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Influence of cerebral glucose metabolism by chronic pain–mediated cognitive impairment in adolescent rats
Get PDFChronic pain during adolescence can lead to mental health disorders in adulthood, but the underlying mechanism is still unclear. Furthermore, the homeostasis of cerebral glucose metabolism and neurotransmitter metabolic kinetics are closely associated with cognitive development and pain progression. The present study investigated changes in cognitive function and glucose metabolism in adult rats, which had experienced chronic pain during their adolescence. Here, spared nerve injury (SNI) surgery was conducted in 4-week-old male rats. Mechanical nociceptive reflex thresholds were analyzed, and SNI chronic pain (SNI-CP) animals were screened. Based on animal behavioral tests (open field, three-chambered social, novel object recognition and the Y maze), the SNI-CP animals showed learning and memory impairment and anxiety-like behaviors, compared to SNI no chronic pain (SNI-NCP) animals. The cerebral glucose metabolism in the prefrontal cortex and hippocampus of adult SNI-CP animals was decreased with positron emission tomography/computed tomography. GABA2 and Glu4 levels in the metabolic kinetics study were significantly decreased in the hippocampus, frontal cortex, and temporal cortex, and the expression of GLUT3 and GLUT4 was also significantly downregulated in the prefrontal cortex and hippocampus of adult rats in the SNI-CP group. These findings suggest that the rats which suffered chronic pain during adolescence have lower cerebral glucose metabolism in the cortex and hippocampus, which could be related to cognitive function during the development of the central nervous system
Performance analysis of selected machine learning techniques for estimating resource requirements of virtual network functions (VNFs) in software defined networks
Get PDFRapid development in the field of computer networking is now demanding the application of Machine Learning (ML) techniques in the traditional settings to improve the efficiency and bring automation to these networks. The application of ML to existing networks brings a lot of challenges and use-cases. In this context, we investigate different ML techniques to estimate resource requirements of complex network entities such as Virtual Network Functions (VNFs) deployed in Software Defined Networks (SDN) environment. In particular, we focus on the resource requirements of the VNFs in terms of Central Processing Unit (CPU) consumption, when input traffic represented by features is processed by them. We propose supervised ML models, Multiple Linear Regression (MLR) and Support Vector Regression (SVR), which are compared and analyzed against state of the art and use Fitting Neural Networks (FNN), to answer the resource requirement problem for VNF. Our experiments demonstrated that the behavior of different VNFs can be learned in order to model their resource requirements. Finally, these models are compared and analyzed, in terms of the regression accuracy and Cumulative Distribution Function (CDF) of the percentage prediction error. In all the investigated cases, the ML models achieved a good prediction accuracy with the total error less than 10% for FNN, while the total error was less than 9% and 4% for MLR and SVR, respectively, which shows the effectiveness of ML in solving such problems. Furthermore, the results shows that SVR outperform MLR and FNN in almost all the considered scenarios, while MLR is marginally more accurate than FNN
Walking into the unknown: a research journey through abuse, trauma, motherhood, poverty, and the Covid pandemic
Get PDFResearching the lives of mothers with children under five from the most deprived areas of a major city in the UK during a pandemic was never going to be easy. In this performative reflection I explore how walking and the side-be-sidedness of our interaction facilitated conversation and understanding, between one young mother and myself, while also considering the way a researcher’s thoughts whirl around and respond to what is being shared, though never voiced at the time
The impact of COVID-19 on group tour operators and the implications for overtourism
Get PDFThis chapter sets out to analyze the impact of the Covid-19 virus on the holidays provided by UK group tour operators (GTOs) and the implications for overtourism. With tourism arrivals expected to fall by up to 30% in 2020 and a slow return to pre-Covid-19 levels for 2021 and beyond, the industry is possibly suffering the loss of up to 100 million travel-related jobs (World Travel and Tourism Council, 2020). GTOs will need to assess and possibly change the way they do business to initially survive and subsequently build up tourism numbers in the coming years.
This chapter identifies how GTOs could alter their holiday proposition to reassure travellers including the challenges of operating international tours when airlines have reduced capacity, the need to consider alternative age demographics who are more likely to travel and assessing existing itineraries to visit rural or small town locations rather than cities where numerous itineraries travel to now.
Finally, this chapter discusses and describes the significance of the findings with insights about possible opportunities based upon the approaches taken by various countries to target potential holidaymakers and the need to create a ‘crisis management plan’ for current and future countries. This may result in operational adjustments to meet these new requirements including the changing outlook of potential customers and the possibility of offering domestic tours to meet the current demand
Employee voice and employee commitment have become a global emergency
Get PDFEmployee voice (EV) and employee commitment (EC) are critical concepts in people management in organizations across the world. There are various definitions of EV and many types of EC in literature. Even though there is no agreed one definition of EV and there are many different forms of EC, both concepts are extremely important to the success of any business. This chapter aimed at exploring the challenges associated with EV and EC across nations; therefore, a systematic review of recent primary studies was adopted to meet the objectives of the chapter. A total of 14 most relevant studies published in 2020 in English, 7 EV and 7 EC studies, were vigorously selected for review across the world through Business Source Complete, Science Direct, Emerald Insight, Oxford Brookes University Library Search, and Google Scholar databases. The search terms that were used to gather relevant studies are “employee voice” and “employee commitment.” The studies were picked from America, Australia, China, Germany, Indonesia, Malaysia, Nigeria, Norway, Pakistan, South Korea, and Thailand because many scholars have presented their interests in EV and EC and considered these countries more. Nevertheless, the first debate that emerged from literature was about the definitions of EVand EC in recent studies, given these concepts are considered across different cultures. After content analysis, the results indicated that there are many factors in recent studies, and these were discussed in this book chapter. Nevertheless, despite the limitations associated with the study selection process that includes bias in selecting the material to consider, the recent studies that were involved in this work exposed EV and EC as a global emergency across industries
Food fraud prevention through traceability within the food supply chain. A scoping review
Get PDFFood fraud is a form of intentional adulteration of a product, usually for the purpose of an economical gain. Food fraud is committed regularly and may vary in form, depending on the nature of the product or target. This research work investigates the effectiveness of various food supply chain traceability methods in combating fraud by reviewing the literature in a systematic way (scoping review).
The scoping review was carried out in accordance with the Joanna Brings Institute methodology which includes three steps search. The review was carried out using 4 different search engines. Only peer reviewed journal articles were included, grey literature was excluded from the review. A set of key words was used with the application of Boolean operators. Screening commenced in two separate stages: (1) title & abstract screening and (2) full text screening. Data has been presented in written and tabular formats.
The initial search resulted in 486 articles, after removal of duplicates the figured decreased to 306. First screening stage led to further exclusion of 248 articles. Another 31 were excluded after second screening leaving 27 peer reviewed journal articles for data extraction.
Main findings indicate that increased traceability has a positive impact on the security of food supply chains in relation to adulteration. However, ‘fraudsters’ seem to stay ahead of the game as new technologies are constantly developed to mask adulteration and falsify test results. ‘Blockchain’ analysis has been outlined as the most popular traceability system used across modern food supply chains. The disadvantages of increased traceability can be attributed to high costs and problems with integrity where human motivation plays a crucial role.
Despite the extensive costs and technical difficulties, application of modern traceability assessments is key to food safety. To help to combat fraud, businesses must focus more on vulnerability assessments
An investigation into the impact of airport operations on ambient air quality: the case study of Sphinx International Airport
Get PDFThis investigation focuses on the predicted environmental impact due to emissions and air pollutants resulting from planned airport operations. The analysis relies on the application of the descriptive and analytical approach on a case study developed at the new Sphinx International Airport (ICAO: HESX, IATA: SPX) serving the city of Giza, Egypt. The theoretical part reviews previous studies in the field to draw the most important results. It contains air pollutant monitoring and measuring in the area where the airport is to be established, weather forecast data, calculation of air traffic and plane emissions as well as the implementation of all necessary data for the emission dispersion modelling mapping program. This is instrumental to predict the pollutant concentrations from all activities at the airport. This research has found that the levels of emission concentrations in the airport area are acceptable and below maximum permissible thresholds. However, the emissions from certain specific sources would be considerably lower if mitigation measures will be implemented. Other sources will increase emissions with lower capacity for mitigation. By way of comparison between the results of the actual analyses of the emissions from planes and the energy consumption, different impact was found on the areas adjacent to the airport. To ensure that the outcomes of the analyses for the emissions comply with national and international standards, recommendations are also suggested. This study indicates that the most important measures must be focused on mitigating emissions during the operational phase
A novel approach for emotion detection and sentiment analysis for low resource Urdu language based on CNN-LSTM
Get PDFEmotion detection (ED) and sentiment analysis (SA) play a vital role in identifying an individual’s level of interest in any given field. Humans use facial expressions, voice pitch, gestures, and words to convey their emotions. Emotion detection and sentiment analysis in English and Chinese have received much attention in the last decade. Still, poor-resource languages such as Urdu have been mostly disregarded, which is the primary focus of this research. Roman Urdu should also be investigated like other languages because social media platforms are frequently used for communication. Roman Urdu faces a significant challenge in the absence of corpus for emotion detection and sentiment analysis because linguistic resources are vital for natural language processing. In this study, we create a corpus of 1021 sentences for emotion detection and 20,251 sentences for sentiment analysis, both obtained from various areas, and annotate it with the aid of human annotators from six and three classes, respectively. In order to train large-scale unlabeled data, the bag-of-word, term frequency-inverse document frequency, and Skip-gram models are employed, and the learned word vector is then fed into the CNN-LSTM model. In addition to our proposed approach, we also use other fundamental algorithms, including a convolutional neural network, long short-term memory, artificial neural networks, and recurrent neural networks for comparison. The result indicates that the CNN-LSTM proposed method paired with Word2Vec is more effective than other approaches regarding emotion detection and evaluating sentiment analysis in Roman Urdu. Furthermore, we compare our based model with some previous work. Both emotion detection and sentiment analysis have seen significant improvements, jumping from an accuracy of 85% to 95% and from 89% to 93.3%, respectively
A comprehensive skills analysis of novice software developers working in the professional software development industry
Get PDFMeasuring and evaluating a learner’s learning ability is always the focus of every person whose aim is to develop strategies and plans for their learners to improve the learning process. For example, classroom assessments, self-assessment using computer systems such as Intelligent Tutoring Systems (ITS), and other approaches are available. Assessment of metacognition is one of these techniques. Having the ability to evaluate and monitor one’s learning is known as metacognition. An individual can then propose adjustments to their learning process based on this assessment. By monitoring, improving, and planning their activities, learners who can manage their cognitive skills are better able to manage their knowledge about a particular subject. It is common knowledge that students’ metacognitive and self-assessment skills and abilities have been extensively studied, but no research has been carried out on the mistakes that novice developers make because they do not use their self-assessment abilities enough. This study aims to assess the metacognitive skills and abilities of novice software developers working in the industry and to describe the consequences of awareness of metacognition on their performance. In the proposed study, we experimented with novice software developers and collected data using Devskiller and a self-assessment log to analyze their use of self-regulation skills. The proposed study showed that when developers are asked to reflect upon their work, they become more informed about their habitual mistakes, and using a self-assessment log helps them highlight their repetitive mistakes and experiences which allows them to improve their performance on future tasks