Rigorous Modeling Approach Research Articles

Design and evaluation of piperidine carboxamide derivatives as potent ALK inhibitors through 3D-QSAR modeling, artificial neural network and computational analysis

Tumor stands as one of the principal contributors to global mortality. As research into tumor treatments advances, tumor inhibitors emerge as pivotal milestones in tumor therapy. Among these inhibitors, Anaplastic Lymphoma Kinase (ALK), a receptor tyrosine kinase, is critical owing to its close association with tumor cell proliferation and growth, which renders it a critical therapeutic target. This work systematically explores the relationship between the chemical structures of 36 piperidine carboxamide derivatives and their efficacy in inhibiting Karpas-299 tumor cell activity by employing a rigorous 3D-QSAR modeling approach. A robust Topomer CoMFA model was generated and was meticulously validated through ANN neural network analysis (q2 = 0.597, r2 = 0.939, F = 84.401, N = 4, SEE = 0.268). Based on the model, 60 new compounds with desirable inhibitory activities were successfully designed. Combined with Lipinski’s rule and ADMET criteria alongside molecular docking and dynamics simulations, a lead compound with high inhibitory activity and good drug-likeness was selected. Further computational analyses, encompassing free energy landscape and binding free energy calculations, provided compelling evidence of the stable binding conformation of the lead compound and the superior affinity with the target protein at the active site, underscoring its potential therapeutic utility. In summary, this investigation offers valuable insights and methodological guidance for advancing tumor therapy and underscores the promise of piperidine carboxamide derivatives as prospective ALK inhibitors.

Promoting Road Safety for Sustainable Communities: Analysing Driving Anger, Aggressive Driving, and Road Accident Proneness with Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach

Objective: Numerous studies have emphasized the web of factors contributing to a driver’s vulnerability to road accidents. These factors often stem from the interaction between drivers’ conduct, environmental conditions, and vehicle-related aspects. Among these elements, road rage and aggressive driving behavior are particularly noteworthy for their roles in road accidents. In this article, we delve into the connection between road rage, aggressive driving, and their combined impact on the likelihood of road accidents among Malaysian drivers. Theoretical framework: Theoretical materials were based on international scientific publications, reports, and scientific papers. Empirical findings from previous studies were critically reviewed for a more complete and objective presentation of the road accident problem. Methodology: This study used a cross-sectional design to administer self-administered questionnaires to drivers. The survey method made it possible to study the factors influencing susceptibility to traffic accidents quantitatively. The analysis reveals compelling insights using a rigorous structural equation modeling approach. Results and Conclusion: Driving anger and aggressive driving significantly influence the likelihood of road accidents among Malaysian drivers. This study holds significant implications for theoretical understanding and practical intervention strategies to reduce road accident proneness within the Malaysian road user community. The findings provide valuable insights and recommendations to enhance road safety, ultimately contributing to the sustainability of communities through reduced road accidents. Originality/value: The originality and value of the study lie in its in-depth examination of the relationship between driving anger, aggressive driving, and the likelihood of road accidents among Malaysian drivers. It goes beyond existing research by employing a rigorous structural equation modelling approach, drawing from international scientific publications and empirical findings. This comprehensive approach offers fresh insights into the specific context of Malaysia and provides a foundation for practical interventions to reduce road accident proneness, contributing to community sustainability and road safety.

Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit

The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of a new normal for the future of transit. This paper addresses this challenge using a novel panel survey data set collected from a representative sample of individuals across the United States. The study involved the estimation of a panel multinomial probit model of mode choice to capture both socio-economic effects and period (pre-, during-, and post-COVID) effects that contribute to changes in mode choice. This paper provides rich insights into the evolution of commute mode use as a result of the pandemic, with a particular focus on public transit. Through a rigorous modeling approach, this paper provides a deep understanding of how transit use has evolved, how it is likely to evolve into the future, and the socio-economic and demographic characteristics that affect the evolution (and expected future use) of public transit. Results suggest that transit patronage is likely to remain depressed by about 30% for the foreseeable future, in the absence of substantial changes in service configurations. This study also shows that minority groups and those living in higher density regions are more likely to exhibit a return to transit use in the post-pandemic period.

Numerical modeling of nonlinear photoelasticity

AbstractWhen molecular photo‐switches, such as azobenzene or norbornadiene, are embedded into a sufficiently soft polymer matrix the resulting compound can undergo a mechanical deformation induced by light of a specific wavelength. These photo‐sensitive compounds have the potential to be applied as soft actuators without the need for hard wired electronics or a separate energy source. Such characteristics are especially attractive in the design of micro‐scale robots but also other applications such as high‐speed data transfer or the conversion of photonic energy into a mechanical response holds great promise. Despite these almost futuristic possibilities, photo‐sensitive polymers have not yet experienced a sufficient attention in industrial applications. One important factor to increase the acceptance of this group of soft smart materials is the formulation of a rigorous constitutive modeling approach in combination with numerical simulation methods. Thus, in this contribution we present a photo‐mechanical modeling approach, departing from the fundamentals published previously. We briefly introduce the necessary constitutive equations which are subsequently utilized in combination with the respective balance laws into a finite element implementation. Finally, the capabilities of the numerical solution approach are illustrated by a simple two‐dimensional bench‐mark example and subsequently extended to a more complex three‐dimensional problem.

Three-Dimensional Modeling of Soil-Structure Interaction for a Bridge Founded on Caissons under Seismic Conditions

In recent years, the urgent need to increase the safety standards of viaducts and bridges—under static and dynamic loading conditions—has required the development of advanced modeling approaches able to accurately predict the expected behavior of such infrastructures in a reliable manner. This paper presents a comparison between the adoption of a simplified modeling approach, widely used in the current practice, where the response of the structural system neglects the effects of the soil-structure interaction (SSI) phenomenon (considering the base of the structure fixed at the ground surface) and a rigorous modeling approach that considers the full 3D problem with all the components of the system (superstructure, foundation, and soil), through a finite element model. The pier of a real-world viaduct in central Italy was considered, with the aim of starting from a specific case study with foundation characteristics that are frequently found in viaducts in Italy, to obtain results that can be generalized to a wide range of similar types. Its behavior was evaluated both in the dynamic range of small oscillations and in the field of the seismic response to low and strong motion events. The results show that, in terms of seismic demand, the fixed-based model appears more conservative, but it significantly underestimates both elastic and residual displacements and rotations

Behavioural and neural indices of perceptual decision-making in autistic children during visual motion tasks

Many studies report atypical responses to sensory information in autistic individuals, yet it is not clear which stages of processing are affected, with little consideration given to decision-making processes. We combined diffusion modelling with high-density EEG to identify which processing stages differ between 50 autistic and 50 typically developing children aged 6–14 years during two visual motion tasks. Our pre-registered hypotheses were that autistic children would show task-dependent differences in sensory evidence accumulation, alongside a more cautious decision-making style and longer non-decision time across tasks. We tested these hypotheses using hierarchical Bayesian diffusion models with a rigorous blind modelling approach, finding no conclusive evidence for our hypotheses. Using a data-driven method, we identified a response-locked centro-parietal component previously linked to the decision-making process. The build-up in this component did not consistently relate to evidence accumulation in autistic children. This suggests that the relationship between the EEG measure and diffusion-modelling is not straightforward in autistic children. Compared to a related study of children with dyslexia, motion processing differences appear less pronounced in autistic children. Exploratory analyses also suggest weak evidence that ADHD symptoms moderate perceptual decision-making in autistic children.

Robust probabilistic principal component regression with switching mixture Gaussian noise for soft sensing

In the era, that data collection is not as challenging as before, data-driven process modeling for prediction of unmeasurable or expensive-to-measure variables is gaining popularity. Probabilistic principal component analysis has powerful features for modeling such as considering uncertainty and dealing with high-dimensional process data. Although data collection is more attainable these days, low quality of data still diminishes model performance. High-fidelity modeling requires high-quality data. The focus of this work is to deal with outlying observations by developing a Robust Probabilistic Principal Component Regression (RPPCR). Here, we have investigated a scenario of mixture Gaussian switching measurement noise to mimic certain type of outliers in a forward-looking approach that extends our previous work. A rigorous modeling approach that can handle switching noise and the solution methodology are discussed in detail. Two case studies, a numerical illustrative example and a real industrial counterpart, are considered to verify the robustness of proposed model.

A Design Methodology for a Computer-Supported Collaborative Skills Lab in Technical Translation Teaching

Aim/Purpose: The aim of this study is to adopt more systematically the collaborative learning dimension in the technical translation teaching at Master Degree level. In order to do so, a computer-supported skills lab approach is targeted. This approach is aimed at enhancing traditional courses on Computer-Assisted Translation (CAT) so that student competences and soft skills are enhanced. Background: In traditional CAT courses, laboratory sessions complement theoretical lessons, thus providing students mainly with tool-oriented operational knowledge, while nowadays more intertwined competences are required by the labor market. Moreover, this sector lacks skills labs which engage students in collaborative activities mimicking professional workflows, thus not exploiting team-based learning potential effectiveness. Methodology: In this paper, therefore, a design methodology to deploy and operate an enhanced skills lab as a remote Computer-Supported Collaborative Simulated Translation Bureau (CS2TB) is proposed and validated. The proposed methodology is based on a set of intertwined methodological frameworks that address: 1) student competences and educational requirements, 2) collaborative aspects, 3) regulatory policies as well as functional and interactional guidelines for the simulated fieldwork. The overall effectiveness of the proposed methodology has been assessed by using pre-post questionnaires to ascertain student feedback. The improvement in technology skills has been evaluated by collecting and examining student help requests as well as system error logs. Contribution: The CS2TB provides a technology-enhanced simulation-based learning environment whose aim is twofold: first, enriching traditional approaches with a Computer-Supported Collaborative Learning (CSCL) experience and, second, incorporating widely adopted approaches for the translation-teaching domain as the required grounding knowledge. Findings: Results demonstrate the effectiveness of CS2TB in improving students’ competences (specifically in the IT area but also in the technical translation area), students’ willingness to operate in a fieldwork-like context and cooperative learning efficacy. Recommendations for Practitioners: The educational implications of the proposed approach concern the development of a full range of competences and soft skills for students in the technical translation teaching at the higher education level, ranging from language and translation proficiency to the usage of IT platforms as well as personal and interpersonal interactional soft skills. Recommendation for Researchers: This study offers a wide overview of all the aspects entailed by the design, implementation, management, and evaluation of a skills lab for technical translation teaching. Researchers may benefit from the rigorous modelling approach as well as from the adopted assessment techniques. Moreover, the study stresses the pivotal role of a tight collaboration between language/translation teaching and computer engineering. Impact on Society: Higher education institutions that already have courses on computer-assisted translation may benefit from the proposed CS2TB approach, which allows them to design new thematic activities leveraging team-based learning, collaborative learning, and fieldwork-situated simulation. Moreover, the presented broad range of assessment approaches can be used to measure the impact of CS2TB on learning outcomes of the involved students. Future Research: Future research activities will be dedicated to examining the impact of a different set of enabling IT platforms on the collaborative learning perspective, to evaluate alternative scaffolding approaches (e.g., chatbots or augmented reality), and to increase simulation fidelity further, so that even more student competences can be fostered.

Rigorous Predictive Noise Modeling Approach for Model-Based Onset Detection and Enhanced Picking of P-Waves in Seismic Signals

Detecting and picking the onset of P-waves in seismic signals has a fairly rich literature, among which model-based (predictive) approaches hold immense promise. A majority of these models are usually built on certain critical assumptions, namely, stationarity, linearity, and Gaussianity. Despite their criticality, very little reported literature exists on validating these assumptions on real seismic data. Furthermore, the predictive capabilities of these models are not utilized to their best potential in detection. Therefore, there exists a strong need for a rigorous approach to model seismic noise and develop a method that builds on the statistical properties of residuals resulting from the noise models. The objectives of this work are (i) to critically study certain long-held assumptions in seismic noise modeling, (ii) to develop rigorous time-series models for background noise that are commensurate with the noise properties, so as to (iii) devise a residual-based method for detection and enhanced picking of P-waves. An important finding of this work, arising from our study on 185 historical data sets, is that these standard assumptions do not hold for most of the data sets under study; rather, they exhibit additional special features such as heteroskedasticity and integrating effects. Consequent to these novel discoveries, we develop auto-regressive integrated moving average- generalized auto-regressive conditionally heteroskedastic (ARIMA-GARCH) models for seismic noise. The proposed residual-based detector and picker is found to be highly effective with a 90% detection rate while picking 91% of the events with an accuracy of ≤ 0.625 seconds based on tests with 100 historical data sets. Further, when the noise model is used in combination with the existing AIC-based pickers, the number of events picked with an accuracy of ≤ 0.625 seconds is 50% more than the existing AR-AIC picker. Therefore, the proposed method can be considered highly competitive and effective in P-wave detection and picking.

Competition and climate influence in the basal area increment models for Mediterranean mixed forests

Competition plays a key role controlling tree growth in mixed forests. Contrary to monocultures, quantifying species mixing influence on tree growth suppose a challenge since the presence of two or more species requires to estimate the degree of intra- and inter-specific competition among trees. Moreover, it is well known that aridity can also influence tree growth, especially in the Mediterranean Basin. In the present context of climate change, it is essential to take into account species mixing and aridity uncertainty in the design of sustainable management guidelines for Mediterranean mixed forests. To achieve that, data from Spanish National Forest Inventory was used in this study to fit new mixed-effects basal area increment (BAI) models for 29 two-species compositions in Spain. A wide range of different competition structures (intra-specific, inter-specific, size-symmetric and size-asymmetric) and aridity conditions (in terms of the De Martonne Index) were included and tested into the BAI models. Parameter estimations were obtained for all possible species, mixtures and combinations by Maximum Likelihood (ML). Models with all the coefficients being significant (p < 0.05) were first selected. Among these models, we used Akaike Evidence Ratios for selecting the best one by species for each mixture. The best model for each species and mixture was used to analyze the competition and climatic influence on tree growth. Regarding competition influence, a common trend among mixtures was found with higher productivity in mixed than pure stands, suggesting that BAI values may increase with the increment of species diversity. Based on intra and inter-specific competition indexes, competition seemed to be the most representative biological interaction in conifer-conifer mixtures, since neutralism and facilitation may occur more frequently in conifer-broadleaved and broadleaved-broadleaved mixtures. Our findings also suggested that tree growth may be significantly limited by arid conditions, excepting for Pinus halepensis and Pinus pinea. Our rigorous modelling approach successfully uncovered not only possible mixing effect among various species but also help us to understand the effect of aridity on tree growth. Thus, models presented in this study can be used in the design and implementation of management and adaptation guidelines under future climate change scenarios.

Factors influencing shared micromobility services: An analysis of e-scooters and bikeshare

This study explores how factors, including weather, day of the week, holidays, and special events, influence the trip frequency of two micromobility modes, shared e-scooters and bikeshare, in Louisville, Kentucky. Negative binomial generalized additive models (NBGAM) were estimated to model the trip frequency of each mode. NBGAM provides a rigorous modeling approach that accounts for temporal autocorrelation among variables. While results showed some differences exist between how various factors impact shared e-scooters and bikeshare trips, several similarities emerged between modes. Rain reduced trips for both, reducing bikeshare by 17% and shared e-scooters by 16%. Mondays, Thursdays, Friday, and Saturdays had increased use of both micromobility services though Tuesdays and Wednesdays only saw significant increases in bikeshare ridership. This study contributes to the existing literature in the micromobility realm by quantifying and comparing time-dependent relationships for e-scooters and bikeshare. Results of this study inform how providers distribute vehicles and how cities manage e-scooter policies.

Separation of multicomponent aromatic/aliphatic mixtures by simulated moving bed adsorption: Modeling and experiments

AbstractSimulated moving bed (SMB) adsorption has potential for efficient separation of many valuable chemical mixtures, but considerably less attention has been devoted to multicomponent feeds relative to binary mixtures. We take a rigorous experimental and modeling approach to study multicomponent separation of aromatics and aliphatics with a mesoporous silica adsorbent, which is relevant in many petrochemical applications such as separation of reformate and distillate streams. Our approach involves refining multicomponent adsorption, mass transfer, and SMB process parameters based upon detailed experimental inputs, with progressive addition of components. We develop a robust model that quantitatively predicts the influence of key operating parameters such as stream flow rates, desorbent/feed ratio, and switch time on the separation results and concentration profiles. The model is validated as a function of feed complexity by SMB experiments and column concentration profile measurements in a 16‐column mini‐plant. Furthermore, conditions for clear separation of each mixture are developed.

A surrogate-based optimization framework for simultaneous synthesis of chemical process and heat exchanger network

Heat-integrated process synthesis is fundamental to achieve higher energy efficiency. The well-known sequential-conceptual methods have been widely adopted to solve the synthesis problem in a hierarchical manner. However, the natural hierarchy fails to consider complex interactions between the unit operation and the heat integration. To address this issue, a surrogate-based optimization framework is proposed for simultaneous synthesis of chemical process and heat exchanger network. An artificial neural network (ANN)-based surrogate model, derived from the simulation data generated via rigorous mechanism modelling approach, is established for process units to replace their complex realistic models. With surrogate model formulation incorporated into heat integration, an enhanced transshipment-based mixed integer nonlinear programming model is introduced to synthesize heat exchanger network with variable flowrates and temperatures, aiming at the maximized annual profit. Finally, two example studies are investigated to demonstrate the effectiveness of the proposed framework.

A simultaneous optimization model for a heat-integrated syngas-to-methanol process with Kalina Cycle for waste heat recovery

The syngas to methanol (STM) process is an energy-intensive chemical production process, and effective utilization of waste heat can improve energy and economy efficiency. To address current challenges that complex interactions between process synthesis and waste heat recovery are not considered, a novel simultaneous optimization model is proposed for a heat-integrated syngas-to-methanol process with Kalina Cycle (KC) for waste heat recovery, where the identified key parameters of KC and STM are optimized simultaneously without reducing the overall conversion of hydrogen to produce methanol. In developing the model, an enhanced Heat Integration model that considers variable temperatures and flowrates is established to perform thermal cycle optimization with process synthesis by combination of simulation-based modelling approach and equation-based mathematical programming approach. The STM process is synthesized based on a rigorous kinetic modelling approach and the effect of process parameters on waste heat recovery is further analyzed by control variable method. The results show that the net power output of the whole system increases with the decrease of reaction pressure. The optimal medium temperature and inlet temperature of reactor are 180 °C and 160 °C, respectively. Moreover, the presented model can achieve the optimal coupling structure of KC and STM process with the maximized net power output of 15,206.3 kW, which increases by 81.6% compared with that of 8371.4 kW derived by the traditional sequential optimization method in previous study.

Political participation and basic values in Europe: Replication and extension of Vecchione etal. (2015).

Vecchione etal. (Br J Psychol, 106, 2015, 84) demonstrated the effects of four higher-order values on political participation across 20 European countries. In their Study 1, they found that political participation was related to three higher-order basic values and that these associations were stronger in more democratic countries. The present study attempted a replication using a more rigorous modelling approach. Multilevel structural equationmodels with latent variables for both participation and basic values replicated the main effects of Self-Transcendence and Conservation values but demonstrated negative effects of Openness values (vs. positive in the original study) and positive effects of Self-Enhancement values (vs. ambiguous or insignificant in the original study), while cross-level interactions indicated weaker effects of values in more democratic countries (vs. the opposite in the original study). These discrepancies appeared mostly due to the original study's implicit assumption that basic values' measurement errors were zero. The new results indicated a counter-intuitive similarity of effects of supposedly opposite values. Self-Transcendence-Self-Enhancement is suggested to be a politically motivating value dimension, whereas Openness to Change-Conservation is a dimension that discourages political participation regardless of the preferred extreme.

Are current tsunami evacuation approaches safe enough?

Developing an effective tsunami evacuation plan is essential for disaster risk reduction in coastal regions. To develop effective tsunami evacuation plans, real transportation network, interaction among evacuees, and uncertainties associated with future tsunami events need to be considered in a holistic manner. This study aims to develop such an integrated tsunami evacuation approach using agent-based evacuation simulation and advanced stochastic tsunami hazard assessment. As a case study, a urban area in Padang, Indonesia, threatened by tsunamis from the Mentawai–Sunda subduction zone, is adopted. The uncertainty of the tsunami hazard is taken into account by generating 900 stochastic tsunami inundation maps for three earthquake magnitudes, i.e. 8.5, 8.75, and 9.0. A simplified evacuation approach considering the evacuees moving directly to evacuation areas (defined a priori) is compared with two more rigorous agent-based modeling approaches: (a) a two-destination-point tsunami evacuation plan developed by the local government and (b) a multiple-destination-point plan developed in this study. The improved agent-based stochastic tsunami evacuation framework with multiple destinations takes advantage of the extensive tsunami hazard analyses to define safe areas in a dynamic manner and is capable of capturing the uncertainty of future tsunami risk in coastal areas. In contrast, the results clearly show that the simplified approach significantly underestimates the evacuation time, and the existing tsunami evacuation routes identified by local authorities may be insufficient to save lives.

Macroscopic Circuit Quantum Electrodynamics: A New Look Toward Developing Full-Wave Numerical Models

Devices built using superconducting circuits are a popular approach being pursued to develop quantum information processing hardware. These systems involve the quantized interactions of electromagnetic fields with large coherent collections of charges in superconductors, and so are typically referred to as macroscopic circuit quantum electrodynamics (QED) devices or simply circuit QED devices. Although significant progress has been made recently, substantial engineering efforts are required to improve performance and scale these devices so they can address problems of practical interest. Performing these engineering efforts is difficult due to the absence of rigorous numerical modeling approaches. This work begins to address this by providing a new mathematical description of a commonly used circuit QED system, a transmon qubit coupled to microwave transmission lines. Expressed in terms of three-dimensional vector fields, our new model is better suited to developing rigorous numerical solvers than approximate lumped element circuit descriptions used in the literature. We present details on the quantization of our model, and derive equations of motion for the coupled field-transmon system. These results can be used in developing full-wave solvers in the future. To make this work more accessible, we assume a limited amount of training in quantum physics and provide many background details.

Comparing multiple statistical software for multiple-indicator, multiple-cause modeling: an application of gender disparity in adult cognitive functioning using MIDUS II dataset

BackgroundThe multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis. It is a special case of structural equation modeling (SEM), which is modeled under latent variable framework. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software packages SAS CALIS procedure, R lavaan package, and Mplus version 8.0.MethodsIn this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N = 4109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented.ResultsIn terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men.ConclusionsOur study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

The Good, The Bad and The Ugly: A Mathematical Model Investigates the Differing Outcomes Among CoVID-19 Patients.

The disease caused by SARS-CoV-2—CoVID-19—is a global pandemic that has brought severe changes worldwide. Approximately 80% of the infected patients are largely asymptomatic or have mild symptoms such as fever or cough, while rest of the patients display varying degrees of severity of symptoms, with an average mortality rate of 3–4%. Severe symptoms such as pneumonia and acute respiratory distress syndrome may be caused by tissue damage, which is mostly due to aggravated and unresolved innate and adaptive immune response, often resulting from a cytokine storm.Cytokine storm: A sudden acute increase in circulating levels of different inflammation causing cytokines including IL-6, IL-1, etc. Here, we discuss how an intricate interplay among infected cells and cells of innate and adaptive immune system can lead to such diverse clinicopathological outcomes. Particularly, we discuss how the emergent nonlinear dynamics of interaction among the components of adaptive and immune system components and virally infected cells can drive different disease severity. Such minimalistic yet rigorous mathematical modeling approaches are helpful in explaining how various co-morbidity risk factors, such as age and obesity, can aggravate the severity of CoVID-19 in patients. Furthermore, such approaches can elucidate how a fine-tuned balance of infected cell killing and resolution of inflammation can lead to infection clearance, while disruptions can drive different severe phenotypes. These results can help further in a rational selection of drug combinations that can effectively balance viral clearance and minimize tissue damage.

Implications of Sectoral Logistical Capabilities for Export Competitiveness: A Public Policy Perspective for Interventions in the Logistics Sector

This article takes a public policy perspective and captures the implications of sectoral logistical capabilities for export competitiveness to obtain directives for developmental interventions in the logistics sector. We specifically develop a logistical policy intervention framework that links trade logistics performance and exports at the country-level with the objective to identify strategies for a nation's logistics policy formulation. Theoretically, the framework relies on the “macro-institutional view of the logistics systems environment,” which is an emerging global value chain perspective to model global trade scenarios. This perspective accounts for the influence of diverse operational capabilities at a sectoral scale, multiple institutions, and socio-economic environments in governing the performance of a nation's logistics systems in global trade. Methodologically, a rigorous nonlinear regression modeling approach highlights nonlinear discontinuities associated with the impact of logistical policy interventions fostering sectoral logistical capabilities (for example, developing logistics infrastructure or improving the state of logistics service quality in the nation) to boost the export viability of a country in the global economy. The results also highlight critical environmental bottlenecks that govern the efficacy of such interventions. This article pioneers to mainstream the public policy underpinning of logistics systems, and via this contextualization, it contributes to the agenda of mastering policy challenges in engineering and technology management.