Validation of the Arabic version of the Social Communication Questionnaire

Validated screening and diagnostic tools for autism spectrum disorder for use in Arabic-speaking individuals are scarce. This study validated the Arabic version of the Social Communication Questionnaire. The total study sample included 206 children with autism spectrum disorder and 206 typically developing children (73.8% male; mean age: 8.5 (standard deviation = 2.6) years). The mean Social Communication Questionnaire total score was significantly higher in autism spectrum disorder children than in typically developing children (p < 0.0001). Scores on the three Social Communication Questionnaire subscales also differed significantly between the groups (p < 0.001). Of the 39 items, 37 were endorsed significantly more often in the autism spectrum disorder group. The total Social Communication Questionnaire score did not vary by age or gender. Internal consistency was excellent (alpha = 0.92). In the receiver operating characteristic analysis, the area under the curve for the total score showed excellent discrimination between autism spectrum disorder and typically developing children (area under the curve = 0.95; 95% confidence interval: 0.93–0.97). The areas under the curve for the scale subscores were 0.923 (95% confidence interval: 0.898–0.949) for the social interaction score, 0.872 (95% confidence interval: 0.838–0.905) for the communication score, and 0.856 (95% confidence interval: 0.819–0.893) for the repetitive behaviors score. The findings support the use of the Arabic Social Communication Questionnaire to successfully differentiate children with clinically diagnosed autism spectrum disorder using the established cutoff value for the English version.The authors would like to thank all the staff of the autism centers and schools who contributed in distributing and collecting the SCQ forms. They also would like to thank Western Psychological Services (WPS) staff for their help during the process of translating and reviewing the Arabic SCQ. They acknowledge Jennifer Holmes, ELS, for medical editing. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by the Qatar National Research Fund (NPRP 6-093-3-024)

Biomarkers in Spinal Cord Injury: Prognostic Insights and Future Potentials

Spinal Cord Injury (SCI) is a major challenge in Neurotrauma research. Complex pathophysiological processes take place immediately after the injury and later on as the chronic injury develops. Moreover, SCI is usually accompanied by traumatic injuries because the most common modality of injury is road traffic accidents and falls. Patients develop significant permanent neurological deficits that depend on the extent and the location of the injury itself and in time they develop further neurological and body changes that may risk their mere survival. In our review, we explored the recent updates with regards to SCI biomarkers. We observed two methods that may lead to the appearance of biomarkers for SCI. First, during the first few weeks following the injury the Blood Spinal Cord Barrier (BSCB) disruption that releases several neurologic structure components from the injured tissue. These components find their way to Cerebrospinal Fluid (CSF) and the systemic circulation. Also, as the injury develops several components of the pathological process are expressed or released such as in neuroinflammation, apoptosis, reactive oxygen species, and excitotoxicity sequences. Therefore, there is a growing interest in examining any correlations between these components and the degrees or the outcomes of the injury. Additionally, some of the candidate biomarkers are theorized to track the progressive changes of SCI which offers an insight on the patients' prognoses, potential-treatments-outcomes assessment, and monitoring the progression of the complications of chronic SCI such as Pressure Ulcers and urinary dysfunction. An extensive literature review was performed covering literature, published in English, until February 2018 using the Medline/PubMed database. Experimental and human studies were included and titles, PMID, publication year, authors, biomarkers studies, the method of validation, relationship to SCI pathophysiology, and concluded correlation were reported. Potential SCI biomarkers need further validation using clinical studies. The selection of the appropriate biomarker group should be made based on the stage of the injuries, the accompanying trauma and with regards to any surgical, or medical interference that might have been done. Additionally, we suggest testing multiple biomarkers related to the several pathological changes coinciding to offer a more precise prediction of the outcome

Crise de abastecimento de água em São Paulo e falta de planejamento estratégico

Embora a crise no abastecimento de água na Região Metropolitana de São Paulo (RMSP) tenha se manifestado de maneira mais intensa no verão de 2013-2014, ela revela um problema crônico que vem afetando toda a Região nos últimos dez anos. Esse problema foi gerado pela falta de um planejamento estratégico que considere questões climatológicas que podem indicar, com meses de antecedência, problemas de recomposição dos níveis dos mananciais, permitindo que ações sejam empreendidas com razoável antecedência, reduzindo os impactos para a população. Este estudo mostra como é possível utilizar informações climáticas na gestão estratégica do sistema de abastecimento da RMSP.Though the crisis in the water supplying system in the Metropolitan Region of São Paulo (RMSP) was more intensively felt in the 2013-2014 summer, it reveals a chronic problem that has been affecting the whole RMSP for the past ten years. This problem is originated from the lack of a strategic planning that takes into consideration climate issues that could, months before, foresee problems to restore the levels of water resources, allowing measures to be implemented within a reasonable anticipation, therefore reducing the impacts on the population. This study shows how it is possible to use climate information in the strategic management of the water supply in the RMSP

Pyrolytic liquid fuel: a source of renewable electricity generation in Makkah

Millions of Muslims from all over the world visit the Holy Cities of Saudi Arabia: Makkah and Madinah every year to worship in form of Pilgrimage (Hajj) and Umrah. The rapid growth in local population, urbanization, and living standards in Makkah city along with continually increasing number of visitors result in huge municipal solid waste generation every year. Most of this waste is disposed to landfills or dumpsites without material or energy recovery, thus posing substantial environmental and health risks. The municipal plastic waste is the second largest waste stream (up to 23% of total municipal waste) that is comprised of plastic bottles, water cups, food plates, and shopping bags. The sustainable disposal of plastic waste is challenging task due to its clogging effects, very slow biodegradation rates, and presence of toxic additives and dyes. Pyrolysis is one of the promising waste-to-energy technology for converting municipal plastic waste into energy (liquid fuel) and value-added products like char. The produced liquid fuel has the potential to be used in several energy-related applications such as electricity generation, transportation fuel, and heating purposes. It has been estimated that the plastic waste in Makkah city in 2016 can produce around 87.91 MW of electricity. This is projected to increase up to around 172.80 MW of electricity by 2040. A global warming potential of 199.7 thousand Mt.CO2 eq. will be achieved with savings of 7.9 thousand tons emission of CH4, if pyrolysis technology is developed in Makkah city. Furthermore, a total savings of 297.52 million SAR from landfill diversion, electricity generation, and carbon credits would be possible to achieve in 2016 from pyrolysis. These economic benefits will increase every year and will reach up to 584.83 million SAR in 2040

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task

A Novel Algorithm to Find the Best Solution for Pentagonal Fuzzy Numbers with Linear Programming Problems

    Fuzzy numbers are used in various fields such as fuzzy process methods, decision control theory, problems involving decision making, and systematic reasoning. Fuzzy systems, including fuzzy set theory. In this paper, pentagonal fuzzy variables (PFV) are used to formulate linear programming problems (LPP). Here, we will concentrate on an approach to addressing these issues that uses the simplex technique (SM). Linear programming problems (LPP) and linear programming problems (LPP) with pentagonal fuzzy numbers (PFN) are the two basic categories into which we divide these issues. The focus of this paper is to find the optimal solution (OS) for LPP with PFN on the objective function (OF) and right-hand side. New ranking function (RF) approaches for solving fuzzy linear programming problems (FLPP) with a pentagonal fuzzy number (PFN) have been proposed, based on new ranking functions (N RF). The simplex method (SM) is very easy to understand. Finally, numerical examples (NE) are used to demonstrate the suggested approach's computing process

Smart Chatbot for User Authentication

Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed or brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple different authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new authentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up to date information on the user from which authentication questions can be dynamically generated. This paper proposes a system that implements JitHDA, which we call Autonomous Inquiry-based Authentication Chatbot (AIAC). AIAC uses anomalous events gathered from a user’s recent activity to create personalized questions for the user to answer, and is designed to improve its own capabilities over time using neural networks trained on data gathered during authentication sessions. Due to using the user’s recent activity, they will be easy for the authentic user to answer and hard for a fraudulent user to guess, and as the user’s recent history updates between authentication sessions new questions will be dynamically generated to replace old ones. We intend to show in this paper that AIAC is a viable implementation of JitHDA

Mesoporous Silica Particles as Drug Delivery Systems—The State of the Art in Loading Methods and the Recent Progress in Analytical Techniques for Monitoring These Processes

Conventional administration of drugs is limited by poor water solubility, low permeability, and mediocre targeting. Safe and effective delivery of drugs and therapeutic agents remains a challenge, especially for complex therapies, such as cancer treatment, pain management, heart failure medication, among several others. Thus, delivery systems designed to improve the pharmacokinetics of loaded molecules, and allowing controlled release and target specific delivery, have received considerable attention in recent years. The last two decades have seen a growing interest among scientists and the pharmaceutical industry in mesoporous silica nanoparticles (MSNs) as drug delivery systems (DDS). This interest is due to the unique physicochemical properties, including high loading capacity, excellent biocompatibility, and easy functionalization. In this review, we discuss the current state of the art related to the preparation of drug-loaded MSNs and their analysis, focusing on the newest advancements, and highlighting the advantages and disadvantages of different methods. Finally, we provide a concise outlook for the remaining challenges in the field

An overview of Conservation Agriculture in the dry Mediterranean environments with a special focus on Syria and Lebanon

Conservation Agriculture (CA), comprising minimum or no mechanical soil disturbance through no-till seeding, organic soil mulch cover, and crop diversification is now practiced on some 157 million ha worldwide, corresponding to about 11% of the global cropped land. CA adoption in the Middle-East is low compared to other regions. Lack of knowledge on CA practices and systems discourages farmers from giving up ploughing. The main reason why farmers in the Middle-East have begun to apply the no-till system has been the cost reduction in fuel, labor and machinery required for land preparation. Soil and water conservation concerns do not appear to be the main drivers in the Middle-Eastern farmers’ decision to adopt or not to adopt CA. The adoption and uptake of CA by Middle Eastern farmers has been slow but it is nonetheless occurring gradually. Collection of information and research parameters related to agricultural practices are needed for designing a suitable soil and water conservation program for sustainable production intensification. Governmental policy encouraging the adoption and spread of CA systems in the Middle-East region is certainly a necessary condition for uptake. The objective of this article is to review the current status of adoption and spread of CA in the Middle-East, focusing mainly on Syria and Lebanon, and the potential beneficial consequences that can be harnessed through CA systems under rainfed conditions in both countries. The benefits include: higher factor productivity, yield and income; improved soil properties; climate change adaptation, including reduced vulnerability to the erratic rainfall distribution; and reduction in machinery, fuel and labor costs