Managing disruptions in the maritime industry – a systematic literature review

PurposeThis paper presents a systematic review of the literature in the domain of maritime disruption management, upon which future research framework and agenda are proposed. Two review questions, i.e. the measures that are employed to manage disruptions and how these contribute to resilience performance, were pursued.Design/methodology/approachThe systematic literature review procedure was strictly followed, including identification and planning, execution, selection and synthesis and analysis. A review protocol was developed, including scope, databases and criteria guiding the review. Following this, 47 articles were eventually extracted for the systematic review to identify themes for not only addressing the review questions but also highlighting future research opportunities.FindingsIt was found that earlier studies mainly focused on measures, which are designed using mathematical models, management frameworks and other technical support systems, to analyse and evaluate risks, and their impacts on maritime players at the levels of organisation, transport system and region in which the organisation is embedded. There is, however, a lack of research that empirically examines how these measures would contribute to enhancing the resilience performance of maritime firms and their organisational performance as a whole. Subsequently, a Digitally Embedded and Technically Support Maritime Disruption Management (DEST-MDM) model is proposed.Research limitations/implicationsThis review is constrained by studies recorded by the Web of Science only. Nevertheless, the proposed research model would expectedly contribute to enhancing knowledge building in the specific domain of maritime disruption management and supply chain management overall while providing meaningful managerial implications to policymakers and managers in the maritime industry.Originality/valueThis research is perhaps one of the first studies which presents a systematic review of literature in maritime disruption management and proposes a future research framework that establishes the link between disruption management and resilience and organisational performance for empirical validation.

DIBR–DOMBI–FUZZY MAIRCA Model for Strategy Selection in the System of Defense

Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–"DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model's sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.

Modeling of Physical Activity Behavioral Interventions Relying on MPC Strategy

Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.

Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies

Healthcare is a critical field of research and equally important for all nations. Providing secure healthcare facilities to citizens is the primary concern of each nation. However, people living in remote areas do not get timely and sufficient healthcare facilities, even in developed countries. During the recent COVID-19 pandemic, many fatalities occurred due to the inaccessibility of healthcare facilities on time. Therefore, there is a need to propose a solution that may help citizens living in remote areas with proper and secure healthcare facilities without moving to other places. The revolution in ICT technologies, especially IoT, 5G, and cloud computing, has made access to healthcare facilities easy and approachable. There is a need to benefit from these technologies so that everyone can get secure healthcare facilities from anywhere. This research proposes a framework that will ensure 24/7 accessibility of healthcare facilities by anyone from anywhere, especially in rural areas with fewer healthcare facilities. In the proposed approach, the patients will receive doorstep treatment from the remote doctor in rural areas or the nearby local clinic. Healthcare resources (doctor, treatment, patient counseling, diagnosis, etc.) will be shared remotely with people far from these facilities. The proposed approach is tested using mathematical modeling and a case study, and the findings confirm that the proposed approach helps improve healthcare facilities for remote patients.

Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

An Epidemiological Model for Tuberculosis Considering Environmental Transmission and Reinfection

As tuberculosis (TB) patients do not have lifetime immunity, environmental transmission is one of the key reasons why TB has not been entirely eradicated. In this study, an SVEIRB model of recurrent TB considering environmental transmission was developed to explore the transmission kinetics of recurrent TB in the setting of environmental transmission, exogenous infection, and prophylaxis. A more thorough explanation of the effect of environmental transmission on recurrent TB can be found in the model's underlying regeneration numbers. The global stability of disease-free and local equilibrium points can be discussed by looking at the relevant characteristic equations. The Lyapunov functions and the LaSalle invariance principle are used to show that the local equilibrium point is globally stable, and TB will persist if the basic reproduction number is larger. Conversely, the disease will disappear if the basic reproduction number is less than one. The impact of environmental transmission on the spread of tuberculosis was further demonstrated by numerical simulations, which also demonstrated that vaccination and reducing the presence of the virus in the environment are both efficient approaches to control the disease's spread.

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

PQSCT: Pseudo-Siamese BERT for Concept Tagging With Both Questions and Solutions

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises, or ignore that exercises may contain multiple concepts. To this end, in this paper, we present a study of concept tagging. First, we propose an improved pre-trained BERT for concept tagging with both questions and solutions (QSCT). Specifically, we design a question-solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions (PQSCT). We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging. IEEE

A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown

LABORATORY PARAMETERS ASSOCIATED WITH CODIV-19 CYTOKINE STORM DEVELOPMENT

BackgroundSince the end of 2019, physicians became more and more familiar with SARS-CoV-2 infection and the variety of forms in which it may present and evolve. There have been a lot of studies trying to understand and predict why some patients develop a dysregulation of the immune response, with an exaggerated release of pro-inflammatory cytokines, called cytokine storm (1–4). There is scarce evidence in Romania regarding this aspect.ObjectivesThis study aims to verify the correlation between some laboratory parameters and the development of cytokine storm in SARS-CoV-2 infection in a cohort of over 200 patients admitted in a tertiary hospital from Romania, hoping that early identification of these risk factors of progression to a severe form of the disease can bring considerable benefit to patient care.MethodsThis is an analytical, observational, case-control study which includes 219 patients (all COVID-19 hospitalized patients on the Internal Medicine 3 department of Colentina Clinical Hospital, Bucharest, from 01 March 2020 to 1 April 2021). A series of data were collected, the laboratory parameters being the most important, including: albumin, lymphocyte (percentage), neutrophil (absolute value), aspartate aminotransferase, alanine aminotransferase, D-dimers, lactate dehydrogenase (LDH), anionic gap, chloremia, potassium and the BUN:creatinine ratio (BUN - blood urea nitrogen). The laboratory parameters used for the statistical analysis represent the average values of the first 7 days of hospitalization for those who did not develop cytokine storm, respectively until the day of its development, for the others. Patients were classified into these groups, those who developed cytokine storm, respectively those who did not have this complication taking into account the clinical and paraclinical criteria (impairment of respiratory function, elevations of certain markers 2-3 times above the upper limit of normal, those who died as a result of SARS-CoV-2 infection). Then Binary Univariate Logistic Regression was applied in order to verify the individual impact of every laboratory parameter on cytokine storm development. Furthermore, all laboratory parameters were subsequently included in the multivariate analysis, using the backward selection technique to achieve a model as predictive as possible.ResultsWe mention that the analysis of demographic data was previously performed, showing no statistically significant relationship between patient gender, age or comorbidities (history of neoplasm, lung diseases, cardiac pathology, obesity, type II diabetes and hypertension) and their evolution to cytokine storm. After performing binary univariate logistic regression we concluded that 8 of the 13 laboratory analyzes have had a significant change between groups (ferritin, PCR, albumin, Lymphocyte, Neutrophils, TGO, LDH, BUN:creatinine ratio). Only 150 patients were then included in the multivariate analysis. After the analysis, some of the variables lost their statistical significance, the final model including C-reactive protein, neutrophilia, LDH, ferritin and the BUN:creatinine ratio. This model correctly predicts the development of cytokine storm in 88% of cases.ConclusionHigh C-reactive protein, neutrophilia, LDH, ferritin and the BUN:creatinine ratio are risk factors for cytokine storm development and should be monitored in all COVID-19 patients in order to predict their evolution.References[1]Pedersen SF et all. SARS-CoV-2: A storm is raging[2]Mehta P et al. COVID-19: consider cytokine storm syndromes and immunosuppression[3]Hu B et al. The cytokine storm and COVID-19.[4]Caricchio R et al. Preliminary predictive criteria for COVID-19 cytokine stormAcknowledgements:NIL.Disclosure of InterestsNone Declared.

Computerized intelligence and mathematical models for COVID-19 management: a short communication

The onset of COVID- 19 pandemic has resulted in the transition from the conventional face to face health care strategies to computerized approaches, considering distances, the importance of quarantine, and early diagnosis and management. As far as the rapid management of the infection is concerned, telemedicine has been introduced as a beneficial approach. The use of telemedicine is thought to decrease the risk of cross contamination. Moreover, it provides the access to the health care for remote locations. The health care staff can use the computational analyses to get rapid access to the accurate epidemiological and laboratory data. The risk assessment provided by the mathematical models seems beneficial for decision-making in regards to the prognosis and management. We aimed to explore the breakthrough of telemedicine regarding the pandemic, also attempting to describe the related problems and challenges.

A Polarized Temporal Network Model to Study the Spread of Recurrent Epidemic Diseases in a Partially Vaccinated Population

Motivated by massive outbreaks of COVID-19 that occurred even in populations with high vaccine uptake, we propose a novel multi-population temporal network model for the spread of recurrent epidemic diseases. We study the effect of human behavior, testing, and vaccination campaigns on infection prevalence and local outbreak control. Our modeling framework decouples a vaccine's effectiveness in protecting against transmission and severe symptom development. Additionally, it captures the polarizing effect of vaccination decisions and homophily, i.e., people's tendency to interact with like-minded individuals. Through a mean-field approach, we analytically derive the epidemic threshold and, under further assumptions, we compute the endemic equilibrium. Our results show that while vaccination campaigns are highly beneficial in reducing pressure on hospitals, they may facilitate resurgent outbreaks, particularly in the absence of testing campaigns. Subsequently, numerical simulations confirm and extend our theoretical findings to more realistic scenarios. Our analytical and numerical results demonstrate that vaccination programs are crucial, but as a sole control measure, they are not sufficient to achieve disease eradication without relying on the population's responsibility or testing campaigns. Furthermore, we show that homophily impedes local outbreak control, highlighting the peril of a polarized network structure. IEEE

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

A common way to model an epidemic — restricted to contagion aspects only — is a modification of the Kermack-McKendrick SIR Epidemic model (SIR model) with differential equations. (Mis-)Information about epidemics may influence the behavior of the people and thus the course of epidemics as well. We have thus coupled an extended SIR model of the COVID-19 pandemic with a compartment model of the (mis-)information-based attitude of the population towards epidemic countermeasures. The resulting combined model is checked concerning basic plausibility properties like positivity and boundedness. It is calibrated using COVID-19 data from RKI and attitude data provided by the COVID-19 Snapshot Monitoring (COSMO) study. The values of parameters without corresponding observation data have been determined using an L2-fit under mild additional assumptions. The predictions of the calibrated model are essentially in accordance with observations. An uncertainty analysis of the model shows, that our results are in principle stable under measurement errors. We also assessed the scale, at which specific parameters can influence the evolution of epidemics. Another result of the paper is that in a multi-domain epidemic model, the notion of controlled reproduction number has to be redefined when being used as an indicator of the future evolution of epidemics.

A mathematical model and simulation scenarios for T and B cells immune response to severe acute respiratory syndrome-coronavirus-2

Severe acute respiratory syndrome coronavirus is a type 2 highly contagious, and transmissible among humans;the natural human immune response to severe acute respiratory syndrome-coronavirus-2 combines cell-mediated immunity (lymphocyte) and antibody production. In the present study, we analyzed the dynamic effects of adaptive immune system cell activation in the human host. The methodology consisted of modeling using a system of ordinary differential equations;for this model, the equilibrium free of viral infection was obtained, and its local stability was determined. Analysis of the model revealed that lymphocyte activation leads to total pathogen elimination by specific recognition of viral antigens;the model dynamics are driven by the interaction between respiratory epithelial cells, viral infection, and activation of helper T, cytotoxic T, and B lymphocytes. Numerical simulations showed that the model solutions match the dynamics involved in the role of lymphocytes in preventing new infections and stopping the viral spread;these results reinforce the understanding of the cellular immune mechanisms and processes of the organism against severe acute respiratory syndrome-coronavirus-2 infection, allowing the understanding of biophysical processes that occur in living systems, dealing with the exchange of information at the cellular level.

An Operations Research-Based Teaching Unit for Grade 10: The ROAR Experience, Part I

We introduce "Ricerca Operativa Applicazioni Reali" (ROAR;in English, "Real Applications of Operations Research"), a three-year project for higher secondary schools. Its main aim is to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. ROAR offers examples and problems closely connected with students' everyday life or with the industrial reality, balancing mathematical modeling and algorithmics. The project is composed of three teaching units, addressed to grades 10, 11, and 12. The implementation of the first teaching unit took place in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). In particular, in this paper, we provide a full description of this first teaching unit in terms of objectives, prerequisites, topics and methods, organization of the lectures, and digital technologies used. Moreover, we analyze the feedback received from students and teachers involved in the experimentation, and we discuss advantages and disadvantages related to distance learning that we had to adopt because of the COVID-19 pandemic.

Within-host modeling to measure dynamics of antibody responses after natural infection or vaccination: A systematic review.

BACKGROUND: Within-host models describe the dynamics of immune cells when encountering a pathogen, and how these dynamics can lead to an individual-specific immune response. This systematic review aims to summarize which within-host methodology has been used to study and quantify antibody kinetics after infection or vaccination. In particular, we focus on data-driven and theory-driven mechanistic models. MATERIALS: PubMed and Web of Science databases were used to identify eligible papers published until May 2022. Eligible publications included those studying mathematical models that measure antibody kinetics as the primary outcome (ranging from phenomenological to mechanistic models). RESULTS: We identified 78 eligible publications, of which 8 relied on an Ordinary Differential Equations (ODEs)-based modelling approach to describe antibody kinetics after vaccination, and 12 studies used such models in the context of humoral immunity induced by natural infection. Mechanistic modeling studies were summarized in terms of type of study, sample size, measurements collected, antibody half-life, compartments and parameters included, inferential or analytical method, and model selection. CONCLUSIONS: Despite the importance of investigating antibody kinetics and underlying mechanisms of (waning of) the humoral immunity, few publications explicitly account for this in a mathematical model. In particular, most research focuses on phenomenological rather than mechanistic models. The limited information on the age groups or other risk factors that might impact antibody kinetics, as well as a lack of experimental or observational data remain important concerns regarding the interpretation of mathematical modeling results. We reviewed the similarities between the kinetics following vaccination and infection, emphasising that it may be worth translating some features from one setting to another. However, we also stress that some biological mechanisms need to be distinguished. We found that data-driven mechanistic models tend to be more simplistic, and theory-driven approaches lack representative data to validate model results.

The Model of Organizational Performance Based on Employees' Dynamic Capabilities-Verification During Crisis Caused by Black Swan Event

The article concerns the potential influence of employees' dynamic capabilities on the performance of entire organization, which operates in crisis caused by Black Swan event. It is the expansion of job performance model based on employees' dynamic capabilities, proposing the possibility of translating the positive influence of those capabilities onto entire organization and underlining the importance of employees' dynamic capabilities during crisis within organization. Based on literature analysis, the shape of the amended model is proposed, in which employees' dynamic capabilities influence organizational performance through elements of the original model (person-job fit, work motivation, job satisfaction, work engagement and job performance), and additional ones: person-organization fit, person-supervisor fit. The proposed model is empirically verified based on the sample of 1160 organization operating in Poland, Italy and USA during an active wave of COVID-19 pandemic (which is an example of Black Swan event). The results obtained using path analysis confirmed that employees' dynamic capabilities indeed influence organizational performance of organizations operating in crisis caused by Black Swan event through elements proposed in the model.

The impact of organizational culture and total quality management on the relationship between green practices and sustainability performance

PurposeThis study aims to examine the direct and indirect effects of organizational culture (OC) and total quality management practices (TQMPs) on the relationship between green practices (GPs) and sustainability performance (SP) by using structural equation modeling (SEM) analysis.Design/methodology/approachThis study proposed a conceptual research model of the relationships and formulated six hypotheses. This study used a structured questionnaire based on previous studies to collect relationship data to test these hypotheses, and 441 full-time managers from various US businesses responded. The complete and valid survey responses were then tested against the hypotheses using IBM SPSS Statistics and SEM-AMOS.FindingsResults supported the relationships proposed in the research model. They indicated that a strong supporting OC and TQMPs might improve positive SP and GPs. Additionally, the more managers are aware of their companies' GPs, the more likely they will feel positive about the organization's SP.Research limitations/implicationsA larger sample size to ensure statistically minimum representation in several major industries would better validate the findings and help identify significant differences in industry-specific OCs, TQMPs, GPs and SPs. Similarly, ensuring a varied geographical representation (both within the USA and internationally) would help determine if the findings vary according to the respondent's location. Furthermore, collecting the data during Year 1 of the COVID-19 pandemic may have skewed the results. Thus, once the working environment has been normalized, the survey should be repeated to determine if the findings are valid post-pandemic.Practical implicationsThe findings of this study provide important strategic guidance for managers who work to balance the implementation of corporate GPs and the triple bottom line dimensions of SP. For practitioners, the results showed that companies could accomplish both profitability and sustainability if they are willing to continuously pay attention to environmental issues and strategically invest in cost-efficient and eco-friendly initiatives.Originality/valueTo the best of the authors' knowledge, this research is one of the first to explore how OC and TQMPs, directly and indirectly, affect the relationship between GPs and the triple bottom line dimensions of SP. These results imply that OC and TQMPs have a significant indirect impact on the relationship between GPs and the SP dimensions.

Contribution of mathematical models and Big Data in epidemiological events such as SARS Cov-2

The pandemic caused by the COVID-19 virus has given rise to numerous analyses and studies due to the implications and serious consequences it has had on all areas of human development worldwide. The data unquestionably reflect the degree of impact it has had, not only on the mortality rate, but also on the economic indices of nations. In analyzing all these indicators, the question arises as to whether some key elements, such as the number of incidences, the variables of the effective reproductive factor of the disease could better reflect the predictability of the cases and, in turn, evaluate the mitigating measures to placate the incidence of new cases. This analysis is especially significant considering that the pandemic is not over, and that more and better resolutions are still needed to address this ongoing crisis. In this context, the present study aims to analyze, from the theoretical mathematical models, what has been the contribution of this area of science to find and predict possible solutions to quell the effects of this global pandemic. For this purpose, statistical analyses based on three models will be used: non-linear phenomenological models, data modeling and the generalized logistic model, which are expected to contribute to a better evaluation and understanding of the measures taken to face this health crisis and, in the future, the importance of understanding the use of data and the technological tools available to mankind today in the face of any new virus. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

16th International Conference on the Modelling of Casting, Welding, and Advanced Solidification Processes

PrefaceThe 16th International Conference on the Modelling of Casting, Welding, and Advanced Solidification Processes (MCWASP XVI) was held from June 18 to 23, 2023, in Banff, Canada, at the Banff Centre for Arts and Creativity. Founded in 1933, the Centre in Treaty 7 Territory within Banff National Park—Canada's first National Park—is a learning organization built upon an extraordinary legacy of excellence in artistic and creative development. The "all-inclusive” nature of the conference and the remote setting meant that participants dined, attended oral and poster presentations, and participated in social activities as a group, fostering outstanding opportunities for networking.Given that the MCWASP community had not met in person since 2015 in Japan (the 2020 edition of MCWASP was virtual owing to COVID-19), the 2023 conference provided the opportunity to renew old friendships and make new ones as well as discuss the science of solidification and related processes—all within the backdrop of the beautiful Canadian Rocky Mountains.The technical program comprised more than 70 oral and poster presentations. In addition to content related to modelling of casting, welding, and advanced solidification processes, keynotes were invited to talk about related subjects (artificial intelligence/machine learning, and permeability modelling in shale rock) as well as the rich diversity of fossils, especially dinosaurs, found in Alberta.The oral technical program was organized with as a single session (i.e., no concurrent presentations). It featured all aspects of solidification modelling, including solidification process technologies (continuous and semi-continuous casting, shape casting, additive manufacturing, and welding), coupled multi-physics simulations, defect formation, fluid flow, micro- and macro-structure formation, numerical methods, and related experimentation, especially in-situ observation of solidification.The four-day technical program was spread over five days to give participants the opportunity to explore the stunning Canadian Rocky Mountains.In these proceedings, the papers are organized by major theme. The dominant topics are Additive Manufacturing and Welding and Microstructure Formation, followed by Continuous Casting – Shape Casting, Heat Transfer and Fluid Flow, Alloy Segregation, Defects, Imaging of Solidification, Thermomechanics, and Materials Properties. In these themes, the authors report advances in numerical modelling techniques, new scientific and process developments in solidification, and related in-situ experimentation.Although significant progress has been made over these past 16 MCWASP conferences covering 43 years, it is clear that the complexity of advanced solidification phenomena as related to conventional and emerging manufacturing technologies still attracts a great deal of scientific and industrial interest to support technological innovation.André PhillionBanff, Canada, June 2023MCWASP XVI 2023List of Peer Reviewers, Sponsors, MCWASP XVI Organizers, International Scientific Committee are available in this Pdf.