The future of liquified natural gas (LNG) in the energy transition: options and implications for the LNG industry in a decarbonising world

A global energy transition is currently taking place, driven primarily by the need to combat climate change. The Intergovernmental Panel on Climate Change (IPCC) has concluded that the current trajectory of global greenhouse gas emissions is not consistent with limiting global warming to below 1.5 or 2 °C, relative to pre-industrial levels, a threshold that could lead to severe economic damage and instability for the coming decades. Fossil fuel combustion, industry, transport, and electricity production contribute to approximately 80% of global greenhouse gas emissions. Energy systems must therefore decarbonise at dramatic rates to move towards a more sustainable environmental development path, but also to cater for population and economic growth in many parts of the world. Natural gas, a fuel with superior environmental credentials than other fossil fuels, has been touted as a “transition fuel” to support the low-carbon transition by promoting fuel-switching and supporting hard-to-abate sectors until largescale electrification with renewable resources and other solutions such as largescale batteries and hydrogen are developed and deployed. Utilising a bespoke meta-framework grounded in institutional theory, combining elements of techno-economic and socio-technical approaches, this study examines how institutional, political, and resource characteristics affect the use of liquified natural gas (LNG), the fastest growing sector within natural gas. Methodology includes the analysis of three country cases (UK, Japan, China). In addition, an in-depth analysis of the LNG industry is conducted, with a focus on the decarbonisation options and implications for the industry, including the impact of development of the hydrogen economy on LNG. The synthesis presents conclusions and findings on LNG’s role in future potential pathways in energy systems in various stages of the energy transition

Reporting and methodologic quality of Cochrane Neonatal review group systematic reviews

<p>Abstract</p> <p>Background</p> <p>The Cochrane Neonatal Review Group (CNRG) has achieved a lot with limited resources in producing high quality systematic reviews to assist clinicians in evidence-based decision-making. A formal assessment of published CNRG systematic reviews has not been undertaken; we sought to provide a comprehensive assessment of the quality of systematic reviews (both methodologic and reporting quality) published in CNRG.</p> <p>Methods</p> <p>We selected a random sample of published CNRG systematic reviews. Items of the QUOROM statement were utilized to assess quality of reporting, while items and total scores of the Oxman-Guyatt Overview Quality Assessment Questionnaire (OQAQ) were used to assess methodologic quality. Two reviewers independently extracted data and assessed quality. A Student t-test was used to compare quality scores pre- and post-publication of the QUOROM statement.</p> <p>Results</p> <p>Sixty-one systematic reviews were assessed. Overall, the included reviews had good quality with minor flaws based on OQAQ total scores (mean, 4.5 [0.9]; 95% CI, 4.27–4.77). However, room for improvement was noted in some areas, such as the title, abstract reporting, a <it>priori </it>plan for heterogeneity assessment and how to handle heterogeneity in case it exists, and assessment of publication bias. In addition, reporting of agreement among reviewers, documentation of trials flow, and discussion of possible biases were addressed in the review process. Reviews published post the QUOROM statement had a significantly higher quality scores.</p> <p>Conclusion</p> <p>The systematic reviews published in the CNRG are generally of good quality with minor flaws. However, efforts should be made to improve the quality of reports. Readers must continue to assess the quality of published reports on an individual basis prior to implementing the recommendations.</p

A review on detecting brain tumors using deep learning and magnetic resonance images

Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained

Exploring ‘Policy Learning Communities’: A case study of the Arabic language curriculum policy community in the United Arab Emirates

This study explores, theoretically and empirically, the concept of Policy Learning Communities (PoliLCs) as a model to facilitate collaborative learning in policymaking. Collaborative learning refers to the process of policymakers collectively updating beliefs and policy choices based on evidence. The notion of Evidence-Informed Practice (EIP) has emerged in policy literature as enhancing the rationality of policymaking decisions. Yet, ensuring that evidence positively impacts the policymaking process remains challenging, largely due to issues around linking researchers and policymakers in ways that promote trust, conversation and sharing of ideas and learning. PoliLCs (Stoll 2008; Brown 2013) are thus defined and used as a framework to explore an interactive, government-initiated learning process connecting policy and non-policy actors in policymaking collectives that address shared policy problems via a critical, ongoing, collaborative, and inclusive process. The literature review provided a critical evaluation of collaborative learning models to establish the theoretical grounding of PoliLCs. The resulting theoretical framework framed the study’s empirical investigation of the Arabic language curriculum policy community, established by the Ministry of Education in the United Arab Emirates. The study sought to identify learning in this case to examine the concept of PoliLCs, provide an account of how and why actors engage in learning and interaction, and identify lessons to further EIP in the UAE. A Social Network Analysis (SNA) survey was used to identify basic levels of interaction within the policy community, as well as a smaller interview sample. Nineteen members completed the survey, and seven were interviewed. Thematic analysis was employed to analyse and report these data. This study argues that the PoliLCs framework can effectively describe and explore learning in policymaking, as it provides a systematic model to engage policy actors in learning and evidence use. While this study sought to examine a case study in UAE of PoliLCs, further research may develop the concept and improve its utility in policymaking and knowledge sharing more globally

Physiochemical Properties and Environmental Levels of Legacy and Novel Brominated Flame Retardants

Polybrominated diphenyl ethers (PBDEs) and ‘novel’ brominated flame retardants (NBFRs) are synthetic chemicals widely used in consumer products to enhance their ignition resistance. Since in most applications, these chemicals are used additively, they can transfer from such products into the environment. PBDEs have been classified as significant pollutants in the environment. Knowledge of PBDE and NBFR physicochemical properties provides information about their potential environmental fate and behaviour. This chapter highlights the most important physiochemical properties such as molecular weight, vapour pressure, octanol/air partitioning coefficient, octanol/water partition coefficient, water solubility and organic carbon/water partitioning coefficient that influence the distribution pattern of these contaminants in the environment. In addition, this chapter provides an evaluation of the concentrations of these chemicals in various environmental media such as indoor and outdoor air, indoor dust, soil and sediment, sewage sludge, biota and food, and human tissues

Choosing an NLP library for analyzing software documentation: a systematic literature review and a series of experiments

To uncover interesting and actionable information from natural language documents authored by software developers, many researchers rely on "out-of-the-box" NLP libraries. However, software artifacts written in natural language are different from other textual documents due to the technical language used. In this paper, we first analyze the state of the art through a systematic literature review in which we find that only a small minority of papers justify their choice of an NLP library. We then report on a series of experiments in which we applied four state-of-the-art NLP libraries to publicly available software artifacts from three different sources. Our results show low agreement between different libraries (only between 60% and 71% of tokens were assigned the same part-of-speech tag by all four libraries) as well as differences in accuracy depending on source: For example, spaCy achieved the best accuracy on Stack Overflow data with nearly 90% of tokens tagged correctly, while it was clearly outperformed by Google's SyntaxNet when parsing GitHub ReadMe files. Our work implies that researchers should make an informed decision about the particular NLP library they choose and that customizations to libraries might be necessary to achieve good results when analyzing software artifacts written in natural language.Fouad Nasser A Al Omran, Christoph Treud

Abnormalities of Hormones and Inflammatory Cytokines in Women Affected With Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a common hormone disorder of women. Women with PCOs have difficulty to pregnat and have a high levels of  androgens with oligomenorrhea, hirsutism, and obesity. The present study was designed to investigate of some hormones and inflammatory markers of women with PCOS, Their ages ranged between 23-24 years old. Means of luteal hormone (LH) , testosterone, Insulin, and Homeostasis model assessment (HOMA-IR) were significantly (p&lt;0.05) higher in women with PCOs when compared with control women. Concerning means of follicle stimulating hormone (FSH) and estradiol hormone were insignificant different (p&gt;0.05) in affected women with PCOs in acomparison with control. Regarding means of inflammatory cytokines including C- Reactive proteins (CRP), interleukin- 6 (IL-6), and leptin have been showed a significant increase (p&lt;0.05) in women with PCOS in a comparison with healthy control women. Data obtained from the present study indicate that women with PCOS have hormones disturbances associated with insnlin resistance and elevated levels of inflammatory cytokines may be in turn aggravate infertility of women with PCOS. Keywords: Polycystic ovary syndrome, insulin resistance, obesity, infertility