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  • Warm-up intensity does not affect the ergogenic effect of sodium bicarbonate in adult men

    Jones, Rebecca Louise; Stellingwerff, Trent; Artioli, Guilherme Giannini; Saunders, Bryan; Sale, Craig; Swinton, Paul; ; University of Bedfordshire; Canadian Sport Institute–Pacific; University of Victoria; et al. (Human Kinetics, 2021-07-07)
    This study determined the influence of a high (HI) vs. low-intensity (LI) cycling warm-up on blood acid-base responses and exercise capacity following ingestion of sodium bicarbonate (SB; 0.3 g·kg-1 body-mass (BM)) or a placebo (PLA; maltodextrin) 3-hours prior to warm-up. Twelve men (21±2 years, 79.2±3.6 kg BM, maximum power output (Wmax) 318±36 W) completed a familiarisation and four double-blind trials completed in a counterbalanced order: HI warm-up with SB (HISB); HI warm-up with PLA (HIPLA); LI warm-up with SB (LISB); and LI warm-up with PLA (LIPLA). LI warm-up was 15-minutes at 60%Wmax, while the HI warm-up (typical of elites) featured LI followed by 2 x 30-sec (3-minute break) at Wmax, finishing 30-minute prior to a cycling capacity test at 110%Wmax (CCT110%). Blood bicarbonate and lactate were measured throughout. SB supplementation increased blood bicarbonate (+6.4 [95%CI: 5.7 to 7.1 mmol·L-1]) prior to greater reductions with high intensity warm-up (-3.8 [95%CI: -5.8 to -1.8 mmol·L-1]). However, during the 30-minute recovery, blood bicarbonate rebounded and increased in all conditions, with concentrations ~5.3mmol·L-1 greater with SB supplementation (P<0.001). Blood bicarbonate significantly declined during the CCT110% with greater reductions following SB supplementation (-2.4 [95%CI: -3.8 to -0.90 mmol·L-1]). Aligned with these results, SB supplementation increased total work done during the CCT110% (+8.5 [95%CI: 3.6 to 13.4 kJ], ~19% increase) with no significant main effect of warm-up intensity (+0.0 [95%CI: -5.0 to 5.0 kJ). Collectively, the results demonstrate that SB supplementation can improve HI cycling capacity irrespective of prior warm-up intensity, likely due to blood alkalosis.
  • Unlink the link between COVID-19 and 5G Networks: an NLP and SNA based approach

    Bahja, Mohammed; Safdar, Ghazanfar Ali; University of Birmingham; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2020-11-18)
    Social media facilitates rapid dissemination of information for both factual and fictional information. The spread of non-scientific information through social media platforms such as Twitter has potential to cause damaging consequences. Situations such as the COVID-19 pandemic provides a favourable environment for misinformation to thrive. The upcoming 5G technology is one of the recent victims of misinformation and fake news and has been plagued with misinformation about the effects of its radiation. During the COVID-19 pandemic, conspiracy theories linking the cause of the pandemic to 5G technology have resonated with a section of people leading to outcomes such as destructive attacks on 5G towers. The analysis of the social network data can help to understand the nature of the information being spread and identify the commonly occurring themes in the information. The natural language processing (NLP) and the statistical analysis of the social network data can empower policymakers to understand the misinformation being spread and develop targeted strategies to counter the misinformation. In this paper, NLP based analysis of tweets linking COVID-19 to 5G is presented. NLP models including Latent Dirichlet allocation (LDA), sentiment analysis (SA) and social network analysis (SNA) were applied for the analysis of the tweets and identification of topics. An understanding of the topic frequencies, the inter-relationships between topics and geographical occurrence of the tweets allows identifying agencies and patterns in the spread of misinformation and equips policymakers with knowledge to devise counter-strategies.
  • Detecting advance fee fraud using NLP bag of word model

    Hamisu, Muhammad; Mansour, Ali; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2021-05-25)
    Advance Fee Fraud (AFF) is a form of Internet fraud prevalent within the Cybercrimes domain in literature. Evidence shows that huge financial assets are stolen from the global economy as a result of AFF. Consequently, this paper presents a fraudulent email classifier (FEC) that detects and classifies an email as fraudulent or non-fraudulent using Natural Language Process (NLP) model referred to as Bag-of-Words (BoW). The classifier is designed and trained to detect and classify AFF that originate from known sources using Nigeria as a Case study. Dataset is obtained and used for the training while testing the classifier logs. Experimentally, the classifier was trained using various machine learning algorithms with BoW generated as predictors. By selecting the best algorithms, the classifier was tested and found to perform satisfactorily.
  • Analysis of cybercrime in Nigeria

    Hamisu, Muhammad; Idris, Abubakar Muhammad; Mansour, Ali; Olalere, Morufu; University of Bedfordshire; Federal University of Technology, Minna, Nigeria (Institute of Electrical and Electronics Engineers Inc., 2021-05-25)
    Nigeria has both the largest economy and population in Africa, and this contribute to the growth and fast expansion of ICT and the use of Internet in Nigeria. Like other technologies, Internet has been used by both good and bad actors. The use of internet and computer to commit crime is costing global economy the loss of billions of dollars. In Nigeria, the majority of the population use the Internet for good but some few are using it to commit criminal activities such as Fraud. Cybercriminals in Nigeria, widely called Yahoo Boys in the country specialize in Internet fraud that target mostly International victims. The Nigeria government is stepping efforts to bring an end the activities of these criminals as their actions tarnishes the image of the country. While the efforts of the government had yielded some positive results, the threat of Cybercrime in Nigeria is still high, as criminals continue to take advantage of flaws in the law enforcement tactical approach in addressing the crime. This paper discusses an overview of Cybercrime in Nigeria, the common types of Cybercrime that is perpetuated from the country and the reason of doing so. It also discusses the government's success and areas of strength in its fight against Cybercrime and highlight the areas of weaknesses. Recommendations and suggestions are made on how law enforcement and the government at large can improve to tackle Cybercrime better in Nigeria.

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