An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu

During the last two decades, sentiment analysis, also known as opinion mining, has become one of the most explored research areas in Natural Language Processing (NIP) and data mining. Sentiment analysis focuses on the sentiments or opinions of consumers expressed over social media or different web sites. Due to exposure on the Internet, sentiment analysis has attracted vast numbers of researchers over the globe. A large amount of research has been conducted in English, Chinese, and other languages used worldwide. However, Roman Urdu has been neglected despite being the third most used language for communication in the world, covering millions of users around the globe. Although some techniques have been proposed for sentiment analysis in Roman Urdu, these techniques are limited to a specific domain or developed incorrectly due to the unavailability of language resources available for Roman Urdu. Therefore, in this article, we are proposing an unsupervised approach for sentiment analysis in Roman Urdu. First, the proposed model normalizes the text to overcome spelling variations of different words. After normalizing text, we have used Roman Urdu and English opinion lexicons to correctly identify users\u27 opinions from the text. We have also incorporated negation terms and stemming to assign polarities to each extracted opinion. Furthermore, our model assigns a score to each sentence on the basis of the polarities of extracted opinions and classifies each sentence as positive, negative, or neutral. In order to verify our approach, we have conducted experiments on two publicly available datasets for Roman Urdu and compared our approach with the existing model. Results have demonstrated that our approach outperforms existing models for sentiment analysis tasks in Roman Urdu. Furthermore, our approach does not suffer from domain dependency

Clinical and Biological Effects of Adjunctive Photodynamic Therapy in Refractory Periodontitis

Introduction: To date, no novel treatment approach is available for optimum outcomes regarding refractory periodontitis. The aim of the present study was to assess the efficiency of photodynamic therapy (PDT) in treating patients diagnosed with refractory periodontitis and compare the clinical and biological outcomes of conventional periodontal treatment with or without adjunctive PDT in these patients, by assessing clinical parameters (plaque index [PI], gingival recession [GR], bleeding on probing [BOP], periodontal probing depth [PPD] and clinical attachment level [CAL]) as well as biological parameters (IL-1β) in the gingival crevicular fluid (GCF).Methods: Sixteen patients within the age of 30 to 60 years, with a mean age of 40 years old, diagnosed with refractory periodontitis were included. In this split mouth design study, 2 quads (1 upper + 1 lower) from the same patient were randomly treated with (scaling and root planing [SRP]+PDT) together. The other 2 quadrants (1 upper + 1 lower) were treated by SRP only and selected to serve as controls. Clinical parameters including PI, GR, BOP, PPD and CAL and biological parameters (IL-1β) in the GCF were measured at baseline, then at, 2 and 6 months after therapy.Results: A statistically significant reduction in several clinical parameters as, BOP (P < 0.001), PI (P < 0.001), PPD (P < 0.001) and CAL (P < 0.001) in quadrant treated with SRP and adjunctive PDT when compared to control group treated with SRP alone was observed and both therapies showed non-statistically significant differences in the reduction of IL-1β level.Conclusion: The inclusion of PDT as an adjunctive measure to nonsurgical conventional periodontal treatment seems to be a useful therapeutic measure in refractory periodontitis treatment

Optimal Government Strategies for BIM Implementation in Low-Income Economies: A Case Study in Syria

Building information modeling (BIM) enables substantial improvement in the architecture, engineering, and construction (AEC) industry. As a leading actor in the AEC industry, policymakers have the means to develop appropriate strategies for addressing the factor affecting BIM implementation. However, the lack of empirical investigation on the relationships between factors to implementing BIM and government strategies prevents the strategies from being effective. This study aimed to establish relationships between critical factors and government strategies for implementing BIM using Syria as a case study. A systematic literature review and semistructured interviews with AEC professionals yielded 27 factors and 12 government strategies for implementing BIM. The collected data were analyzed using descriptive statistics, a chi-squared test, exploratory factor analysis (EFA), and partial least-squares structural equation modeling (PLS-SEM). The EFA classified the factors into four underlying constructs (technology, project environment, governmental and organizational, and people) and government strategies into two underlying constructs (soft and hard strategies). The structural equation model revealed that soft strategies positively affect technology, project environment, and people. Moreover, hard strategies positively affect technology. These findings provide new insights into the body of knowledge on optimal government strategies for implementing BIM in low-income economies. Policymakers can use the findings of this study to prioritize efforts and resources when promoting BIM implementation in the local AEC industry

Pathogenetic profiling of COVID-19 and SARS-like viruses.

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA-DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein-chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein-chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic

A new estimate of carbon for Bangladesh forest ecosystems with their spatial distribution and REDD+ implications

In tropical developing countries, reducing emissions from deforestation and forest degradation (REDD+) is becoming an important mechanism for conserving forests and protecting biodiversity. A key prerequisite for any successful REDD+ project, however, is obtaining baseline estimates of carbon in forest ecosystems. Using available published data, we provide here a new and more reliable estimate of carbon in Bangladesh forest ecosystems, along with their geo-spatial distribution. Our study reveals great variability in carbon density in different forests and higher carbon stock in the mangrove ecosystems, followed by in hill forests and in inland Sal (Shorea robusta) forests in the country. Due to its coverage, degraded nature, and diverse stakeholder engagement, the hill forests of Bangladesh can be used to obtain maximum REDD+ benefits. Further research on carbon and biodiversity in under-represented forest ecosystems using a commonly accepted protocol is essential for the establishment of successful REDD+ projects and for the protection of the country’s degraded forests and for addressing declining levels of biodiversity

Novel composite materials of modified roasted date pits using ferrocyanides for the recovery of lithium ions from seawater reverse osmosis brine

In this paper, novel composite materials from modified roasted date pits using ferrocyanides were developed and investigated for the recovery of lithium ions (Li+) from seawater reverse osmosis (RO) brine. Two composite materials were prepared from roasted date pits (RDP) as supporting material, namely potassium copper hexacyanoferrate-date pits composite (RDP-FC-Cu), and potassium nickel hexacyanoferrate-date pits composite (RDP-FC-Ni). The physiochemical characterization of the RO brine revealed that it contained a variety of metals and salts such as strontium, zinc, lithium, and sodium chlorides. RDP-FC-Cu and RDP-FC-Ni exhibited enhanced chemical and physical characteristics than RDP. The optimum pH, which attained the highest adsorption removal (%) for all adsorbents, was at pH 6. In addition, the highest adsorption capacities for the adsorbents were observed at the initial lithium concentration of 100 mg/L. The BET surface area analysis confirmed the increase in the total surface area of the prepared composites from 2.518 m2/g for RDP to 4.758 m2/g for RDP-FC-Cu and 5.262 m2/g for RDP-FC-Ni. A strong sharp infrared peak appeared for the RDP-FC-Cu and RDP-FC-Ni at 2078 cm−1. This peak corresponds to the C≡N bond, which indicates the presence of potassium hexacyanoferrate, K4[Fe(CN)6]. The adsorption removal of lithium at a variety of pH ranges was the highest for RDP-FC-Cu followed by RDP-FC-Ni and RDP. The continuous increase in the adsorption capacity for lithium with increasing initial lithium concentrations was also observed. This could be mainly attributed to enhance and increased lithium mass transfer onto the available adsorption active sites on the adsorbents’ surface. The differences in the adsorption in terms of percent adsorption removal were clear and significant between the three adsorbents (P value < 0.05). All adsorbents in the study showed a high lithium desorption percentage as high as 99%. Both composites achieved full recoveries of lithium from the RO brine sample despite the presence of various other competing ions.This work was made possible by Qatar University collaborative internal grant # [QUCG-CAS-20/21-2]. The findings achieved herein are solely the responsibility of the author[s]