Launching the LSE Media Policy Project Brief 1: Public responses

The LSE Media Policy Project’s first policy brief, entitled ‘Creative Destruction and Copyright Protection’, was successfully launched last week on the eve of the judicial review for the Digital Economy Act (DEA) began a few days later on March 23. The report authors, Bart Cammaerts and Bingchun Meng advocate file-sharing as an important activity in participatory culture responsible for fostering creativity and innovation; rather than as a criminal activity requiring often repressive copyright enforcement measures

Expert meeting on media literacy

The LSE Media Policy Project hosted an expert meeting on media literacy last week and is pleased with the outcome – stimulating debate and lively discussion! The slides are hosted on Scribd

Peggy Valcke on EU Approaches to Monitoring Media Pluralism

Professor Peggy Valcke, director of the Interdisciplinary Centre for Law & ICT (ICRI) and research professor at K.U.Leuven, spoke at yesterday’s Media Policy Project expert workshop on ‘Assessing Media Plurality’. Professor Valcke addressed OfCom’s approach to media pluralism in light of the EU Media Pluralsim Monitor and provided an excellent overview to media pluralism in the EU

Book review: the ambivalent internet: mischief, oddity, and antagonism online by Whitney Phillips and Ryan M. Milner

In The Ambivalent Internet: Mischief, Oddity and Antagonism Online, Whitney Phillips and Ryan M. Milner explore the contradictions and paradoxes of the internet as a realm of ‘vernacular creativity’ . This is a thoughtprovoking and original study that diverges from a ‘good or bad’ binary to instead demonstrate the messy ambivalence of internet culture today, writes Dr Zoetanya Sujon

Understanding the social in a digital age

Datafication, algorithms, social media and their various assemblages enable massive connective processes, enriching personal interaction and amplifying the scope and scale of public networks. At the same time, surveillance capitalists and the social quantification sector are committed to monetizing every aspect of human communication, all of which threaten ideal social qualities, such as togetherness and connection. This Special Issue brings together a range of voices and provocations around ‘the social’, all of which aim to critically interrogate mediated human connection and their contingent socialities. Conventional methods may no longer be adequate, and we must rethink not only the fabric of the social but the very tools we use to make sense of our changing social formations. This Special Issue raises shared concerns with what the social means today, unpicking and rethinking the seams between digitization and social life that characterize today’s digital age

Leveraging social media to optimize tourism in Cox´s Bazar : qualitative insights from accommodation businesses

There has been a great revolution in the tourism industry, especially in destination promotion and tourist engagement, as a result of the use of social media in recent times. Cox’s Bazar, the world’s longest sea beach, has seen significant growth in its accommodation facilities. The objective of this research was to examine how accommodation businesses in Cox’s Bazar were using social media such as Facebook, Instagram, and YouTube for the promotion of their services and building relationships with tourists. The research focused on various aspects of digital engagement, including content types like images, videos, reviews, posting frequencies, dedicated marketing teams, as well as performance metrics. The research was qualitative-based and the data was collected using a survey from the managers of ten selected accommodation businesses. Furthermore, benchmarking research was carried out using their social media content to identify challenges and best practices among them. The results of the study revealed that the examined hotels utilized a number of social media strategies, including posting frequency, immediate response to customer queries, and user-generated content. However, managers found the approach helpful for increasing their brand visibility and developing customer relationships on social media. Based on qualitative data, the study provided some early recommendations on how social media strategies can be improved by accommodation providers in Cox’s Bazar. These enhancements may help digital engagement more efficiently and assist further processes regarding sustainable tourism development in the region

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Exploring the doctoral journey and good supervisorial practice

Drawing from my own doctoral journey, this paper examines some key challenges facing doctoral students and what they mean for good supervisorial practice. These include high levels of pressure and uncertainty, resulting in often strong unmet emotional needs made visible through feelings of imposter syndrome. These challenges can be manifested in writing, in part because writing is complex, involving a writing-into-being of the professional self and authorial voice. These kinds of challenges appear to be commonplace in the doctoral journey, indicating that good supervisorial practice must account for students’ different contexts and relationships to their current and emerging professional identities.

Viscous Dissipation and Heat Absorption effect on Natural Convection Flow with Uniform Surface Temperature along a Vertical Wavy Surface

The effect of viscous dissipation and heat absorption on a steady two-dimensional natural convection laminar flow of viscous incompressible fluid with uniform surface temperature along a vertical wavy surface has been investigated. Using the appropriate variables; the basic equations are transformed to convenient form and then solved numerically employing very efficient method, namely Keller-Box method (KBM). Numerical results are presented by the shearing stress in terms of the local skin-friction coefficient; the rate of heat transfer in terms of local Nusselt number, streamline and isotherms, respectively for a wide range of the viscous dissipation parameter N and heat absorption parameter Q. Increasing Q and lessening N cause the enhancement of heat transfer rate

Empirically Tuning HPC Kernels with iFKO

iFKO (iterative Floating point Kernel Optimizer) is an open-source iterative empirical compilation framework which can be used to tune high performance computing (HPC) kernels. The goal of our research is to advance iterative empirical compilation to the degree that the performance it can achieve is comparable to that delivered by painstaking hand tuning in assembly. This will allow many HPC researchers to spend precious development time on higher level aspects of tuning such as parallelization, as well as enabling computational scientists to develop new algorithms that demand new high performance kernels. At present, algorithms that cannot use hand-tuned performance libraries tend to lose to even inferior algorithms that can. We discuss our new autovectorization technique (speculative vectorization) which can autovectorize loops past dependent branches by speculating along frequently taken paths, even when other paths cannot be effectively vectorized. We implemented this technique in iFKO and demonstrated significant speedup for kernels that prior vectorization techniques could not optimize. We have developed an optimization for two dimensional array indexing that is critical for allowing us to heavily unroll and jam loops without restriction from integer register pressure. We then extended the state of the art single basic block vectorization method, SLP, to vectorize nested loops. We have also introduced optimized reductions that can retain full SIMD parallelization for the entire reduction, as well as doing loop specialization and unswitching as needed to address vector alignment issues and paths inside the loops which inhibit autovectorization. We have also implemented a critical transformation for optimal vectorization of mixed-type data. Combining all these techniques we can now fully vectorize the loopnests for our most complicated kernels, allowing us to achieve performance very close to that of hand-tuned assembly