Search

Pregnancy-related diabetes carries dangers for both the mother and her growing foetus. Pre-conception care, screening, diagnosis, prenatal care, and postpartum care are all included in the management of the condition at the level of primary care. Its overall care requires a multidisciplinary approach. At the primary care level, there are many approaches to managing diabetes during pregnancy. The following clinical practice guidelines (CPG) on this topic were created to give healthcare clinicians a thorough, evidence-based management guidance to help them deliver high-quality care.

Electric vehicles (EVs) are important elements in the global strategy to tackle climate change; however, research often fails to sufficiently identify the range of barriers which affect their adoption. Taking Saudi Arabia as a case study, this paper analyses responses from 698 potential drivers in order to identify and rank the infrastructure, performance, financial, social, and policy barriers to EV adoption in a major oil-producing nation with a hot climate and a desert terrain. According to this study’s findings, the most important barriers in this context are the lack of charging infrastructure and the additional load placed on the national grid, while others include the safety and effectiveness of batteries at high temperatures, and the ability of EVs to perform in desert conditions. Common themes also include concerns that EVs may damage Saudi’s oil-based economy, cost of purchase and maintenance, low resale value, and the absence of awareness about EVs. The study concludes that EV manufacturers must demonstrate that their vehicles are suitable for the Saudi climate. Governments should also provide subsidies, or other incentives, to promote adoption of EVs as the study also found that variations in the cost of different EV models in Saudi Arabia, for example, the Tesla Model 3, is up to 40% more expensive to own than a Toyota Camry, mean that owning EVs can cost significantly more than small sized internal combustion engine-based vehicles (ICEVs). This paper identifies and ranks the barriers to EV ownership in a desert nation which is a leading petroleum producer and compares the relative costs of EVs and ICEVs in the country. As such, it has immediate relevance in countries with similar economic, geographic, and climatic conditions.

Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The “significance and effects” of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the “length-lanes” significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors.

Background: In the global effort to combat SARS CoV2 infection, adoption of the vaccination has been an essential component. The goal of this research was to determine the quality of web-based information gathered during COVID-19 and participants’ awareness and acceptance for the booster dose for COVID-19. Methods: This cross-sectional study was carried out to gauge interest in and willingness for a booster dose, as well as the satisfaction with the availability and accuracy of Internet resources. This study included 631 people from the cities of Riyadh, Al Majma’ah, Al Ghat, and Zulfi in the Riyadh Area. Chi-square and Fischer’s exact tests, with a 95% confidence interval, and a threshold of p < 0.05, were used to analyze the significance of associations between variables. Results: Out of 631 respondents, 347 people who reported willingness to receive the immunization were women (319, 91.9%), with only 28 (8.1%) being men. There was a statistically significant correlation between individuals who worried about booster dosage adverse effects and those who did not receive the immunization. Knowledge of the efficacy of the vaccine, confidence in the capacity of the vaccine to avert problems, and willingness to receive a third dosage were all shown to be substantially correlated (p < 0.001). Attitude and behavior ratings were substantially correlated with prior COVID-19 immunization status (p < 0.005). Conclusions: There was a significant correlation between vaccination knowledge, confidence in the capacity of the vaccine to prevent problems, and willingness to receive a third dose. Therefore, our research can help policymakers develop more precise and scientific roll-out strategies for the COVID-19 booster vaccination.

The purpose of this paper is to improve the English teaching levels of colleges and universities. First, the primary attributes of dynamic nonlinear system theory in second language acquisition are analyzed. Second, the influencing factors of English grammar application ability in theoretical dimension and measurement methods are proposed. Six first-year students majoring in English in a university were randomly selected as research objects, who were followed up for a semester, during which three identical oral English tasks were finished (in September, October, and November of 2020). The fluency, accuracy, and complexity of students’ utterances are statistically analyzed using SPSS 26.0. Results demonstrate that the fluency of students’ utterances has increased from 110 syllables per minute on average in September to 129 syllables per minute on average in November. The proportion of error-free clauses has increased from 0.5 to 0.55, and the number of clauses in the T unit has increased from 1.42 to 1.51. Students [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] have a gradually increasing accuracy of utterance, while student [Formula: see text] shows a gradually decreasing utterance accuracy. Students [Formula: see text] and [Formula: see text] have a decreasing utterance accuracy, while students [Formula: see text] and [Formula: see text] present an increasing utterance accuracy. Besides, the utterance accuracy of students [Formula: see text] and [Formula: see text] first decreases and then increases. The performances in utterance complexity of students [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] have been increasing gradually, while the performance of student [Formula: see text] has been decreasing gradually. The normal distribution of each student’s test scores is different, as well. Hence, any minor changes will affect the overall oral English levels of students.

Introduction -- Natural and man-made disasters are threatening countries and causing fatal damages to the economy, the environment and the livelihood and well-being of people across the globe (Zhang and Huang, 2018). Scholars in many disciplines and practitioners in several fields have realized the significance of natural and environmental disasters for decades. The study of environmental disasters is interdisciplinary and requires a broad range of knowledge and research to understand the natural and human dimensions of disasters. Some environmental disasters are known to be directly caused by human actions and behavior, while others are beyond human control and understanding. Reducing associated risks with existing and potential hazards that threaten people and societies is the ultimate goal of disaster management (O'Brien et al., 2006) at local, regional and international scales. Disaster management is based on the belief that human intervention and action can help humans mitigate and adapt to disasters. There is an extensive scholarly and practitioner literature related to disaster management, as virtually all jurisdictions have to grapple with environmental disasters. Yet, capacity for human intervention, ‘management’ and action remain limited in the context of uncertainty related to many types of natural disasters. In addition, there are a wide range of government, non-government and private sector actors involved in environmental disaster management. This thesis focuses on government-led regimes related to environmental disaster management in the Great Lakes region. The introduction covers the significance of disasters globally, in Canada and the United States, and in the Great Lakes region. It introduces key concepts and dimensions of environmental disasters; outlines the objectives of this research project; the central research questions; and provides an overview of the structure of the thesis. Prior to a focus on environmental disasters in the Great Lakes, the researcher had an interest of exploring environmental disaster management in the Red Sea region in Saudi Arabia. The goal was to investigate transboundary and national government arrangements related to environmental disasters in the Red Sea within the borders of the Kingdom of Saudi Arabia, Egypt and Sudan. A comparative research approach was considered to study both regions and compare government arrangements. However, due to the lack of publically available government documents and some government restrictions on data related to environmental disasters management in the Red Sea, a focus on environmental disaster management in that region and a comparative analysis was not feasible to conduct. Preliminary research indicated that there was not a lot of research on this topic in the Great Lakes region. Therefore, this thesis focuses on environmental disaster management in the Great Lakes region of North America providing some research foundations for possible comparative research in the future.

Monkeypox disease (MPX) is currently considered a global threat after COVID-19. European Medicines Agency (EMA) approved Tecovirimat in capsule dosage form (200 mg) as the first treatment for MPX in January 2022. This article highlights Tecovirimat’s development and patent literature review and is believed to benefit the scientists working on developing MPX treatments. The literature for Tecovirimat was gathered from the website of SIGA Technologies (developer of Tecovirimat), regulatory agencies (EMA, United States Food and Drug Administration (USFDA), and Health Canada), PubMed, and freely accessible clinical/patent databases. Tecovirimat was first recognized as an anti-orthopoxvirus molecule in 2002 and developed by SIGA Technologies. The USFDA and Health Canada have also recently approved Tecovirimat to treat smallpox in 2018 and 2021, respectively. The efficacy of Tecovirimat was verified in infected non-human primates (monkeys) and rabbits under the USFDA’s Animal Rule. Most clinical studies have been done on Tecovirimat’s safety and pharmacokinetic parameters. The patent literature has revealed inventions related to the capsule, injection, suspension, crystalline forms, amorphous form, and drug combinations (Tecovirimat + cidofovir) and process for preparing Tecovirimat. The authors foresee the off-label use of Tecovirimat in the USA and Canada for MPX and other orthopoxvirus infections. The authors also trust that there is immense scope for developing new Tecovirimat-based treatments (new drug combinations with other antivirals) for orthopoxvirus and other viral diseases. Drug interaction studies and drug resistance studies on Tecovirimat are also recommended. Tecovirimat is believed to handle the current MPX outbreak and is a new hope of biosecurity against smallpox or orthopoxvirus-related bioterrorism attack.

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the “context windows” of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

Since past several years, finding relevant documents from plethora of web repositories has become prime attention of the scientific community. To find out relevant research articles, state-of-the-art techniques employ content, metadata, citations, and collaborative filtering based approaches. Among all of them, citation based approaches hold strong potential because most of the time, authors cite relevant papers. Bibliographic coupling is one of the well-known citation based approaches for recommending relevant papers. In this paper, we present an approach SwICS that harnesses number of common references between pair of documents as similarity measure whereas the distribution of in-text citations within the text are not analyzed. The proposed approach explores the in-text citation frequencies within contents of the paper and in-text citation patterns between different logical sections for bibliographically coupled papers. For evaluation, the employed data set contains 1150 research documents are obtained from a well-known autonomous citation index known as: CiteSeer. A comprehensive user study is conducted to build a gold standard for comparing the proposed approach. The approach is compared with the state-of-the-art bibliographic coupling and content similarity based techniques. The comparison results revealed that proposed approach significantly performs better than the contemporary approaches. The comparison result with gold standard yielded an average of 0.73. The average gain achieved by the proposed approach is 60% from state-of-the-art: bibliographic coupling. Whereas, the correlation between gold standard and content based approach remains 20%. The proposed approach can play a significant role for search engines and citation indexers in terms of improving the quality of their results.

In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation, and others. It is a task that first identifies appropriate syntactic categories for each word in running text and second assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work. Previously, we presented conditional random field (CRF)-based POS tagger with both language dependent and independent feature set. However, in the current study, we offer: 1) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and 2) to highlight diverse challenges which cause Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN), and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward long short-term memory (LSTM)-RNN and LSTM-RNN with CRF output.

This paper examines the impact of large-scale transport investment and the resulting increase of accessibility on the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia (KSA). Spatial and temporal economic data for the 13 regions of the country from 1999 to 2018 are analyzed using static and dynamic panel data models. Both first difference GMM (Generalised Method of Moments) models and system GMM models fit the data satisfactorily, with system GMM models generally offering better modelling accuracies. The results show that the elasticity of the one-year lagged GDP variable is positive and statistically significant in all specifications considered, indicating the presence of a dynamic pattern toward economic growth. Generally, the value of transport investment for the one-year lag shows positive and significant statistical relationships with regional GDP among several specifications of the dynamic models. On the other hand, railway accessibility value presents positive and significant impact on GDP in two years lag. Our study finds that the impact of the monetary value of transport investment, in general, was manifested immediately in the following year. However, the railway accessibility improvement requires a period to deliver its benefits.

In Information Retrieval systems, stemming handles the words that can occur in different morphological forms, and hence matches the terms of the documents and the queries that are related in meanings. In this article, we have proposed a cognitive inspired language-independent stemming that learns group of morphologically related words from the ambient corpus without any linguistic knowledge or human intervention and it behaves in a way the human brain works. The main idea of our proposed algorithm is to determine only those variants of the words from the ambient corpus that match the original intent of the query terms. We conducted ad-hoc retrieval experiments in a number of languages of varying morphological complexity using standard TREC, FIRE, and CLEF document collection. The results indicate that stemming improves the retrieval accuracy and the effectiveness of stemming algorithm increases with the increase in the morphological complexity of algorithm. The results also indicates that the performance of our proposed algorithm is better than the stemmers based on linguistic knowledge and other state-of-the-art statistical stemmers in almost all the languages under study. In multi-lingual setup these results are quite encouraging.

Part of speech (POS) tagging, the assignment of syntactic categories for words in running text, is significant to natural language processing as a preliminary task in applications such as speech processing, information extraction, and others. Urdu language processing presents a challenge due to the dual behaviour of various Urdu POS tags in differing situations (morphosyntactic ambiguity). This paper addresses this challenge by developing a novel tagging approach using linear-chain conditional random fields (CRF). Our work is the first instance of a CRF approach for Urdu POS tagging. The proposed model employs a strong, stable and balanced language-independent as well as language dependent feature set. The language-dependent feature considered includes part-of-speech tag of the previous word and suffix of the current word while the language-independent features includes the ‘context words window’. Our approach was evaluated against support vector machine techniques for Urdu POS—considered as state of the art—on two benchmark datasets. The results show our CRF approach to improve upon the F-measure of prior attempts by 8.3–8.5%.

Background: Breast cancer is one of the most common malignant cancers in women around the world. Until now, despite great improvements in treatment the high incidence rate and mortality worldwide are exponential. Thus identification of novel molecular cancer drivers and evaluation of existing breast cancer biomarkers is a critical step. Objective:This study aims to explore one of the driven genes-ERBB2 receptor- a good predictive cancer biomarker, its specific TK receptor domain mutations, biological pathways, and oncoproteins challenging the apoptotic process, involved in the progression of breast cancerusingInsilicoapproach. Methods: We used the multiple advanced bioinformatics tools performing structure-based Domain architecture analysis with predicted functional protein association of ERBB2 gene and the role of post-translational modification-- in instigating TK receptor domain mutations providing an understanding of the underlying mechanism of tumor invasion and metastasis. Results:Considering all approaches as complementary the TK inhibitors and the role of the ERBB2 gene can improve drug targeting prediction strategy highlighting the importance of driver genes. Our results suggest that functional validation is a positive prediction and we provided answers to some of the imperative questions although important concerns in the field remain unresolved like the implications for therapeutics etc. for future biological and clinical accomplishments. Conclusion:The evaluation of the existing cancer candidate gene and the interacting proteins and pathways particularly ERBB2- downstream signaling pathway has the potential to generate novel hypotheses in oncology. Thusprovide a baseline to identify target protein-based pathways involved through wet experiments.&nbsp

The authors wish to make the following corrections to their paper [...]

Background Oestrogen is the primary female sex hormone and has important functions in both female and male physiology. Recently oestrogen abuse among male gym participants had raised as it is considered to enhance gym performance and breast size. Aims This study assesses the prevalence of oestrogen use among male gym patrons in Saudi Arabia, their practice related to oestrogen abuse, and the profiles of users. Methods A cross-sectional study was conducted from February 2017 to May 2017 and included 4,860 male gym patrons. The participants were given a questionnaire with a total of 19 questions regarding socioeconomic information, knowledge and practices related to oestrogen, and lifestyle habits. Results The participants had a mean age of 28.6+6.2 years, 6.1 per cent of them abused oestrogen, and the most common forms used were ethinylestradiol (0.03mg) and drospirenone (3mg). Furthermore, 80.7 per cent of the users used it before exercise only. Breast enlargement was the main reason for oestrogen use, and local drug stores were the main source. Compared to non-users, oestrogen users were older (P=0.322), reported lower incomes (P=0.395), were more likely to be active smokers (P=0.597), and had a longer duration of gym participation (P < 0.001). Conclusion The results indicate that 6.1 per cent of the surveyed male participants abused a combination of oestrogen and progesterone for breast enlargement, which was significantly more likely among males who had longer durations of gym participation.