Towards Sustainable Environment in G7 Nations: The Role of Renewable Energy Consumption, Eco-innovation and Trade Openness

Some of the globe’s most economically advanced nations make up the G7 (Canada, Japan, France, Germany, Italy, United States and United Kingdom). Nevertheless, in tandem with such strong economic growth, the environmental conditions in these nations have deteriorated, raising serious issues among stakeholders. Therefore, we examine the effect of eco-innovation and trade openness on CO2 emissions in G7 nations. We also take into account the role of renewable energy, economic growth and nonrenewable energy use using a dataset covering the period from 1990–2019. We employed recent econometric techniques such as slope heterogeneity (SH) and cross-sectional dependence (CSD), Westerlund cointegration, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), panel quantile regression and panel causality tests to assess these associations. The outcomes of the CSD and SH tests disclosed that using a first-generation unit root test will produce biase outcomes. Furthermore, the outcomes of the Westerlund cointegration disclosed support long-run association between CO2 and its drivers. In addition, the results of the long-run estimators (FMOLS and DOLS) unveiled that nonrenewable energy and trade openness contribute to the damage to the environment while economic expansion, renewable energy and eco-innovation enhance the quality of the environment. Furthermore, the outcomes of GDP, REC and ECO curb CO2 while NREC energy and TO surge CO2. Finally, the outcomes of the panel causality test unveiled that CO2 emissions can be predicted by all the exogenous variables. Copyright © 2022 Olanrewaju, Irfan, Altuntaş, Agyekum, Kamel and El-Naggar

Determinants of load capacity factor in an emerging economy: The role of green energy consumption and technological innovation

Brazil’s ability to provide safe and dependable resources that can assist the nation achieve its goal of becoming carbon neutral by 2060 will have a significant impact on the nation’s sustainable development. Therefore, this study performs ARDL and frequency domain causality tests to evaluate the effect of disintegrated energy, technological innovation and economic growth on load capacity factor in South Africa between 1990 and 2018. The ARDL bounds test affirms a long-run interrelationship between the selected indicators in South Africa. The long-run elasticities show that economic expansion and nonrenewable energy deteriorate ecological quality, while green energy and technological innovation significantly boost ecological quality. The results of the frequency causality show that in the long-term, renewable energy, economic growth, technological innovation and nonrenewable energy Granger cause load capacity factor suggesting that the regressors can forecast the environmental quality in South Africa. Overall, these results demonstrate the significance of renewable energy in the fight against ecological deterioration. According to the aforementioned findings, South Africa’s environmental damage may be greatly reduced by renewable energy. Copyright © 2022 Liu, Olanrewaju, Agyekum, El-Naggar, Alrashed and Kamel.Ministry of Education in Saudi ArabiaPrince Sattam bin Abdulaziz University, PSAU, (IF-PSAU-2021/01/18921)Funding text 1: This research is funded by Prince Sattam Bin Abdulaziz University, grant number IF- PSAU-2021/01/18921.Funding text 2: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the project number (IF-PSAU-2021/01/18921)

Linking Financial Development and Environment in Developed Nation Using Frequency Domain Causality Techniques: The Role of Globalization and Renewable Energy Consumption

The topic of whether globalization, energy consumption and financial development can substantially reduce emissions during the globalization era remains unanswered. In this context, this research highlights empirical indications supporting this theoretical discord; assessing the effect of globalization, energy consumption and financial development on the CO2 emissions in Japan (utilizing a dataset that spans between 1990 and 2019). The study employed the Autoregressive Distributed Lag (ARDL) technique and frequency domain causality to probe these relationships. Unlike other conventional causality tests, the frequency domain causality test can capture causality at different frequencies. The findings from the ARDL analysis disclosed that globalization and renewable energy contribute to the mitigation of CO2 emissions while fossil fuel, economic growth and financial development caused an upsurge in CO2 in Japan. Furthermore, the frequency domain demonstrated that all the exogenous variables can forecast CO2 mostly in the long-term which implies that any policy initiated based on the exogenous variables will impact emissions of CO2. Based on the results obtained, Japan has to improve its financial systems and increase its use of renewable energy. Furthermore, Japan needs to restructure its policy regarding globalization owing to the fact that it contributes to the degradation of the environment. Since globalization is a major driver of economic growth, the government should concentrate on luring and licensing investors that use environmentally beneficial (net-zero) technology. Copyright © 2022 Mosleh, Al-Geitany, Lawrence Emeagwali, Altuntaş, Agyekum, Kamel, El-Naggar and Agbozo

Descriptive epidemiology of salivary gland neoplasms in Nigeria: An AOPRC multicenter tertiary hospital study

Objectives: Accurate diagnosis of salivary gland neoplasms (SGN) in many centers in Africa is limited by poor diagnostic resources and ancillary services. Hence, we have carried out a multicenter epidemiological study to understand the true burden of SGN in Nigeria. Method: In this descriptive cross‐sectional study, we have deployed resources available to members of the African Oral Pathology Consortium (AOPRC) to examine the burden of salivary gland lesions in Nigeria, using a multicenter approach. Data from seven major tertiary health institutions in northern, western, and southern Nigeria were generated using a standardized data extraction format and analyzed using the Epi‐info software (Version 7.0, Atlanta, USA). Result: Of the 497 cases examined across the seven centers, we observed that SGN occurred more in females than males. Overall, pleomorphic salivary adenoma (PA) was found to be the most common. PA was found to be the commonest benign SGN while adenocystic carcinoma (ADCC) was the commonest malignant SGN. Regional variations were observed for age group, diagnosis, and gender distribution. Significant statistical differences were found between males and females for malignant SGNs (p‐value=0.037). Conclusion: We found regional variation in the pattern of distribution of SGN in Nigeria. This is the largest multicenter study of SGN in Nigeria, and our findings are robust and representative of the epidemiology of this neoplasm in Nigeria

Interphase cytogenetics of multicentric renal cell tumours confirm associations of specific aberrations with defined cytomorphologies

To demonstrate associations of certain chromosomal aberrations with defined renal cell tumour (RCT) subtypes, we analysed 239 tumour nephrectomy cases for specimens with multicentric tumours. Chromosomal in situ hybridization was then performed on 15 cases with 34 foci (16 conventional renal cell carcinomas (RCCs), and 18 papillary RCTs (11 carcinomas and seven adenomas) for specific chromosomal aberrations, using α-satellite probes for chromosomes 3, 7 or 17. Particular preference was given to cases which had separate foci with different cytomorphologies. Furthermore, we compared aberrations in relation to tumour size, stage, grade and between different foci in a specimen. Thirty-four cases had multiple tumours. Forty-seven per cent of the multicentric tumours were conventional RCCs and 53% papillary RCTs (against 83% solitary conventional RCCs and 5% solitary papillary RCTs). Three conventional RCCs sized 8 mm (G3), 13 cm (pT2, G2) and 15 cm (pT3b, G3), respectively, revealed monosomy 3, and 13 were disomic. Seventeen papillary RCTs (11 carcinomas and six adenomas) displayed trisomy 17, irrespective of size or grade. Four papillary carcinomas and six papillary adenomas had trisomy 7, and the rest (seven papillary carcinomas and one papillary adenoma) revealed disomy 7. In conclusion, papillary RCTs were tendentially multicentric. Although specific for conventional RCCs heedless of size, monosomy 3 was only observed in high-grade and/or advanced tumours. Trisomy 17 was only detectable in papillary RCTs irrespective of tumour state, showing increased copies with tumour growth. Papillary RCTs also appeared to lose some copies of chromosome 7 with tumour progress, possibly reflecting malignancy. © 2000 Cancer Research Campaig

Oval Domes: History, Geometry and Mechanics

An oval dome may be defined as a dome whose plan or profile (or both) has an oval form. The word Aoval@ comes from the latin Aovum@, egg. Then, an oval dome has an egg-shaped geometry. The first buildings with oval plans were built without a predetermined form, just trying to close an space in the most economical form. Eventually, the geometry was defined by using arcs of circle with common tangents in the points of change of curvature. Later the oval acquired a more regular form with two axis of symmetry. Therefore, an “oval” may be defined as an egg-shaped form, doubly symmetric, constructed with arcs of circle; an oval needs a minimum of four centres, but it is possible also to build polycentric ovals. The above definition corresponds with the origin and the use of oval forms in building and may be applied without problem until, say, the XVIIIth century. Since then, the teaching of conics in the elementary courses of geometry made the cultivated people to define the oval as an approximation to the ellipse, an “imperfect ellipse”: an oval was, then, a curve formed with arcs of circles which tries to approximate to the ellipse of the same axes. As we shall see, the ellipse has very rarely been used in building. Finally, in modern geometrical textbooks an oval is defined as a smooth closed convex curve, a more general definition which embraces the two previous, but which is of no particular use in the study of the employment of oval forms in building. The present paper contains the following parts: 1) an outline the origin and application of the oval in historical architecture; 2) a discussion of the spatial geometry of oval domes, i. e., the different methods employed to trace them; 3) a brief exposition of the mechanics of oval arches and domes; and 4) a final discussion of the role of Geometry in oval arch and dome design

A common classification framework for neuroendocrine neoplasms: an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert consensus proposal

The classification of neuroendocrine neoplasms (NENs) differs between organ systems and currently causes considerable confusion. A uniform classification framework for NENs at any anatomical location may reduce inconsistencies and contradictions among the various systems currently in use. The classification suggested here is intended to allow pathologists and clinicians to manage their patients with NENs consistently, while acknowledging organ-specific differences in classification criteria, tumor biology, and prognostic factors. The classification suggested is based on a consensus conference held at the International Agency for Research on Cancer (IARC) in November 2017 and subsequent discussion with additional experts. The key feature of the new classification is a distinction between differentiated neuroendocrine tumors (NETs), also designated carcinoid tumors in some systems, and poorly differentiated NECs, as they both share common expression of neuroendocrine markers. This dichotomous morphological subdivision into NETs and NECs is supported by genetic evidence at specific anatomic sites as well as clinical, epidemiologic, histologic, and prognostic differences. In many organ systems, NETs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, and/or the presence of necrosis; NECs are considered high grade by definition. We believe this conceptual approach can form the basis for the next generation of NEN classifications and will allow more consistent taxonomy to understand how neoplasms from different organ systems inter-relate clinically and genetically

The prognostic significance of allelic imbalance at key chromosomal loci in oral cancer

Forty-eight primary oral squamous cell carcinomas (SCC) were screened for allelic imbalance (AI) at 3p24–26, 3p21, 3p13, 8p21–23, 9p21, 9q22 and within the Rb, p53 and DCC tumour suppressor genes. AI was detected at all TNM stages with stage 4 tumours showing significantly more aberrations than stage 1–3. A factional allelic loss (FAL) score was calculated for all tumours and a high score was associated with development of local recurrence (P = 0.033) and reduced survival (P = 0.0006). AI at one or more loci within the 3p24–26, 3p21, 3p13 and 9p21 regions or within the THRB and DCC genes was associated with reduced survival. The hazard ratios for survival analysis revealed that patients with AI at 3p24–26, 3p13 and 9p21 have an approximately 25 times increase in their mortality rate relative to a patient retaining heterozygosity at these loci. AI at specific pairs of loci, D3S686 and D9S171 and involving at least two of D3S1296, DCC and D9S43, was a better predictor of prognosis than the FAL score or TNM stage. These data suggest that it will be possible to develop a molecular staging system which will be a better predict of outcome than conventional clinicopathological features as the molecular events represent fundamental biological characteristics of each tumour. © 1999 Cancer Research Campaig

Prognostic biomarkers in patients with human immunodeficiency virusâ positive disease with head and neck squamous cell carcinoma

BackgroundWe examined the prognostic value of a panel of biomarkers in patients with squamous cell carcinoma of the head and neck (SCCHN) who were human immunodeficiency virus (HIV) positive (HIVâ positive head and neck cancer) and HIV negative (HIVâ negative head and neck cancer).MethodsTissue microarrays (TMAs) were constructed using tumors from 41 disease siteâ matched and ageâ matched HIVâ positive head and neck cancer cases and 44 HIVâ negative head and neck cancer controls. Expression of tumor biomarkers was assessed by immunohistochemistry (IHC) and correlations examined with clinical variables.ResultsExpression levels of the studied oncogenic and inflammatory tumor biomarkers were not differentially regulated by HIV status. Among patients with HIVâ positive head and neck cancer, laryngeal disease site (P = .003) and Clavienâ Dindo classification IV (CD4) counts <200 cells/μL (P = .01) were associated with poor prognosis. Multivariate analysis showed that p16 positivity was associated with improved overall survival (OS; P < .001) whereas increased expression of transforming growth factorâ beta (TGFâ β) was associated with poor clinical outcome (P = .001).ConclusionDisease site has significant effect on the expression of biomarkers. Expression of tumor TGFâ β could be a valuable addition to the conventional risk stratification equation for improving head and neck cancer disease management strategies.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139994/1/hed24911.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139994/2/hed24911_am.pd

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods