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• A framework computes workers trustworthiness based on trust and reputation factors. • It produces a degree of trustworthiness score for a fairer decision. • Sentiment analysis using Naïve Bayes to identify potential malicious workers. • Introduces the use of referrals to alleviate new workers' cold start problem. • Results show that our approach achieves greater accuracy in task allocation. Unlike crowdsourcing, Spatial Crowdsourcing (SC) requires workers to travel to a specific physical location to accomplish a task. Due to its open concept, the platform accepts any interested individual as workers or task requesters, including those who may be unreliable and untrustworthy. Deploying untrustworthy workers in spatial tasks can negatively impact the quality of the completed tasks, thus threatening the sustainability of the SC platform. Recent research has been carried out to evaluate workers' trustworthiness based on the Trust and Reputation (TR) system. Current TR system approaches for evaluating workers' trustworthiness are mostly relying on a single trust or reputation factor, and the decisions are mainly binary. This binary representation of trustworthiness is considerably rigid and may cause severe repercussions like, an untrustworthy worker who could be the victim of partial ratings may end up not getting any kind of spatial tasks from the system, or a trustworthy worker who may have malicious intention may be allocated a spatial task. To address these limitations, we propose a novel framework that allocates every spatial task according to a workers' degree of perceived trustworthiness computed based on multi-criteria trust and reputation factors using a Mamdani fuzzy inference system. Our work considers historical ratings to calculate reputation value, applies sentiment analysis to infer trust value, implements Mamdani fuzzy inference to determine trustworthiness degree, and introduces the concept of referral to mitigate worker cold-start problems in spatial crowdsourcing. Our experimental findings on the Yelp real-world datasets demonstrate the reliability of the proposed framework to allocate every spatial task of various types to the most trustworthy workers from huge crowds of available workers. When evaluated against other baseline approaches, our approach achieves greater accuracy in allocating the right tasks to the most trustworthy workers. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is an autoimmune disorder in which the body's defense system wrongly attacks healthy body tissues. The objective of this current setup was to quantify and compare the serum concentration of ascorbic acid (Vit-C), malondialdehyde (MDA), c-reactive protein (CRP) and trace elements (Cu, Fe, Mn and Zn) in SLE and normal subjects. The proposed case-control study was performed with 25 SLE patients and 25 healthy subjects as case and control, respectively. The serum level of malondialdehyde (MDA) and vitamin C was evaluated by UV spectrophotometric method. For the determination of CRP, the latex agglutination method was used, whereas serum trace elements were estimated by atomic absorption spectroscopy (AAS). This analysis demonstrated that patients with SLE possessed a significant (p < 0.001) higher level of MDA and lower level of vitamin C compared to control subjects. Pearson's correlation analysis found negative correlation between the serum level of MDA and vitamin C (r= -0.023, p = 0.887) for patients while control group also possessed similar result (r= -0.157, p = 0.453). The current findings have also revealed that serum level of Zn and Cu in SLE patients was significantly (p < 0.05) lowered to that of the control group, while serum level of Mn also showed a similar scenario. During Pearson's correlation analysis a significantly (p < 0.05) negative correlation was found between Zn and Mn (r= -0.410, p = 0.042) in patients' group. Although our study was limited to a small sample size and confined to a particular area of the country, the study results support a significant role of antioxidants, CRP, and trace elements in the generation of SLE and, therefore, recommends a large spectrum study of the associations between SLE and these biochemical parameters. [ABSTRACT FROM AUTHOR]

In present study, performance and emissions of a 100 kVA common rail turbocharged diesel engine electric generator installed with an emulsifier free emulsion fuel supply system, known as Real-Time Non-Surfactant Emulsion Fuel Supply System (RTES) was investigated experimentally. This system uses a novel mixing system design upgraded from previous RTES design, with an omission of ultrasonic agitator component. In this system, water was injected into an emulsification device where water and diesel fuel were mixed and supplied to the engine as an emulsion fuel. It was found that water-in-diesel emulsions produced from this system increased the engine's Brake Thermal Efficiency (BTE) to a maximum point of 42.7%, a marked increase of 28.6% from the base diesel. Furthermore, Brake Specific Fuel Consumption (BSFC) was reduced as water percentage increases. Additionally, Nitrogen Oxides (NOx) emissions and smoke opacity were also reduced. However, water-in-diesel emulsion emitted increased carbon monoxide (CO) in lower to middle loads before a reduction was observed in higher loads. Interestingly, unburned hydrocarbons (UHC) emission reduced substantially for water-in-diesel emulsions with water percentages between 14% and 22%. In short, emulsifier free emulsion fuel produced by RTES was able to improve thermal efficiency and reduce fuel consumption and harmful emissions. • Implementation of a novel in-line emulsifier was used to produce water-in-diesel fuel. • A turbocharged common rail diesel electric generator was used in the experiments. • Thermal efficiency was enhanced for all water-in-diesel concentrations. • Smoke opacity decreased substantially with increasing engine load. • NOx reduction was not as high as previously reported studies. [ABSTRACT FROM AUTHOR]