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Greenfield investment is considered the backbone of emerging economies and developing countries. This research is carried out to investigate the causal impact of Greenfield investment as a target variable and some other controlled variables for the sample of 23 Latin American and Caribbean (LA&C) developing countries. The period is 1998–2017, and Levin, Lin and Chu (LLC) and System-Generalized Method of Moment (Sys-GMM) techniques are employed for analytical analysis. The Sys-GMM technique estimates show that Greenfield investment has a significant positive impact on these countries’ economic growth, health, education, and welfare. Furthermore, controlled variables remittances have a significant and positive impact, while foreign aid has a negative effect on the dependent variables. The rest of the other controlled variables show mixed results. From the analysis, it is suggested that Greenfield investment has improved per capita income, education and health sector that further enhanced the welfare of the society. In addition, new foreign investment creates job employment and brings innovations that improve labour skills. On the other hand, foreign aid must be avoided, which harms the economic activities of developing countries. Therefore, it is concluded that governments of Latin American and Caribbean developing countries adopt more friendly policies to attract Greenfield investment.

Electricity theft is the largest type of non-technical losses faced by power utilities around the globe. It not only raises revenue losses to the utilities but also leads to lethal fires and electric shocks at distribution side. In the past, field operation groups were sent by the utilities to conduct inspections of suspicions electric equipments stated by the public. Advanced metering infrastructure based recent development in the smart grids makes it easy to detect electricity thefts. However, the conventional supervised learning techniques have low theft detection performance mainly due to imbalance datasets available for training. Therefore, in this paper, we develop a novel theft detection model with twofold contribution. A unique hybrid sampling technique named as hybrid oversampling and undersampling using both classes (HOUBC) is proposed to balance the dataset. HOUBC first performs undersampling and then oversampling using both the majority (normal) and minority (theft) classes. A new deep learning method, fractal network is applied with light gradient boosting method to extract and learn important characteristics from electricity consumption profiles for identifying electricity thieves. The proposed model relies on smart meter's data for theft detection and hence, a rapid and widespread adaption of this model is feasible, which shows its main advantage. The performance of the model is evaluated with real-world smart meter's data, i.e., state grid corporation of China. Comprehensive simulation results describe the effectiveness of the proposed model against conventional schemes in terms of electricity theft detection.

Aspect-based sentiment analysis refers to the task of determining the sentiment polarity associated with particular aspects mentioned in a sentence or document. Previous studies have used attention-based neural network models to connect aspect terms with context words, but these models often perform poorly due to limited interaction between aspect terms and opinion words. Furthermore, these models typically focus only on explicitly stated aspect objects, which can be overly restrictive in certain scenarios. Current sentiment analysis methods that rely on aspect categories also often fail to consider the implicit placement of aspect-category information within the context. While existing models may produce strong results, they often lack domain knowledge. To address these issues, this study proposes an Aspect-position and Entity-oriented Knowledge Convolutional Graph (APEKCG) consisting of two modules: the Aspect position-aware module (APA) and the Entity oriented Knowledge Dependency Convolutional Graph (EKDCG). The APA module is designed to integrate aspect-specific sentiment features for sentiment classification by incorporating information about aspect categories into different parts of the context. The EKDCG module incorporates entity-oriented knowledge, dependency labels, and syntactic path using a dependence graph. Experimental results on five benchmarks Natural Language Processing (NLP) datasets of the English language demonstrate the effectiveness of the proposed APEKCG framework. Furthermore, the APEKCG outperformed previous state-of-the-art models with its accuracy, achieving 89.13%, 84.32%, 89.02%, 79.64%, and 90.22% on the MAMS, Laptop, Restaurant, AWARE, and SemEval-15&16 datasets, respectively.

Over the years, there has been a rise in the number of fabricated and fake news stories that utilize both textual and visual information formats. This coincides with the increased likelihood that users will acquire their news from websites and social media platforms. While there has been various research into the detection of fake news in text using machine learning techniques, less attention has been paid to the problem of multimedia data fabrication. In this paper, we propose a Stylometric, and Semantic similarity oriented for Multimodal Fake News Detection (SSM). There are five distinct modules that make up our methodology: Firstly, we used a Hyperbolic Hierarchical Attention Network (Hype-HAN) for extracting stylometric textual features. Secondly, we generated the news content summary and computed the similarity between Headline and summary. Thirdly, semantic similarity is computed between visual and textual features. Fourthly, images are analyzed for forgery. Lastly, the extracted features are fused for final classification. We have tested SSM framework on three standard fake news datasets. The results indicated that our suggested model has outperformed the base line and state-of-the-art methods and is more likely to detect fake news in complex environments.

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.

News media agencies are known to publish misinformation, disinformation, and propaganda for the sake of money, higher news propagation, political influence, or other unfair reasons. The exponential increase in the use of social media has also contributed to the frequent spread of fake news. This study extends the concept of symmetry into deep learning approaches for advanced natural language processing, thereby improving the identification of fake news and propaganda. A hybrid HyproBert model for automatic fake news detection is proposed in this paper. To begin, the proposed HyproBert model uses DistilBERT for tokenization and word embeddings. The embeddings are provided as input to the convolution layer to highlight and extract the spatial features. Subsequently, the output is provided to BiGRU to extract the contextual features. The CapsNet, along with the self-attention layer, proceeds to the output of BiGRU to model the hierarchy relationship among the spatial features. Finally, a dense layer is implemented to combine all the features for classification. The proposed HyproBert model is evaluated using two fake news datasets (ISOT and FA-KES). As a result, HyproBert achieved a higher performance compared to other baseline and state-of-the-art models.

Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the performance of deep learning models depends on the values of the hyperparameters. Determining suitable values for hyperparameters is a cumbersome task. The goal of this study is to increase the accuracy of stacked autoencoders for sentiment analysis using a heuristic optimization approach. In this study, we propose a hybrid model GA(SAE)-SVM using a genetic algorithm (GA), stacked autoencoder (SAE), and support vector machine (SVM) for fine-grained sentiment analysis. Features are extracted using continuous bag-of-words (CBOW), and then input into the SAE. In the proposed GA(SAE)-SVM, the hyperparameters of the SAE algorithm are optimized using GA. The features extracted by SAE are input into the SVM for final classification. A comparison is performed with a random search and grid search for parameter optimization. GA optimization is faster than grid search, and selects more optimal values than random search, resulting in improved accuracy. We evaluate the performance of the proposed model on eight benchmark datasets. The proposed model outperformed when compared to the baseline and state-of-the-art techniques.

Numerous studies have been conducted to meet the growing need for analytic tools capable of processing increasing amounts of textual data available online, and sentiment analysis has emerged as a frontrunner in this field. Current studies are focused on the English language, while minority languages, such as Roman Urdu, are ignored because of their complex syntax and lexical varieties. In recent years, deep neural networks have become the standard in this field. The entire potential of DL models for text SA has not yet been fully explored, despite their early success. For sentiment analysis, CNN has surpassed in accuracy, although it still has some imperfections. To begin, CNNs need a significant amount of data to train. Second, it presumes that all words have the same impact on the polarity of a statement. To fill these voids, this study proposes a CNN with an attention mechanism and transfer learning to improve SA performance. Compared to state-of-the-art methods, our proposed model appears to have achieved greater classification accuracy in experiments.

Background In the UK, the National Health Service has various incentivisation schemes in place to improve the provision of high-quality care. The Quality Outcomes Framework (QOF) and other Pay for Performance (P4P) schemes are incentive frameworks that focus on meeting predetermined clinical outcomes. However, the ability of these schemes to meet their aims is debated.Objectives (1) To explore current incentive schemes available in general practice in the UK, their impact and effectiveness in improving quality of care and (2) To identify other types of incentives discussed in the literature.Methods This systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched: Cochrane, PubMed, National Institute for Health and Care Excellence Evidence, Health Management Information Consortium, Embase and Health Management. Articles were screened according to the selection criteria, evaluated against critical appraisal checklists and categorised into themes.Results 35 articles were included from an initial search result of 22087. Articles were categorised into the following three overarching themes: financial incentives, non-financial incentives and competition.Discussion The majority of the literature focused on QOF. Its positive effects included reduced mortality rates, better data recording and improved sociodemographic inequalities. However, limitations involved decreased quality of care in non-incentivised activities, poor patient experiences due to tick-box exercises and increased pressure to meet non-specific targets. Findings surrounding competition were mixed, with limited evidence found on the use of non-financial incentives in primary care.Conclusion Current research looks extensively into financial incentives, however, we propose more research into the effects of intrinsic motivation alongside existing P4P schemes to enhance motivation and improve quality of care.

Cadmium (Cd) is a heavy metal and a highly toxic pollutant that is released into the environment as a byproduct of most modern factories and industries. Cd enters our body in significant quantities from contaminated water, cigarette smoke, or food product to many detrimental health hazards. Based on causal association all the Cd-related or derived compounds have been classified as carcinogens. In this study, we present an overview of the published literature to understand the molecular mechanisms for Cd-induced carcinogenesis and its prevention. In acute Cd poisoning production of reactive oxygen species is a key factor. However, chronic Cd exposure can transform cells to become more resistant to oxidative stress. Also, as an epigenetic mechanism Cd acts indirectly on DNA repair mechanisms via alteration of reactions upstream. Those transformed cells acquire resistance to apoptosis and deregulation of calcium homeostasis. Leading to uncontrolled carcinogenic cell proliferation and inherent DNA lesions. Flavonoids commonly found in plant foods have been shown to have a protective effect against Cd-induced carcinogenicity. A wide variety of tumorigenic mechanisms involved in chronic Cd exposure and the beneficial effects of flavonoids against Cd-induced carcinogenicity necessitate further investigations.

Introduction: Various drug–food interactions exist that may hinder treatment and can sometimes be lethal. Our aim was to assess the level of public knowledge and awareness in Jeddah city, Western Saudi Arabia, about drug–food interactions, along with the effects of demographics on their knowledge. Methods: A survey questionnaire was administered in this cross-sectional study to participants spread across multiple locations in Jeddah, including in malls and public gatherings. Participants included both males and females. Sample size was calculated through Raosoft® software. Data analysis was executed using IBM Statistic SPSS and the level of statistical significance was set at p < 0.05. Results: A total of 410 people participated in the study and only 92.68% (380) of responses were enrolled in the study; 7.32% (30) were not enrolled due to the exclusion criteria. Surprisingly, only six out of eighteen questions regarding drug–food interactions in the administered questionnaire were correctly answered by 380 participants. Data indicated that the participants had a poor to intermediate level of both knowledge and awareness with respect to drug–food interactions. Furthermore, participants showed moderate to strong awareness of the effects of alcohol and tea generally, and their interaction with medication. Conclusion: Participants in our study showed inadequate knowledge of basic and fundamental information about drug–food interactions, which highlights the dire need to increase awareness.

Background: It is widely recognized that the incidence of allergic diseases is on the rise globally Allergies in the population have impact on the quality of life by interfering with the daily activities, sleep disturbances, relationship problems etc. Increasing frequency of allergic diseases in the world are huge burden on economy. Identifying the prevalence and characteristics of allergies among medical students would provide constructive information and implementation of preventive measures. Materials and Methods: This study is descriptive cross sectional study and was conducted among 289 students, (both male and female) of MBBS with equal participation from first year to final year students. Participants were selected using systematic randomized methods. The data was collected from February 2017 to June 2017 through questionnaire based on the manifestations of the Allergic symptoms. The frequencies were calculated and data was analyzed using IBM Statistical Packages for Social Sciences (SPSS) version 23. Results: Out of 289 students, 59% (n=171) have allergic symptoms while 41% (n=118) 118 (41%) have not any allergic symptoms. Among those, 120 (70%) students have a positive family history. Dust is the most common triggering factor in 50.29% of the students. The frequency of nasal, eye, skin and throat symptoms are 78%, 70%, and 49% respectively. Conclusion: Government should take steps to control dust in the environment by continuous monitoring of air quality across the major industrial and urban areas. Health education regarding environmental pollution is required at individual and collective level to tackle the menace of air pollution.