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LAMPD: a combined lean and agile model for product development
No full textDespite apparent differences, Lean and Agile product development methodologies share significant commonalities. This research integrates Lean and Agile product development methodologies for hardware contexts through a unified lean and agile model for product development (LAMPD). We examined case study implementations of both approaches, explored their integration potential, developed a comprehensive model, and evaluated its potential outcomes through expert assessment. Working with Atlas Copco Henrob, we systematically harmonised Lean PD principles with Agile frameworks by mapping relationships between 12 Agile Principles and the 5-phase Set-Based Concurrent Engineering process, identifying synergies and resolving conflicts. The resulting model integrates stage-gate processes with Scrum ceremonies and Kanban visualisation techniques across three development phases. Expert evaluation with 17 R&D professionals revealed strong support for the model, with practitioners identifying nine benefit categories, including enhanced communication, better risk management, and improved knowledge capture. The LAMPD process model provides a structured method to enhance efficiency and adaptability in hardware development while challenging traditional methodology dichotomies.International Journal of Agile Systems and Managemen
Emergence of calling in the domain of creative work, and the role of context: the stories of manga artists
No full textDespite growing attention to calling and its benefits for workers and organizations, little is known regarding how calling emerges in the domain of creative work as a distinct type of nonstandard work, and how context shapes its emergence. To address this knowledge void, we offer a unique qualitative study of the career journey of Japanese manga artists who draw manga and view creative work as their calling. We show that manga artists' calling emerges through the gradual enactment of their existential passion for drawing manga, beginning in their formative years, and is intertwined with and mutually reinforced by multi-layered validationβe.g., social circle, professional, and continuous validation. In their career journey, we demonstrate that context operates dually, both constraining and enabling the emergence of calling. Based on these insights, we theorize a model and show manga artists' transition from a metaphorical βshellββsymbolizing an initial solitary existenceβtoward breaking free as their existential passion is enacted and their professional calling emerges. We finally contribute to the literature on calling by offering insights into creative work and foregrounding the crucial role of context in shaping the emerge of calling.Journal of Vocational Behavio
Fuel-rich ammonia catalytic combustion on gadolinium-doped ceria (GDC) supported copper oxide catalyst
No full textAmmonia combustion faces challenges such as high ignition temperature, low flame speed, and potential NOx emissions. Catalytic combustion offers an alternative solution to burn ammonia with high efficiency while minimizing NOx formation. This study aims to develop a fuel-rich, catalytically stabilized ammonia combustion and study the reactivity and NOx formation of a novel gadolinium-doped ceria (GDC) supported CuO catalyst (CuO/GDC). The CuO/GDC catalyst was synthesized using the wet impregnation method and characterized using physisorption, X-ray diffraction (XRD), scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), and Transmission Electron Microscopy (TEM). Its performance was tested in a fixed-bed reactor and compared to CuO/Ξ³-Al2O3, with an equivalence ratio (ER) of 1.5 and temperatures ranging from 100Β°C to 800Β°C. The reaction mechanism of ammonia combustion over CuO/GDC was investigated with X-ray photoelectron spectroscopy (XPS) analysis and microkinetic modeling integrated with Density Functional Theory calculations. The CuO/GDC catalyst demonstrated a high NH3 conversion of 100% with a great N2 selectivity of over 99% at 800Β°C and minimal NOx formation, which was attributed to the strong adsorption of N and O species on its surface and mitigation of nitrogen oxide formation. Apart from altering the overall adsorptive properties of the catalyst, the mixed ionic-electronic conductivity of GDC also facilitates oxygen ion transport on the catalyst surface, promoting redox-driven catalytic activity. These findings highlight the potential of the use of mixed conductor as an effective catalyst support for fuel-rich ammonia catalytic combustion.Engineering and Physical Sciences Research Council; EP/X03593X/1Combustion Science and Technolog
A decreasing horizon model predictive control for landing reusable launch vehicles
Get PDFA novel approach to model predictive control (MPC) with a decreasing horizon is analysed for guiding and controlling reusable launch vehicles (RLVs) during powered descent phases. Conventional MPC methods typically use receding horizons, where optimal control inputs are computed over fixed time intervals. However, when applied directly, these methods can cause a hovering-like behaviour, preventing the vehicle from reaching the landing platform, as the landing time is continually deferred at each iteration. The proposed solution addresses this problem by adjusting the prediction horizon dynamically, reducing its length over time. This dynamic adjustment is driven by a time-scaling factor and the time elapsed since the previous MPC iteration. Optimal control solutions are derived through convex optimization techniques. To evaluate the algorithmβs robustness against initial conditions, a Monte Carlo analysis is performed by varying initial position, velocity and mass. This method can also be used as a viable methodology for selecting tuning parameters for the MPC to ensure a successful and safe landing for a wide range of initial conditions.Aerospac
Enhancing wind power forecasting and ramp detection using long shortβterm memory networks and the swinging door algorithm
Get PDFAccurate prediction of shortβterm wind power ramps is essential for effective smart grid management. This study introduces the swinging door algorithm for ramp detection, which outperforms traditional methods by precisely identifying ramp events. Additionally, a long shortβterm memory (LSTM) network is evaluated against established models such as support vector machines, artificial neural networks, convex multiβtask feature learning, and random forest for wind power ramp forecasting. The LSTM model demonstrates superior performance, achieving the lowest weighted mean absolute percentage error of 8.36% and normalized root mean squared error of 0.60, alongside the highest Rβsquared (R2) value of 0.73, indicating strong predictive accuracy and correlation with observed data. Furthermore, the combined swinging door algorithmβLSTM framework improved ramp event detection by 15% compared to traditional methods, showcasing its robustness in capturing both mild and extreme ramp events. This research underlines LSTM's effectiveness in wind power forecasting, marking a notable advancement in prediction methodologies. By illustrating the strengths of LSTM and swinging door algorithm, the study contributes to the refinement of prediction models for smart grid applications, highlighting their potential to transform wind power ramp prediction and detection.IET Renewable Power Generatio
Leveraging machine learning and optimization models for enhanced seaport efficiency
Get PDFThis study provides an overview of the application of predictive and prescriptive analytics in seaport operations and explore the potential of integrating predictive outputs into prescriptive analytics to advance research in this field. A systematic review of 124 papers was performed to identify and classify key topics based on application areas, types of applications, and employed techniques. Our findings show a growing interest in developing either predictive or prescriptive analytics models to improve seaport operational efficiency. However, there is limited research combining predictive outputs with prescriptive analytics for data-driven decision-making. Additionally, the hybridization of machine learning and operations research techniques remains underexplored. One promising area is applying machine learning models, such as reinforcement learning, to solve optimization problems. Predictive maintenance and data-enabled operational control measures for port equipment and facilities are also highlighted as interesting future research areas.This work was supported by the Engineering and Physical Sciences Research Council UK [grant number EP/Y024605/1] and Department of Transport UK.Maritime Economics & Logistic
CRISPR-enabled sensors for rapid monitoring of environmental contaminants
Get PDFThere is increasing attention on the impacts of contaminants on environmental and human health. To better understand the potential threat to ecosystems and human health, biosensing has played an important role in monitoring contaminants and biomarkers. In the past decade, the integration of CRISPR-Cas systems with technologies like microfluidic devices and isothermal amplification methods has paved the way for developing advanced sensors for environmental surveillance. Here we discuss the recent progress of various CRISPR-Cas systems to develop new biosensing devices, ranging from the fundamental mechanisms to their practical applications. We present a comprehensive and critical overview on the current state-of-the-art of CRISPR-Cas-based sensing platforms, including for both nucleic acid and non-nucleic acid contaminants, as well as portable engineered systems for on-site detection. We also provide the prospects of CRISPR-Cas systems for next-generation environmental surveillance, together with emerging technologies such as data science and artificial intelligence.Royal Academy of Engineering, Natural Environment Research Council, Biotechnology and Biological Sciences Research Council, National Natural Science Foundation of China, Leverhulme TrustThe work is supported by UK Royal Academy of Engineering (FF\1920\1\36), UKRI BBSRC EBIC Engineering Biological Innovation Centre (BB/Y008332/1) and (BB/X012840/1), and UKRI BBSRC EBNet, National Key R&D Program of China (2023YFF1204500), "Pioneer" and "Leading Goose" R&D Program of Zhejiang (2024C03011). ZY thanks Leverhulme Trust Research Leadership Awards (RL-2022-041) and UKRI NERC Fellowship grant (NE/R013349/2).TrAC Trends in Analytical Chemistr
International interlaboratory study to normalize liquid chromatography-based mycotoxin retention times through implementation of a retention index system
Get PDFMonitoring for mycotoxins in food or feed matrices is necessary to ensure the safety and security of global food systems. Due to a lack of standardized methods and individual laboratory priorities, most institutions have developed their own methods for mycotoxin determinations. Given the diversity of mycotoxin chemical structures and physicochemical properties, searching databases, and comparing data between institutions is complicated. We previously introduced incorporating a retention index (RI) system into liquid chromatography mass spectrometry (LC-MS) based mycotoxin determinations. To validate this concept, we designed an interlaboratory study where each participating laboratory was sent N-alkylpyridinium-3-sulfonates (NAPS) RI standards, and 36 mycotoxin standards for analysis using their pre-optimized LC-MS methods. Data from 44 analytical methods were submitted from 24 laboratories representing various manufacturer platforms, LC columns, and mobile phase compositions. Mycotoxin retention times (tR) were converted to RI values based on their elution relative to the NAPS standards. Trichothecenes (deoxynivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol) showed tR consistency (Β± 20β50 RI units, 1β5 % median RI) regardless of mobile phase or type of chromatography column in this study. For the remaining mycotoxins tested, the RI values were strongly impacted by the mobile phase composition and column chemistry. The ability to predict tR was evaluated based on the median RI mycotoxin values and the NAPS tR. These values were corrected using Tanimoto coefficients to investigate whether structurally similar compounds could be used as anchors to further improve accuracy. This study demonstrated the power of employing an RI system for mycotoxin determinations, further enhancing the confidence of identifications.Genome Canada, FWF Austrian Science Fund, Agriculture and Agri-Food Canada, Ministry of Education, Universities and Research, National Research Council Canada, MitacsThis research was supported by the NRC (Biotoxin Metrology, Nova Scotia), the ALIFAR project (Italian Ministry of University, Dipartimenti di Eccellenza 2023β2027), Genome Canada Technology Development Grant and MITACS scholarship, with resources provided by the VetCore Facility (Mass Spectrometry) of the University of Veterinary Medicine Vienna.Moreover, this research was supported by the Austrian Science Fund (FWF, P33188), the Mass Spectrometry Centre of the Faculty of Chemistry and the Exposome Austria Research Infrastructure at the University of Vienna.Journal of Chromatography
Preliminary analysis and design of an optical space surveillance and tracking constellation for LEO coverage
Get PDFAccurately tracking space debris and operational satellites is the foundation of the long-term sustainability of space operations. To improve upon some of the inherent limitations of ground radars, a constellation of satellites carrying optical sensors for the surveillance of the Low Earth Orbit (LEO) region is analysed. This analysis aims to understand the performance drivers of such a system in terms of constellation geometry and provide a general methodology for the preliminary design of the system. First, a method for decoupling the design of the optical payload and the constellation geometry while retaining statistically significant results is shown. Using the resulting estimate for the maximum observable distance, an approximate method for computing the coverage of the system is proposed. The expected daily and yearly variation of coverage depending on its own dynamics and the position of the Sun is analysed, showing that it has a small impact on the design process. The dependence of the coverage on constellation parameters such as altitude, inclination and distribution of satellites is investigated through parametric analysis, retrieving an estimate for the Pareto front of the system. Building upon the previous results, a random search method is shown to be effective in finding a design point lying on the Pareto front that is robust to both random satellite loss and deployment strategy. Finally, a reduced budget architecture is proposed to achieve acceptable performance while using only a few tens of satellites. The resulting work answers the problems of estimating and optimising the performance of a distributed system for space-based surveillance of the LEO region, a stepping stone for future costβbenefit analyses for the enhancement of space surveillance networks.Acta Astronautic
Multi-criteria decision making in evaluating digital retrofitting solutions: utilising AHP and TOPSIS
Get PDFIn an era of digital transformation, evaluating effective strategies for upgrading manufacturing systems is crucial to maintaining competitiveness. Digital retrofitting has become a strategic approach integrating new digital technologies into legacy systems to share data and align with Industry 4.0 principles. However, various techniques and criteria exist for implementing digital retrofitting. Despite its importance, there is a notable lack of studies assessing these retrofitting approaches using multi-criteria decision making (MCDM) methodologies. This study addresses this gap by employing two MCDM techniques: the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS). It assesses three digital retrofitting alternatives, starter kit solutions, embedded gateway solutions, and IoT hardware-based solutions, against ten critical criteria. These criteria were weighted through pairwise comparison analysis based on a survey of twelve industry practitioners to reflect industry preferences. The aim is to determine the most effective digital retrofitting approach to aid manufacturers in transitioning to Industry 4.0. This study addresses the complexities of managing conflicting criteria in digital transformation. Moreover, the results contribute to decision-making methodologies by demonstrating their practical applications, thus guiding manufacturers through the intricate landscape of digital retrofitting.12th CIRP Global Web Conference (CIRPe 2024)Procedia CIR