New insights on the long-run relationship between economic growth and environmental quality

One of the widespread debates in the field of environmental economics that started at the beginning of the 1990s concerns the relation between environmental pollution and economic growth. This research aims to investigate the most likely pattern of the long-run relationship between CO₂ emissions and economic growth, identify the factors that drive CO₂ emissions and propose policy recommendations for reducing CO₂ emissions. The study utilizes panel data on seven variables – per capita CO₂ emissions, GDP per capita, energy consumption, human population, trade openness, financial development and corruption in 65 countries over 51 years, from 1960 to 2010. Employing graphical tool and econometric techniques such as panel unit root test, panel cointegration test, FMOLS (Fully Modified Least Squares) estimates, Granger causality and IAA (Innovative Accounting Approach) analysis, the study finds that the most likely pattern of the relationship is a sigmoid curve showing that a country’s per capita CO₂ emissions increase when the country transitions from a low-income status to a middle-income status to a high-income status. Also, the study documents that the potential factors driving global CO₂ emissions are economic growth, financial development, energy consumption and corruption. An appropriate combination of emissions standards, pollution tax on fossil fuel based energy sources, anti-corruption strategies, socio-environmental standards for global trade, mass education and awareness about the adverse effects of CO₂ emissions on the environment and human health are potential policy measures

A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning

Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to model various NP-hard combinatorial optimization problems in the form of binary variables. The Hamiltonian function is often used to formulate QUBO problems where it is used as the objective function in the context of optimization. Recently, PI-GNN, a generic scalable framework, has been proposed to address the Combinatorial Optimization (CO) problems over graphs based on a simple Graph Neural Network (GNN) architecture. Their novel contribution was a generic QUBO-formulated Hamiltonian-inspired loss function that was optimized using GNN. In this study, we address a crucial issue related to the aforementioned setup especially observed in denser graphs. The reinforcement learning-based paradigm has also been widely used to address numerous CO problems. Here we also formulate and empirically evaluate the compatibility of the QUBO-formulated Hamiltonian as the generic reward function in the Reinforcement Learning paradigm to directly integrate the actual node projection status during training as the form of rewards. In our experiments, we observed up to 44% improvement in the RL-based setup compared to the PI-GNN algorithm. Our implementation can be found in https://github.com/rizveeredwan/learning-graph-structure

Modification of polyvinyl chloride by organic molecule for the improvement of its thermal stability

Improving the stability of PVC became a task for many research groups to improve its properties and lower plastic pollution. An invented Schiff base was applied as a heat-resistant agent to alter polyvinyl chloride (PVC) behavior under varying temperatures. The efficacy of the Schiff base-treated PVC films in terms of thermal stability was assessed through weight-loss analysis, Fourier transform infrared (FTIR) spectroscopy, an optical microscope, and atomic force microscopy (AFM). The outcomes demonstrated that incorporating the altered PVC extended the polymer's stability duration, consequently lowering its inclination towards degradation. Furthermore, the Schiff base led to a marked decrease in the presence of PVC's conjugated double bonds, consequently reducing weight loss. The enhancement observed can be credited to the Schiff base's strong ability to neutralize HCl and its effectiveness in protecting unstable chlorine atoms within the polymer chains. These alterations, when combined, resulted in a prolonged delay in thermal degradation and alterations in color, affirming the success of the modification method in improving the thermal stability of PVC

Beyond No Blame:Practical Challenges of Conducting Maternal and Perinatal Death Reviews in Eastern Ethiopia

Performing effective maternal death reviews as part of the maternal death surveillance and response program has been hindered by challenges including poor attendance, defensive attitudes, and blame shifting. Reviews of maternal and perinatal deaths should be based on a “no blame” principle. Emphasis should be on learning lessons and health professionals should feel safe to discuss the circumstances surrounding death. Meaningful reduction in maternal mortality requires a depoliticizing paradigm shift, a professional body to address patients’ worries, and clear medicolegal guidance to encourage providers to identify care deficiencies

Intraoperative Changes in Cerebrospinal Fluid Gas Tensions Reflect Paraplegia During Thoracoabdominal Aortic Surgery

Background: In this study, gas tensions in cerebrospinal fluid (CSF) were prospectively evaluated as intraoperative markers for the detection of neurological deficits. Methods: Spinal fluid, serum, and heart lung machine (HLM) perfusate were monitored for gas tensions (po2/pCo2) and related parameters (pH, lactate, and glucose) during thoracoabdominal aortic repair and correlated with perioperative neurological examination and electrophysiological testing. Results: Forty-seven patients were assessed for the study, and 40 consecutive patients were finally included. The patients were divided into 3 groups: group A (23 patients, 57.5%): no clinical or laboratory signs of neurological damage; group B (14 patients, 35%) who developed subclinical deficits; and group C (3 patients, 7.5%) who had paraplegia. Significant intraoperative changes in CSF gas tensions were observed with postoperative paraplegia. Glucose ratio between serum and CSF showed higher variability in group C, confirming a damage of the blood–brain barrier (BBB). Conclusion: Major neurological damage is reflected by early changes in CSF gas tensions and glucose variability, suggesting damage of the BBB in these patients

Predisplacement Abuse and Postdisplacement Factors Associated With mental Health Symptoms After Forced Migration Among Rohingya Refugees in Bangladesh

Importance: At the end of August 2017, violence and persecution in Myanmar\u27s Rakhine state forced nearly 1 million Rohingyas to flee to Bangladesh for their lives and seek shelter. Many refugees, after their traumatic experiences leaving Myanmar, experience mental health problems. Objectives: To identify the prevalence of posttraumatic stress symptoms (PTSSs) among displaced Rohingya adults and investigate the association of predisplacement abuse and postdisplacement factors with PTSSs. Design, setting, and participants: This cross-sectional analysis from a household survey of 1184 Rohingya adults aged 18 years or older was conducted in 8 refugee camps within Cox\u27s Bazar, Bangladesh, from September 17, 2019, to January 11, 2020. Main outcomes and measures: The Impact of Event Scale-Revised was used to assess PTSSs. The possible range of scores was 0 to 88; moderate PTSSs were classified using a score cutoff of 33 to 38 and severe PTSSs were classified using a score cutoff of 39 and above. Adjusted prevalence ratios (aPRs) were estimated using a multivariable logistic regression model adjusted for potential confounders. Results: Of 1184 participants (625 men [52.8%]; mean [SD] age, 35.1 [13.4] years), 552 (46.6%) had severe PTSSs, and 274 (23.1%) had moderate PTSSs. In Bangladesh, refugees are not legally permitted to work in refugees camps, but 276 of 1165 respondents (23.7%) had temporary paid jobs. Moreover, 113 of the 276 working participants (40.9%) and 430 of the 889 nonworking participants (48.4%) reported severe PTSSs. A total of 496 respondents (41.9%) reported inadequate humanitarian aid for their families, and among them, 281 (56.7%) reported severe PTSSs. A total of 136 of 1177 respondents (11.6%) experienced both physical and sexual abuse in Myanmar, and 87 (64.0%) of them had severe PTSSs. The multivariable analysis showed a reduced risk of PTSSs with appropriate humanitarian assistance (aPR, 0.50; CI, 0.38-0.65). Experiencing both physical and sexual abuse before displacement had a significant association with PTSSs (aPR, 2.09; CI, 1.41-3.07). Opportunities for paid employment in refugee camps also reduced the risks of PTSSs (aPR, 0.69; CI, 0.52-0.91)

Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning.

Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient's health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet's superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains

An Interim Report on the Editorial and Analytical Work of the AnonymClassic Project

In this collective article, members of the AnonymClassic project discuss various aspects of their work on the textual tradition Kalīla and Dimna. Beatrice Gruendler provides a general introduction to the questions being considered. This is followed by a number of short essays in specific areas, organized into three categories: codicology, literary history and theory, and the digital infrastructure of the project. Jan J. van Ginkel summarizes the challenges involved in editing the Syriac versions of Kalīla and Dimna; Rima Redwan explains the AnonymClassic team’s approach vis-à-vis the transcription and textual segmentation of Arabic manuscripts; Khouloud Khalfallah follows this with an overview of the types of data that are recorded for each codex that is integrated into the project; Beatrice Gruendler, in a second contribution, shares some preliminary results from the analysis of interrelationships among manuscripts; and Rima Redwan, also in a second contribution, discusses the sets of illustrations, or »image cycles«, that are found in many copies of Kalīla wa-Dimna. Moving into the realm of literary history and theory, Isabel Toral poses a range of questions relating to the status of Kalīla and Dimna, as (arguably) anonymous in authorship and as a fundamentally translated book; Johannes Stephan explores the references to Kalīla wa-Dimna found in various medieval Arabic scholarly works; and Matthew L. Keegan confronts the problem of the genre(s) to which Kalīla wa-Dimna might be assigned and the exceptional »promiscuity« of the text. The last section of the article, on digital infrastructure, contains two contributions: Theodore S. Beers describes a web application that the team has created to facilitate the consultation of published versions of Kalīla and Dimna, and, finally, Mahmoud Kozae and Marwa M. Ahmed offer a more comprehensive discussion of the digital tools and methods – specialized and in some cases developed »in-house« – on which the AnonymClassic project relies

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe