► This study uses text mining techniques to investigate hidden patterns in consumers’ attitudes towards global brands. ► The study found that Twitter can be used as a reliable method in analyzing ...attitudes towards global brands. ► Results show that companies can effectively use the blogosphere to redesign their marketing and advertising campaigns.
Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers’ sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands’ tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.
In this comprehensive review we analyze the intellectual structure of the halal food research based on 290 Web of Science (WoS) core collection documents written by 633 authors from 41 countries over ...three decades (1990–2019). The review adopts a knowledge domain visualization technique to explore the basic bibliometric/scientometric profile of the field, its influential authors, its core journals, its representative references, its collaborative patterns, its research hotspots/emerging trends, its changing paradigms and its knowledge/intellectual structure. We also borrow concepts from social network analysis to uncover and map out structural attributes of communication networks in the field as well as to illuminate how different actors exchange information. Social network analysis shows that halal food research networks reflect what is sometimes called the Matthew Effect in sociology-a mechanism akin to the preferential attachment in which a small number of hubs have larger number of connections compared to the rest of actors. We further use burst detection techniques to visually explore halal food research fronts and its distinct schools of thought (the so-called ‘invisible colleges’). By so doing, we provide a window through which the knowledge domain of halal food landscape can be examined.
•The study reviews the intellectual structure of halal food research.•Basic bibliometric structure of halal food is investigated.•Collaboration patterns in the field are examined.
Photosynthesis is a fundamental biosynthetic process in plants that can enhance carbon absorption and increase crop productivity. Heat stress severely inhibits photosynthetic efficiency. Melatonin is ...a bio-stimulator capable of regulating diverse abiotic stress tolerances. However, the underlying mechanisms of melatonin-mediated photosynthesis in plants exposed to heat stress largely remain elucidated. Our results revealed that melatonin treatment (100 μM) in tomato seedlings increased the endogenous melatonin levels and photosynthetic pigment content along with upregulated of their biosynthesis gene expression under high-temperature stress (42 °C for 24 h), whereas heat stress significantly decreased the values of gas exchange parameters. Under heat stress, melatonin boosted CO2 assimilation, i.e., Vc,max (maximum rate of ribulose-1,5-bisphosphate carboxylase, Rubisco), and Jmax (electron transport of Rubisco generation) and also enhanced the Rubisco and FBPase activities, which resulted in upregulated photosynthetic related gene expression. In addition, heat stress greatly reduced the photochemical chemistry of photosystem II (PSII) and photosystem I (PSI), particularly the maximum quantum efficiency of PSII (Fv/Fm) and PSI (Pm). Conversely, melatonin supplementation increased the chlorophyll a fluorescence parameters led to amplifying the electron transport efficiency. Moreover, heat stress decreased the actual PSII efficiency (ΦPSII), electron transport rate (ETR) and photochemical quenching coefficient (qP), while increasing nonphotochemical quenching (NPQ); however, melatonin reversed these values, which helps to fostering the dissipation of excess excitation energy. Taken together, our results provide a concrete insight into the efficacy of melatonin-mediated photosynthesis performance in a high-temperature regime.
•Melatonin increased photosynthetic pigment contents and CO2 assimilation of tomato seedlings exposed to heat stress.•Melatonin protected the photosystem II and photosystem I reaction center and reduced photoinhibition under thermal stress.•Melatonin enhanced photosynthesis efficiency by elevating the Rubisco and FBPase enzyme activities.•Melatonin treatment efficiently enhanced the heat-induced reduction of chlorophyll a fluorescence photochemistry.
The outbreak of coronavirus disease (COVID-19) not only affected health and economics, but also its effect extended to include other aspects, such as the environment. Using Egypt as a case study, ...this paper presents the impact of COVID-19 pandemic on air pollution levels by studying nitrogen dioxide (NO2), ozone (O3), particulate matter represented in absorbing aerosol index (AAI), carbon monoxide (CO), and greenhouse gas (GHG) emissions. The paper also highlights the impact of COVID-19 pandemic on other environmental indicators including environmental noise, medical and municipal solid wastes. The paper presents the Egyptian COVID-19 story from its different angles including the development of confirmed COVID-19 cases, containment measures from the government, the impact on the country's economy and the national energy consumption so as to effectively evaluate the effect on both the air pollution levels and the other studied environmental indicators. For the other environmental indicators, a strong link was observed between COVID-19 lockdown and the reduction in environmental noise, beaches, surface and groundwater pollution. For environmental noise, this has been confirmed by officially governmental announcements which reported that the level of environmental noise in Egypt was reduced by about 75% during the lockdown period. On the other hand, there are some negative effects, including an increase in medical solid waste (from 70 to 300 ton/day), municipal solid waste, as well as a less efficient solid waste recycling process. For air pollution levels, the data were obtained from National Aeronautics and Space Administration (NASA) and European Space Agency satellite data sets. The data for the lockdown period in 2020 have been extracted and compared to the corresponding months in the selected baseline period (2015–2019) to identify the effect that the lockdown period had on the air pollution levels in Egypt with focus on Cairo and Alexandria governorates. It was found that the AAI decreased by about 30%, the NO2 decreased by 15 and 33% over Cairo and Alexandria governorates, respectively, and that the CO decreased by about 5% over both governorates. In addition, the GHG emissions in Egypt were reduced by at least 4% during the pandemic. In contrast, ozone levels increased by about 2% over Cairo and Alexandria governorates. It can be concluded that the implemented containment measures during COVID-19 pandemic had resulted in both positive and negative environmental impacts. The positive environmental impacts are not sustainable and deterioration on them is expected to occur after the lockdown as it was before the pandemic. Therefore, stricter laws must be enacted to protect the environment in Egypt.
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•NO2 decreased over Cairo (~ 15%) and Alexandria (~ 33%).•AAI and GHG emissions of Egypt decreased by about 30% and 4%, respectively.•An increase in ozone level (2%) and a reduction in CO (5%) over Cairo and Alexandria.•Reduction in environmental noise, beaches, surface and groundwater pollution.•Increase municipal and medical wastes and less efficient waste recycling.
Nanoscale zero-valent iron (nZVI) particles were investigated for the removal of methylene blue (MB) from aqueous solutions and the treatment of textile industry effluents. The nZVI material was ...characterized by XRD, TEM, EDS, FTIR, and SEM. It was demonstrated that several functional groups such as C-H, C = C, C-C, and C-O contributed to MB reduction. At initial MB concentration of 70 mg/L, the optimum pH was 6, achieving a removal efficiency of 72.1% using an nZVI dosage of 10 g/L, stirring rate of 150 rpm, and temperature of 30 °C within 30 min. The adsorption isotherm was described by the Langmuir model with monolayer coverage of 5.53 mg/g, and the Freundlich equation with multilayer adsorption capacity of 1.59 (mg/g)·(L/mg)
. The removal mechanisms of MB included reduction into colorless leuco-MB, precipitation as Fe(II)-MB, adsorption as ZVI-MB or FeOOH-MB, and/or degradation using
OH radicals. The synthesized nZVI particles were applied to reduce various organic and inorganic compounds, as well as heavy metal ions from real textile wastewater samples. The removal efficiencies of COD, BOD, TN, TP, Cu
, Zn
, and Pb
reached up to 91.9%, 87.5%, 65.2%, 78.1%, 100.0%, 29.6%, and 99.0%, respectively. The treatment cost of 1 m
of textile wastewater was estimated as 1.66 $USD.
Carbon emissions from products contribute to anthropogenic climate change. Because of the growing concern over the environmental impact of production and consumption of consumer goods, carbon ...footprint information started to appear on labels of several products. In this paper we use both parametric and non-parametric econometric models in order to estimate Egyptian consumers' willingness to pay (WTP) for carbon-labeled products. Contingent valuation methods based on log-logistic, log-normal and Weibull regression models revealed that consumers in Egypt are willing to pay a price premium of approximately 75 Egyptian pounds (EP) for carbon-labeled products based on the single-bound dichotomous choice (SBDC) model and up to 90 EP based on the double-bound dichotomous choice (DBDC) model. From a socio-economic perspective, results have also revealed that income, age, gender, and educational level have a significant influence on the respondent's WTP. Implications of this study highlight the fact that understanding consumers' preferences for eco-friendly products may play an important role in formulating environmental policy changes to face complex problems as diverse as environmental pollution or global climate change.
Polymeric nanocomposites (PNC) have an outstanding potential for various applications as the integrated structure of the PNCs exhibits properties that none of its component materials individually ...possess. Moreover, it is possible to fabricate PNCs into desired shapes and sizes, which would enable controlling their properties, such as their surface area, magnetic behavior, optical properties, and catalytic activity. The low cost and light weight of PNCs have further contributed to their potential in various environmental and industrial applications. Stimuli-responsive nanocomposites are a subgroup of PNCs having a minimum of one promising chemical and physical property that may be controlled by or follow a stimulus response. Such outstanding properties and behaviors have extended the scope of application of these nanocomposites. The present review discusses the various methods of preparation available for PNCs, including in situ synthesis, solution mixing, melt blending, and electrospinning. In addition, various environmental and industrial applications of PNCs, including those in the fields of water treatment, electromagnetic shielding in aerospace applications, sensor devices, and food packaging, are outlined.
This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on green consumption in Kuwait. SOM is a machine learning method that can be used to ...explore patterns in large and complex datasets for linear and non-linear patterns. The results show that major variables affecting green consumption are related to altruistic values, environmental concern, environmental knowledge, skepticism towards environmental claims, attitudes toward green consumption, and intention to buy green products. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.
Floods are among the devastating types of disasters in terms of human life, social and financial losses. Authoritative data from flood gauges are scarce in arid regions because of the specific type ...of dry climate that dysfunctions these measuring devices. Hence, social media data could be a useful tool in this case, where a wealth of information is available online. This study investigates the reliability of flood related data quality collected from social media, particularly for an arid region where the usage of flow gauges is limited. The data (text, images and videos) of social media, related to a flood event, was analyzed using the Machine Learning approach. For this reason, digital data (758 images and 1413 video frames) was converted into numeric values through ResNet50 model using the VGG-16 architecture. Numeric data of images, videos and text was further classified using different Machine Learning algorithms. Receiver operating characteristics (ROC) curve and area under curve (AUC) methods were used to evaluate and compare the performance of the developed machine learning algorithms. This novel approach of studying the quality of social media data could be a reliable alternative in the absence of real-time flow gauges data. A flash flood that occurred in the United Arab Emirates (UAE) from March 7-11, 2016 was selected as the focus of this study. Random forest showed the highest accuracy of 80.18% among the five other classifiers for images and videos. Precipitation/rainfall data were used to validate social media data, which showed a significant relationship between rainfall and the number of posts. The validity of the machine learning models was assessed using the area under the curve, precision-recall curve, root mean square error, and kappa statistics to confirm the validity and accuracy of the model. The data quality of YouTube videos was found to have the highest accuracy followed by Facebook, Flickr, Twitter, and Instagram. These results showed that social media data could be used when gauge data is unavailable.
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