Search

Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared. A focus has been placed on the analysis of a transition to dimensionless input quantities. The methods used in this paper are the support vector machine, two-tiered support vector machine, random forest, gradient-boosted trees and long-short term memory. The performance between different models is remarkably similar, with the support vector machine attaining a slightly better accuracy score. The similarity could indicate issues with the dataset, but further study is required to confirm this. Both the two-tiered model and long-short term memory performed below expectations. The former could be attributed to an implementation which did not allow error propagation between tiers. The latter could be attributed to high noise and low frequency of the input signals. Dimensionless models experienced an expected decrease in performance, caused by a loss of information in the conversion. However, random forest and gradient boosted trees experienced a significantly lower decrease, making them more suitable for dimensionless predictors. From the disruption detection times, it was concluded that several disruptions could be predicted at more than 600 ms in advance. A feature importance study using the random forest indicated the negative impact of high noise and missing data in the database, suggesting improvements in data preparation for future work and the potential reevaluation of some of the selected portable features due to poor performance.

Data-driven and machine-learning-based methods are increasingly used in attempts to master the challenges of the world. But are they really the best approaches to manage complex dynamical systems? Our aim is to gain more insights into this question by studying various popular reinforcement learning methods for traffic signal control, namely in disrupted scenarios characterized by significant, unpredictable variations. The results are expected to be relevant in subject areas ranging from traffic physics to transportation theory, from dynamics in networks to complex systems, from control theory to self-organization, and from adaptive heuristics to machine learning., IEEE Access, 11, ISSN:2169-3536

Disruptions create both new opportunities and challenges in higher education. In settled times, education systems plod along with an assumed and uncritical acceptance of normalcy of the status-quo. When the status quo is disrupted, suddenly the patched-up cracks reveal the depth and magnitude of the simmering problems of the sector in graphic ways. Access and success are arguably the two most poignant indicators of the performance of higher education systems. In post-colonial societies such as South Africa, access is used to estimate progress in broadening participation in higher education, particularly to young people from previously disadvantaged communities. Access has two broad meanings: increased enrolments and enhanced epistemological impact. Success, on the other hand is measured variously but mainly through graduation and progression rates across different socio-economic higher education students groups and also on the quality of their performances. In this article we provide a theoretical discussion of the notions of disruptions and their impact in higher education; examine the questions of access and success in higher education; and conclude that the chasm lying between access by participation and access by success requires substantial transformation of a knowledge system that is alien to the cultural context of the country; rebalancing and recalibrating the broader ideological environment that privileges liberalism while paying token attention to social justice and inclusion beyond mere symbolism; and a persistent refocusing on emancipatory pedagogies, designed to liberate rather than subjugate graduates into pigeon holed choices in the labour market which are designed to serve the needs of owners of capital as the primary motive of employment. We conclude by identifying critical factors that appear to lead to a failure by universities to bridge the gap between access by participation and access by success or epistemological access. Most of these tend to be structurally embedded in the fabric of higher education institutions and the sector and include, a persistent coloniality of the sector, disjuncture between the intended ideological framework guiding national development and the operating economic models and institutional inertia to move beyond the canonical bases of higher education based on western epistemes.

This paper aims to analyze the effect of a recent disruption of Mexico's gasoline supply chain on the usage of public bike-sharing systems in Mexico City and Guadalajara.,The authors use a detailed data set to understand the usage patterns of Ecobici and Mibici. The authors assess both systems with a differences-in-differences econometric model using the least popular stations as a control group.,The authors find that the number of rides increased significantly shortly after the event because less popular stations became more utilized.,The authors show that when the effects of gasoline shortages were noticeable, usage rates increased in Guadalajara and Mexico City, but the rise primarily came from the users selecting more bikes from the less popular stations. Therefore, the authors show that citizens in both cities regarded bike-sharing as an adequate means of transportation, maximizing system usage during a disruptive time. This finding suggests that cities should invest in improving public bike-sharing systems to reduce carbon emissions and increase their population's well-being.,The authors use a publicly available data set to understand how citizens answered to a major disruption. Furthermore, this is one of the first papers that align supply chain risk management with sustainable transportation and analyzes its effects on citizen behavior in a Latin American setting.,El proposito de este articulo es analizar el efecto de una interrupcion en la cadena de suministro de la gasolina en Mexico sobre el uso del sistema de bicicletas publicas en Ciudad de Mexico y Guadalajara.,Usamos una base de datos detallada para entender los patrones de uso de Ecobici y Mibici. Analizamos ambos sistemas a traves de un modelo econometrico de diferencias en diferencias utilizando las estaciones menos afectadas como grupo de control.,Encontramos que la utilizacion del sistema aumento en numero de viajes de manera significativa luego del evento. Esto es debido a que las estaciones menos populares se empezaron a utilizar de manera mas intensiva.,Demostramos que los habitantes de las dos ciudades decidieron buscar alternativas de transporte cuando los efectos de la escasez de gasolina se sintieron con fuerza. Esto significa que los ciudadanos consideran que las bicicletas publicas son un medio de transporte adecuado y que durante episodios inoportunos decidieron buscar opciones en las estaciones menos populares maximizando el uso del sistema. Por lo que las ciudades deberian invertir en la mejora de dichos sistemas para que sean mas utilizados y asi se reduzcan las emisiones de carbono y para que aumente el bienestar en la poblacion.,Utilizamos una base de datos abierta para entender como los ciudadanos respondieron a una interrupcion importante. Adicionalmente, este es uno de los primeros trabajos que alinea la gestion de riesgos en la cadena de suministros con transporte sostenible y analiza su efecto sobre el comportamiento de los usuarios en un contexto latinoamericano.

Supply and Demand Shocks on Construction Projects caused by COVID-19 WHO officially announced outbreak of COVID-19 disease on March 11th 20201. COVID-19 has caused local, regional & worldwide economic and medical problems. COVID-19 disruption and delay implications & affects on construction projects (but not limited to): negatively affected engineering and construction projects in different ways. labor and skilled worker shortages due to travel bans and temporarily or permanent self-isolations reduces the ability for a return to the engineering and construction projects. interrupted supply chain and interrupted procurement processes for construction materials, plants and equipment's relevant for the progress of construction project. disruptions and delays to deliver the project in time to a certain extent. contractor may bar and tight up still to a concrete and fixed delivery time for the project and liability for delays associated with a contractual liquidated damage provision or penalized milestone. no experience with such pandemic for entire construction world but adversely affect global and local supply chains and demand. ultimately, the contractor’s ability to perform and progress will be restricted or even collapse. If the progress of the contractor falls behind schedule or is suspended he is not able to invoice with negative affect to the contractors cashflow.

Our study examines the literature that has been published in important journals on supply chain disruptions, a topic that has emerged the last 20 years, with an emphasis in the latest developments in the field. Based on a review process important studies have been identified and analyzed. The content analysis of these studies synthesized existing information about the types of disruptions, their impact on supply chains, resilience methods in supply chain design and recovery strategies proposed by the studies supported by cost-benefit analysis. Our review also examines the most popular modeling approaches on the topic with indicative examples and the IT tools that enhance resilience and reduce disruption risks. Finally, a detailed future research agenda is formed about SC disruptions, which identifies the research gaps yet to be addressed. The aim of this study is to amalgamate knowledge on supply chain disruptions which constitutes an important and timely as the frequency and impact of disruptions increase. The study summarizes and builds upon the knowledge of other well-cited reviews and surveys in this research area.

In a complex environment, how to construct a resilient supply chain network (SCN) that can resist disruptions is a problem of great concern in supply chain management. In order to provide some insights for constructing a resilient SCN, this paper studies the influence of network structures on SCN resilience from the perspective of complex networks. Considering the exit and reselection of enterprises that have been ignored in previous studies, we propose a new SCN model in which nodes are connected with each other based on degree, fitness and distance. Subsequently, different disruption scenarios are simulated and the resilience of the SCN generated by the proposed model is compared with that of previous models. The simulation results show that the SCN generated by the proposed model is resilient to random disruptions, but vulnerable to targeted disruptions. In particular, the resilience of the SCN will be seriously affected when the strong-strong alliance is broken. Through the research on the influence of the parameters (e.g., $\alpha $ , $\beta $ and $f$ ) of the proposed model on SCN resilience, we find that $\alpha $ has a significant impact on SCN resilience. The larger the value of $\alpha $ is, the worse the resilience of the corresponding SCN is. The parameter $\beta $ has a slight effect on SCN resilience, and the more uniform the distribution of $f$ in the network is, the better the resilience of the corresponding SCN is. This study may contribute to the design and resilience optimization of SCNs.

Freight transport disruptions in recent years caused production shutdowns in the industry, supply shortages and high economic damage around the world. In practice, contingency strategies from transport operators are often weak and disruption management fails to handle the situation sufficiently. Despite availability of data collection, academic optimization approaches, and multiple initiatives for coordinated multimodal management in case of disruptions, there are multiple infrastructural, decisional and organizational issues, which limit their applicability. We study those factors by a literature analysis, and a survey in which we ask practitioners, authorities and academic experts to discuss specific aspect in structured and unstructured form. We summarize some parameters, which can be influenced by strategic, tactical, operational actions, and those others referring to systemic properties; or disruption characteristics, which can only be hedged against. Hereby, we offer a knowledge base to future projects aiming to optimize multimodal management at strategic tactical and operational scope, to counter disruptions in freight transport networks., IEEE Open Journal of Intelligent Transportation Systems, 2, ISSN:2687-7813

International audience; Given the inevitable globalisation in the food sector and the specific security challenges this industry faces, designing food supply chains has become a substantial topic for academics and practitioners. The integration of food product-specific characteristics and potential disruptions has continuously gained importance because it better reflects real-world problems and responds to a crucial need for resilience, robustness, and competitiveness. In this article, a generic two-stage mixed-integer mathematical model is developed to integrate key features of location-allocation and inventory-replenishment decisions. Then, food-specific disruptions with ripple effects are incorporated through plausible scenarios. For such a setting, three resiliency strategies – namely, readiness, flexibility, and responsiveness – are used to deal with uncertainties. Based on extensive numerical experiments, the solutions obtained highlight behaviour of different design models to hedge against ripple effects as well as the importance of incorporating food-specific assumptions and risk aversion attitudes.

Supply chains have been facing many disruptions due to natural and man-made disasters. Recently, the global pandemic caused by COVID-19 outbreak, has severely hit trade and investment worldwide. Companies around the world faced significant disruption in their supply chains. This study aims to explore the impacts of COVID-19 outbreak on supply chain risks (SCRs). Based on a comprehensive literature review on supply chain risk management, 70 risks are identified and listed in 7 categories including demand, supply, logistics, political, manufacturing, financial and information. Then, a modified failure mode and effects analysis (FMEA) is proposed to assess the identified SCRs, which integrates FMEA and best-worst method to provide a double effectiveness. The results demonstrate the efficiency of the proposed method, and according to the main findings, "insufficient information about demand quantities", "shortages on supply markets", "bullwhip effect", "loss of key suppliers", "transportation breakdowns", "suppliers", "on-time delivery", "government restrictions", "suppliers' temporary closure", "market demand change" and "single supply sourcing" are the top 10 SCRs during the COVID-19 outbreak, respectively. Finally, the practical implications are discussed and useful managerial insights are recommended. WOS:000777221200003

Disruptions in public transport can have major implications for passengers and service providers. Our study objective is to develop a generic approach to predict how often different disruption types occur at different stations of a public transport network, and to predict the impact related to these disruptions as measured in terms of passenger delays. We propose a supervised learning approach to perform these predictions, as this allows for predictions for individual stations for each time period, without the requirement of having sufficient empirical disruption observations available for each location and time period. This approach also enables a fast prediction of disruption impacts for a large number of disruption instances, hence addressing the computational challenges that rise when typical public transport assignment or simulation models would be used for real-world public transport networks. To improve transferability of our study results, we cluster stations based on their contribution to network vulnerability using unsupervised learning. This supports public transport agencies to apply the appropriate type of measure aimed to reduce disruptions or to mitigate disruption impacts for each station type. Applied to the Washington metro network, we predict a yearly passenger delay of 5.9 million hours for the total metro network. Based on the clustering, five different types of station are distinguished. Stations with high train frequencies and high passenger volumes located at central trunk sections of the network show to be most critical, along with start/terminal and transfer stations. Intermediate stations located at branches of a line are least critical.

Among the many factors that cause project delays or cancellations are disruptions, that is, unforeseen events occurring during the implementation of a project, which postpone or interrupt the performance of project activities. Examples of disruptions include employee absenteeism, addition of new activities, and others. One way to deal with this type of events is to predict potential disruptions and prepare redundant resources to be used should a disruption occur (proactive approach). The focus of the present paper are human resources, in particular redundant project team competence frameworks, which allow to continue work on a project in the event of a disruption. Previous studies on planning competence frameworks regard insensitivity (robustness) to one type of disruption, caused by employee absenteeism (an absence of one, two, or three employees). The goal of this article is to present a proactive procedure that allows to seek competence frameworks robust to two types of disruptions: absence of one employee and addition of new activities not included in the project plan. Examples are provided to illustrate how the proposed approach can be used in practice.

Place-based research faces multiple threats, including both natural and global health hazards and political conflicts, which may disrupt fieldwork. The current COVID-19 pandemic shows how these threats can drastically affect social-ecological research activities given its engagement with different local stakeholders, disciplines, and knowledge systems. The crisis reveals the need for adaptive research designs while also providing an opportunity for a structural shift towards a more sustainable and inclusive research landscape.

In Izzo et al (2022 Nucl. Fusion 62 096029), state-of-the-art modeling of thermal and current quench (CQ) magnetohydrodynamics (MHD) coupled with a self-consistent evolution of runaway electron (RE) generation and transport showed that a non-axisymmetric (n = 1) in-vessel coil could passively prevent RE beam formation during disruptions in SPARC, a compact high-field tokamak projected to achieve a fusion gain Q > 2 in DT plasmas. However, such suppression requires finite transport of REs within magnetic islands and re-healed flux surfaces; conservatively assuming zero transport in these regions leads to an upper bound of RE current ∼ 1 M A compared to ∼ 8.7 M A of pre-disruption plasma current. Further investigation finds that core-localized electrons, within r / a < 0.3 and with kinetic energies ∼ 0.2 – 15 M e V , contribute most to the RE plateau formation. Yet only a relatively small amount of transport, i.e. a diffusion coefficient ∼ 18 m 2 s − 1 , is needed in the core to fully mitigate these REs. Properly accounting for (a) the CQ electric field’s effect on RE transport in islands and (b) the contribution of significant RE currents to disruption MHD may help achieve this.

El siguiente trabajo propone articular una reflexión teórico-crítica sobre la novela La Mucama de Omicunlé (2015) de la escritora dominicana Rita Indiana Hernández centrada en las conexiones que se formulan entre desastre y cuerpo. En relación a estas conexiones nos interesa específicamente indagar y problematizar aquellas dislocaciones que opera la novela al exponer los cuerpos a un atípico y disruptivo relato del fin. The following article proposes to articulate a theoretical-critical reflection about the novel La Mucama de Omicunlé (2015) from the Dominican writer Rita Indiana Hernández focused on the connections that are formulated between disaster and body. In relation to these connections, we are specifically interested in investigate and problematize those dislocations that the novel operates when exposing the bodies to atypical and disruptive story of the end.

Purpose of Review To describe how mitigation measures against COVID-19 have impacted HIV and TB research in South Africa. Recent Findings South Africa has the highest number of COVID-19 (34%) cases in Africa, accounting for 43% of all reported COVID-19-related deaths on the continent. The country accounts for 20% of all people living with HIV and ranked third in the world for new TB infections in 2019. Summary While South Africa’s investments in its HIV and TB responses enabled it to pivot rapidly to respond to the emerging COVID-19 epidemic, it negatively impacted the HIV and TB response through temporary suspension of research, diversion of key resources for HIV and TB control, and patient access to health care facilities; the full extent of this has yet to emerge. Success in integrating responses to the colliding epidemics could potentially enhance survival outcomes and ensure gains made to date in HIV and TB are not reversed and we stay on track toward achieving the UN 2030 Sustainable Development Goals.

Disruptions in agri-food systems caused by crises, such as the COVID-19-pandemic, reveal the vulnerability of global food supply chains. Such crises might consequently impact consumer perceptions about the relevance of local food production and consumption. In this light, this study aims to (i) identify whether the COVID-19 outbreak led to short-term changes in perceptions about local food consumption and (ii) capture how the role of local agri-food systems is perceived in times of crisis. For the first purpose, this study analyzes two waves of survey data collected from an Austrian sample (n = 351) to compare pre-and post-COVID-19 levels of consumer values, beliefs, and attitudes towards local food. For the second purpose, the paper assesses consumer perceptions about the reliability and resilience of the local agri-food sector in response to the COVID-19 outbreak. The results reveal that while consumer perceptions driving local food consumption at an early phase of the pandemic remained stable at large, the perceived relevance of the local agri-food sector attenuated. Consumers showed strong beliefs in the local agriculture as a reliable and trustworthy partner during the pandemic guaranteeing food supply security. Based on these findings, the paper discusses how these insights into consumer perceptions in response to macro-level disruptions might help to better understand short-term demand-side implications of other forms of external crises affecting local food production and supply. Finally, the paper provides recommendations for practitioners and avenues for future research to determine implications from a long-term perspective.

The COVID-19 pandemic has disrupted universities across the globe in unprecedented ways, requiring many teaching faculty to reexamine and transform approaches to pedagogy. As higher-education institutions have grappled with various methods of hybrid and remote delivery in an effort to best preserve student instruction through the pandemic, most have fervently looked ahead for a more satisfying “new normal.” Yet this moment of unease and transformation is one of critical opportunity for universities and their teaching faculty. Educators are seeing in vivid form how an unexpected “threat”—in this case, a global health challenge—can profoundly disrupt pedagogy, and the immense adaptive innovation necessary to preserve universities’ most important functions through a sustained period of difficulty. Equally important are lessons concerning the varying degrees of success experienced between institutions based on different levels of proactive planning and the quality of resilience-building strategies.

This study assessed the COVID-19 related disruptions on the food environment in Zimbabwe. Information on disruptions in personal life, changes in food habits, changes at food acquisition points and nutrition information required during the lockdown were collected from 243 adult respondents using online survey. The pandemic disrupted work, social events, food behaviors and habits. Food consumption behaviours changed during the COVID-19 lockdown included panic buying (68%) and stockpiling (60%). The changes in food related habits were associated with a reduction in eating out at restaurants (89%) and ordering fast foods (71%). Temperature checks, physical distancing, hand sanitization and restricted access to food stores when not wearing a mask were the changes made at the point of food acquisition and adhered to by respondents. Respondents required nutrition advice on what type of food to eat (58%), food availability in area (52%), and ways to reduce food wastages (49%). Important policy implications emanate from this study. Retailers should ensure adequate supplies of affordable food commodities. The public and private sector should provide information on nutritious foods, ways to reduce food wastages, and food safety during and after the COVID-19 pandemic. There is scope of using social media as one of the platforms to disseminate such information. Innovative interventions such as online purchasing and home delivery of food supplies should be piloted to reduce human physical contact. This requires improved broadband connectivity and collaboration between mobile network companies and food suppliers to pilot online purchases.

In recent years, a new inversion method has been adapted to JET tomographic diagnostics. It is based on the Maximum Likelihood (ML) approach and has been applied to most systems of interest: the neutron, gamma ray and bolometric tomographies. In addition to its accuracy and reliability, the main competitive advantage of the ML inversion is the fact that it can provide reliable estimates of the uncertainties in the reconstructions. Recently the same approach has been deployed for the investigation of the hard X-rays and gamma rays, emitted by beams of runaway electrons during disruptions. This application is a particularly challenging proposition, given the potential detrimental effects of the errors in the magnetic topology, during the current quench. In this perspective, particular attention has been devoted to producing good quality, high time resolution equilibria. The ML inversion technique has given promising results and the developed routines have the potential of constituting a good complement to the other diagnostics, such as the infrared cameras, typically used to investigate the runaway electrons.

COVID-19 had a large impact on the global economy, including the global tourism industry, which had to adapt due to travel restrictions and health concerns among consumers. This study aims to find how the hotel industry adapts in response to the changing customer segments and customer demands in response to a disruption. In particular, it offers a case study of the response of Hilton Worldwide to COVID-19. The research finds the strategy of Hilton Worldwide changed from growth focus to cost management, causing a more centralized governance structure. Moreover, Hilton Worldwide has put the Hilton Honors App in the spotlight, which can offer a fully digitalized and contactless check-in procedure. This research analyses this via a BPMN model. To cope with the loss of business travelers, a new business model for Hilton is proposed; renting out rooms as workplaces. However, this imposes new cyber risks such as; key Bluetooth signal interception, key interception when shared, a data leak and unauthorized access to Hilton Honors account. Finally, the findings are discussed in light of the pre-COVID-19 business strategy of Hilton Worldwide to identify opportunities and threats in the new strategy.

Background: The animal feed supply chain in South Africa is an important industry that has experienced numerous changes as it evolved. However, not much is known empirically about the dynamics of implementing supply chain management (SCM) in the animal feed industry in specific South African contexts. Objectives: This study investigates the SCM-related factors for growth (enablers), barriers and disruptions within the animal industry in the Western Cape Province of South Africa. Method: This study used a qualitative method in which managers drawn from the animal feed industry in the Western Cape Province were interviewed. The collected data were examined using thematic analysis. Results: This study showed that the use of technology to achieve goals, bargaining power of the industry and unity, and procurement and supply chain strategy served as the main SCM enablers. The lack of infrastructure, ineffective change management, health and safety, nature of the industry, and skills and qualifications are the primary barriers hindering the effective implementation of SCM. Economic factors, natural disasters and diseases, and a lack of customer loyalty within the animal feed industry were the main SCM disruptions highlighted in the study. Conclusion: This study provides information regarding the factors (enablers) that deserve attention when developing interventions to improve the performance of the animal feed supply chain. The information on SCM barriers and disruptions is relevant when developing risk management initiatives in the animal feed industry.

I engage with the impact of disruptions on my work life, and consider the transformative potential that these disruptions offer. I focus on four parts of my life: as a researcher, teacher, administrator and editor. In each, I examine the nature of the disruption and the way I deal with it. I also consider how the present disruption may facilitate a transformation of current practices that lead to a better world at the individual and institutional levels. Rather than lamenting the inconvenience of a crisis, I prefer to celebrate the opportunity to do better.

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease., Grey matter covariance networks are extracted from structural MRI. In the DIAN autosomal dominant Alzheimer’s disease family cohort, we demonstrate for the first time the trajectory of network properties in Alzheimer’s disease. These network properties related to other eurodegeneration markers and can form a non-invasive tool for studying Alzheimer’s disease progression., Graphical Abstract Graphical Abstract

Notwithstanding the efforts exerted over many years, disruptions remain a major impediment on the route to a magnetic confinement reactor of the Tokamak type. Machine learning predictors, relying on adaptive strategies, have recently proved to achieve very good performance. Even if their last generation implement a "from scratch" approach to learning, i.e. they can start predicting after the first example of each class (safe and disruptive), it would be extremely useful to profit from the experience of previous devices, when new machines come on online, to reduce excessive errors at the beginning of the learning process. In this paper, adaptive predictors, based on ensemble classifiers, have been operated on a series of AUG campaigns and then they have been deployed on several JET campaigns with the ILW, all together covering more than order of magnitude in plasma current. The criteria to normalise the signals and to translate the parameters of the predictors from one device to the other are discussed in detail. With regard to mitigation, the overall performance, both in terms of success rate and false alarms, are quite positive (98% success rate and only 1.9% false alarm rate). Encouraging results have also been obtained for prevention (94.2% success rate and only 7.7 % false alarm rate), by providing as inputs to the classifiers appropriate profile indicators. Even if they require significant refinements, adaptive predictors, capable of capitalising on the experience of smaller devices, have therefore become a serious candidate for deployment in the next generation of machines.

Risk is a significant issue for supply chain managers. Not only must they contend with multiple dimensions of risk in decision‐making, they must reconcile decision‐making with broader organizational interests. This study examines the influence of organizational communication regarding supply chain risk on individual decision‐making strategies and the perceptions of risk. A multi‐stage experimental design is applied, in which decision‐makers make decisions across three dimensions of risk and adjust their risk‐taking behavior after being presented with organizational communication regarding supply chain risk levels. The relationship between organizational communication and the perceptions of supply chain risk is then explored after decision‐makers are allowed to adjust their supply chain strategies. The results suggest that decision‐makers adapt sourcing strategies in response to organizational communication regarding supply chain risk. Specifically, they make riskier decisions when the organization communicates improvements in supply chain risk levels. However, when given specific instructions to reduce risk, they do not adjust their supply chain strategies.

The permutation flow shop problem is a complex combinatorial optimization problem. Over the last few decades, a good number of algorithms have been proposed to solve static permutation flow shop problems. However, in practice, permutation flow shop problems are not static but rather are dynamic because the orders (where each order contains multiple jobs) arrive randomly for processing and the operation of any job may be interrupted due to resource problems. For any interruption, it is necessary to reschedule the existing jobs that are under process at different stages in the production system and also any orders that were previously accepted that are waiting for processing. In this paper, a memetic algorithm-based rescheduling approach has been proposed to deal with both single and multiple orders while considering random interruptions of resources. The experimental results have shown that the performance of the proposed approach is superior to traditional reactive approaches.

In the last years, it has become apparent that detecting disruptions with sufficient anticipation time is an essential but not exclusive task of predictors. It is also important that the prediction is accompanied by appropriate qualifications of its reliability and it is formulated in mathematical terms appropriate for the task at hand (mitigation, avoidance, classification etc.). In this paper, a wide series of rule-based predictors, of the Classification and Regression Trees (CART) family, have been compared to assess their relative merits. An original refinement of the training, called noise-based ensembles, has allowed not only to obtain significantly better performance but also to increase the interpretability of the results. The final predictors can indeed be represented by a tree or a series of specific and clear rules. Such performance has been proved by analysing large databases of shots on JET with both the carbon wall and the ITER Like Wall. In terms of performance, the developed tools are therefore very competitive with other machine learning techniques, with the specificity of formulating the final models in terms of trees and simple rules.

On the route to the commercial reactor, the experiments in magnetical confinement nuclear fusion have become increasingly complex and they tend to produce huge amounts of data. New analysis tools have therefore become indispensable, to fully exploit the information generated by the most relevant devices, which are nowadays very expensive to both build and operate. The paper presents a series of innovative tools to cover the main aspects of any scientific investigation. Causality detection techniques can help identify the right causes of phenomena and can become very useful in the optimisation of synchronisation experiments, such as the pacing of sawteeth instabilities with ion cyclotron radiofrequency heating modulation. Data driven theory is meant to go beyond traditional machine learning tools, to provide interpretable and physically meaningful models. The application to very severe problems for the tokamak configuration, such as disruptions, could help not only in understanding the physics but also in extrapolating the solutions to the next generation of devices. A specific methodology has also been developed to support the design of new experiments, proving that the same progress in the derivation of empirical models could be achieved with a significantly reduced number of discharges.

The outbreak of the COVID-19 pandemic in 2020 has posed unprecedented challenges for fish trade worldwide. This study examines these challenges through an observation of China’s fish import and export activities and a qualitative study on evolving policies in the first nine months of 2020. The results indicate that control measures responding to uncertainty regarding the source of the disease and transmission path, the pressing need for restriction of the movement of people and goods, and fear of a second wave of outbreaks have substantially disrupted fish trade. To meet these challenges, certain coping measures are adopted to offset negative effects in the short term. These measures indicate the government’s pivotal role in stabilizing the supply chain and striking a balance between control requirements and efficiency required in trading activities, as well as the importance to find alternative markets to mitigate risks brought by the delayed supply and the plummeting consumption market. It concludes with some suggestions on improving international cooperation and domestic regulation for building resilience in long run.

Using a structural time-varying-parameter Bayesian vector autoregression (TVP-BVAR) framework, this paper documents that oil price increases caused by oil supply disruptions did not affect food commodity prices before the start of the millennium, but had positive spillover effects in more recent periods and particularly in the era around the Great Recession. Likewise, shortfalls in global food commodity supply resulting from bad harvests have positive effects on crude oil prices since the early 2000s, in contrast to the preceding era. The econometric evidence suggests that these developments are not the consequence of the popular biofuels narrative and more likely the result of informational frictions about the global business cycle and information discovery in financialized commodity markets. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

First, it is shown that during electromagnetic transients in COMPASS-U the poloidal field coils must drain sizeably the current from the vessel and, therefore, reduce disruption forces and their duration. Next, the role of poloidal eddy current (which is absent in some approaches) in the dynamics of vertical and radial forces is found to be essential. Finally, to verify the CarMa0NL modelling for COMPASS-U, the numerical results are cross-validated with general analytical predictions (Pustovitov 2015 Nucl. Fusion 55 113032): the computed vertical force on the tokamak wall is found to be almost zero during rapid ( jump-like) transients, as it should be because of strong skin-effect. This test proves the credibility of the simulation model and computational realization 2021 IAEA, Vienna.

Biomass to biofuel supply chain is subject to several potential disruptions such as flood, drought, pest attack, and equipment failure. These disruptions must be considered while designing the supply chain; especially if capital cost intensive components such as regional biomass pre-processing depots (RBPD) are to be implemented. This work develops a supply chain design optimization model that incorporates the possibility of such disruptions at the design stage. The objective function is the sum of the total cost incurred during the non-disruption and disruption scenarios weighted by their respective probability of occurrence. This also quantifies the expected disruption cost (EDC) on the operation of the supply chain. The decision variables are the locations and capacities of RBPDs and biorefinery, as well as the biomass flow across the supply chain. The model was applied to two separate case studies, namely, procurement of corn stover from farms arranged in a generic grid pattern, and procurement of corn stover, switch grass, and Miscanthus for a region of thirteen counties in Southern Illinois. The simulation results showed that the consideration of resiliency in design reduced the EDC of supply chain by up to 38% by optimizing the RBPD locations. The results were shown to depend on the intensity and nature of disruptions. This was especially true for feedstock with higher yield such as Miscanthus. Local parameters such as yield and biomass price also affected the optimal results. Moreover, the presence of RBPDs was shown to increase supply chain resiliency. (C) 2016 Elsevier Ltd. All rights reserved.

Over the last couple of years, design activism has attracted increasing attention among designers and design researchers, but the approach seems not to have found it’s way yet to the Design & Emotion research community. In this paper, we introduce design activism as a new approach valuable for probing into the role of mixed emotions in interpersonal interaction. To set the scene, we critically examine recent sources on design activism in order provide a general characterisation of what constitute the approach. Next, we argue for how social game can be seen as an instantiation of design activism used, in a current research project, to inquiry into how teenagers feel about visiting their incarcerated fathers in top secure prisons. Finally, we relate our findings to discourse on ambiguous emotions and use this to visualize the eliciting pattern of ambiguous emotions.

NPS NRP Executive Summary Managing Materiel Distribution in an Uncertain Environment N4 - Fleet Readiness & Logistics This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp Chief of Naval Operations (CNO) Approved for public release. Distribution is unlimited.

The role of the COMPASS tokamak in research of generation, confinement and losses of runaway electron (RE) population is presented. Recently, two major groups of experiments aimed at improved understanding and control of the REs have been pursued. First, the effects of the massive gas injection (~1021 Ar/Ne particles) and impurity seeding (~1018 particles) were studied systematically. The observed phenomena include generation of the post-disruption RE beam and current conversion from plasma to RE. Zero loop voltage control was implemented in order to study the decay in simplified conditions. A distinctive drop of background plasma temperature and electron density was observed following an additional deuterium injection into the RE beam. With the loop voltage control the parametric dependence of the current decay rate dI/dt can be studied systematically and possibly extrapolated to larger facilities. Second, recent results of experiments focused on the role of the magnetic field in physics of RE were analysed. In this contribution, special attention is given to the observed effects of the resonant magnetic perturbation on the RE population. The benefits of the RE experiments on COMPASS was reinforced by diagnostic enhancements (fast cameras, Cherenkov detector, vertical ECE etc) and modelling efforts (in particular, coupling of the METIS and LUKE codes).

The rapid growth of urbanisation and e-commerce has increased the number of home deliveries that need to be made in retail operations. Consequently, there is also an increase in unexpected incidents, such as adverse traffic, unavailability of parking space, and vehicle breakdowns. These disruptions result in delays, higher costs, and lower service levels in the last-mile delivery operation. Motivated by free, innovative, and efficient tools, such as the Google application programming interface (API) and Google OR, we built a model to measure the impact of disruptions in the last-mile delivery operation. Our model considers customers’ geographic information, speed estimation between nodes, routing optimisation, and disruption evaluation. Disruptions are considered here as external factors such as accidents and road works that imply the closure of or slow access to certain roads. Computational experiments, based on a set of real data from three different cities around the world, which contrast in size and characteristics (i.e., Boston, US, Bogotá, Colombia, and Pamplona, Spain), were conducted to validate our approach. The tests consider 50 different instances of up to 100 customers per city and analyse the impact of disruptions in terms of travelled time and distance. Our results provide managerial insights for key stakeholders (i.e., carriers, consumers, and government) to define policies and development plans that improve the resilience and capabilities of cities’ transportation systems.

Agri-food supply chains in North America have become remarkably efficient, supplying an unprecedented variety of items at the lowest possible cost. However, the initial stages of the COVID-19 pandemic and the near-total temporary loss of the foodservice distribution channel, exposed a vulnerability that many found surprising. Instead of continued shortages, however, the agri-food sector has since moved back to near normal conditions with prices and production levels similar to those typically observed in years prior to the pandemic. Ironically, the specialization in most food supply chains designed for “just-in-time” delivery to specific customers with no reserve capacity, which led to the initial disruptions, may have also been responsible for its rapid rebound. A common theme in assessing the impacts across the six commodities examined is the growing importance of understanding the whole supply chain. Over the longer term, a continuation of the pandemic could push the supply chain toward greater consolidation of firms and diversification of products given the increasing option value of maintaining flexibility. Other structural changes will be felt through input markets, most notably labour, as the trend toward greater automation will continue to accelerate as a response to meeting concerns about a consistent supply of healthy and productive workers. The economic fall out from the pandemic may lead to greater concentration in the sector as some firms are not able to survive the downturn and changes in consumer food buying behaviour, including movement toward online shopping and enhanced demand for attributes associated with resiliency, such as local. On the other hand, online shopping may provide opportunities for small producers and processors to shorten supply chains and reach customers directly. In the long term, COVID-19 impacts on global commerce and developing country production are more uncertain and could influence poverty reduction. While COVID-19's impacts on North American agriculture should have minimal effect on the Sustainable Development Goals (SDGs) through food prices, the ongoing global trends in trade and agribusiness accelerated by the pandemic are relevant for achievement of the SDGs., Highlights • Initial stages of the COVID-19 pandemic exposed a vulnerability in agri-food sector supply chains. • Most sectors have since moved back to near normal conditions. • Importance of understanding the whole supply chain is growing. • The trend toward greater automation will continue to accelerate.

UIDB/00066/2020 Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action. publishersversion published

The current state-of-the-art in timetable analysis in the presence of disruptions is to use railway microsimulation, which typically yields detailed results on infrastructure or timetable performance. However, micro-simulation is time-consuming and requires a detailed infrastructure model. This paper outlines a macroscopic approach which aims at reducing execution time by restricting the level of detail to high-level relations between significant events. In particular, the effect of disruptions is modelled by sampling delay times from probability distributions obtained from historical data. In this paper, we test whether this approach, given common disruption scenarios, still allows accurate results on delays to be obtained. Two disruption scenarios were simulated in RailSys and with the new method, using limited parameter tuning. In the results, visually similar delay distributions were observed. Although there is some room for improvements in accuracy, the new approach appears promising, and we found no evidence against its suitability in the presence of disruptions.

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy 'from scratch' has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a first-stage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters.; For this novel problem, a sophisticated branch-and-cut framework based on Benders decomposition is designed and complemented by several non-trivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zero-half cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

This paper studies optimal spatial layout of transshipment facilities and the corresponding service regions on an infinite homogeneous plane R 2 that minimize the total cost for facility set-up, outbound delivery and inbound replenishment transportation. The problem has strong implications in the context of freight logistics and transit system design. This paper first focuses on a Euclidean plane and shows that a tight upper bound can be achieved by a type of elongated cyclic hexagons, while a cost lower bound based on relaxation and idealization is also obtained. The gap between the analytical upper and lower bounds is within 0.3%. This paper then shows that a similar elongated non-cyclic hexagon shape, with proper orientation, is actually optimal for service regions on a rectilinear metric plane. Numerical experiments are conducted to verify the analytical findings and to draw further insights.

The significant role of magnetic perturbations in mitigation and losses of runaway electrons (REs) was documented in dedicated experimental studies of RE at the COMPASS tokamak. REs in COMPASS are produced both in low density quiescent discharges and in disruptions triggered by massive gas injection (MGI). The role of the RE seed produced in the beginning of the discharge on the subsequent RE population proved significant. Modulation of the RE losses by MHD instabilities was observed at several characteristic frequencies, as well as by magnetic field oscillations related to power supplies. Magnetic islands seem to suppress the losses as the HXR signal is low and coherent with the island rotation frequency. Moreover, periods of increased losses of REs observed in the current quench (CQ) and early RE beam plateau phase of the MGI disruptions seem to be linked to the bursts of magnetic perturbation, and to the observation of filaments in the fast visible camera images.

Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.

Within the last decade, several cases of the supply chain vulnerability to major disruptions have been observed. The typical mitigation strategies such as safety inventory and excess capacity are inadequate to cover the major disruptions. Furthermore it is not economical to invest in such costly proactive strategies to recover from infrequent disruptions. The objective of this paper is to provide a decision making tool achieving robust supply flow by incorporating strategic stock and reconfigurable back-up supplier in mitigating disruptions. We consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive due to re-configurability characteristics. We present a multi-stage robust optimization model to determine optimal strategic stock levels and layout configuration of the back-up supplier for a supply chain subject to random realization of disruptions and available capacity during the ramp-up time. Furthermore, the partial available capacity of the backup supplier during the response time has been modelled using queuing theoretical models. The results show the optimality of highly scalable configuration as the decision maker becomes more risk averse.

Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operations. The nonlinear model is linearized and solved using CPLEX. A number of computational experiments are conducted for the liner shipping route, passing through ECAs. Important managerial insights reveal that the proposed methodology can assist liner shipping companies with efficient vessel schedule recovery, minimize the monetary losses due to disruptions in vessel schedules, and improve energy efficiency as well as environmental sustainability.

Climate change and peak oil are likely to have a significant impact on future tourism growth in the Asia Pacific region. Dealing with these issues and the policies and strategies required for mitigation and adaptation need to be given far greater attention by the tourism industry and the public sector than has hitherto been the case. Existing approaches based on crisis and disaster management may be inadequate and a new approach to deal with shocks of this nature is required. This regional spotlight suggests a new approach based on the concept of disruption which is defined as an event that causes substantial and long-term change in the structure of the tourism industry., Highlights • Peak oil and climate change are likely to adversely affect projected tourism growth in the Asia Pacific region. • Current definitions of the terms crisis and disaster are inadequate to describe the impact of large scale events such as peak oil and climate change. • The term ‘disruption’ provides a more comprehensive approach to dealing with these issues. • The tourism industry in the Asia Pacific region needs to become more proactive in planning to cope with future large scale disruptions.