Implications for Futures: The Missing Section in Sustainable Information Systems Research

This paper argues in favor of a deeper understanding of the importance of futures in information systems (IS) research and proposes an initial set of guidelines for the inclusion, in IS publications, of an optional section devoted to the discussion of futures. The proposal was inspired by reflections on the nature of disruptive innovations and was grounded on a cross disciplinary literature review in the areas of IS, management, and computer science. The proposal includes examples of preferable futures and a discussion of their exploration. The anticipation of sustainable futures has the potential to (1) enrich the debate in IS research, (2) contribute to the development of preferable futures, and (3) create knowledge bases of scenarios and trajectories for such development. As far as theory is concerned, the paper advances the incorporation of futures in IS research. For the practice of the discipline, the paper proposes an additional dimension for the publication of impactful research that takes into account the needs of today’s managers, namely those who deal with uncertain environments

An architecture to predict anomalies in industrial processes

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internet of Things (IoT) and machine learning algorithms (ML) are enabling a revolutionary change in digitization in numerous areas, benefiting Industry 4.0 in particular. Predictive maintenance using machine learning models is being used to protect assets in industry. In this paper, an architecture for predicting anomalies in industrial processes was proposed in which SMEs can be guided in implementing an IIoT architecture for predictive maintenance (PdM). This research was conducted to understand what machine learning architectures and models are generally used by industry for PdM. An overview of the concepts of the Industrial Internet of Things (IIoT), machine learning (ML), and predictive maintenance (PdM) was provided, and through a systematic literature review, it was possible to understand their applications and which technologies enable their use. The survey revealed that PdM applications are increasingly common and that there are many studies on the development of new ML techniques. The survey conducted confirmed the usefulness of the artifact and showed the need for an architecture to guide the implementation of PdM. This research can be a contribution for SMEs, allowing them to become more efficient and reduce both production and maintenance costs in order to keep up with multinational companies

Key challenges in designing CHO chassis platforms

Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.This work has been supported the Federal Ministry for Digital and Economic Affairs (bmwd), the Federal Ministry for Transport, Innovation and Technology (bmvit), the Styrian Business Promotion Agency SFG, the Standortagentur Tirol, Government of Lower Austria and ZIT - Technology Agency of the City of Vienna through the COMET-Funding Program managed by the Austrian Research Promotion Agency FFG. A.H. has been supported by the Portuguese NORTE-08-5369-FSE-000053 operation. Additional funding came from the PhD program BioToP (Biomolecular Technology of Proteins) of the Austrian Science Fund (FWF Project W1224) and MIT-Portugal PhD program (Bioengineering Systems). The funding agencies had no influence on the conduct of this research. Open Access Funding by the University of Vienna.info:eu-repo/semantics/publishedVersio

EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier handling is vital for predictive maintenance as the variables distribution initially showed a right-skewed pattern but after rectifying, it became more centralized, with correlations between specific sensors showing potential for further exploration. Q2) Data balancing through SMOTE and feature engineering is essential due to the rarity of actual failure instances. Substantial challenges are observed when predicting \u27Failure\u27 instances, with a lower true positive rate (73%), resulting in low precision (0.02) and recall (0.73) for \u27Failure\u27 predictions. This is further reflected in the low F1-Score (0.03) for \u27Failure,\u27 indicating a trade-off between precision and recall. Despite a commendable overall accuracy of 94%, the class imbalance within the dataset (92,200 \u27Running\u27 instances vs. 126 \u27Failure\u27 instances) remains a contributing factor to the model\u27s limitations. Q3) Machine learning algorithm performance varies, with Catboost excelling in accuracy and failure detection. The choice of algorithm and continuous model refinement are critical for enhanced predictive accuracy in industrial contexts. The main conclusions are: Q1) Addressing outliers in data preprocessing significantly enhances the accuracy of machine failure prediction models. Q2) focuses on addressing the issue of equipment failure parameter imbalance. It was found in the research findings that there was a significant imbalance in the failure data, with only 0.14% of the dataset representing actual failures and 99.86% of the dataset pertaining to non-failure data. This extreme class disparity can result in biased models that underperform on underrepresented classes, which is a common problem in machine learning. Q3) Catboost outperforms other algorithms in predicting machine failures with remarkable accuracy and failure detection rates of 92% accuracy and 99% times it is correct, and further exploration of diverse data and algorithms is needed for tailored industrial applications. Future research areas include advanced outlier handling, sensor relationships, and data balancing for improved model accuracy. Addressing rare failures, enhancing model performance, and exploring diverse machine learning algorithms are critical for advancing predictive maintenance

Transition to sustainable chemistry through digitalization

Modern chemistry is the backbone of our society, but it is also a major contributor to global environmental pollution and the ongoing climate crisis. The transition toward a sustainable future requires a radical transformation of how chemistry is designed, developed, and used. This represents a “break it or make it” challenge for the chemical industry with significant technology lock-in and high entry barriers to radical innovations. We propose that urgently required systemic changes in chemical industry, research and development (R&D), chemicals assessment and management, and education to advance sustainable chemistry are attainable through increased and more rapid adoption of digitalization and new digital tools. This will enable flexible data exchange, increased transparency of information flows along cross-country chemical, material, and product life cycles, and chemistries that are safe and sustainable by design, addressing the complexity of chemicals-environment-health interactions and lowering the costs of entry into chemical R&D and manufacture, and new, more sustainable and collaborative business models

Exit velocity: the media election

The previous campaign of 2010 produced electoral firsts in media terms (the televised leaders' debates), drama and unpredictability (‘Cleggmania’) and memorable moments (Gordon Brown's ‘bigoted woman’ comments) all of which disrupted the parties' planned scripts. Arguably, the 2015 election seems to have been its very antithesis. The plodding six-week campaign has been widely been portrayed as dull, stage-managed, narrowly focused and lacking in surprise moments, but with a dramatic ending on election night, as the broadcasters announced the shock exit poll. The disbelieving former Liberal Democrat leader Paddy Ashdown declared ‘he would eat his hat’ if his party suffered the losses predicted by the forecast; in fact the result was even worse. Ashdown like so many of his fellow commentators, whether of the traditional offline or online media varieties, was stunned by the apparent failure of the opinion polls to foresee the Conservative victory. What followed was the political equivalent of ‘exit velocity’ in the aftermath of a plodding election, with frenetic, intensive debate over the future of the UK sparking the kind of passion lacking in the preceding campaign. The 2015 campaign as reported in the media was predicated on the assumption that the outcome would be another hung Parliament and, possibly, coalition government. This was constantly reinforced by a stream of experts and opinion-formers fixated on what might happen after the election rather what had just happened in the previous Parliament. This augmented the potential power-broking role of emerging ‘challengers’ such as UKIP, the SNP and Greens at the marked expense of the Liberal Democrats, clear beneficiaries of the added exposure they had received in 2010. Yet if the campaign differed in terms of its focus on these growing political parties it was also reminiscent of the previous one with its similar emphasis on polls and other aspects

Open innovation to create value & address sustainability concerns in fashion industry: H&M case

This work project is a case study that aims to contribute to the body of educational tools for open innovation directed towards the sustainability concerns. The case is built around the H&M Foundation’ Global Change Award, an innovation competition that focuses to diminish the negative impact of the fashion industry on climate. Globally, it is recognized like the Nobel Prize of Fashion and the projects that enter the competition have to show potentials to reinvent the industry by providing ideas such as digital technologies, waste recovery and reuse or any problem related to the impact of fashion industry. As the H&M Foundation leverages principles of Open Innovation and works towards meeting SDGs goals, the case can serve for teaching not only innovation management courses, but also Sustainability or Social Impact related courses

Modern mediators : intermediaries’ informational roles in sourcing from China

Purpose Sourcing intermediaries, commonly known as agents or trading companies, represent a useful organisational solution for assisting companies to manage supply risks and to overcome the liability of foreignness. However, the landscape of global business is experiencing rapid and fundamental changes, which leads us to ask whether intermediaries will continue to play a role in global sourcing. This paper aims to understand how sourcing intermediaries ensure a lasting position in the changing setting of global sourcing and information sharing. Design/methodology/approach This paper investigates the operations of both Chinese and Nordic (Finnish and Swedish) intermediaries in sourcing from China by analysing qualitative data collected over a period of four years. Findings Through the lens of information asymmetry, this paper identifies four distinct informational roles that are used by intermediaries to reduce information asymmetry between suppliers and buyers located in different countries. The paper also examines intermediaries’ signalling activities under these roles in a cross-border triad. Originality/value The paper contributes to the scientific debate on the usefulness of intermediaries by underlining intermediaries’ informational advantage, which provides a new explanation for the survival of intermediaries in a rapidly changing business context. Additionally, this study contributes to research on intermediation strategies by empirically examining both Chinese and Western intermediaries, highlighting the importance of institutional contexts in affecting intermediaries’ informational roles.©2021 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

The Power of Patents: Leveraging Text Mining and Social Network Analysis to Forecast IoT Trends

Technology has become an indispensable competitive tool as science and technology have progressed throughout history. Organizations can compete on an equal footing by implementing technology appropriately. Technology trends or technology lifecycles begin during the initiation phase. Finally, it reaches saturation after entering the maturity phase. As technology reaches saturation, it will be removed or replaced by another. This makes investing in technologies during this phase unjustifiable. Technology forecasting is a critical tool for research and development to determine the future direction of technology. Based on registered patents, this study examined the trends of IOT technologies. A total of 3697 patents related to the Internet of Things from the last six years of patenting have been gathered using lens.org for this purpose. The main people and companies were identified through the creation of the IOT patent registration cooperation network, and the main groups active in patent registration were identified by the community detection technique. The patents were then divided into six technology categories: Safety and Security, Information Services, Public Safety and Environment Monitoring, Collaborative Aware Systems, Smart Homes/Buildings, and Smart Grid. And their technical maturity was identified and examined using the Sigma Plot program. Based on the findings, information services technologies are in the saturation stage, while both smart homes/buildings, and smart grid technologies are in the saturation stage. Three technologies, Safety and Security, Public Safety and Environment Monitoring, and Collaborative Aware Systems are in the maturity stage