Integrated Gasification Combined Cycle from coal

An integrated gasification combined cycle (IGCC) is a technology that uses a high pressure gasifier to turn coal, a carbon based fuels into pressurized gas, this is also known as synthesis gas or syngas. The IGCC system consist of 4 main structures; air compression and separation unit, gasifier, combustion and steam turbine and heat recovery generator.A meta-analysis was conducted to investigate possible relationships between the efficiency and types of gasifiers used in the integrated gasification combined cycle in terms of the key thermodynamic laws. Through this analysis correlations were established between varying coal compositions, types of gasification systems and thermal efficiency. It was found that the updraft gasifier had the highest efficiency across most reports, thus making this procedure the most efficient with today’s current knowledge in terms of the thermodynamic principles associated with coal-fired power plants. It was also established that coal with lower moisture content will generally allow a system to be more efficient

Rotationally-Driven Fragmentation for the Formation of the Binary Protostellar System L1551 IRS 5

Either bulk rotation or local turbulence is widely invoked to drive fragmentation in collapsing cores so as to produce multiple star systems. Even when the two mechanisms predict different manners in which the stellar spins and orbits are aligned, subsequent internal or external interactions can drive multiple systems towards or away from alignment thus masking their formation process. Here, we demonstrate that the geometrical and dynamical relationship between the binary system and its surrounding bulk envelope provide the crucial distinction between fragmentation models. We find that the circumstellar disks of the binary protostellar system L1551 IRS 5 are closely parallel not just with each other but also with their surrounding flattened envelope. Measurements of the relative proper motion of the binary components spanning nearly 30 yr indicate an orbital motion in the same sense as the envelope rotation. Eliminating orbital solutions whereby the circumstellar disks would be tidally truncated to sizes smaller than are observed, the remaining solutions favor a circular or low-eccentricity orbit tilted by up to $\sim$25$^\circ$ from the circumstellar disks. Turbulence-driven fragmentation can generate local angular momentum to produce a coplanar binary system, but which bears no particular relationship with its surrounding envelope. Instead, the observed properties conform with predictions for rotationally-driven fragmentation. If the fragments were produced at different heights or on opposite sides of the midplane in the flattened central region of a rotating core, the resulting protostars would then exhibit circumstellar disks parallel with the surrounding envelope but tilted from the orbital plane as is observed.Comment: Accepted for publication in Ap

W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition

Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with the same labels and increase the distance between tokens with different labels. The effect of this setup may, however, in the medical domain, there are a lot of entities annotated as OUTSIDE (O), and they are undesirably pushed apart to other entities that are not labeled as OUTSIDE (O) by the current contrastive learning method end up with a noisy prototype for the semantic representation of the label, though there are many OUTSIDE (O) labeled entities are relevant to the labeled entities. To address this challenge, we propose a novel method named Weighted Prototypical Contrastive Learning for Medical Few Shot Named Entity Recognization (W-PROCER). Our approach primarily revolves around constructing the prototype-based contractive loss and weighting network. These components play a crucial role in assisting the model in differentiating the negative samples from OUTSIDE (O) tokens and enhancing the discrimination ability of contrastive learning. Experimental results show that our proposed W-PROCER framework significantly outperforms the strong baselines on the three medical benchmark datasets

Second-generation bio-based plastics are becoming a reality - Non-renewable energy and greenhouse gas (GHG) balance of succinic acid-based plastic end products made from lignocellulosic biomass:NREU and GHG balance of succinic acid-based PBS products made from lignocellulosic biomass

Bio-based and bio-degradable plastics such as polybutylene succinate (PBS) have the potential to become sustainable alternatives to petrochemical-based plastics. Polybutylene succinate can be produced from bio-based succinic acid and 1,4-butanediol using first-generation (1G) or second-generation (2G) sugars. A cradle-to-grave environmental assessment was performed for PBS products in Europe to investigate the non-renewable energy use (NREU) and greenhouse gas (GHG) impacts. The products investigated are single-use trays and agricultural film, with incineration, industrial composting and degradation on agricultural land as end-of-life scenarios. Both end products manufactured from fully bio-based PBS and from partly bio-based PBS (made from bio-based succinic acid and fossil fuel-based 1,4 butanediol) were analysed. We examine corn (1G) as well as corn stover, wheat straw, miscanthus and hardwood as 2G feedstocks. For the cradle-to-grave system, 1G fully bio-based PBS plastic products were found to have environmental impacts comparable with their petrochemical incumbents, while 2G fully bio-based PBS plastic products allow to reduce NREU and GHG by around one third under the condition of avoidance of concentration of sugars and energy integration of the pretreatment process with monomer production. Without energy integration and with concentration of sugars (i.e., separate production), the impacts of 2G fully bio-based PBS products are approximately 15–20% lower than those of 1G fully bio-based PBS products. The environmental analysis of PBS products supports the value proposition related to PBS products while also pointing out areas requiring further research and development

Using Machine Learning to Make Computationally Inexpensive Projections of 21st Century Stratospheric Column Ozone Changes in the Tropics

Stratospheric ozone projections in the tropics, modeled using the UKESM1 Earth system model, are explored under different Shared Socioeconomic Pathways (SSPs). Consistent with other studies, it is found that tropical stratospheric column ozone does not return to 1980s values by the end of the 21st century under any SSP scenario as increased ozone mixing ratios in the tropical upper stratosphere are offset by continued ozone decreases in the tropical lower stratosphere. Stratospheric column ozone is projected to be largest under SSP scenarios with the smallest change in radiative forcing, and smallest for SSP scenarios with larger radiative forcing, consistent with a faster Brewer-Dobson circulation at high greenhouse gas loadings. This study explores the use of machine learning (ML) techniques to make accurate, computationally inexpensive projections of tropical stratospheric column ozone. Four ML techniques are investigated: Ridge regression, Lasso regression, Random Forests and Extra Trees. All four techniques investigated here are able to make projections of future tropical stratospheric column ozone which agree well with those made by the UKESM1 Earth system model, often falling within the ensemble spread of UKESM1 simulations for a broad range of SSPs. However, all techniques struggle to make accurate projects for the final decades of the SSP5-8.5 scenario. Accurate projections can only be achieved when the ML methods are trained on sufficient data, including both historical and future simulations. When trained only on historical data, the projections made using models based on ML techniques fail to accurately predict tropical stratospheric ozone changes. Results presented here indicate that, when sufficiently trained, ML models have the potential to make accurate, computationally inexpensive projections of tropical stratospheric column ozone. Further development of these models may reduce the computational burden placed on fully coupled chemistry-climate and Earth system models and enable the exploration of tropical stratospheric column ozone recovery under a much broader range of future emissions scenarios

University Staff’s Perceptions of Community College Transfer Students’ Transition Experiences Within a “2+2” Pathway in an Asian Educational Context

Various countries have alternative pathway policies for 2-year community college graduates to articulate to 2-year university study, forming a “2+2” pathway. However, few studies have explored university staff members’ perceptions of this “2+2” transfer pathway and their understanding of transfer students’ (TSs) transition experiences. This descriptive qualitative study addressed this research gap. Forty-two academic and supporting staff participated in the focus group interviews. Specifically, the study explored the assets and challenges of the “2+2” pathway from the university staff perspective in Hong Kong. The articulation pathway and TSs are highly recognized for their prior learning, academic performances, and the value of the second chance. However, while the university staff were sympathetic to the challenges filling these transfer pathways, their offering of help was limited by government funding and policies restrictions. It is recommended that policies should be established at government and university levels to recognize and tackle TSs’ unique needs to alleviate their heavy workloads through better articulation between community college and university studies. Improving articulation will allow TSs time for social involvement in university life and thus enhance their mental well-being

A multi-voiced account of family entrepreneuring research: expanding the agenda of family entrepreneurship

Purpose This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms. Design/methodology/approach Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward. Findings Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context. Originality/value This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring