Marshallnewton6418
Philosophy of science has traditionally focused on the epistemological dimensions of scientific practice at the expense of the ethical and political questions scientists encounter when addressing questions of policy in advisory contexts. In this article, I will explore how an exclusive focus on epistemology and theoretical reason can function to reinforce common, yet flawed assumptions concerning the role of scientific knowledge in policy decision making when reproduced in philosophy courses for science students. In order to address this concern, I will argue that such courses should supplement the traditional focus on theoretical reason with an analysis of the practical reasoning employed by scientists in advisory contexts. Later sections of this paper outline a teaching strategy by which this can be achieved that consists of two steps the first examines idealized examples of scientific advising in order to highlight the irreducible role played by moral reasoning in justifying policy recommendations. The second employs argument analysis to reveal implicit moral assumptions in actual advisory reports that form the basis for class discussion. This paper concludes by examining some of the wider benefits that can be expected from adopting such an approach.The COVID-19 pandemic has significantly affected the supply chains (SCs) of many industries, including the oil and gas (O&G) industry. This study aims to identify and analyze the drivers that affect the resilience level of the O&G SC under the COVID-19 pandemic. The analysis helps to understand the driving intensity of one driver over those of others as well as drivers with the highest driving power to achieve resilience. Through an extensive literature review and an overview of experts' opinions, the study identified fourteen supply chain resilience (SCR) drivers of the O&G industry. These drivers were analyzed using the integrated fuzzy interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) approaches. The analysis shows that the major drivers of SCR are government support and security. These two drivers help to achieve other drivers of SCR, such as collaboration and information sharing, which, in turn, influence innovation, trust, and visibility among SC partners. Two more drivers, robustness and agility, are also essential drivers of SCR. However, rather than influencing other drivers for their achievement, robustness and agility are influenced by others. The results show that collaboration has the highest overall driving intensity and agility has the highest intensity of being influenced by other drivers.Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.Integrated assessment models (IAM) study the interlinkages between human and natural systems and play a key role in assessing global strategies to reduce global warming. However, they largely neglect the role of materials and the circular economy. With the Plastics Integrated Assessment model (PLAIA), we included plastic production, use, and end-of-life in the IAM IMAGE. PLAIA models the global plastics sector and its impacts up to 2100 for 26 world regions, providing a long-term, dynamic perspective of the sector and its interactions with other socioeconomic and natural systems. This article summarizes the model structure, mathematical formulation, assumptions, and data sources. The model links the upstream chemical production with the downstream production of plastics, their use in different sectors, and their end of life. Therefore, PLAIA can assess material use and emission mitigation strategies throughout the whole life cycle in an IAM, including the impacts of the circular economy on mitigating climate change. PLAIA projects plastics demand, production pathways and specifies the annual plastic waste generation, collection, and the impact of waste management strategies. It shows the fossil and bio-based energy and carbon flows in product stocks, landfills, and the emissions in production and at the end of life.•We included plastics production, use, and waste management into an Integrated Assessment Model (IAM).•Our model PLAIA provides a long-term, dynamic perspective of the global plastics sector until 2100 and its interactions with other sectors and the environment.•PLAIA can assess the impact of material use and emission mitigation strategies throughout the whole life cycle of plastics.Fourier Transform Infrared Spectroscopy has been employed to investigate the composition of the shells of two marine bivalves Mytilus galloprovincialis and Corbula gibba from four samples collected from the Mar Piccolo of Taranto (Ionian Sea, Southern Italy). Bivalve shells are composed of 95-99.9% calcium carbonate (CaCO3), while the remaining portion is constituted by organic matrix, which in some cases may incorporate pollutants from the surrounding environment. Recognizing the role of bivalves in the carbon biogeochemical cycle and their economic importance for aquaculture, we aimed to develop a methodology for shells powder samples preparation and analysis. The main objective of the study was to demonstrate the feasibility of Fourier Transform Infrared photoacoustic spectroscopy to perform a fast sample analysis in order to detect the possible presence of pollutants in the shells. The results revealed an unbiased differentiation between the shell compositions of the two bivalve selected species. Moreover, the spectra interpretation indicated that C. gibba specimens recorded a shell matrix contaminated by organic pollutants present in the surrounding environment. In conclusion, the described methodology including sample preparation tailored for photoacoustical investigations demonstrated to be a tool for the characterization of bivalve shells contamination enhancing environmental studies of polluted marine areas.•Bivalve species were collected from sampling stations located in the Mar Piccolo of Taranto (Ionian Sea, Southern Italy).•Samples preparation stages include sonication, grinding and fractioning by sieving.•FT-IR PAS spectral region of interest is in the mid-infrared between 4000 and 400 cm-1.Online reviews play an important role in consumer purchase decisions and have received much research attention. However, previous research has typically examined the effects of online review characteristics independent of firm marketing messages. We argue that how much average review rating influences consumers' decisions depends on the presence of a scarcity appeal and its congruence with review volume information. Selleckchem Tivantinib Through a lab experiment and analyses of real-world data from Amazon.com, we show that claiming a product to have limited supply moves consumers toward more heuristic processing but only when review volume is consistent with the scarcity information. In contrast, when review volume is incongruent with the supply-based scarcity message, the incongruence prompts consumers to process information more carefully and reduces their reliance on review valence.A high Mandard score may indicate the tumor is insensitive to chemotherapy. We analyzed tumor regression and lymph node response under different Mandard scores to assess the impact of Mandard score on prognosis. Methods. Mandard scores and ypN stage of postoperative pathological reports were recorded. The results were reviewed by a professional pathologist. The radiologist compared the tumor regression before and after chemotherapy by computed tomography (CT). The survival of all patients was obtained by telephone follow-up. Multivariate Cox regression was used to assess the relationship between overall risk of death and Mandard score, imaging evaluation, and ypN stage. Results. In the Mandard score (4-5) group, the median survival time for PR and ypN0 patients was 68.5 and 76.7 months. While in the Mandard score (1-2) group, the median survival time for PD and ypN3a patients was 15.6 and 14.5 months. Imaging evaluation of tumor regression (PR 68.5 months, SD 27.8 months, and PD 10.2 months) and lymph node remission (ypN0 76.7 months, ypN1 61.6 months, ypN2 18.0 months, ypN3a 18.7 months, and ypN3b 18.3 months) showed improved survival. Mandard score, imaging evaluation, and ypN stage are important prognostic factors affecting prognosis. Conclusion. A high Mandard score does not mean neoadjuvant chemotherapy is ineffective in gastric cancer. Patients with imaging evaluation of tumor regression and ypN stage reduction may benefit from neoadjuvant chemotherapy.
We identified relevant cohort studies that assessed the relationship between liver fibrosis scores (e.g., FIB-4, NAFLD fibrosis score (NFS), and aspartate aminotransferase to platelet ratio index (APRI)) and associated prognosis outcomes by searching the PubMed, EMBASE, and medRxiv databases. The potential dose-response effect was performed using a stage robust error meta-regression.
Sixteen studies with 8,736 hospitalized patients with COVID-19 were included. One-point score in FIB-4 increase was significantly associated with increased mechanical ventilation (RR 2.23, 95% CI 1.37-3.65,
=0.001), severe COVID-19 (RR 1.82, 95% CI 1.53-2.16,
< 0.001), and death (RR 1.47, 95% CI 1.31-1.65,
< 0.001), rather than hospitalization (RR 1.35, 95% CI 0.72-2.56,
=0.35). Furthermore, there is a significant positive linear relationship between FIB-4 and severe COVID-19 (
=0.12) and mortality (
=0.18). Regarding other liver scores, one unit elevation in APRI increased the risk of death by 178% (RR 2.78, 95% CI 1.10-6.99,
=0.03). Higher NFS (≥-1.5) and Forns index were associated with increased risk of severe COVID-19 and COVID-19-associated death.
Our dose-response meta-analysis suggests high liver fibrosis scores are associated with worse prognosis in patients with COVID-19. For patients with COVID-19 at admission, especially for those with coexisting chronic liver diseases, assessment of liver fibrosis scores might be useful for identifying high risk of developing severe COVID-19 cases and worse outcomes.
Our dose-response meta-analysis suggests high liver fibrosis scores are associated with worse prognosis in patients with COVID-19. For patients with COVID-19 at admission, especially for those with coexisting chronic liver diseases, assessment of liver fibrosis scores might be useful for identifying high risk of developing severe COVID-19 cases and worse outcomes.
