How Do Privacy Laws Impact the Value for Advertisers, Publishers and Users in the Online Advertising Market? A Comparison of the EU, US and China

Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws

The feasibility of estimating biological parameters using electronic length frequency analysis (ELEFAN): The Bohai Sea as a case study

The feasibility of estimating biological parameters using electronic length frequency analysis (ELEFAN): The Bohai Sea as a case stud

Hadoop performance modeling for job estimation and resource provisioning

MapReduce has become a major computing model for data intensive applications. Hadoop, an open source implementation of MapReduce, has been adopted by an increasingly growing user community. Cloud computing service providers such as Amazon EC2 Cloud offer the opportunities for Hadoop users to lease a certain amount of resources and pay for their use. However, a key challenge is that cloud service providers do not have a resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user's responsibility to estimate the required amount of resources for running a job in the cloud. This paper presents a Hadoop job performance model that accurately estimates job completion time and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model builds on historical job execution records and employs Locally Weighted Linear Regression (LWLR) technique to estimate the execution time of a job. Furthermore, it employs Lagrange Multipliers technique for resource provisioning to satisfy jobs with deadline requirements. The proposed model is initially evaluated on an in-house Hadoop cluster and subsequently evaluated in the Amazon EC2 Cloud. Experimental results show that the accuracy of the proposed model in job execution estimation is in the range of 94.97 and 95.51 percent, and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model

Modeling the Oceanographic Impacts on the Spatial Distribution of Common Cephalopods During Autumn in the Yellow Sea

Modeling the Oceanographic Impacts on the Spatial Distribution of Common Cephalopods During Autumn in the Yellow Se

Impacts of COVID-19 on construction project management: a life cycle perspective

Purpose: The impacts of COVID-19 on construction projects have attracted much attention in the construction management research community. Nevertheless, a systematic review of these studies is still lacking. The purpose of this paper is to systematically analyze the impacts of COVID-19 on the different stages of a project life-cycle, and comprehensively sort out the epidemic response measures adopted by project participants. In addition, the study also attempts to explore the challenges and opportunities faced by project management practitioners under the context of COVID-19. Design/methodology/approach: This study comprehensively demonstrates the systematic review process of COVID-19 related research in the construction industry, systematically summarizes the research status of the impact of COVID-19 on construction projects, and defines the strategies to deal with COVID-19 in project management; and through the visualization research, determines the current key research topics and future research trends. Findings: This study identifies 11 construction activities in the project management life cycle that are affected by COVID-19 and finds that the COVID-19 epidemic has the greatest impact on construction workers, construction standards, construction contracts and construction performance. The study further summarizes the six main epidemic countermeasures and mitigation measures taken within the construction industry following the arrival of the epidemic. In addition, the results of this study identify opportunities and future trends in intelligent construction technology, rapid manufacturing engineering and project management in the construction industry in the post-epidemic era through literature results, which also provide ideas for related research. Practical implications: COVID-19 has brought severe challenges to society. It is of great significance for the future sustainable development of the construction industry to identify the impact of COVID-19 on all phases of the project and to promote the development of coping strategies by project stakeholders. Originality/value: First of all, there is little study comprehensively reviewing the impacts of COVID-19 on the different stages of construction projects and the strategies to deal with the negative impacts. In addition, from a life cycle perspective, the used articles in this study were grouped into different categories based on project stages. This promotes an integrated and comprehensive understanding of historical studies. Moreover, on the basis of a comprehensive review, this paper puts forward future research directions to promote the sustainable development of the construction sector

Understanding the effects of climate and anthropogenic stresses on distribution variability of Setipinna taty in the Yellow Sea

Understanding the effects of climate and anthropogenic stresses on distribution variability of Setipinna taty in the Yellow Se

Numerical simulation of micro-galvanic corrosion in Al alloys: Effect of geometric factors

A finite element model for simulating the propagation of intermetallic particle driven micro-galvanic corrosion in an Al-matrix model system is presented. The model revealed dynamic changes related to localized corrosion, including the moving dissolution boundary, the deposition of reaction products (Al(OH)3), and their blocking effect. Modelling was focused on the effects of key geometric parameters, including the radius of cathodic particle (range 0.5 to 4 μm) and the width of an assumed anodic ring surrounding the particle (range 0.1 to 2 μm). Simulations revealed the dynamic flow of molecular and ionic species, along with influence of geometrical constraints. For ring widths below 0.5 μm, deposition of Al(OH)3 inside the dissolving volume was inhibited due to limited transport of OH− and O2 into a constrained volume − resulting in local acidification. An increase in cathodic particle radius at given ring width resulted in a greater dissolution by providing a larger cathodic area for O2 reduction, quantifying the effect of cathode/anode ratio. The model was also developed to include two cathodic particles to explore any interaction. The present study reveals a physicochemical model that contributes toward a framework for multi-process localized corrosion systems, which can be further adapted to commercial alloy systems

Simulation-based analyses and improvements of the smart line management system in canned beverage industry: A case study in Europe

Canned water is one of the thriving markets in the food and beverage industry. Given the tight competition in this market, realistic analysis in such production lines has become even more attractive for all participating parties. In this paper, we apply a KPI-driven simulation-based approach to a smart production plant of a key player in the European beverage market. The project covers realistic discrete-event modeling and analysis of the system together with the suggested scenario-based optimization for performance improvement. Here, the smart line management system is modeled and re-coded while considering machine characteristics, failures, and their overall influence on the production process. Our proposed optimized scenario demonstrates noticeably better results in all performance indicators when compared to the existing state of the system. The total increment of the production speed reaches up to 45 percent, resource utilization is evenly optimal, and the overall work-in-progress inventory is reduced significantly

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

© 1997-2012 IEEE. Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically handcrafted with extensive expertise in both CNNs and the investigated data, which consequently hampers the widespread adoption of CNNs for less experienced users. Evolutionary deep learning (EDL) is able to automatically design the best CNN architectures without much expertise. However, the existing EDL algorithms generally evaluate the fitness of a new architecture by training from scratch, resulting in the prohibitive computational cost even operated on high-performance computers. In this paper, an end-to-end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. The proposed performance predictor shows the promising performance in term of the classification accuracy and the consumed computational resources when compared with 18 state-of-the-art peer competitors by integrating into an existing EDL algorithm as a case study. The proposed performance predictor is also compared with the other two representatives of existing performance predictors. The experimental results show the proposed performance predictor not only significantly speeds up the fitness evaluations but also achieves the best prediction among the peer performance predictors

Coupling spatio-temporal distribution to provide more informative population status and management recommendations

Coupling spatio-temporal distribution to provide more informative population status and management recommendation