The application of data analytics to product usage data has the potential to enhance engineering and decision-making in product planning. To achieve this effectively for cyber-physical systems (CPS), it is necessary to possess specialized expertise in technical products, innovation processes, and data analytics. An understanding of the process from domain knowledge to data analysis is of critical importance for the successful completion of projects, even for those without expertise in these areas. In this paper, we set out the foundation for a toolbox for data analytics, which will enable the creation of domain-specific pipelines for product planning. The toolbox includes a morphological box that covers the necessary pipeline components, based on a thorough analysis of literature and practitioner surveys. This comprehensive overview is unique. The toolbox based on it promises to support and enable domain experts and citizen data scientists, enhancing efficiency in product design, speeding up time to market, and shortening innovation cycles.

The variability in ground manoeuvre occurrences for aircraft landing gear is intrinsically linked to the airport geometries served by aircraft in-service and consequently, the cyclic loads that landing gear carry are driven by the route network and characteristics of aircraft operators. Currently, assumptions must be made when deriving fatigue load spectra for aircraft landing gear, which may fail to capture the operator characteristics, potentially leading to design conservatism. This paper presents the enhanced characterisation of ground turning manoeuvres within the Automatic Dependent Surveillance-Broadcast (ADS-B) trajectories for six narrow-body aircraft across a full-service carrier (FSC) and a low-cost carrier (LCC) fleet. The methodology presented within this paper employs ADS-B latitude and longitude information to overcome limitations of previous approaches, increasing the rate of correct manoeuvre identification within ADS-B trajectories to 77% of flights from the 50% rate achieved previously. When characterising the ground manoeuvres across 3,000 flights, significant differences in manoeuvre occurrences were observed between individual aircraft within the LCC fleet and between the FSC and LCC fleets. The occurrence of tight and pivot turns were shown to vary across the six aircraft with six and eight fatigue-critical turns being performed by the FSC and LCC fleet for every 10 flights performed. In addition, it was observed that the direction of fatigue critical turns is biased in specific directions, suggesting that individual main landing gear assemblies will accumulate fatigue damage at an increased rate, leading to greater justification for operator-specific spectra and structural health monitoring of aircraft landing gear.

This paper examines the potential role of network analysis in understanding the powerful elites that pose a significant threat to peace and state-building within post-conflict contexts. This paper makes a threefold contribution. First, it identifies a caveat in the scholarship surrounding international interventions, shedding light on shortcomings in their design and implementation strategies, and elucidating the influence these elites wield in the political and economic realms. Next, it delineates the essentials of the network analysis approach, addressing the information and data requirements and limitations inherent in its application in conflict environments. Finally, the paper provides valuable insights gleaned from the international operation in Guatemala known as the International Commission for Impunity in Guatemala, which specifically targeted illicit networks. The argument asserts that network analysis functions as a dual-purpose tool—serving as both a descriptive instrument to reveal, identify, and address the root causes of conflict and a predictive tool to enhance peace agreement implementation and improve decision-making. Simultaneously, it underscores the challenge of data analysis and translating network interventions into tangible real-life consequences for long-lasting results.

The potential of artificial intelligence (AI) in water management is widely recognised by research and practice communities alike, with an increasing number of applications showed tackling water supply, stormwater and wastewater management challenges. However, there is a critical knowledge gap in understanding the fundamental role of AI in the development of urban water infrastructure (UWI). This review aimed to provide an analysis of how AI could be aligned to support the future development of UWI systems. Four types of AI analytics – descriptive, diagnostic, predictive and prescriptive – are discussed and linked to the improvement in the performance of UWI systems from three categories: reliability, resilience and sustainability. It is envisioned that AI technology will play a pivotal role in UWI transitioning to the future through underpinning the five development pathways: decentralisation, circular economy, greening, decarbonisation and automation. The barriers in improving AI adoption in the real world are also highlighted from four dimensions: cyber-physical infrastructure, institutional governance, social-economic systems and technological development in wider society. Embedding AI in the development pathways and tackling the barriers can ensure that AI-empowered systems are deployed in an equitable and responsible way to improve the resilience and sustainability of future UWI systems.

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.

Landing gear are exposed to cyclic loads from the ground manoeuvres that aircraft perform in-service. Variability is observed in the loading magnitude associated with ground manoeuvres, along with the per-flight variability in ground manoeuvre occurrence and sequencing. Whilst loading magnitude variability has been widely characterised, significant assumptions are required regarding manoeuvre occurrence and sequencing when constructing landing gear load spectra for fatigue design. These assumptions are required due to the limited availability of data concerning ground manoeuvre occurrence and sequencing relating to aircraft in-service and require validation to facilitate the design of more efficient components. ‘Big-Data’ approaches, employing Automatic Dependent Surveillance-Broadcast (ADS-B) transponder data, enable aircraft ground tracks to be identified. This paper presents a methodology to characterise the variability in ground manoeuvre occurrence and sequencing using ADS-B data sourced from Flightradar24® for a wide-body aircraft fleet. Using statistics generated for the fleet, it was identified that significant variability exists in the occurrence and sequencing of turning and braking manoeuvres. The statistics also validate existing assumptions, including that the proportional share of left and right turning manoeuvres is equal. Finally, this paper discusses the utility of ADS-B datasets for constructing landing gear load spectra and monitoring of landing gear in-service.

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V’s) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V’s) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.