Search results

The purpose of this paper is to present a discrete compartmental susceptible-asymptomatic-infected-dead (SAID) model to address the expansion of plant pests. The authors examined the case of Xylella fastidiosa in almond trees in the province of Alicante (Spain) to define the best eradication/contention protocol depending on the environmental parameters such as climatic factors, distance between trees, isolation of the plots, etc.

This approach considers the expansion of the disease among the almond trees orchards by means of a grid model. The cells of the grid represent a tree (or even a group of trees) that can be susceptible (healthy), asymptomatic (infected by the bacterium but without symptoms), infected or dead. When time passes, the status of the cells is determined by binary rules that update following both a neighborhood and a delay pattern. The model assumes that the environmental parameters have a crucial impact on the expansion of the disease, so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids.

This proposal shows how the grid architecture, along with an update rule and a neighborhood pattern, is a valuable tool to model the pest expansion. This model has already been analyzed in previous works and has been compared with the corresponding continuous models solved by ordinary differential equations, coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between susceptible and asymptomatic, asymptomatic and infected, infected and recovered/dead) may have a crucial impact on both the peak of infected and the recovery/death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and is also currently under study in the case of expansion of COVID-19.

This work develops a new approach for the analysis of expansion of plant pests. This approach provides both behavioral variability at the cell level (by its capability to modify the neighborhood and/or the update rule and/or the delays) and modularity (by easy scaling the number of grids). This provides a wide range of possibilities to deal with realistic scenarios.

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

The purpose of this paper is to identify the strain of oyster mushroom (OM) Pleurotus sapidus cultivated in a local (commercial) farm, and to generate a total quality index (TQI) on the strain using different modified atmosphere packaging (MAP) gas mixtures.

A phylogenetic tree was constructed using the MEGA software to identify the specific strain of P. sapidus grown in a local farm. The effects of MAP on fresh fruiting bodies of the identified strain were determined under three conditions: high carbon dioxide packaging (HCP: 20 per cent CO₂, 15 per cent O₂), low carbon dioxide packaging (LCP: 2 per cent CO₂, 30 per cent O₂) and high nitrogen packaging (HNP: 85 per cent N₂, 15 per cent O₂). All samples were stored at 4 ^oC for up to ten days, and subjected to total phenolic content (TPC), colour retention (CR) and sensory analysis. Quality parameters such as chewiness and odour were used to obtain the TQI.

From the phylogenetic analysis, a new strain (P. sapidus strain QDR) with 99 per cent similarity to P. sapidus was identified. Among the three MAP treatments, HCP recorded the highest TPC (2.85 mg GAE/g) and CR (60.36) after ten days, although only its CR was significantly different (p<0.05) from the control. Feedback from 30 sensory panellists indicated that both HCP and LCP were generally more effective in retaining the colour–odour of OM. The optimum TQI for HCP was obtained based on the observed parameters, which is useful for the large-scale packaging of OM.

Scientific evidence has revealed that packaging trend for commercially grown OM affects consumer’s acceptance.

This chapter investigates the ‘green skills gap’ as economies move towards net zero emissions. Building on the work of Moon, Walmsley, Apostolopoulos, and Zollo (2020), the author identifies critical skills needed in the green economy to ensure that current skills gaps identified by the International Labour Organization (2018) are bridged. However, the ILO (2018) report predominantly refers to macro level changes in society as economies transition to carbon neutrality (job destruction, job creation and job reallocation). There is little evidence of detailed action plans nor their implementation and the ILO report concludes that skills development programmes are yet to be mainstreamed in policy discussions. This chapter thus highlights the green skills needed, identifies some of the barriers preventing economies from mainstreaming such skills development programmes into policy and provides recommendations for governments, enterprises and universities in the short to medium term. The chapter includes a review of global progress in developing skills for a greener future (ILO, 2019) but focusses on implications for European policy in particular.

This paper aims to look at two recently published studies from the UK that report on using geo‐engineering solutions

The paper examines the geo‐engineering solutions suggested by the two publications: to remove CO₂ from the atmosphere, or reflect solar radiation back into space.

The paper finds that using geo‐engineering solutions to remove CO₂ from the atmosphere, or reflect solar radiation back into space, may well be needed in order help avoid dangerous temperature increases and climate change. The publications agree that certain solutions, ranging from artificial trees and algae covered buildings to fleets of sea salt spraying cloud ships are technically possible, could be effective and so need to be investigated further. However, both studies also emphasize that geo‐engineering is no silver bullet that can combat climate change in isolation. Moreover, it must not be seen as a diversion from efforts to reduce carbon emissions through mitigation. However, as reduction efforts to date have yet to deliver anything like what is needed, the studies state that geo‐engineering solutions of the type they propose should be further investigated, and those with merit invested in and used to buy the world time to decarbonize the global economy.

The two recently published studies from the UK provide useful information on geo‐engineering solutions to help avoid dangerous temperature increases and climate change.

The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic.

The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model.

The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model.

At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

The role of urban forests and urban trees in creating vibrant and resilient cities is widely recognised. Urban forest governance as the strategic decision and rule making for urban tree resources is no longer solely the domain of governmental actors, but occurs rather often as network governance also involving businesses and civic society. However, governance theory usually does not consider the role of non-human agency, which can be considered problematic due to, for example, the important role of urban trees in place making. The purpose of this paper is to provide further insight into the importance of considering tree agency in governance.

Taking an environmental governance and actor network theory perspective, the paper presents a critical view of current urban forest governance, extending the perspective to include not only a wide range of human actors, but also trees as important non-human actors.

Urban forest governance has become more complex and involves a greater range of actors and actor networks. However, the agency of trees in urban forest governance is seldom well developed. Trees, in close association with local residents, create places, something which needs to be better recognised in governance. Case studies show that this type of non-reflexive agency of urban trees often has emerged in the case of acute threats to urban trees or woodlands. New approaches such as those of biophilic urbanism and biocultural diversity can assist with better integration of tree agency in governance.

Urban forest governance is an emerging field of research which has seldom addressed tree agency. Thus the perspective on urban forest governance is enriched, beyond the dominant post-neoliberal and anthropocentric perspective.

The purpose of this paper is to design an electromechanical actuator which can inherently tolerate a stuck or loose failure without any need for fault detection isolation and reconfiguration.

Generalized design methodology for a thrust vector control application is adopted to reduce the design iterations during the initial stages of the design. An optimum ball screw pitch is selected to minimize the motor sizing and maximize the load acceleration.

A high redundancy electromechanical actuator for thrust vector control has lower self-inertia and higher reliability than a direct drive simplex configuration. This configuration is a feasible solution for thrust vector control application because it offers a more acceptable and graceful degradation than a complete failure.

Future work will include testing on actual hardware to study the transient disturbances caused by a fault and their effect on launch vehicle dynamics.

High redundancy electromechanical actuator concept can be extended to similar applications such as solid motor nozzle in satellite launch vehicles and primary flight control system in aircraft.

High redundancy actuators can be useful in safety critical applications involving human beings. It can also reduce the machine downtime in industrial process automation.

The jam tolerant electromechanical actuator proposed for the launch vehicle application has a unique configuration which does not require a complex fault detection isolation and reconfiguration logic in the controller. This enhances the system reliability and allows a simplex controller having a lower cost.