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Transformational Leadership and Creativity
No full textCreativity in sport is increasingly recognised as a developmental resource to be nurtured in all participants. Yet existing literature on how coaches nurture creativity has largely centred on task-based drills and prescriptive instruction. This chapter shows how transformational leadership serves as a supplement to these task-based approaches to foster creativity. Drawing on Amabile’s Componential Theory of Creativity, creativity arises from the interaction of four key elements: sport-specific skills, flexible thinking, task motivation, and a supportive social environment. These elements are linked to the four I’s of transformational leadership (idealized influence, inspirational motivation, intellectual stimulation, individualized consideration) to show how everyday leadership behaviours create conditions that help athletes think and act in new ways. Building on research in transformational leadership and creativity, the chapter presents ten creativity-nurturing principles for coaches that translate theory into daily coaching practice. Each principle includes a brief description and a concrete example. Together, the ten principles form a practical approach that coaches can use in daily training to embrace risk-taking, idea-sharing, and fresh solutions. The chapter closes with applied notes on adapting the principles for different athletes and sport settings and outlines directions for future research, including exploring barriers and enablers to implementation
A Two-Stage Deep Learning Approach for Accurate Day-Ahead Electricity Price Forecasting
Full text linkParticipants in the energy market are at greater risk of making decisions due to the nonlinear and volatile characteristics of electricity prices. Accurate short-term electricity price forecasting (EPF) is essential to ensure improved resource allocation, grid stability and enable market participants to manage their decisions efficiently. This study proposes a novel two-stage forecasting framework for day-ahead EPF using time series decomposition methods and hybrid deep learning algorithms. In the first stage, features related to EPF at the next time step are predicted. In this stage, the highest-frequency component extracted via Empirical Mode Decomposition (EMD) is further decomposed using Variational Mode Decomposition (VMD) so as to better capture rapid fluctuations and improve the overall prediction accuracy. Moreover, a decentralized deep learning architecture is designed in which Gated Recurrent Unit (GRU) networks are employed for high-frequency components, while Long Short-term Memory (LSTM) networks are used for the remaining components. In the second stage, EPF is generated using a hybrid LSTM and GRU structure, which incorporates both features estimated in the first stage and historical electricity price data. Finally, hyperparameters of the deep learning models are optimized using Bayesian Optimization to enhance performance. To validate the proposed framework, real market data from the DK1 region of Denmark is used. The proposed hybrid prediction framework is evaluated against both machine learning methods and deep learning-based architectures. Experimental results demonstrate that the proposed method achieves approximately 27.15 % lower RMSE compared to traditional machine learning models, and around 28.24 % lower RMSE compared to LSTM-based models.<br/
Thermocatalytic ammonia synthesis beyond conventional Haber-Bosch: Principles, advances, challenges and opportunities
Full text linkTransforming ammonia (NH3) synthesis from the energy-intensive, fossil-fuel-dependent conventional Haber-Bosch (HB) process to a flexible, green hydrogen-based process is pivotal for decarbonization and enabling NH3 utilization in the energy sector. The conventional HB process, operating under high temperature and pressure, is incompatible with green hydrogen systems and economically unviable for downscaled NH3 production integrated with intermittent renewable energies . Therefore, developing alternatives capable of synthesizing NH3 under moderate conditions is crucial for achieving green NH3 production. This necessity has driven the development of a range of emerging technologies, including thermocatalytic, electrocatalytic, photocatalytic, and plasma-assisted processes, amongst which thermocatalysis stands out in terms of production rate, technology readiness, and economic feasibility, demonstrating the greatest potential for NH3 synthesis transformation. This review provides a comprehensive overview of advanced thermocatalytic NH3 synthesis beyond conventional HB process and the system integration with renewable sources. It highlights key limitations and advances in implementing new materials and auxiliary techniques, outlining the challenges and mitigation strategies for achieving high NH3 productivity under mild conditions. Alongside multiscale modeling studies, the review covers catalyst development, reactor intensification, process integration, and system evaluation, examining progress and conducting meta-analysis in reaction mechanisms, emerging separation technologies, and system integration. Scientific obstacles, economic analysis, and environmental impacts are thoroughly discussed, offering state-of-the-art insights into mild NH3 synthesis from fundamental research to practical applications. Additionally, recent industrial projects of green NH3 production are summarized, showcasing scalability and commercial viability. Finally, the remaining challenges and opportunities in advanced thermocatalytic NH3 synthesis are outlined, identifying future research frontiers.</p
Using simulations to help students visualize and intuit radio frequency and communications concepts
No full textMulti-material and thickness optimization of sandwich structures subject to failure criteria
No full textSandwich structures are essential for lightweight design, but their multi-material composition introduces complexities, particularly additional failure modes that must be addressed during design. Structural optimization provides an efficient means to manage these complexities and accelerate development of high-performance designs. Discrete Material and Thickness Optimization (DMTO) enables such optimization of general multi-material and multi-layered structures with varying thickness. The objective of this work is to extend the DMTO framework for sandwich structure design by incorporating sandwich failure criteria. The problems are parametrized using the Discrete Material and Direct Thickness Optimization (DMDTO), a variant of DMTO, allowing the layer thickness to vary independently in each sandwich face sheet and core. A sandwich failure analysis approach that includes key sandwich failure criteria is presented in this context, particularly shear crimping and face wrinkling criteria — novel additions within multi-material and thickness optimization. These criteria are formulated to allow utilizing efficient gradient-based solvers with adjoint design sensitivity analysis to compute problem gradients. Several numerical examples are solved to demonstrate the approach, including a simplified wind turbine blade main spar that highlights the potential for industrial application of the approach
Optimal data-driven predictive control for transcritical CO2 refrigeration systems with application to supermarket
No full textDenne undersøgelse præsenterer et optimalt datadrevet prædiktivt styringsrammeværk (DDPC) til transkritiske CO2-supermarkedskølesystemer, karakteriseret ved høj sammenkobling og ikke-lineær dynamik. Klassiske feedforward PID (FFPID) regulatorer er effektive til Multiple Input Multiple Output (MIMO) systemer med lav kobling, men har problemer i stærkt sammenkoblede systemer uden præcise modeller. For at overvinde disse begrænsninger og nødvendigheden af at have en præcis model i mere avancerede modelbaserede regulatorer foreslås en DDPC-tilgang, der anvender Singular Value Decomposition (SVD) til at eliminere behovet for regularisering og inkorporerer den målbare forstyrrelse i Hankel-matricerne for at opnå højere ydeevne inden for forstyrrelsesafvisning. Et centralt fokus i dette arbejde er på forstyrrelsesafvisning, hvilket er mere udfordrende end banesporing på grund af forstyrrelsernes pludselige karakter i modsætning til de mere jævne ændringer drevet af omgivelsestemperaturen. Rammens effektivitet demonstreres i to alvorlige scenarier. I det første tester en belastningsforøgelse på 45 % systemets evne til at stabilisere fordamperens lufttemperatur og gaskølertrykket og dermed overgå konventionelle metoder. Det andet scenarie, der involverer forstyrrelser fra en on/off-fordamper, fremhæver algoritmens robusthed i forhold til at opretholde suge- og gaskølertryk under krævende forhold. Resultaterne understreger det offset-fri DDPC-rammeværks evne til at forbedre energieffektivitet, robusthed og praktisk anvendelighed, hvilket giver et pålideligt alternativ til håndtering af den komplekse dynamik i transkritiske CO2-systemer.This study presents an optimal data-driven predictive control (DDPC) framework for transcritical CO2 supermarket refrigeration systems, characterized by high interconnection and nonlinear dynamics. Classical feedforward PID (FFPID) controllers are effective for Multiple Input Multiple Output (MIMO) systems with low coupling but struggle in highly interconnected systems without precise models. To overcome these limitations and the necessity of having a precise model in more advanced model-based controllers, a DDPC approach is proposed that utilizes Singular Value Decomposition (SVD) to eliminate the need for regularization and incorporates the measurable disturbance in the Hankel matrices to achieve higher performance in disturbance rejection. A key focus of this work is on disturbance rejection, which is more challenging than trajectory tracking due to the sudden nature of disturbances, as opposed to the smoother changes driven by ambient temperature. The framework’s effectiveness is demonstrated in two severe scenarios. In the first, a 45 % load increase tests the system’s ability to stabilize evaporator air temperature and gas cooler pressure, outperforming conventional methods. The second scenario, involving disturbances from an on/off evaporator, highlights the algorithm’s robustness in maintaining suction and gas cooler pressures under demanding conditions. The results underscore the offset-free DDPC framework’s capability to enhance energy efficiency, robustness, and practicality, providing a reliable alternative for managing the complex dynamics of transcritical CO2 systems
Numerical investigation of sorption-enhanced ammonia synthesis for hydrogen storage: CFD modeling, experimental validation, and scale-up
Full text linkAmmonia (NH3) is increasingly recognized as a clean hydrogen (H2) carrier due to its high H2 content, carbon neutrality, and ease of storage and transport. Realizing its potential as an energy vector requires synthesis technologies that operate under mild conditions and are compatible with water electrolysis. This study develops a comprehensive computational fluid dynamics (CFD) model of sorption-enhanced NH3 synthesis with alternating catalyst and sorbent layers, validated against experimental data, to elucidate the in-situ sorption effects. The model is subsequently employed to scale up the process and to optimize the operation conditions of a pilot-scale reactor. Results indicate a 66.8% increase in H2 conversion with a six-layer configuration, while a 60-layer pilot reactor achieves over 90% conversion and 99.5% NH3 capture in single-pass operation. These findings demonstrate the scalability and potential of sorption-enhanced NH3 synthesis, providing a moderate and efficient pathway for H2 storage and transport.</p
Clinical practice guideline for nonpharmacological prevention and management of patient agitation in the adult intensive care unit (CALM ICU)
Full text linkBackground Agitation affects 32–70% of adult patients in the intensive care unit (ICU) and is associated with disruption of life-saving treatment, prolonged hospitalisation, and psychological trauma. While nonpharmacological interventions are increasingly encouraged to reduce the reliance on sedatives, existing guidelines predominantly focus on pharmacological management. This contributes to inconsistent practices and underutilisation of effective person-centred nonpharmacological alternatives. Objective The objective of this study was to develop evidence-based recommendations for the nonpharmacological prevention and management of patient agitation in the adult ICU. Method The clinical practice guideline for non-pharmacological prevention and management of patient agitation in the adult ICU (CALM ICU) was developed following the Australian National Health and Medical Research Council Guidelines for Guidelines and the Danish Health Authority’s manual on guideline development. The process included stakeholder consultation with ICU clinicians, researchers, patients, and family members on the initial scope of the guideline, a systematic review and an umbrella review, a three-round modified Delphi study involving 114 participants from Denmark and Australia, and finally, stakeholder and methodological reviews of the draft guideline. The Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess the certainty of the evidence. Results The guideline offers 14 recommendations, including four conditional recommendations and 10 consensus recommendations. These address early and systematic assessment, identifying and treating underlying causes of agitation, prioritising nonpharmacological interventions, and using multicomponent interventions. The recommendations also include using de-escalation strategies, reorientation, promoting sleep, adjusting stimuli, supporting comfort and relaxation, encouraging mobilisation, and involving family members. The guideline also includes two additional recommendations highlighting the importance of fundamental person-centred care and organisational support for ICU staff. Conclusions The CALM ICU guideline provides the best available evidence for reducing patient agitation through nonpharmacological strategies. It should be integrated into standard ICU care and serve as a foundation for education and practice. Further research is needed to strengthen the evidence base and explore implementation in diverse ICU settings. This guideline has been endorsed by the Australian College of Critical Care Nurses
