5,001 research outputs found
Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022
In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet
Digitization of Macro-Logistics Systems in Ukraine
This article delves into the multifaceted realm of digitizing macrologistical systems, scrutinizing their nature, influencing factors, and emerging trends. It intricately explores how the integration of digital technologies empowers transport enterprises to deliver superior logistics services while concurrently curtailing costs. A comparative analysis meticulously dissects various facets of the macrologistical system, elucidating the interplay and correlation of its elements within the realms of commodity-money and contractual relationships. The study accentuates the pivotal role of the macro-logistics system in supply chain management.In scrutinizing Ukraine’s Logistics Performance Index scores vis-à -vis EU countries, the authors attribute the disparity to the enduring conflict. A notable European logistics market trend is identified: a surge in digitization investments by individual companies and the establishment of cohesive online platforms. The paper advocates for the integration of automation in transport logistics, particularly in the backdrop of a wartime economy.European standards for a contemporary transport management system are analyzed, culminating in the selection of the CargoClix logistics platform. The article expounds on the platform’s merits in adeptly digitizing the operations of transport and logistics enterprises, offering real-time tracking crucial for prompt responses amid wartime exigencies.The article concludes by substantiating findings with pertinent diagrams and tables. In essence, the digitization of macrologistical systems entails a holistic amalgamation of digital technologies, data analytics, and decision-making solutions for information management in logistics and supply chain operations. This transformative journey, leveraging technologies such as the Internet of Things, unmanned technologies, identification technologies, blockchain, big data, robotic systems, artificial intelligence, and neural networks, aspires to amplify efficiency, visibility, and coordination throughout the supply chain. This paradigm shift is pivotal for evaluating the efficacy of transport and logistics companies, determining their competitive stance in today’s market. Future research trajectories encompass formulating strategies for national companies operating in the domestic logistics market, identifying and implementing key competitive advantages, and devising measures and tools to ensure efficiency during wartime
Customers’ Continued Adoption of Mobile Apps and Their Satisfaction with Restaurants: The Case of McDonald’s
Background: The major purpose of this research is to examine Contactless Technology (CT) users’ post-adoption phenomena in the context of mobile apps (MA) run by a Quick Service Restaurant (QSR). It applies the Post-Adoption Model of Information System Continuance (PAMISC) to examine how QSR customers’ technology anxiety (TA), confirmation of initial expectations, perceived usefulness (PU), and satisfaction with CT relate to their continued intention of use. Furthermore, the study investigates the relationship between customers’ satisfaction with CT and their overall satisfaction with QSR.
Methods: To test the research model, we collected survey data from 245 users of MA provided by McDonald’s restaurants in the US, which are analyzed through Partial Least Square analysis using SmartPLS 4.0.
Results: The theoretical relationships in the PAMISC hold true in the context of QSR’s MA. Current QSR customers’ TA is negatively associated with their perceived usefulness, but is not related to the degree of confirmation of using MA. Customers’ continued intention of use and satisfaction with MA are positively related to overall satisfaction with QSR.
Conclusion: Our study is among the first to provide empirical/practical evidence of the PAMISC in the context of IT-enabled hospitality services. It also extends the model in two important ways. First, the study examines the role of TA, an important personal trait relevant to individuals’ use of QSR technology. Second, it highlights customers’ satisfaction with firm-provided technology to improve their overall satisfaction with the firm in the context of QSRs. For practitioners, it is important for QSR managers to understand the impact of TA on customers’ adoption of MA, so that they can design their MA with simpler interfaces and more human aspects. Managers should also make sure that MA is well-designed to satisfy customers’ needs, which will then lead to those customers’ overall satisfaction with the QSR
The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments
In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident.
In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion.
This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture.
Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data.
As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis
Who watches the watchmen? Assessing potential regulatory capture through an examination of historical Surface Transportation Board (STB) decisions on shipper/railroad disputes
This thesis will examine a series of historical decisions made by a major U.S. regulatory body, the Surface Transportation Board (STB) in the surface freight transportation sector. As a federal regulator overseeing a major industry, the STB (and the ICC before it) were created to operate as a neutral economic regulator acting in the public interest, managing relationships and disputes between surface railroads (in this case, railroads) and their shipper customers. But understanding the incentives and consequences described by Stigler (1971) in the context of economic regulation and capture, in particular the U.S. freight rail sector continues to operate under some controversy because of the questionable regulatory objectivity of the STB as the railroad regulator (Gallamore, 2014). Much of the prior regulatory research about the STB has focused on its scope along with key issues resulting from the agency’s long term regulation of the rail sector (Goldman, 2022), as well as the impact of regulation on the operation and management of the U.S. freight rail system. Other related literature tries to gain insight on decision processes as well as rationalizing the outcomes of the STB decisions over various freight disputes (Warren, 2018). But in spite of this body of research, to our knowledge there have been few if any analytic attempts to assess the fairness or objectivity of the STB regulatory decisionmaking. One interesting feature of the STB crucial to our assessment is that the agency maintains an online compendium or database of its decisions, going back well into the 1990’s and overall numbering into the thousands. As a qualitative database it can be difficult to use for analytics, but it is detailed and allows us to set up both empirical and qualitative assessments of regulatory objectivity. A further underlying factor in formulating this thesis was the effort required to identify and code the sub-set of relevant STB decisions that were both thematically consistent (i.e. rate disputes between a railway and a shipper) as well as independent over time to the present. This extensive vetting yielded individual decisions/data points that were used to conduct our initial statistical analysis and subsequent qualitative work.
After reviewing related literature on assessments of regulatory objectivity in other industries, the empirical part of the thesis estimates various statistical tests (randomness tests, tests of distributional differences) on the case decision data to identify whether or not the data were generated by a neutral decision-maker. To supplement the statistical analysis and to help facilitate understanding of the reasonability and justifiability of STB decisions, we further qualitatively analyze the same cases to add insight on regulatory behavior. Overall, we hope this study will contribute to a better understanding about the decision-making process of a major U.S. economic regulator. Further, we hope this work might help improve STB performance by improving future objectivity in regulatory decision-making within the US freight rail sector
An intelligent intrusion detection system for 5G-enabled internet of vehicles
The deployment of 5G technology has drawn attention to different computer-based scenarios. It is useful in the context of Smart Cities, the Internet of Things (IoT), and Edge Computing, among other systems. With the high number of connected vehicles, providing network security solutions for the Internet of Vehicles (IoV) is not a trivial process due to its decentralized management structure and heterogeneous characteristics (e.g., connection time, and high-frequency changes in network topology due to high mobility, among others). Machine learning (ML) algorithms have the potential to extract patterns to cover security requirements better and to detect/classify malicious behavior in a network. Based on this, in this work we propose an Intrusion Detection System (IDS) for detecting Flooding attacks in vehicular scenarios. We also simulate 5G-enabled vehicular scenarios using the Network Simulator 3 (NS-3). We generate four datasets considering different numbers of nodes, attackers, and mobility patterns extracted from Simulation of Urban MObility (SUMO). Furthermore, our conducted tests show that the proposed IDS achieved an F1 score of 1.00 and 0.98 using decision trees and random forests, respectively, which means that it was able to properly classify the Flooding attack in the 5G vehicular environment considered
Equipment users’ experiences of a manufacturer’s smart services
Purpose: The use of a manufacturers’ equipment and industrial services is dependent on the users’ readiness and capabilities. In a business-to-business context, different users may have different experiences with intelligent product features and related smart services, and the experiences need to be understood, when a manufacturer develops and delivers its industrial services. The goal of this study is to identify user experience patterns concerning intelligent product features and related smart services for industrial equipment. The focus is on the early phases of adopting the intelligent product features and related smart services. Design/Methodology/Approach: A qualitative case study was implemented with two customers of a machine manufacturer. Data were collected through interviews, and user experiences were analysed concerning intelligent features, services, and the service supplier. Findings: The cross-case analysis reveals that all users do not experience benefits from intelligent features and related smart services. Four different user experience patterns are reported: feature-centric, competence-centric, development-oriented, and decision-oriented. Originality/Value: The study adopts a users’ perspective to industrial services, thereby offering a more nuanced idea of customer experiences and potentially explaining why digital servitization proceeds slowly within customer firms.publishedVersionPeer reviewe
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
Towards a circular maritime industry : identifying strategy and technology solutions
Shipping is considered one of the most energy-efficient modes, considering the amount of cargo that can be carried. On the other hand, the circular economy approach is not well-established in the maritime industry, which currently lags behind different transport modes. The maritime industry needs scientific support to “close the loop”, minimise waste and increase the revenue stream. Therefore, this study aims to address a critical gap in the maritime industry by first showing the understanding of the stakeholders and identifying suitable strategy and technology solutions that can fit the characteristics of the maritime industry. Moreover, the potential benefits of these solutions have been demonstrated through high-speed marine engine remanufacturing. A cost-benefit analysis has shown that remanufactured engine acquisition cost is nearly half of the cost of a new engine with similar operating performance and operating cost. This study is a novel contribution to maritime industry stakeholders to demonstrate the advantages of circular end-of-life applications
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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