Augmented Model Research Articles

Re-solving wicked problems in global value chains: how probing into modern slavery can advance MNE research and support better policymaking

PurposeThis paper aims to respond to the response pieces by Burmester (2024) and by Dindial and Voss (2024) to the original paper on “Taming wicked problems through international business policy: recommendations for addressing modern slavery”. Beyond engaging with the issues raised by Burmester (2024) and Dindial and Voss (2024), the follow-up helps further clarify the key difference between so-called “grand challenges” and “wicked problems” for both international business (IB) policymaking and multinational enterprise (MNE) research.Design/methodology/approachIn response to Burmester (2024), the paper juxtaposes key literature on grand challenges and wicked problems to show the theoretical value of applying a wicked problem lens to modern slavery. In engaging with some of the issues raised by Dindial and Voss (2024), this paper further builds on the most current review papers on navigating control and coordination issues within MNEs and the literature on global value chains (GVCs).FindingsThe paper operationalises the field of IB policy of relevance to modern slavery research and proposes an augmented conceptual model of MNEs’ control and coordination mechanisms to address modern slavery under conditions of distributed responsibility in their GVCs.Originality/valueThis paper problematizes the grand challenges’ label imposed on modern slavery and leverages a wicked problem theoretical toolkit that can help better guide modern slavery’s global and multi-level governance nexus. The proposed augmented conceptual model also provides a significant attempt to address some of the key theoretical gaps in GVC and MNE control−coordination literature.

African Continental Free Trade Area and Regional Trade in ICT and Digital Technologies

This study examines the impact of the African Continental Free Trade Area (AfCFTA) on regional trade in the Information and Communication Technology (ICT) and Digital Technologies (DT) sector across 43 African countries from 2014 to 2021. Employing the augmented gravity model and confidence-level estimations, it highlights AfCFTA's mediating role in enabling ICT&DT trade on the continent. Using hierarchical regression analysis of a panel dataset comprising 5,160 observations, the findings imply that trade openness and productive capacities not only facilitate trade in the ICT&DT sector but also result in positive spillover effects across various economic sectors. This study contributes to the international business literature by refining the application of the gravity model to capture the need for sector-specific analyses to unpack institutional dynamics and dis-enablers of trade. It identifies AfCFTA as a pivotal yet underexplored element in the global trade landscape, highlighting its potential as Africa seeks a more prominent role on the global stage. The research stresses the significance of digital empowerment and policy reforms to maximise the benefits of regional integration under AfCFTA.

TPMS Microarchitectures for Vertical Bone Augmentation and Osteoconduction: An In Vivo Study.

Triply periodic minimal surface microarchitectures (TPMS) were developed by mathematicians and evolved in all kingdoms of living organisms. Renowned for their lightweight yet robust attributes, TPMS structures find application in diverse fields, such as the construction of satellites, aircrafts, and electric vehicles. Moreover, these microarchitectures, despite their intricate geometric patterns, demonstrate potential for application as bone substitutes, despite the inherent gothic style of natural bone microarchitecture. Here, we produced three TPMS microarchitectures, D-diamond, G-gyroid, and P-primitive, by 3D printing from hydroxyapatite. We explored their mechanical characterization and, further, implanted them to study their bone augmentation and osteoconduction potential. In terms of strength, the D-diamond and G-gyroid performed significantly better than the P-primitive. In a calvarial defect model and a calvarial bone augmentation model, where osteoconduction is determined as the extent of bony bridging of the defect and bone augmentation as the maximal vertical bone ingrowth, the G-gyroid performed significantly better than the P-primitive. No significant difference in performance was observed between the G-gyroid and D-diamond. Since, in real life, the treatment of bone deficiencies in patients comprises elements of defect bridging and bone augmentation, ceramic scaffolds with D-diamond and G-gyroid microarchitectures appear as the best choice for a TPMS-based scaffold in bone tissue engineering.

The Military Expenditure – Economic Growth Nexus Revisited: Evidence from the United Kingdom

Abstract The relationship between government defence expenditure and economic growth is a debated topic. This study uses UK data for the period of 1960–2012 and applies two of the most prevailing theories used within the literature, the ‘Feder-Ram’ and the ‘augmented Solow’ models, to assess this question. We utilise traditional model specifications, alongside extensively altered versions of both models, enabling a comprehensive comparison between them. The alterations to the models include re-evaluating how core variables are expressed, inclusion of measures of conflict, the impact of recession, etc. The results show that the augmented Solow model outperforms the Feder-Ram model, and we provide some explanations for this result. In addition, our results suggest that military expenditure has a positive effect on economic growth within the UK, implying that the decision to reduce defence spending may have been detrimental to the UK economy.

Initial decoding with minimally augmented language model for improved lattice rescoring in low resource ASR

Initial decoding with minimally augmented language model for improved lattice rescoring in low resource ASR

Integrated deviance information criterion for spatial autoregressive models with heteroskedasticity

In this study, we introduce the integrated deviance information criterion (DIC) for nested and non-nested model selection problems in heteroskedastic spatial autoregressive models. In a Bayesian estimation setting, we assume that the idiosyncratic error terms of our spatial autoregressive model have a scale mixture of normal distributions, where the scale mixture variables are latent variables that induce heteroskedasticity. We first derive the integrated likelihood function by analytically integrating out the scale mixture variables from the complete-data likelihood function. We then use the integrated likelihood function to formulate the integrated DIC measure. We investigate the finite sample performance of the integrated DIC in selecting the true model in a simulation study. The simulation results show that the integrated DIC performs satisfactorily and can be useful for selecting the correct model in specification search exercises. Finally, in a spatially augmented economic growth model, we use the integrated DIC to choose the spatial weights matrix that leads to better predictive accuracy.

Social distancing and COVID-19 under violence: Evidence from Colombia

Did violence increase social distancing and decrease COVID-19 cases? We investigated the effects of massacres on social distancing and subsequent impacts on COVID-19 cases in Colombia. Using an augmented synthetic control method model, we find that massacres reduced human mobility toward parks by six percentage points compared to unaffected areas. However, we did not find significant changes in workplace mobility. Moreover, alterations in social interactions caused by the violence had minimal effects on the spread of COVID-19. Following the occurrence of the first massacre, there was a decrease in 35 new cases per 100,000 inhabitants in the subsequent four months, with no evidence of changes in COVID-19 deaths. By leveraging an exogenous shock unrelated to the fear of the disease or its previous levels, we demonstrate the effect of social distancing and offer insights into social dynamics and public health.

Are vehicle lifespan caps an effective and efficient method for reducing US light-duty vehicle fleet GHG emissions?

With light duty vehicles (LDVs) responsible for 17% of annual US greenhouse gas (GHG) emissions, integrating emerging GHG-reducing technologies into the fleet is essential. However, the slow rate of vehicle turnover presents a significant barrier to the market penetration of new technologies, with adoption delayed by the low number of vehicles needing replacement each year. A strategy of accelerated vehicle turnover through a vehicle lifespan cap could potentially mitigate this limit. While older studies reach differing conclusions on their effectiveness, two newer studies that incorporate life cycle assessment find that accelerated turnover strategies can be effective if coupled with high levels of electric vehicle deployment. We seek to determine whether a vehicle lifespan cap strategy can be an effective and efficient (cost-effective) method for reducing US LDV fleet GHG emissions. We augment the capabilities of the Fleet Life Cycle Assessment and Material Flow Estimation (FLAME) fleet life cycle assessment model, integrating vehicle lifespan caps and comprehensive calculations of cost along with sensitivity analysis for electric vehicle survival curves and battery degradation. The augmented FLAME model is used to analyse the impact of vehicle lifespan caps of varying lengths on a suite of scenarios, including a business as usual (BAU) scenario and eight scenarios modelling different technology improvement assumptions. This work confirms that vehicle lifespan caps have limited effectiveness in reducing GHG emissions under a BAU scenario but show potential to meaningfully reduce GHG emissions in a scenario with accelerated deployment of electric vehicles. However, abatement costs are high, exceeding 2020 USD 1000/tCO2eq under baseline assumptions, but falling within the range of current estimates of the social cost of carbon under more optimistic assumptions. Overall, vehicle lifespan caps must be carefully considered as they accelerate both the benefits and costs of new vehicle technologies, and are best positioned as part of a larger integrated strategy for tackling transportation GHG emissions.

An improved hybrid solar cell defect detection approach using Generative Adversarial Networks and weighted classification

Quality control has a vital role in manufacturing processes. Electroluminescence (EL) imaging is one of the main non-destructive inspection methods for quality assessment in the Photovoltaic (PV) module production industry. EL test reveals PV cell defects such as micro cracks, broken cells, finger interruptions and provides detailed information about production quality. In recent years, automated detection and classification systems using deep neural networks for PV module inspection have gained increasing attention. However, deep learning-based systems usually require large amount of labeled data and high computational power. In this work, we proposed a compact classification framework based on hybrid data augmentation and deep learning models for detection of the defective solar cells. In the proposed method, the limited and imbalanced EL datasets were augmented through various Generative Adversarial Networks (GAN), and defect detection was achieved by customized pre-trained Convolutional Neural Networks (CNN). A novel hybrid EL dataset for training was formed by combining public ELPV dataset, our custom real-world dataset and synthetic images created with different GAN architectures such as GAN, cGAN and WGAN-GP. The datasets were classified through proposed customized VGG-16 and other state-of-the-art CNN models. The best results obtained by the proposed CNN model with WGAN-GP augmented dataset are 94.11% mean accuracy in the ELPV test dataset and 93.08% mean accuracy in the custom real-world test dataset. Therefore, the proposed detection system achieved superior performance with lesser resource usage and limited data.

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

In this paper, we study the dynamics of the CoVid-19 outbreak in Semarang, Indonesia, using a fractional CoVid-19 model. We first determine the effects of the isolation rate ∊ and infection rate β on the reproduction number R0 and infected number V. We find that R0 is directly proportional to β and inversely proportional to ∊. For V, the effect of physical distancing is not as significant as changing ∊. As ∊ increases, V decreases, the number of susceptible individuals increases, the number of quarantined individuals decreases sharply, and the number of recovered individuals decreases. Moreover, the effect of vaccination is also considered. The combination of physical distancing, isolation, and vaccination has a significant impact on reducing the number of infected individuals. Analysis of dynamical systems allows us to understand the characteristics of our model, such as its boundedness and non-negativity, the existence of equilibrium points, the existence and uniqueness of solutions, and the local and global stability. To validate our fractional CoVid-19 model, we introduce the fractional extended Kalman filter (FracEKF) as a prediction method and compare the results against reported CoVid-19 data. FracEKF is a modified version of the basic extended Kalman filter with a time-fractional memory effect. The prediction results illustrate the accuracy of this model in terms of the root mean square error (RMSE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) for each fractional-order. Varying ∊ reproduces the trends observed in the reported data for the number of infected individuals, i.e., when ∊ increases, the infected number decreases. Moreover, a higher fractional-order results in higher model accuracy. Furthermore, higher values of the process noise Qf give smaller errors, whereas higher values of the observation noise Rf produce higher errors. Qf and the fractional-order α are inversely proportional to RMSE,NRMSE, and MAPE, whereas Rf is directly proportional to RMSE,NRMSE, and MAPE.

A Panel Data Analysis of Antidumping Duties in Seafood Trade: A Gravity Model Approach

ABSTRACT This study uses a panel of monthly data to investigate the effects of antidumping (AD) duties imposed on shrimp imports to the U.S. The results from the augmented gravity model show that initiating AD investigations and the final AD duty rate harm trade with the named countries. Distribution of AD duties among domestic industry producers stimulates a positive supply response. Rejection of imports by European countries for food safety reasons deflects trade to the U.S. The pass-through effects of the AD duty rate (and exchange rate) on import prices are small, indicating minimal adverse effects of AD policy on consumer welfare.

Coordinated management of oxygen excess ratio and cathode pressure for PEMFC based on synthesis variable-gain robust predictive control

In this research endeavor, a novel synthesis variable-gain robust model predictive control (SVGRPC) method using the min-max technique with parameter dependent Lyapunov functions is introduced to achieve the desired trajectory tracking of oxygen excess ratio (OER) and cathode pressure in the polymer electrolyte membrane fuel cells (PEMFCs) system. Initially, a simplified control-oriented model of the air supply system is developed and then transformed into a polytopic form to accommodate the inherent uncertainties in the PEMFC. Subsequently, the polytopic model is integrated with the reference trajectory to construct an augmented state-space model for deriving an error state representation. In the framework of robust model predictive control (RPC) utilizing a parameter-dependent Lyapunov function, a series of variable feedback gains are employed to mitigate conservatism. Additionally, the online solution process imposes a significant computational burden, presenting challenges for the real-time implementation of RPC-based control strategy. Therefore, the offline computing is introduced to significantly reduce the computational burden, resulting in the development of the SVGRPC strategy proposed in this paper. Subsequently, the SVGRPC strategy computes explicit linear state-feedback control laws by solving linear matrix inequalities (LMIs) using an offline control algorithm. The effectiveness of the proposed controller is then validated through assessments conducted under three distinct operating conditions of the PEMFC system. The outcomes of this study affirm that the proposed controller improves the requirements of control performance when compared to the online RPC with a quadratic Lyapunov function while significantly alleviating the computational workload.

Open-ti: open traffic intelligence with augmented language model

Open-ti: open traffic intelligence with augmented language model

Augmented Bifactor Models and Bifactor-(S-1) Models are Identical. A Comment on Zhang, Luo, Zhang, Sun & Zhang (2023)

In a recent study, Zhang et al. (2023) proposed the augmented oblique bifactor model as a new methodological contribution, in which an oblique bifactor model is augmented by one or more indicator(s). Zhang et al. (2023) argued that the augmented oblique bifactor model is superior to existing bifactor modeling approaches. In this comment, we show that the augmented oblique bifactor model by Zhang et al. (2023) is identical and therefore indistinguishable from bifactor-(S-1) type of models presented by Eid et al. (2017). The augmentation approach has already been recommended in the original work of Eid et al. (2017) and by Eid (2020). Finally, we highlight strengths of the simulation studies conducted by Zhang et al. (2023), but also show that several of the conclusions drawn by Zhang et al. (2023) are incorrect. We conclude that there are no differences when applying augmented and bifactor-(S-1) type of models.

A cross-attention augmented model for event-triggered context-aware story generation

Despite recent advancements, existing story generation systems continue to encounter difficulties in effectively incorporating contextual and event features, which greatly influence the quality of generated narratives. To tackle these challenges, we introduce a novel neural generation model, EtriCA, that enhances the relevance and coherence of generated stories by employing a cross-attention mechanism to map context features onto event sequences through residual mapping. This feature capturing mechanism enables our model to exploit logical relationships between events more effectively during the story generation process. To further enhance our proposed model, we employ a post-training framework for knowledge enhancement (KeEtriCA) on a large-scale book corpus. This allows EtriCA to adapt to a wider range of data samples. This results in approximately 5% improvement in automatic metrics and over 10% improvement in human evaluation. We conduct extensive experiments, including comparisons with state-of-the-art (SOTA) baseline models, to evaluate the performance of our framework on story generation. The experimental results, encompassing both automated metrics and human assessments, demonstrate the superiority of our model over existing state-of-the-art baselines. These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.

Modeling Residents’ Mega Sport Event Social Value: Integrating Social and Economic Mechanisms

Social value is the difference between monetized social impacts and related economic investments. Stimulating positive social value is a leading concern and focus for sport event stakeholders. However, insights on this socioeconomic phenomenon have concentrated on social or economic mechanisms, not both, and are siloed to host city residents, largely overlooking nonhost city residents central to events. Thus, we integrated social and economic mechanisms to examine host city and nonhost city residents’ mega sport event social value. Data from 1,880 Canadians revealed varying social values (Vancouver and Provincial = negative; Venue-City = neutral; National = positive). Applying a reverse contingent valuation method, findings confirmed the need to integrate (monetized) social and economic mechanisms to calculate social value. Testing an augmented social exchange theory model, findings highlight residents’ perceptual ambivalence to social impacts and the importance of income to estimate social value. Stakeholders should effectively leverage events for social impacts and reconsider event public funding allocation policies.

Macroeconomic, energy, and emission effects of solar PV deployment at utility and distributed scales in Saudi Arabia

For the first time, this paper assesses the macroeconomic, energy, and emission impacts of solar photovoltaic (PV) deployment in Saudi Arabia by linking an energy-and-environment-sector augmented macroeconometric model with a power model and a distributed generation model while distinguishing between utility scale and distributed-generation scale of the PV deployment. We analyze three policy scenarios of deploying 5.5 TWh PV for the period 2021–2030: (S1) fully government-funded utility-scale PV deployment, (S2) half-government-funded utility-scale PV deployment, and (S3) household-funded distributed generation scale PV deployment with little government support. We find that: S1 is best suited if the objective is fiscal expansion, 2 is best suited to improve non-oil fiscal positions, increase non-oil value added and employment, and increase household consumption, and S3 is best suited to reduce energy consumption and associated emissions and enhance international trade. We show that government and foreign investment play an important role, and it would be desirable for the Saudi government to partner in financing renewable energy projects, saving budgetary resources to invest in other projects to support economic growth.

Achieving environmental sustainability goals through capitalizing on renewable energy channels: Role of green finance, resources productivity and geopolitical situation in the MENA region

Environmental sustainability is essential to the country's financial position and economic growth. Geopolitical situations and green activities significantly contribute to enhancing sustainable development goals. The purpose of this study is to evaluate the impact of green finance, investment in renewable energy sources and geopolitical risk on the environmental sustainability of the Middle East and North America (MENA) region over the period 2000–2021. When using a cross‐sectional dependence autoregressive distributed lag model (CS‐ARDL), it is discovered that a rise in geopolitical risk will decrease environmental performance in the form of carbon dioxide emission. At the same time, the other factors (green finance and investment in renewable energy) have a positive relationship with sustainability. This observation can be ascribed to the association between increased geopolitical risk and the inclination of crude oil consumers, who are most affected by such risk, to contemplate renewable energy as an alternative to conventional energy sources. This finding could also be attributed to the fact that clean energy is becoming more affordable. In addition, the augmented mean group model findings provide more evidence that a negative linkage is found between geopolitical risk and environmental sustainability. The findings have repercussions for regulators as well as investors who are active in the renewable energy markets.

Enhancing Email Security - End to End Encryption

Abstract: Nowadays, most of people and organizations use the e-mail for different needs to exchange information between users. E-mail application is an important network application. It is significant when business1, health and educational communities use it for exchange of critical information such as business information, health patient record and so on. over the Internet or internal networks, It allows data and messages to be transferred easily between senders and receivers allowing messages to be received, responded to, stored, forwarded and broadcast among recipients. These extensive capabilities have caused the email to be widely adopted as the official communications method for many organizations. Also common for personal use, electronic mail is available through a diverse number of compatible software clients, and also via web-browser. An existing enterprise email implementation second service model adds security processing. The service provider is responsible for monitoring all threats using email as a channel , and for providing an email user interface (UI) In the enterprise augmentation model, an existing on-premise email deployment is augmented by additional cloud-based services and functionalist. This paper explores identity federation and data loss prevention and describing best practices for evaluating, developing, installing and using cloud-based email security services.1.1Intended Audience Email security services are viewed from two perspectives: the providers of these services and the consumers or purchasers of email security services. key service features is needed for both sides to be aware of and plan for and how these features are used to tackle threats to email security.

Real Time Object Detection System Using Yolov

Improving object detection models in computer vision is a crucial area of focus. Object detection represents a more sophisticated approach to image classification, where a neural network identifies objects within an image and delineates them using bounding boxes. YOLO (You Only Look Once) revolutionized object detection by introducing an end-to-end neural network architecture that simultaneously predicts bounding boxes and class probabilities. Unlike earlier methods that adapted classifiers for detection, YOLO's approach streamlines the process by integrating classification and localization tasks, accurately delineating objects with bounding boxes. The project mainly focuses on augmenting YOLOv4, a state-of-the-art object detection architecture, to address the limitations like small object detection, adaptive lighting handling, occlusion challenges, detecting objects at different angles, and specialized object features. Additionally, we aim to improve the model's ability to recognize specialized object features for superior classification. Furthermore, by incorporating functionalities like license plate recognition using Tesseract OCR, object counting (total and class-wise), and detection cropping, this project extends the model's applicability to real-world scenarios. These functionalities make the model valuable in areas like autonomous vehicles, traffic monitoring, and industrial automation. The augmented model's improvements aim to overcome drawbacks identified in existing research, enhancing its efficacy in various scenarios.