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A simple design method for concrete fuse blocks at small dam spillways
Full text linkConcrete fuse blocks are a promising solution for enhancing small dam safety while increasing their storage capacity. Developed by the NGO HydroCoop, this technology addresses critical needs in regions where small dams face significant challenges. While the development of a practical and safe design method is required to facilitate the large scale implementation of fuse blocks, such a method is currently lacking. Considering systematic experimental tests at the laboratory scale, an analytical equation to predict fuse block tilting head is developed and validated. It is then used to define a standard block geometry. A simple table providing, without calculation, a safe design of fuse blocks for every dam height configuration is finally derived
Quality management system auditing and ISO 19011: Fundamentals for future standardization of the method
Full text linkAuditing is used to assess whether the requirements laid down in standards are being met. This paper studies the process of auditing quality management systems (QMS) to enhance our understanding of the process and develop a systematic approach. We focus on the information gathering and judgement parts of it. We compare the scarce literature about QMS auditing with the management system auditing standards ISO 19011 and ISO/IEC 17021-1. We incorporate insights from philosophical literature to develop a process model regarding the main steps of reliable auditing: observing reality; recalling memory and understanding the observed reality; contemplating and understanding abstract objects; expressing what is understood; judging: comparing what is understood and concluding about fulfilment of a requirement; and expressing the judgement. The methods described in these two standards are insufficient. Future researchers can use our model in developing theory on conformity assessment. The International Organization for Standardization (ISO) may use it to improve its conformity assessment standards
Aircraft Takeoff Weight Estimation: The EUROCONTROL PRC 2024 Data Challenge
Full text linkEUROCONTROL\u27s Performance Review Commission launched the 2024 PRC Data Challenge in July 2024 with the aim of engaging with data scientists and aviation enthusiasts for the development of an open model to estimate an aircraft\u27s take-off weight. The dataset for the challenge represents a unique instance of otherwise difficult-to-obtain flight information and could be reused for educational purposes or to further improve the outcome of the challenge.
Water governance and immigrants in Western democracies: A systematic review
Full text linkImmigrants in western democracies are becoming an important social and demographic group. The extent to which water governance processes and structures are positioned to create space for engagement for these newcomers is not well-understood. We employ a systematic review of the literature to determine the extent to which participative (including collaborative) water governance approaches incorporate voices from immigrant communities. We conduct a systematic search of the relevant literature on participatory water governance over the five-year period 2015-2019 to assess the nature of participation by immigrants in water governance. Results from review of articles that directly focus on participatory to water governance indicate that the water governance research community has been slow to recognize distinctive immigrant voices in research. We discuss how such lack of attention is closely tied with issues of justice and fairness as well as its implications for effectiveness of policies aimed water sustainability
Optimizing Aircraft Operations in Case of Increasing Demand for Limited Airport Capacity
Full text linkThe purpose of this paper is to minimize the total difference between the requested and assigned departure time of aircraft to enhance the efficiency of using limited airport capacity. The mathematical model was formed by employing mixed integer linear programming. The parameters, decision variables, and constraints were defined to cover the problem. The baseline and alternative scenarios were compared to present the improved results. Sensitivity analysis was performed to test the ability of the model with different parameters. Also, in order to test the mathematical model, distinct from both the baseline and alternative scenarios, CHQ airport was based by using its number of parking positions and taxi-in/out durations in the sensitivity analysis. The proposed model reduced the total difference by 20.78% for 30 aircraft for 5 parking positions in the alternative scenario. The results showed that it may well serve to improve the imbalance regarding operational conditions
Combining Machine Learning Models to Improve Estimated Time of Arrival Predictions
Full text linkAll aviation stakeholders require accurate estimated times of arrival in order to run flight operations as efficiently as possible. The time of arrival, however, is difficult to predict because it is affected by the uncertainties of the previous flight phases, with take-off time variability being the most significant contributor. At present, estimated time of arrival predictions are computed by the Enhanced Traffic Flow Management System, which collects data from a variety of sources to provide the best estimate throughout the entire duration of the flight. This paper introduces a novel approach that leverages existing machine learning models to enhance the accuracy of estimated time of arrival predictions, also during the pre-departure phase. More specifically, the first model (Knock-on) anticipates rotational reactionary delays arising from unrealistic available turn-around times; the second model (FADE) forecasts the evolution of air traffic flow management delays for regulated flights; and the third model, AirborneTime, was trained to identify systematic discrepancies between reported and actual airborne times. Using a dataset comprised of historical traffic and meteorological data collected during one year, this paper presents a comprehensive evaluation of this ensemble of models, referred to as PETA, against the current predictions across various time horizons, ranging from 6 hours before departure to the moment of take-off. The results indicate that the proposed solution surpasses the existing system in approximately two-thirds of the predictions. When the proposed solution performs better, the average and median improvements are 14 minutes and 7 minutes, respectively. However, when it underperforms, the average and median deteriorations are 7 minutes and 4 minutes, respectively. The optimal time frame appears to be between 2 and 6 hours before the departure time. This quantitative data is supported by feedback from European airlines, air navigation service providers and airports who used PETA in a live trial
Unweaving the Technique: Embroidering Autonomous Landscapes
Full text linkThrough a critical experience of reconstruction at the Isthmus of Tehuantepec, Oaxaca, Mexico,we present various ontological components that manifest as spe-cific cosmotechnics of this territory. These cosmotechni-cal manifestations enunciate singularity, resistance and emancipation.
Placemaking and Blue Green Infrastructure for Liveable, Resilient Places: Insights from Dundee, Scotland
Full text linkClimate change means that urban areas are experiencing more extreme weather events. Although effective, grey infrastructure solutions to flooding have been criticised for harming the environment, having a negative impact on place and not delivering social and wellbeing benefits. There is increasing interest in using natural elements, such as rain gardens and green roofs, in combination with a placemaking approach to facilitate solutions to flooding that deliver multiple benefits. However, the means to achieve this is poorly understood.
This paper contributes to the limited knowledge base on how placemaking and adaptation measures can be integrated in urban areas to meet the needs of communities. This was achieved through research undertaken in the Dundee local authority area. This area faces threats from groundwater, coastal and surface water flooding and a range of socio-economic challenges. Findings from 24 semi-structured interviews with practitioners and community group members (CGMs) suggest that while placemaking and blue green infrastructure (BGI) can deliver multiple benefits, the realisation of these can be hampered by a range of obstacles. For example, a lack of clear consensus on who is responsible for maintenance and a preference for grey infrastructure solutions. Practical guidance is provided to help overcome the obstacles identified to enhance flood resilient and liveable places. This guidance will be particularly relevant to colleagues in academia, planners, policy makers and a range of practitioners with a remit in flood risk management, climate change and water management and the communities they serve.
Ethical Compliance: This study received ethical approval on January 30 2024 from the School of Humanities, Social Sciences and Law Ethics Committee at the University of Dundee, approval number UoD-SHSL-EES-PGR-23/24-001
Open Machine Learning Models for Actual Takeoff Weight Prediction
Full text linkAircraft weight is a key input in flight trajectory prediction and environmental impact assessment tools. However, the lack of openly available data regarding the actual aircraft weight throughout the flight requires the development of mass estimation approaches to be incorporated into these tools. This study uses large-scale open aviation data made available by Eurocontrol\u27s Performance Review Commission to develop an open-source machine learning model to predict commercial flights\u27 actual takeoff weight. The data combines detailed flight, trajectory, and meteorological information for 369,013 flights that transited through the European airspace in 2022. Several operational features are created to represent each flight\u27s horizontal and vertical profiles accurately. For model learning, we employ CatBoost, LightGBM, XGBoost, artificial neural networks, and an ensemble of these models, which were selected for their robust performance in structured data analysis and potential for high predictive accuracy. The models are evaluated based on their efficiency, accuracy, and applicability to real-world data. The best-performing model is found to predict the aircraft takeoff weights with a mean percentage of error of 1.73%
Sampling-Based Aircraft Path Planning with Soft Actor-Critic
Full text linkThis paper investigates the usage of reinforcement learning for a global path planning task in terminal airspace, specifically for training a policy that can generate paths from any given position in the airspace to the runway. To do this, the Soft Actor-Critic (SAC) algorithm is trained on a simplified version of the Dutch airspace and compared to the solutions generated by the Dijkstra algorithm for varying discretization resolutions. SAC, which uses a Gaussian distribution for the action policy, has previously been shown to be successful in other global planning tasks in continuous environments. However, evaluating the policy by following the mean of the learned distribution, which is the standard evaluation method, may yield suboptimal performance when dealing with complex cost functions that deviate from a normal distribution. To address this, the paper proposes and evaluates a sampling-based strategy, which generates an ensemble of paths by sampling from the learned policy distribution. These three methods: mean-based SAC, Dijkstra and sampling-based SAC, are then tested on a bi-criterion cost function which includes both fuel and noise emissions in varying ratios. It was found that Dijkstra outperforms mean-based SAC for all cost ratios at the best discretization resolution, regardless of the neural network architectures used. However, sampling-based SAC results in consistently lower costs than both Dijkstra and mean-based SAC, particularly for the more complex cost functions that have a higher focus on noise mitigation. These findings highlight some limitations in mean-based evaluation for distribution models and indicate potential performance benefits that can be obtained with better-tailored evaluation strategies.