Assessment of the Feedstock Availability for Covering EU Alternative Fuels Demand

Modern economies rely on the efficiency of their transportation sector; however, the environmental impact of the sector remains a growing concern. Among the various proposed solutions, the production and deployment of alternative fuels is a major option. However, concerns exist that the actual availability of sustainable feedstock might lower the current level of ambition. This paper addresses this issue by reviewing recent studies and policy targets, to match forecasts for expected demand and feedstock availability for road, aviation, and maritime sectors in the EU in 2030. The existing literature is fragmented and based on a variety of different approaches, and a consistent assessment of the potential overall demand for transport is still missing. In spite of the challenges posed by the numerous uncertainties, this research provides an estimate of potential European demand for alternative fuels that ranges between 20 and 33 Mtoe. We aimed to answer the question about the availability of sustainable feedstock to cover this potential demand. The analysis confirmed, even under very conservative assumptions, that feedstock may not be the major barrier today. Other issues, such as the feedstock costs, the price volatilities, the existing logistical infrastructures, etc., are relevant aspects contributing to the puzzle. Whilst feedstock is present across European regions, a critical element which requires detailed analysis at the implementation value chain level is the effectiveness of its sustainable mobilisation alongside the synergies and trade-offs that may arise.

全人照護及其教育訓練與評值

<p>目的：全人照護是對病人身體、心理、社會層面及靈性全面照護的態度與行為，需要醫療院所投注多專 業、跨領域且有效率又有品質的整合服務，才能滿足病人「一站式服務、一次購足」的需求。本文之目的在於詮釋全人照護之真諦、內涵、教育訓練與評值。</p> <p>方法：藉由闡釋全人照護的素養與能力，強調推廣全人照護需要多面向的教育訓練加以養成，並發展以能力為基礎的評值方法。</p> <p>結果：醫療人員需要終身持續學習有關全人照護的核心知識與技能、醫療專業照護、同理心與心理支持、就醫行為和家庭照護，以及生命關懷及和臨終照護，並佐以適合及一致性的評值標準，方能系統性的推廣全人照護。</p> <p>結論：本文探討全人照護的真諦、如何推動全人照護及如何實施全人照護之教育訓練與評值，作為醫療院所推動全人照護之參考。</p> <p>&nbsp;</p><p>Objective: Holistic care is an approach that involves consideration of the physical, emotional, social, and spiritual wellbeing of a patient. It involves multidisciplinary, interprofessional integrated teams with members who educate, cooperate with, and respect each other to offer one-stop fulfillment of patient needs. This article explains the core values and intentions of holistic care and the relevant education, training, and assessments.</p> <p>Methods: By explaining the competencies of holistic care, this article argues that the promotion of holistic care necessitates multidimensional education, training, and competency-based assessments.</p> <p>Results: To systematically promote holistic care, accompanied by appropriate and consistent assessment standards, health care providers must continue to learn about the main components of holistic care, including its required knowledge and competencies, professional medical care empathy and psychological support, the praxeology of healthcare seeking, family care, and end-of-life and hospice care.</p> <p>Conclusion: This article discusses the core values of holistic care; how to promote holistic care; and how to implement education, training, and assessments in the field of holistic care and may therefore serve as a reference for holistic care training in medical settings.</p> <p>&nbsp;</p>

Need analysis on the usage of multimodal reading assessment among secondary school teachers

Strategically developed assessment rubrics are essential to ascertain fair and consistent assessment grading. Nevertheless, devising assessment rubrics poses certain drawbacks as scores that determine students’ capability are awarded holistically. In order to assure that students’ grades indeed reflect their current capability and to provide effective feedback on aspects that demand improvement, rubrics must accurately evaluate and measure the performances displayed by students. Hence, this study identified the suitability and acceptability of multimodal reading assessment upon assessing reading skills among secondary school students. This study, which involved English language teachers from several schools located across Negeri Sembilan, Malaysia, had adopted the survey research method to design a multimodal reading assessment rubric and to obtain teachers’ views on multimodal reading and viewing (MMRV). The study outcomes signified that although the teachers acknowledged and were aware of the advantages of applying MMRV, the absence of such rubric that specifically assesses MMRV seemed to limit this practice in classroom. This study concludes that it is imperative to formulate a comprehensive MMRV-based rubric to enable teachers assess their students’ reading skills in a more accurate manner.

Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organizations, governmental authorities, and investors and portfolio owners are increasingly required to account for climate-related physical risks. In response to these societal challenges, we present an extension to the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation) for modelling economic impacts of wildfires in a globally consistent and spatially explicit approach. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 km using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global perspective of this study, we conducted two case studies on the recent megafires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment, near-real-time impact estimates or physical climate risk disclosure.

The challenges and opportunities of adopting artificial intelligence (AI) in Jordan’s healthcare transformation

Artificial Intelligence (AI) is the greatest development and promise in the present technology world, as it promises big contribution, massive changes, modernization, and coordination with and within people’s progressing life. This paper aims to provide an analysis of Jordan health care that are co-connected and interconnected with the consequences formed by Artificial Intelligence (AI) and focuses on the strengths and weaknesses of adopting AI in health sector. It also discusses the local awareness and familiarization of Artificial Intelligence (AI) in Jordan healthcare providers and gives a consistent assessment of current and future best practices. Data was gathered by using interviews from Jordan IT and health care providers. The investigation found that AI is consistently changing the way healthcare is to be directed in Jordan. AI can provide solid healthcare services to the stakeholders. As a developing country, Jordan has not fully adopted Artificial Intelligence (AI) in its healthbsector. Keywords: Artificial Intelligence (AI); Challenges; Health care System; Jordan; Opportunities

Globally consistent assessment of coastal eutrophication

Eutrophication is an emerging global issue associated with increasing anthropogenic nutrient loading. The impacts and extent of eutrophication are often limited to regions with dedicated monitoring programmes. Here we introduce the first global and Google Earth Engine-based interactive assessment tool of coastal eutrophication potential (CEP). The tool evaluates trends in satellite-derived chlorophyll-a (CHL) to devise a global map of CEP. Our analyses suggest that, globally, coastal waters (depth ≤200 m) covering ∼1.15 million km2 are eutrophic potential. Also, waters associated with CHL increasing trends—eutrophication potential—are twofold higher than those showing signs of recovery. The tool effectively identified areas of known eutrophication with severe symptoms, like dead zones, as well as those with limited to no information of the eutrophication. Our tool introduces the prospect for a consistent global assessment of eutrophication trends with major implications for monitoring Sustainable Development Goals (SDGs) and the application of Earth Observations in support of SDGs.

Simulating sedimentary burial cycles – Part 2: Elemental-based multikinetic apatite fission-track interpretation and modelling techniques illustrated using examples from northern Yukon

Compositionally dependent apatite fission track (AFT) annealing is a common but underappreciated cause for age dispersion in detrital AFT samples. We present an interpretation and modelling strategy that exploits multikinetic AFT annealing to obtain thermal histories that can provide more detail and better resolution compared to conventional methods. We illustrate our method using a Permian and a Devonian sample from the Yukon, Canada, both with complicated geological histories and long residence times in the AFT partial annealing zone. Effective Cl values (eCl; converted from rmr0 values), derived from detailed apatite elemental data, are used to define AFT statistical kinetic populations with significantly different total annealing temperatures (~110–245 °C) and ages that agree closely with the results of age mixture modelling. These AFT populations are well-resolved using eCl values but exhibit significant overlap with respect to the conventional parameters, Cl content or Dpar. Elemental analyses and measured Dpar for Phanerozoic samples from the Yukon and Northwest Territories confirm that Dpar has low precision and that Cl content alone cannot account for the compositional and associated kinetic variability observed in natural samples. An inverse multikinetic AFT model, AFTINV, is used to obtain thermal history information by simultaneously modelling multiple kinetic populations as distinct thermochronometers with different temperature sensitivities. A nondirected Monte Carlo scheme generates a set of statistically acceptable solutions at the 0.05 significance level and then these solutions are updated to the 0.5 level using a controlled random search (CRS) learning algorithm. The smoother, closer-fitting CRS solutions allow for a more consistent assessment of the eCl values and thermal history styles that are needed to satisfy the AFT data. The high-quality Devonian sample (39 single grain ages and 202 track lengths) has two kinetic populations that require three cycles of heating and cooling (each subsequent event of lower intensity) to obtain close-fitting solutions. The younger and more westerly Permian sample with three kinetic populations only records the latter two heating events. These results are compatible with known stratigraphic and thermal maturity constraints and the QTQt software produces similar results. Model results for these and other samples suggest that elemental-derived eCl values are accurate within the range, 0–0.25 apfu (rmr0 values of 0.73–0.84), which encompasses most of the data from annealing experiments. Outside of this range, eCl values for more exotic compositions may require adjustment relative to better constrained apatite compositions when trying to fit multiple kinetic populations. Our results for natural and synthetic samples suggest that an element-based multikinetic approach has great potential to increase the temperature range and resolution of thermal histories dramatically relative to conventional AFT thermochronology.

Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organisations and governmental authorities, as well as investors and portfolio owners, are increasingly required to account for climate-related physical risks. In this study we present a globally consistent and spatially explicit approach to modelling wildfire impacts using the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation). All input data is free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 kilometers using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global 10 perspective of this study, we conducted two case studies on the recent mega fires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment or physical climate risk disclosure.

MATRIX RECOVERY METHODOLOGY BASED ON MOBILE DATA

The study is aimed at comparing the methods of restoring the correspondence matrices and a more detailed study of the algorithm for restoring the correspondence matrix based on innovative mobile technologies. Previously, the correspondence matrices were reconstructed by interviewing vehicle drivers and applying gravity models, then matrix estimation methods are used to calculate traffic flows. Each of these approaches has a number of advantages and disadvantages, but there is no evidence to assess the quality of the data obtained. The results show that, in general, processing da-ta from the mobile network allows for a more reliable estimate without taking into account survey data or similar observations, and using this type of research can lead to a more consistent assessment of trips with a larger sample size