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Latent Active Transportation Methodology, Maria Chierichetti, Fatemeh Davoudi Kakhki Apr 2025

Latent Active Transportation Methodology, Maria Chierichetti, Fatemeh Davoudi Kakhki

Mineta Transportation Institute

Understanding and estimating latent demand for active transportation, such as walking and cycling, is essential for designing infrastructure and policies that promote sustainable mobility. Unlike traditional demand models that focus on observed trips, latent demand estimation seeks to quantify the unrealized potential for active travel due to barriers such as inadequate infrastructure, safety concerns, or lack of connectivity. This study develops a comprehensive latent demand model tailored for California, integrating geospatial analysis and multimodal accessibility assessments. The methodology employs a GIS-based corridor analysis approach, utilizing spatial accessibility metrics and distance decay functions to evaluate potential demand. It incorporates employment and …

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Ask An Hci Research Ethicist: A Recurring Column On Research Ethics Challenges, Casey Fiesler, Jessica Vitak, Michael Zimmer Apr 2025

Ask An Hci Research Ethicist: A Recurring Column On Research Ethics Challenges, Casey Fiesler, Jessica Vitak, Michael Zimmer

Computer Science Faculty Research and Publications

Created in 2016, the SIGCHI Research Ethics Committee advises SIGCHI conferences and communities on ethical issues that arise in the course of conducting research. The committee also provides guidance and feedback on research ethics issues that arise during the peer review process; as a result, we have a broad sense of novel and persisting open questions within our community. Through this recurring column, we will continue this work by raising awareness and increasing discussion of ethics in the context of conducting HCI research.

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Fuel Cell System Development For Heavy Duty Vehicles, Yu Yang, Hen-Geul Yeh, Bryan Aguirre Apr 2025

Fuel Cell System Development For Heavy Duty Vehicles, Yu Yang, Hen-Geul Yeh, Bryan Aguirre

Mineta Transportation Institute

As California advances its ambitious goals for transportation electrification to combat climate change, hydrogen-powered fuel cells are emerging as a viable solution for overcoming the challenges of heavy-duty vehicles, offering an efficient alternative to lithium-ion batteries because they produce minimal chemical, thermal, and carbon emissions. One type of hydrogen fuel cell technology called proton exchange membrane fuel cells (PEMFCs) has garnered the most attention due to its distinct advantages, including relatively low operating temperatures (60–80 °C) and reliable performance at high current densities. However, despite their promise, PEMFCs face challenges, including in optimizing stack power output and safety concerns. To …

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Draft Final 2022 Unreclaimed Sites Sampling: Ur-12 Site Evaluation Summary Report, Pioneer Technical Services, Inc. Apr 2025

Draft Final 2022 Unreclaimed Sites Sampling: Ur-12 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Draft Final 2022 Unreclaimed Sites Sampling: Ur-06 Site Evaluation Summary Report, Pioneer Technical Services, Inc. Apr 2025

Draft Final 2022 Unreclaimed Sites Sampling: Ur-06 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Women Overcoming Barriers In Stem: How Mentors, Allies, And Sponsors Assist Career Trajectories In Higher Education, Leila Marie Romeo Apr 2025

Women Overcoming Barriers In Stem: How Mentors, Allies, And Sponsors Assist Career Trajectories In Higher Education, Leila Marie Romeo

Electronic Theses and Dissertations

This study focused on the personal narratives of women in STEM in both public and private higher education institutions within various roles. Specifically, the researcher aimed to determine the following: (a) if mentors/allies influence the career trajectories of women in STEM in higher education institutions and (b) if mentors/allies aid in the support of women in STEM in higher education institutions. The researcher used semistructured interviews with a narrative analysis to determine areas of struggle for the sample within their careers and how mentors, allies, and sponsors were present throughout their careers to help the women overcome these challenges. Data …

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Impact Of A Transplant Nurse Navigator In The Dialysis Setting, Judith D. Pozzerle Apr 2025

Impact Of A Transplant Nurse Navigator In The Dialysis Setting, Judith D. Pozzerle

Electronic Theses and Dissertations

Patients with kidney disease have better patient outcomes and quality of life with a kidney transplant. However, the transplant process is complicated with inherent inequalities and biases and patients frequently fail to initiate a referral for transplant evaluation. The Centers for Medicare and Medicaid have now placed financial incentives to improve the number of patients transplanted. The only way to increase the number of patients transplanted is to start with a referral for evaluation. Studies have been done highlighting the barriers, but there is a lack of studies implementing strategies to improve these numbers. Utilizing Kotter’s change theory and describing …

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Dps: Design Pattern Summarisation Using Code Features, Najam Nazar, Sameer Sikka, Christoph Treude Apr 2025

Dps: Design Pattern Summarisation Using Code Features, Najam Nazar, Sameer Sikka, Christoph Treude

Research Collection School Of Computing and Information Systems

Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques -- and more recently, large language models -- have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored.Our approach utilises code features and JavaParser to parse the code and create a JSON representation. …

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Enhancing Llm-Based Coding Tools Through Native Integration Of Ide-Derived Static Context, Yichen Li, Yun Peng, Yintong Huo, R. Michael Lyu Apr 2025

Enhancing Llm-Based Coding Tools Through Native Integration Of Ide-Derived Static Context, Yichen Li, Yun Peng, Yintong Huo, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being trained on in-file contexts, current LLMs are quite effective in completing code for single source files. However, it is challenging for them to conduct repository-level code completion for large software projects that require cross-file information. Existing research on LLM-based repository-level code completion identifies and integrates cross-file contexts, but it suffers from low accuracy and limited context length of LLMs. In this paper, we argue that Integrated Development Environments (IDEs) can provide direct, accurate and …

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On Minimizing Adversarial Counterfactual Error In Adversarial Reinforcement Learning, Roman Belaire, Arunesh Sinha, Pradeep Varakantham Apr 2025

On Minimizing Adversarial Counterfactual Error In Adversarial Reinforcement Learning, Roman Belaire, Arunesh Sinha, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. The challenge inherent to adversarial perturbations is that by altering the information observed by the agent, the state becomes only partially observable. Existing approaches address this by either enforcing consistent actions across nearby states or maximizing the worst-case value within adversarially perturbed observations. However, the former suffers from performance degradation when attacks succeed, while the latter tends to be overly conservative, leading to suboptimal performance in benign settings. We hypothesize that these limitations stem from their failing to account for …

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Semantic Loss-Guided Data-Efficient Supervised Fine-Tuning For Safe Responses In Llms, Yuxiao Lu, Pradeep Varakantham, Pradeep Varakantham Apr 2025

Semantic Loss-Guided Data-Efficient Supervised Fine-Tuning For Safe Responses In Llms, Yuxiao Lu, Pradeep Varakantham, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or rely on the less dependable option of using another LLM to generate corrective data. In this paper, we aim to take this problem and overcome limitations of requiring significant high-quality human data. Our method requires only a small set of unsafe responses to toxic prompts, easily obtained from the unsafe LLM itself. By employing a semantic cost combined with a negative Earth Mover Distance (EMD) …

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2025 April, Morehead State University. Office Of Communications & Marketing. Apr 2025

2025 April, Morehead State University. Office Of Communications & Marketing.

Morehead State Press Release Archive, 1961 to the Present

Press releases for April of 2025.

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Color Constancy In Virtual Environments With Head-Mounted And Flat-Panel Displays Across Different Illuminants And Lightness Levels, Andrea Avendano Martinez Apr 2025

Color Constancy In Virtual Environments With Head-Mounted And Flat-Panel Displays Across Different Illuminants And Lightness Levels, Andrea Avendano Martinez

Theses

Virtual reality (VR) is commonly used as a tool for enhancing immersion within virtual environments, with one of its primary goals being to approximate real-world perception as closely as possible. In everyday life, variations in lighting conditions significantly influence how colors are perceived across different times and settings. However, the human visual system compensates for these changes, maintaining relatively stable color perception; a phenomenon known as color constancy. This process is driven by chromatic adaptation, the visual system’s adjustment to changes in illumination. Despite the growing use of VR, little research has explored how color constancy operates in virtual environments …

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Isolated, Accommodated, Or Elevated? English Language Development Teachers In Professional Learning Communities, Kristina Robertson Apr 2025

Isolated, Accommodated, Or Elevated? English Language Development Teachers In Professional Learning Communities, Kristina Robertson

Theses & Dissertations

This qualitative study examined the experiences of ten elementary English Language Development (ELD) teachers in interdisciplinary Professional Learning Communities (PLCs) across the Twin Cities metro area. Although PLC teamwork improves student academic outcomes through collaboration and collective efficacy, ELD teachers often faced challenges integrating their language expertise into these structures, limiting their ability to address English Learner (EL) academic achievement and language growth. Guided by DuFour’s six elements of effective PLCs and Wenger’s Communities of Practice (CoP) theory, the study analyzed data from ten semi-structured interviews, teacher-submitted artifacts, and district guidance documents. Data were coded and thematically analyzed to explore …

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Democratic Training Against Universal Adversarial Perturbations, Bing Sun, Jun Sun, Wei Zhao Apr 2025

Democratic Training Against Universal Adversarial Perturbations, Bing Sun, Jun Sun, Wei Zhao

Research Collection School Of Computing and Information Systems

Despite their advances and success, real-world deep neural networks are known to be vulnerable to adversarial attacks. Universal adversarial perturbation, an inputagnostic attack, poses a serious threat for them to be deployed in security-sensitive systems. In this case, a single universal adversarial perturbation deceives the model on a range of clean inputs without requiring input-specific optimization, which makes it particularly threatening. In this work, we observe that universal adversarial perturbations usually lead to abnormal entropy spectrum in hidden layers, which suggests that the prediction is dominated by a small number of “feature” in such cases (rather than democratically by many …

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Can Llms Replace Manual Annotation Of Software Engineering Artifacts?, Toufique Ahmed, Premkumar Devanbu, Christoph Treude, Michael Pradel Apr 2025

Can Llms Replace Manual Annotation Of Software Engineering Artifacts?, Toufique Ahmed, Premkumar Devanbu, Christoph Treude, Michael Pradel

Research Collection School Of Computing and Information Systems

Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings. However, human-subject studies in our field are challenging, due to the cost and difficulty of finding and employing suitable subjects, ideally, professional programmers with varying degrees of experience. Meanwhile, large language models (LLMs) have recently started to demonstrate human-level performance in several areas. This paper explores the possibility of substituting costly human subjects with much cheaper LLM queries in evaluations of code and code-related artifacts. We study this idea by applying six …

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Graph-Assisted Offline-Online Deep Reinforcement Learning For Dynamic Workflow Scheduling, Yifan Yang, Gang Chen, Hui Ma, Cong Zhang, Zhiguang Cao, Mengjie Zhang Apr 2025

Graph-Assisted Offline-Online Deep Reinforcement Learning For Dynamic Workflow Scheduling, Yifan Yang, Gang Chen, Hui Ma, Cong Zhang, Zhiguang Cao, Mengjie Zhang

Research Collection School Of Computing and Information Systems

Dynamic workflow scheduling (DWS) in cloud computing presents substantial challenges due to heterogeneous machine configurations, unpredictable workflow arrivals/patterns, and constantly evolving environments. However, existing research often assumes homogeneous setups and static conditions, limiting flexibility and adaptability in real-world scenarios. In this paper, we propose a novel Graph assisted Offline-Online Deep Reinforcement Learning (GOODRL) approach to building an effective and efficient scheduling agent for DWS. Our approach features three key innovations: (1) a task-specific graph representation and a Graph Attention Actor Network that enable the agent to dynamically assign focused tasks to heterogeneous machines while explicitly considering the future impact of …

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Rethinking Light Decoder-Based Solvers For Vehicle Routing Problems, Ziwei Huang, Jianan Zhou, Zhiguang Cao, Yixin Xu Apr 2025

Rethinking Light Decoder-Based Solvers For Vehicle Routing Problems, Ziwei Huang, Jianan Zhou, Zhiguang Cao, Yixin Xu

Research Collection School Of Computing and Information Systems

Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals …

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Node-Time Conditional Prompt Learning In Dynamic Graphs, Xingtong Yu, Zhenghao Liu, Xinming Zhang, Yuan Fang Apr 2025

Node-Time Conditional Prompt Learning In Dynamic Graphs, Xingtong Yu, Zhenghao Liu, Xinming Zhang, Yuan Fang

Research Collection School Of Computing and Information Systems

Dynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique. However, they are generally pre-trained on the link prediction task, leaving a significant gap from the objectives of downstream tasks such as node classification. To bridge the gap, prompt-based learning has gained traction on graphs, but most existing efforts focus on static graphs and neglect the evolution of dynamic graphs. In this paper, we propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. …

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Comadice: Offline Cooperative Multi-Agent Reinforcement Learning With Stationary Distribution Shift Regularization, The Viet Bui, Tien Mai, Hong Thanh Nguyen Apr 2025

Comadice: Offline Cooperative Multi-Agent Reinforcement Learning With Stationary Distribution Shift Regularization, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Offline reinforcement learning (RL) has garnered significant attention for its ability to learn effective policies from pre-collected datasets without the need for further environmental interactions. While promising results have been demonstrated in single-agent settings, offline multi-agent reinforcement learning (MARL) presents additional challenges due to the large joint state-action space and the complexity of multi-agent behaviors. A key issue in offline RL is the distributional shift, which arises when the target policy being optimized deviates from the behavior policy that generated the data. This problem is exacerbated in MARL due to the interdependence between agents' local policies and the expansive joint …

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Bootstrapping Language Models With Dpo Implicit Rewards, Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin Apr 2025

Bootstrapping Language Models With Dpo Implicit Rewards, Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin

Research Collection School Of Computing and Information Systems

Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM. Our approach is to use the rewards from a current LLM model to construct a preference dataset, which is then used …

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Open-Set Graph Anomaly Detection Via Normal Structure Regularisation, Qizhou Wang, Guansong Pang, Mahsa Salehi, Xiaokun Xia, Christopher Leckie Apr 2025

Open-Set Graph Anomaly Detection Via Normal Structure Regularisation, Qizhou Wang, Guansong Pang, Mahsa Salehi, Xiaokun Xia, Christopher Leckie

Research Collection School Of Computing and Information Systems

This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as *seen anomalies*) to detect both seen anomalies and *unseen anomalies* (*i.e*., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. Further, existing open-set AD models were introduced …

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An Evaluation And Improvement Of The Quality Of The Smart Ration Card By Iso 37301:2023, Shaimaa Salah Saifi Gomair Elias, Ghani Dahham Al Zubaidi Apr 2025

An Evaluation And Improvement Of The Quality Of The Smart Ration Card By Iso 37301:2023, Shaimaa Salah Saifi Gomair Elias, Ghani Dahham Al Zubaidi

Journal of Economics and Administrative Sciences

In evaluating the Iraqi smart ration card system in ISO 37301:2023 compliance management, this study reveals how far the system is compliant with international requirements, what gaps exist, and what areas need strengthening for efficiency and transparency purposes. Case study methods greatly complemented by the use of checklists, interviews, and various forms of statistical analysis, were employed to study the level of compliance of the smart ration card. The results show that there exist considerable gaps in documentation, compliance risk assessment, and resource support, which weakens the system's ability to ensure food security and prevent fraud. While high levels of …

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Forecasting Of The Dollar Exchange Rate Using Exogenous Variables With The (Iv4) Method And Some Kernel Functions, Mustafa Ali Fakhri, Firas A. Mohammed Almohana Apr 2025

Forecasting Of The Dollar Exchange Rate Using Exogenous Variables With The (Iv4) Method And Some Kernel Functions, Mustafa Ali Fakhri, Firas A. Mohammed Almohana

Journal of Economics and Administrative Sciences

This paper proposes a hybrid approach to dollar exchange rate forecasting, wherein both types of forecasting models-linear and non-linear models-have been incorporated to improve the efficiency of predictions. The work essentially combined MISO ARX model with GARCH-X models within MISO ARX framework to enhance the data. These three estimation techniques, namely IV4, RELS-HF, and RELS-SE, could optimize the modeling performance of the MISO ARX model, whereas Quasi Maximum Likelihood Estimation (QMLE) was applied for GARCH-X models. An evaluation metric such as the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) was used for the comparison between model performances. …

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Choosing An Appropriate Wavelet For Varx Time Series Model Analysis, Intisar Ibrahim Elias, Taha Hussein Ali Apr 2025

Choosing An Appropriate Wavelet For Varx Time Series Model Analysis, Intisar Ibrahim Elias, Taha Hussein Ali

Journal of Economics and Administrative Sciences

The paper purports to improve the accuracy of VARX (vector autoregressive with exogenous variables) models adopted for economic time series analysis through wavelet transform techniques applied for noise reduction. The research assessed various wavelet types, including Coiflets, Daubechies, Symlets, Biorthogonal, and Reverse Biorthogonal, for the most appropriate wavelet to be used for improving the performance of the models. Furthermore, it made use of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for evaluating the efficacy of each wavelet in the dimension of noise reduction and predictive accuracy enhancement. It is conclusively deduced that preprocessing through wavelet-based techniques markedly …

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The Effect Of Aggressive Tax Practices On Equity Financing Decisions Under The Interactive Role Of The Profitability Variable: An Applied Study In A Sample Of Non-Financial Companies Listed In The Iraq Stock Exchange, Maryam Bahaa Babat, Mohammed Hwueish Al-Shujairi Apr 2025

The Effect Of Aggressive Tax Practices On Equity Financing Decisions Under The Interactive Role Of The Profitability Variable: An Applied Study In A Sample Of Non-Financial Companies Listed In The Iraq Stock Exchange, Maryam Bahaa Babat, Mohammed Hwueish Al-Shujairi

Journal of Economics and Administrative Sciences

The purpose of this study is to evaluate and test the relationship between aggressive tax practices and decisions to choose the appropriate financing structure in companies listed on the Iraq Stock Exchange. Aggressive tax policies of a company may have an impact on the specific considerations related to equity issuance as a financing method, and the impact of aggressive tax practices on equity financing decisions in non-financial companies listed on the Iraq Stock Exchange can be formulated as a research problem by examining the relationship between tax practices and equity financing. Both the descriptive inductive approach and the quantitative inductive …

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Digital Transformation And The Future Of Work: Closing The Digital Skills Gap, Siu Loon Hoe Apr 2025

Digital Transformation And The Future Of Work: Closing The Digital Skills Gap, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

The purpose of this article is to discuss the near future digital technology landscape and propose several specific in-demand digital skills for organizations and individuals in the next few years. This article reviews some recent publications from representative inter-governmental, governmental, non-governmental, and commercial organizations on the rise of digital technologies and corresponding growth in digital jobs. Within this context, several specific in-demand skills are proposed by the author who has written a book on the topic of digital transformation. Rapid advancements in digital technologies continue to shape organizational practices and the future of work. To take advantage of emerging digital …

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Exploring The Role Of Artificial Intelligence In Higher Education: A Comparative Study On Grading Methods And The Technology Acceptance Model, Roberto Bello Apr 2025

Exploring The Role Of Artificial Intelligence In Higher Education: A Comparative Study On Grading Methods And The Technology Acceptance Model, Roberto Bello

AMTP Proceedings 2025

The integration of artificial intelligence (AI) and large language models (LLMs) like ChatGPT has transformed higher education, offering innovative solutions for personalized learning and grading. This study investigates students’ perceptions and acceptance of AI-assisted grading compared to traditional teaching assistant (TA) grading using the Technology Acceptance Model (TAM). The research explores how grading methods and exam formats influence key TAM constructs such as perceived usefulness, ease of use, and behavioral intentions. Findings suggest that mixed exam formats (70% multiple-choice, 30% short-answer) yield the highest acceptance rates for AI-assisted grading, emphasizing the importance of balanced assessment structures. The study highlights the …

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Decarbonising Digital Infrastructure And Urban Sustainability In The Case Of Data Centres, Felicia H. M. Liu, Karen P. Y. Lai, Bertrand Seah, Winston T. L. Chow Apr 2025

Decarbonising Digital Infrastructure And Urban Sustainability In The Case Of Data Centres, Felicia H. M. Liu, Karen P. Y. Lai, Bertrand Seah, Winston T. L. Chow

Research Collection College of Integrative Studies

This paper critically assesses the complex interplay between urban transitions of digitisation and sustainability. Building on a mixed-method research design, we unpack the challenges of decarbonising digital infrastructure while attending to urban sustainability goals in a land- and water-scarce country facing significant physical climate risks. We identify transferrable lessons on the economic, technological, and environmental synergies and trade-offs behind data centre development and argue that stewarding the global data centre sector towards sustainability requires an ecosystem-wide approach. We identify implementation gaps across five key dimensions: technological innovation, policy and regulation, finance, infrastructure, and people. We find that the progress and …

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From A Learning To A Smart Nation: The Rise Of The Digitalization Megatrend And Singapore's Development, Siu Loon Hoe Apr 2025

From A Learning To A Smart Nation: The Rise Of The Digitalization Megatrend And Singapore's Development, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose: The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program. Design/methodology/approach: This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former …

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