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This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal speeds as an operational tool for compliance with the International Maritime Organization (IMO) carbon intensity indicator (CII).

The TCE at different speeds have been calculated for four standard Capesize specifications: (1) standard Capesize with ecoelectronic engine; (2) standard Capesize with non-eco engine (3) standard Capesize vessel with an eco-electronic engine fitted with scrubber and (4) standard Capesize with non-eco engine and no scrubber fitted.

Calculations imply that in a highly inflationary bunker price context, the dollar per ton freight rates equilibrates at levels that may push optimal speeds below the speeds required for minimum CII compliance (C Rating) in the Australia–China trade. The highest deviation of optimal speeds from those required for minimum CII compliance is observed for non-eco standard Capesize vessels without scrubbers. Increased non-eco Capesize deployment would see optimal speeds structurally lower at levels that could offer CII ratings improvements.

While most of the studies have covered the use of speed as a tool to improve efficiency and emissions in the maritime sector, few have been identified in the literature to have examined the interplay between the commercial and operational performance in the dry bulk sector stemming from the freight market equilibrium. The originality of this paper lies in examining the above relation and the resulting optimal speed selection in the Capesize sector against mandatory environmental targets.

This study aims to focus on verifying the possibility of monitoring the condition of a turboprop engine using data recorded by on-board avionics Garmin G1000. This approach has potential benefits for operators without the need to invest in specialised equipment. The main focus was on the inter-turbine temperature (ITT). An unexpected increase in temperature above the usual value may indicate an issue with the engine. The problem lies in the detection of small deviations when the absolute value of the ITT is affected by several external variables.

The ITT is monitored by engine sensors and stored by avionics 1× per second onto an SD card. This process generates large amount of data that needs to be processed. Therefore, an algorithm was created to detect the steady states of the engine parameters. The ITT value also depends on the flight parameters and surrounding environment. As a solution to these effects, the division of data into clusters that represent the usual flight profiles was tested. This ensures a comparison at comparable ambient pressures. The dominant environmental influence then remain at the ambient air temperature (OAT). Three OAT compensation methods were tested in this study. Compensation for the standard atmosphere, compensation for the standard temperature of the given flight level and compensation for the speed of the generator, where the regression analysis proved the dependence between the ambient temperature and the speed of the generator.

The influence of ambient temperature on the corrected ITT values is noticeable. The best method for correcting the OAT appears to be the use of compensation through the revolutions of the compressor turbine NG. The speed of the generator depends on several parameters, and can refine the corrected ITT value. During the long-term follow-up, the ITT differences (delta values) were within the expected range. The tested data did not include the behaviour of the engine with a malfunction or other damage that would clearly verify this approach. Therefore, the engine monitoring will continue.

This study presents a possible approach to turbine engine condition monitoring using limited on board avionic data. These findings can support the development of an engine condition monitoring system with automatic abnormality detection and low operating costs.

This article represent a practical description of problems in monitoring the condition of a turboprop engine in an aircraft with variable flight profiles. The authors are not aware of a similar method that uses monitoring of engine parameters at defined flight levels. Described findings should limit the influence of ambient air pressure on engine parameters.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

The aviation industry has seen consistent growth over the past few decades. To maintain its sustainability and competitiveness, it is important to have a comprehensive understanding of the environmental impacts across the entire life cycle of the industry, including materials, processes and resources; manufacturing and production; lifetime services; reuse; end-of-life; and recycling. One important component of aircraft engines, integral rotors known as Blisks, are made of high-value metallic alloys that require complex and resource-intensive manufacturing processes. The purpose of this paper is to assess the ecological and economical impacts generated through Blisk production and thereby identify significant ‘hot-spots’.

This paper focuses on the methodology and approach for conducting a full-scale Blisk life cycle assessment (LCA) based on ISO 14040/44. Unlike previous papers in the European Aerospace Science Network series, which focused on the first two stages of LCA, this publication delves into the “life cycle impact assessment” and “interpretation” stages, providing an overview of the life cycle inventory modeling, impact category selection and presenting preliminary LCA results for the Blisk manufacturing process chain.

The result shows that the milled titanium Blisk has a lower CO₂ footprint than the milled nickel Blisk, which is less than half of the global warming potential (GWP) of the milled nickel Blisk. A main contributor to GWP arises from raw material production. However, no recycling scenarios were included in the analysis, which will be the topic of further investigations.

The originality of this work lies in the detailed ecological assessment of the manufacturing for complex engine components and the derivation of hot spots as well as potential improvements in terms of eco-footprint reduction throughout the products cradle-to-gate cycle. The LCA results serve as a basis for future approaches of process chain optimisation, use of “greener” materials and individual process improvements.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

This paper focusses on the aftermath of disruptions and the importance of the two largest canals (Suez and Panama), commenting on how during the pandemic the canal fees were lowered. Considering the ongoing efforts to decarbonize shipping, some of the ongoing disruptions will help reach these objectives faster.

Following a literature review of route choice in shipping, and a presentation of significant disruptions in recent years, the author deploys a simplified fuel consumption model and conduct case study analyses to compare different routes environmentally and economically.

The results explain why at times of low fuel prices as in 2020, canals provided discounts to entice ship operators to keep transiting these, instead of opting for longer routes. Considering the ongoing repercussions of the pandemic in supply chains, as well as the potential introduction of market-based measures in shipping, the value of transiting canals will be much higher in the coming years.

The main limitation in this work is that the author used the publicly available information on canal tolls, for the different ship types examined.

The envisioned model is simple, and it can be readily used for any ship and route (port to port) combination available, if ship data are available to researchers.

It is possible that canal tolls will increase, to account for the additional environmental benefits brought to ship operators.

The methodology is simple and transferable, and the author proposes several interesting research questions for follow-up work.

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

Given that online search visibility is influenced by various determinants, and that influence may vary across industries, this study aims in investigating the major predictors of online search visibility in the context of blood banks.

To formalize the online visibility, the authors have found theoretical foundations in activity theory, while to quantify online visiblity the authors have used the search engine optimization (SEO) Index, ranking, and a number of visitors. The examined model includes ten hypotheses and was tested on data from 57 blood banks.

Results challenge shallow domain knowledge. The major predictors of online search visibility are Alternative Text Attribute (ALT) text, backlinks, robots, domain authority (DA) and bounce rate (BR). The issues are related to the number of backlinks, social score, and DA. Polarized utilization of SEO techniques is evident.

The methodology can be used to analyze the online search visibility of other industries or similar not-for-profit organizations. Findings in terms of individual predictors can be useful for marketers to better manage online search visibility.

The acute blood donation problems may be to a certain degree level as the information flow between donors and blood banks will be facilitated.

This is the first study to analyze the blood bank context. The results provide invaluable inputs to marketers, managers, and policymakers.