entities seems to be of utter importance in orofacial pain patients. The Hybrid model could play an important role in this direction.

Pulmonary myiasis is an unusual form of myiasis in humans and has been recently identified as a cause of eosinophilic pneumonia. We report the case of a 13-year-old Omani boy who presented to the Royal Hospital, Muscat, Oman, in October 2014 with respiratory distress. Bronchial aspirates revealed features of eosinophilic pneumonia. Possible larvae identified in the cytology report, a high immunoglobulin E level and the patient history all indicated bronchial myiasis. The patient was treated with steroids and ventilation and has since been diseasefree with no long-term side-effects. To the best of the authors’ knowledge, this is the first case of bronchial myiasis in Oman.

Aims: to evaluate the effect of implant length and diameter on the values and distribution of stresses of immediate loading implants by using three-dimensional (3D) finite elements (FE) analysis. Materials and Method: A 3-D FE model of an implant embedded in a block of bone was used in this study. The implant was LEADER/ ITALIA-Fix type which is specially designed for immediate loading. ANSYS V.12 program was used to build solid model of the implant and bone, and performing the finite element analysis. Two lengths of implant were used (11mm. and 13mm)., and two diameters (3.75mm. and 4.25mm.). Results: The results showed that the areas of maximum stress of all lengths and diameters are located at the neck of implants, the increase in the implant length from 11mm. to 13mm. leading to slightly increase in the stress at the implant-bone interface, while the increase in the diameter from 3.75mm. to 4.25mm. having no significant effect in increasing the value of stresses around dental im-plants. Conclusions: The increase in the implant length is negatively affect on stress values, but it leads to better dissipation of stresses around dental implant. The increase in the diameter having no obvious effect on stress values. However, it reduce the area of maximum stress at the implant neck

Abstract Aims: To investigate the incidence of handpiece asepsis in general dental practice in Mosul City and to evaluate the problems associated with routine handpiece sterilization which are commonly needed by those practitioners. Materials and Methods: A questionnaire was designed to collect the information about handpiece asepsis techniques performed by dentists. One hundred twenty dentists in Mosul City were randomly selected and the data were analyzed. Results: About 55.8% of the respondents know about the importance of handpiece sterilization, but no one do ideal sterilization between each patient due to absence of sufficient number of handpiece and lack of autoclave system in clinics. No one disinfect the handpiece scientifically; 59.38% smear the handpiece between each patient by one of the antiseptic solutions. Conclusion: Handpiece asepsis in Mosul dental clinics is poor. Most of dentists depend on disinfection by disinfectant solution to prevent cross infection by handpieces. Most dentists, because of their poor equipment in both private and national clinics, cannot sterilize handpieces after each patient

Abstract The aim of this study was to evaluate the antimicrobial effect of 10% water extraction of Salvadora persica (Miswak) when used clinically as an endodontic irrigant. Twenty four uniradicular teeth with necrotic pulps were chosen. The patients were divided randomly into 2 groups: Experimental group, in which water extract of Salvadora persica (10%) was used as a root canal irrigant; and control group, in which distilled water was used as a root canal irrigant. Bacteriological samples were obtained from the canal at the step of working length determination (before the canal was subjected to instrumentation and irrigation procedures), and at the end of the biomechanical instrumentation procedures by using a sterile K–file. The file was separated from the handle using a sterile wire cutter, and the severed portion was placed in a sterile screw–capped vial containing 5 ml of thioglycollate broth as a transport media. A 0.1 ml of thioglycollate broth was inoculated on each of two brain–heart infusion agar plates: One plate was incubated under aerobic conditions, and the other was incubated under anaerobic conditions using anaerobic jar and gas pack anaerobic system. Both plates were incubated at 37 ºC for 24 hours; then, the number of bacterial colonies was counted. The results revealed that 10% water extraction of Salvadora persica is an effective antimicrobial agent when utilized clinically as an irrigant in the endodontic treatment of teeth with necrotic pulps.

Abstract The aim of this study is to evaluate the effect of type of the tooth and the placement site of electrode on electrical pulp testing of the anterior teeth. Twenty persons were participated in this study. The six maxillary and mandibular anterior teeth were checked to evaluate the response threshold to electrical pulp testing; each tooth was examined at four placement sites on its labial surface, which are the incisal edge, incisal third, middle third and the cervical third. The results showed that the response threshold of the mandibular anterior teeth was lower than that of the maxillary anteriors. The canines may respond to the electrical stimulus in a highest threshold than the lateral incisors and the central incisors which had the lowest response threshold. Also, this study showed that the best placement site of electrode was in the incisal edge of the tooth and the pulp tester reading was increased as the electrode moved toward the cervical region of the tooth.

This paper investigates the influence of Renewable Portfolio Standards (RPSs) on the reliability of grids while considering key indicators, including the System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI), both without and with the inclusion of Major Event Days (MEDs). The study adopted the instrumental variable quantile regression (IVQR) method for estimation purposes. The first-stage 2SLS results confirm the robustness and validity of the instruments used, including the Democrats' Participation Rate, Real Average Household Income, and Renewable Energy Capacity, which show strong relevance and satisfy the conditions of homogeneity. Our findings demonstrated that the adoption of RPS policies has significantly decreased the interruption of power in all of the considered quantiles. The impact is more potent in states facing more severe reliability challenges. The results proved the important role of the policies of RPSs in improving the stability of grids in the context of states having relatively poor reliability. In summary, the obtained findings demonstrate significant insights related to the policies of renewable energy in the resilience of infrastructure. Finally, the findings suggest that the government authorities should promote the RPS as a tool for enhancing the performance of grids in different states. [ABSTRACT FROM AUTHOR]

This research paper explores the relationship between economic complexity and environmental degradation by highlighting the moderating role of income level. The paper focuses on the BRICS member economies "Brazil, Russia, India, China, South Africa" and utilizes data for the period 1998–2022. Several suitable econometric estimators such as "Fixed Effects (FE)", "Feasible Generalized Least Squares (FGSL)", and "Two Stages Least Squares (2SLS)" are used to obtain results. The main findings show that economic complexity degrades the quality of the environment significantly. However, the results further indicated that income level helps the economic complexity to improve environmental quality. Moreover, the results also revealed that trade openness, income level, and energy use have also significantly degraded the quality of the environment. The causality analysis performed demonstrated a one-way causal relationship running from economic complexity to environmental degradation. Our results have important policy implications for the policymakers of the BRICS economies. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the relationship between financial development and renewable energy consumption using a fractional response model. The study examines a sample of 34 advanced economies and 64 emerging markets and developing economies from 2008 to 2020. The findings from the fractional response model indicate that financial development has a positive impact on renewable energy consumption in advanced economies. However, in emerging and developing economies, financial development negatively affects the consumption of renewable energy. Additionally, the findings illustrate that financial development has a more pronounced positive impact in advanced economies. This effect is especially strong in countries with higher levels of financial development. On the other hand, in emerging and developing economies, the consumption of renewable energy is more strongly affected by the negative impact of financial development on countries with lower financial development. [ABSTRACT FROM AUTHOR]

In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users' ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP considers various factors such as response time, throughput, CPU usage, and other metrics to dynamically model interactions between microservice performance indicators for diagnosis and prediction. Using experimental data from our monitoring tool, stakeholders can build various networks for probabilistic analysis of microservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service (QoS) level. We generated a dataset of microservices with 2726 records across four benchmarks including CPU, memory, response time, and throughput to demonstrate the efficacy of the proposed MPDP architecture. We validate MPDP and demonstrate its capability to predict microservice performance. We compared various Bayesian networks such as the Noisy-OR Network (NOR), Naive Bayes Network (NBN), and Complex Bayesian Network (CBN), achieving an overall accuracy rate of 89.98% when using CBN. [ABSTRACT FROM AUTHOR]