Convergence behaviours of energy series and GDP nexus hypothesis: A non-parametric Bayesian application.

With the EU Green Deal initiatives, European members seek to launch the first climate neutral continent by 2050. This paper assesses the stochastic convergence of per capita energy consumption series for an illustrative sample of 15 EU countries with memberships prior to the 2004 enlargement, using data spanning the 1970-2018 period. Results from the confidence interval subsampling (asymmetric and equal-tailed) highlight that 11 out of the 15 EU series exhibit a long-run memory behaviour, while a diverging pattern was observed for the UK, Germany, Portugal, and Italy. Finally, per capita energy use series persist but fail to reveal one of the above dynamics for Ireland and Spain. Also, findings from the non-parametric Bayesian application (ANOVA/linear Dependent Dirichlet Process (DDP) mixture model) show how economic growth operates as a converging energy consumption-enabler over the long-run, from which the EU membership cannot be excluded. In particular, we highlight how the nature of energy-GDP hypotheses vary with the stochastic properties of energy use (converging behaviour with temporary shocks, converging pattern with permanent shocks, and diverging dynamic). Finally, our conclusions overcome the well-established development stage argument as we claim that countries with similar energy convergence patterns may need to adopt similar energy policies.

What does the EKC theory leave behind? A state-of-the-art review and assessment of export diversification-augmented models.

Over the past three decades, researchers have extensively examined the environmental Kuznets curve (EKC) hypothesis. Despite their early focus on the ecological impacts of anthropogenic development, associated conclusions differ and often conflict. In this study, we conducted a state-of-the-art review of this topic and shed light on the methodological challenges that the literature attempted to overcome so far. Since China is going through structural economic changes and environmental reforms, we relied on this illustrative case and developed an augmented-EKC framework to investigate whether this hypothesis holds between export product diversification and environmental pollution, stratifying by carbon energy content: renewable (Model 1) and fossil energy (Model 2). Quarterly data are collected over the most available and recent period (i.e., 1990Q1-2018Q4) and computed by applying the Quadratic Match-Sum Method (QMS) on annual series. Besides, per capita income and foreign direct investments are included as additional factors to the baseline models specifications. The empirical analysis comprises the Clemente-Montanes-Reyes unit root test with structural break and additive outlier, the autoregressive distributed lag (ARDL) bounds test for cointegration, the Granger causality test, and dynamic (DOLS) and fully modified OLS (FMOLS) estimators, followed by robustness checks confirming the stability of the coefficients exhibited in the two autoregressive settings. For both models, empirical results failed to support the existence of an inverted-U-shaped relationship among export product diversification and carbon release from fuel combustion in China. Also, as income grows, low-carbon resources seem improving export diversification and vice versa. Related findings are thought to bring robust inferences able to complement the existing literature and open a fruitful research direction.

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

Life cycle assessment of greywater treatment systems for water-reuse management in rural areas.

Water scarcity is a major concern worldwide. Population growth, as well as the intensive use of water resources for industrial and agricultural activities, among others, have caused water stress in various regions of the world. Rural areas are usually more affected due to water scarcity and a lack of sanitary infrastructure. The current practices associated with urban water management have been considered inefficient to respond to these problems. In recent years, the reuse of greywater has emerged as a promising and sustainable alternative. Several pilot greywater treatment systems have been implemented in rural areas of different countries, however, studies about the environmental impacts of these decentralized systems under different scenarios are lacking. In this work, the life cycle assessment of greywater treatment systems considering several scenarios was studied. Our results showed that the decrease in environmental impacts due to the saving of drinking water is more evident when the water supply is carried out through cistern trucks. This occurs because the environmental impact of land transport of water is extremely high and represents over 89% of the global warming indicator [kg CO2 eq] and 96% ozone depletion [kg CFC-11 eq] contributions of the system. Greywater treatment systems with backwashing and solar panels as a source of energy have lower environmental impacts, reducing CO2 and CFC emissions by 50% for the maintenance phase and by 85% (CO2) and 47% (CFC) for the operation phase. Furthermore, the acquisition of solar panels was economically feasible, with a payback of 19.7 years. This analysis showed the environmental feasibility of small-scale greywater treatment systems in rural areas affected by water scarcity. Furthermore, the proposed approach has contributed to understand the impact of greywater treatment systems in rural areas, which could become a support tool to integrate greywater reuse practices in different communities.

Machine Learning Classification of Inflammatory Bowel Disease in Children Based on a Large Real-World Pediatric Cohort CEDATA-GPGE® Registry.

Introduction: The rising incidence of pediatric inflammatory bowel diseases (PIBD) facilitates the need for new methods of improving diagnosis latency, quality of care and documentation. Machine learning models have shown to be applicable to classifying PIBD when using histological data or extensive serology. This study aims to evaluate the performance of algorithms based on promptly available data more suited to clinical applications. Methods: Data of inflammatory locations of the bowels from initial and follow-up visitations is extracted from the CEDATA-GPGE registry and two follow-up sets are split off containing only input from 2017 and 2018. Pre-processing excludes patients in remission and encodes the categorical data numerically. For classification of PIBD diagnosis, a support vector machine (SVM), a random forest algorithm (RF), extreme gradient boosting (XGBoost), a dense neural network (DNN) and a convolutional neural network (CNN) are employed. As best performer, a convolutional neural network is further improved using grid optimization. Results: The achieved accuracy of the optimized neural network reaches up to 90.57% on data inserted into the registry in 2018. Less performant methods reach 88.78% for the DNN down to 83.94% for the XGBoost. The accuracy of prediction for the 2018 follow-up dataset is higher than those for older datasets. Neural networks yield a higher standard deviation with 3.45 for the CNN compared to 0.83-0.86 of the support vector machine and ensemble methods. Discussion: The displayed accuracy of the convolutional neural network proofs the viability of machine learning classification in PIBD diagnostics using only timely available data.

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm.

Although the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960-2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

Can biomass energy curtail environmental pollution? A quantum model approach to Germany.

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO2 emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments - through a software capable of emulating a quantum system - to data over the period of 1990-2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany.

NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values.

This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO2) concentrations and COVID-19-related deaths in France. The concentration of NO2 linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO2 in spreading the epidemic. The underlying hypothesis is that NO2, as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO2 connected to COVID-19 range between 15.8 µg/m3 for Lyon, 21.8 µg/m3 for Marseille and 22.9 µg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 µg/m³ imposed by Directive 2008/50/EC of the European Parliament.

The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence.

While the deployment of technological innovation was able to avert a devastating global supply chain fallout arising from the impact of ravaging COronaVIrus Disease 19 (COVID-19) pandemic, little is known about potential environmental cost of such achievement. The aim of this paper is to identify the determinants of logistics performance and investigate its empirical linkages with economic and environmental indicators. We built a macro-level dataset for the top 25 ranked logistics countries from 2007 to 2018, conducting a set of panel data tests on cross-sectional dependence, stationarity and cointegration, to provide preliminary insights. Empirical estimates from Fully Modified Ordinary Least Squares (FMOLS), Generalized Method of Moments (GMM), and Quantile Regression (QR) model suggest that technological innovation, Human Development Index (HDI), urbanization, and trade openness significantly boost logistic performance, whereas employment and Gross Fixed Capital Formation (GFCF) fail to respond in such a desirable path. In turn, an increase in the Logistic Performance Index (LPI) is found to worsen economic growth. Finally, LPI exhibits a large positive effect on carbon emissions, which is congruent with a strand of the literature highlighting that the modern supply chain is far from being decarbonized. Thus, this evidence further suggest that more global efforts should be geared to attain a sustainable logistics.

Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy?

Municipal solid waste (MSW) is one of the most urgent issues associated with economic growth and urban population. When untreated, it generates harmful and toxic substances spreading out into the soils. When treated, they produce an important amount of Greenhouse Gas (GHG) emissions directly contributing to global warming. With its promising path to sustainability, the Danish case is of high interest since estimated results are thought to bring useful information for policy purposes. Here, we exploit the most recent and available data period (1994-2017) and investigate the causal relationship between MSW generation per capita, income level, urbanization, and GHG emissions from the waste sector in Denmark. We use an experiment based on Artificial Neural Networks and the Breitung-Candelon Spectral Granger-causality test to understand how the variables, object of the study, manage to interact within a complex ecosystem such as the environment and waste. Through numerous tests in Machine Learning, we arrive at results that imply how economic growth, identifiable by changes in per capita GDP, affects the acceleration and the velocity of the neural signal with waste emissions. We observe a periodical shift from the traditional linear economy to a circular economy that has important policy implications.

The relationship between air pollution and COVID-19-related deaths: An application to three French cities.

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland.

Municipal solid waste generation is becoming a prominent issue in the environmental arena. The aim of this paper is to investigate the relationship among municipal waste generation, greenhouse gas emissions, and GDP in Switzerland over the period 1990-2017. We apply both time series procedures (stationarity and causality tests) and a Machine Learning approach. Empirical findings underline a bidirectional causal relationship between municipal solid waste generation and GDP, indicating that the Environmental Kuznets Curve hypothesis holds for Switzerland. Moreover, we found that waste recovery (recycling and composting) is a key driver in mitigating greenhouse gas emissions. In fact, in the Tree Model, the probability that a change in the waste recovery variable could lead to a reduction in the greenhouse gas emissions registered a value of 87%.

A novel conditional NPM-ALK-driven model of CD30+ T-cell lymphoma mediated by a translational stop cassette.

Targeted expression of transgenes is essential for the accurate representation of human disease in in vivo models. Current approaches to generate conditional transgenic mouse models are cumbersome and not amenable to high-throughput analysis since they require de novo generation and characterization of genetically modified mice. Here we describe a new system for lineage-restricted expression of transgenes based on a retroviral vector incorporating a translational stop cassette flanked by loxP recombination sites. Conditional transgene expression in chimeric mice is achieved by retroviral infection and transplantation of hematopoietic stem cells (HSC) derived from transgenic mice expressing Cre-recombinase from a lineage-specific promoter. For validation, we directed expression of NPM-ALK, the fusion oncogene driving a subset of anaplastic large cell lymphoma (ALCL), to T-cells by infecting hematopoietic stem cells from Lck-Cre-transgenic mice with a retroviral construct containing the NPM-ALK cDNA preceded by a translational stop cassette. These mice developed T-cell lymphomas within 12-16 weeks, featuring increased expression of the ALCL hallmark antigen CD30 as well as other cytotoxic T-cell markers, similar to the human disease. The new model represents a versatile tool for the rapid analysis of gene function in a defined lineage or in a developmental stage in vivo.

[Polycystic ovary syndrome and congenital adrenal hyperplasia: a different entity for comparable phenotypes?]. / Syndrome des ovaires potykystiques et syndrome adrénogenital: une entité différente pour un phenotype comparable?

The phenotypes of the polycystic ovarian syndrome (PCOS) and congenital adrenal hyperplasia syndrome (CAHS) present a number of similarities. The main symptoms of PCOS are obesity, menstrual disorders, hirsutism, and low fertility in which the pituitary and adrenal glands are spared. The CAHS is a group of various entities all characterised by different degrees of malfunction of the 21-hydroxylase (CYP21) enzyme. The consequences are a downfall of the levels of aldosterone and cortisol, and the hyperproduction of adrenal androgen hormones. It is capital to be able to recognise these 2 entities in terms of identification of high risk families because the female foetuses suffering from CAHS will undergo severe virilization of there genitals in utero, which can efficiently be prevented by a administration of corticotherapy to the mother throughout the pregnancy.

Cystadenocarcinoma of the abdominal wall following caesarean section: case report and review of the literature.

OBJECTIVES: Endometriosis affecting surgical scars is a well-described entity that can complicate surgery involving hysterotomies. Malignant transformation is a rare event that has been reported complicating ovarian endometriosis mainly. CASE: A 60-year-old woman having in the past two uneventful caesarean deliveries presented with a solid lower abdominal mass. A surgical biopsy and then a radical resection of the lower half of the abdominal wall were performed, with a diagnosis of adenocarcinoma of endometrial origin. A diagnostic curettage excluded primary endometrial carcinoma. At 1 year she is NED. CONCLUSIONS: This case report illustrates the carcinomatous transformation of an endometrial implant. Despite the rarity of such a diagnosis, it should be borne in mind when endometriosis in abdominal wall is suspected because an oncological resection is required.

[Mastectomy and reconstruction: what kind of safety, which attitude?]. / Mastectomie et reconstruction: quelle sécurité, quelle attitude?

Breast reconstruction should be regarded as a significant part of breast cancer treatment. Immediate reconstruction has the advantages of a better cosmetic result, a better illness approach and of avoiding the need of a second major operative procedure. Immediate and early delayed reconstruction does not significantly increase the risk of local or systemic disease recurrence and the type of surgery performed should depend on the further therapy chosen. Skin-sparing mastectomy--a new technique--preserves the skin envelope and should be proposed to patients with central breast cancer.