Time Cost Research Articles

A Lidar wind speed measurement system based on SSA-1DCNN algorithm

When traditional algorithms are used for calculating wind speed using, noise or distortions in the Lidar echo signals can compromise the accuracy and reliability, and developing denoising algorithms could increase time cost. To overcome these shortcomings, this paper designed an experimental platform and introduced the SSA-1DCNN algorithm. In the SSA-1DCNN algorithm, 1D-CNN is used for feature extraction, while SSA is employed to determine optimal parameters for 1D-CNN. Specifically, the experimental platform employs both an anemometer and a Lidar to sample wind at varying speeds. The anemometer's sampled values are used to label the Lidar's sampled values. Then, a dataset is established. Subsequently, the aforementioned dataset is used to train the wind speed measurement model. Using this model, we developed a networked Lidar-based wind speed measurement system, which is capable of transmitting wind speed data to remote locations. Compared to baseline algorithms, our algorithm demonstrates a distinct advantage.

Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study

Background: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery. EPI significantly impacts patient outcomes and healthcare costs, making its early detection vital. Objectives: To develop, validate, and clinically implement a machine-learning-based model for diagnosing EPI post-cardiac surgery, enhancing postoperative care. Methods: In this multi-center cohort study spanning 2020 to 2022, data from four medical centers involved 2001 participants. Of these, 1400 were used for trainingand 601 for validation. Several machines-learning algorithms, including XGBoost, random forest, support vector machines, least absolute shrinkage and selection operator, and single-layer neural networks, were applied to develop predictive models. These were compared against a traditional logistic regression model. The model with the highest area under the receiver operating characteristic curve (AUROC) was deemed optimal. Implemented across four centers since 1 January 2023, a retrospective real-world study assessed its clinical applicability. Among 400 patients with an estimated EPI risk above 10%, identified by the optimal model, 55 followed its antibiotic upgrade recommendations (DEICS group). The remaining 345 patients upgraded antibiotics empirically, with 55 in the control group, matched 1:1 with the DEICS group. Clinical utility was evaluated through antibiotic use density (AUD), hospital costs, and ICU stay duration. Results: The XGBoost model achieved the highest performance with an AUROC of 0.96 (95% CI: 0.93–0.98). The calibration curve exhibited strong agreement with Brier scores of 0.02. According to the XGBoost model, the DEICS group significantly demonstrated reduced AUD (P < 0.01) in the matched cohort, along with decreased ICU stay time (median: 5 vs. 6 days, P = 0.01) and hospital costs (median: ¥150 000 vs. median: ¥200 000, P = 0.01) in the EPI cohort. Conclusion: The successful implementation of the XGBoost model facilitates accurate EPI diagnosis, improves postoperative recovery, and lowers hospital costs.

Implementation of Additive Manufacturing Technologies for the Growth and Sustainability of Manufacturing SMEs in Saudi Arabia: An Exploratory Study

International distribution compels global value chain members to demand highly innovative and technological advancements in their products and processes. The members include small, medium, and large enterprises from both the manufacturing and service categories. Due to short product lifecycles and resource limitations, SMEs are unable to fulfill global value chain requirements in terms of new product development, consequently struggling to sustain manufacturing. In Saudi Arabia, more than 10% of SMEs currently belong to the manufacturing sector, and the majority continue to use conventional manufacturing technologies. Against this backdrop, and to strengthen existing SMEs and enhance their growth, the author conducted a comprehensive literature review on the applicability of additive manufacturing technologies in the manufacturing of various products. The review indicated that the implementation of additive manufacturing technologies faces several difficulties; thus, the author selected two manufacturing SMEs to obtain information on the necessary requirements to implement additive manufacturing technologies. The author interacted with executives from two manufacturing companies, one located in Sudair Industrial City and the other in the industrial center of Dammam. These interactions revealed that better financing, industry–academia collaborations, and stronger inter-company ties boost the adoption of additive manufacturing and support SME growth. Optimizing the use of resources, minimizing the use of materials during the production process through the use of 3D printing technologies, and optimizing time and labor costs help to enhance the economy, which is one of the main components of sustainability.

Dynamic trust management framework using blockchain for zero-trust-based authentication in BYOD environments

ABSTRACT With evolving work patterns, employees access company resources from various devices, making traditional management approaches obsolete. Ensuring secure access is crucial. We propose a blockchain-based trust management framework utilizing smart contracts for continuous verification and behavior analysis. Suspicious activity triggers dynamic access adjustments. Blockchain ensures immutability and traceability in authentication. Our design mitigates centralization risks and enhances granularity. Experimental results show minimal time costs during verification, maintaining efficiency despite additional security checks.

Solving Multi-Criteria Shortest Path by Optimization with Morphological Filters

The challenge of determining the shortest path within a multimodal transportation network involves identifying the most efficient travel route while considering various interconnected modes of transportation, such as roads, railways, and public transit. This problem becomes increasingly complex when numerous criteria and modes are involved, complicating the decision-making process. This study proposes a novel approach to computing the shortest path in multimodal networks, focusing on four modes of transportation: metro, trams, buses, and taxis. The optimization criteria include distance, travel time, and monetary cost. The proposed method utilizes a new metaheuristic called Optimization by Morphological Filters (OMF), inspired by image processing techniques. This approach was compared with the Genetic Algorithms (GA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Experiments were carried out using graph models of multimodal transport networks that closely resemble real-world scenarios varying in size. Furthermore, the proposed method was evaluated using a real network from the city of Lyon, France. The results demonstrate that the OMF approach performs well in terms of convergence to optimal solutions and computation time.

Multi-Target Firefighting Task Planning Strategy for Multiple UAVs Under Dynamic Forest Fire Environment

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios.

Influencing Factors' Analysis for the Performance of Parallel Evolutionary Test Case Generation for Web Applications

ABSTRACTEvolutionary test case generation plays a vital role in ensuring software quality and reliability. Since Web applications involve a large number of interactions between client and server, the dynamic evolutionary test case generation is very time‐consuming, which makes it difficult to apply in actual projects. Obviously, parallelization provides a feasible way to improve the efficiency and effectiveness of evolutionary test generation. In our previous research, the idea of parallelism has been introduced into the evolutionary test generation for Web applications. However, its performance is affected by many factors, such as migration scale, migration frequency, the number of browser processes and subpopulations, and so on. The analysis of influencing factors can guide enhancing the performance of evolutionary test generation. For this reason, this paper analyzes the factors that influence parallel evolutionary algorithms and how they affect the performance of test generation for Web applications. At the same time, different parallel evolutionary test generation methods are designed and implemented. Experiments are conducted on open‐source Web applications to generate test cases that meet the server‐side sensitive paths coverage criterion, providing guidance and suggestions for the parameter setting of parallel evolutionary test case generation for Web applications. The experimental results show that (1) compared with the global parallelization model, the evolutionary algorithm based on the parallel island model has a greater improvement in test case generation performance. In more detail, when generating test cases with the same server‐side sensitive paths coverage, the number of iterations required is reduced by 49.6%, and the time cost is reduced by 58.7%; (2) for the test case generation based on the parallel island model, if the migration scale is large, appropriately increasing the migration frequency can reduce its time cost; (3) if the number of subpopulations is fixed, appropriately increasing the number of browser processes can reduce the time cost of Web application test case evolution, but the number of browser processes should not be too large; otherwise, it may increase the time cost.

Climate effects on honey bees can be mitigated by beekeeping management in Kenya.

In recent decades, worldwide concerns about the health of honey bees motivated the development of surveys to monitor the colony losses, of which Sub-Saharan Africa has had limited representation. In the context of climate change, understanding how climate affects colony losses has become fundamental, yet literature on this subject is scarce. For the first time, we conducted a survey to estimate the livestock decrease of honey bee colonies in Kenya for the year 2021-2022 to explore the effects of environmental conditions, such as temperature and precipitation, on livestock decrease. We define "livestock decrease" from the beekeeper's perspective, including dead colonies but also, in the specific context of the tropics, the colonies that absconded from the apiary. A total of 589 beekeepers from a variety of areas participated in the survey. Kenyan beekeepers had an average of 36.6% livestock decrease in 2021-2022, with higher decreases during the dry and hot (31.9%) than during the wet and cold season (20.2%). We found that livestock decreases were more important with temperature for both dry and hot and wet and cold seasons. Interestingly, we found that precipitation mitigated temperature effects on livestock decrease for both seasons. Finally, we found that beekeepers practicing water supplementation had up to 10% less livestock decrease during the dry and hot season than those that did not, suggesting it to be a relevant adaptive strategy to mitigate livestock decrease. It is worth noting that beekeepers can renew their stock by trapping swarms, yet this represents a cost in time and baiting materials. Based on climate change projections, we predicted that annual and seasonal livestock decrease would remain in the same range at horizon 2050 and horizon 2100. These results pinpoint difficulties in maintaining livestock for beekeepers in Kenya and provide clues for strategies to pursue in the context of climate change.

Digital vs. conventional removable complete dentures: A retrospective study on clinical effectiveness and cost-efficiency in edentulous patients: Clinical effectiveness and cost-efficiency analysis of digital dentures.

This study aimed to compare the clinical effectiveness and cost-efficiency of conventional and digital prosthodontic protocols, with a specific focus on milled and 3D-printed digital removable complete dentures. This retrospective clinical study was conducted at the Removable Prosthodontics Department of the University of Siena. 60 patients were divided into two groups: 30 received conventional complete dentures, while the remaining 30 received digital complete dentures (15 milled and 15 3D-printed). Clinical outcomes were assessed using OHIP-14, bite force measurements, and masticatory performance tests before and six months after treatment. Treatment-related metrics, including chairside time, follow-up time, and laboratory costs, were recorded and compared across the groups. Statistical analysis was performed to determine the significance of differences between conventional and digital complete dentures, as well as between milled and 3D-printed digital complete dentures. The study revealed that digital complete dentures significantly reduced chairside time compared to conventional dentures, with average times of 154.31 ± 13.19 min for digital dentures and 218.00 ± 20.75 min for conventional dentures (p < 0.0001). Laboratory costs were found to be statistically lower for digital dentures, (€378.79 ± 137.46 vs. €459.15 ± 63.72, p = 0.0059) compared to conventional dentures. No statistically significant differences were observed in bite force or masticatory performance between the groups. OHIP-14 scores indicated slightly lower patient satisfaction with digital dentures, but the difference was not clinically significant. Digital removable dentures, including both milled and 3D-printed models, offer a practical and efficient alternative to conventional dentures, particularly in terms of reducing chairside time and laboratory costs. Digital removable dentures offer a practical and efficient alternative to conventional dentures, particularly in terms of reducing chairside time and costs. These simplified digital protocols could be applied in residential facilities or within national healthcare programs, improving access to prosthetic care for a larger number of patients.

Cortical Structure of left superior parietal cortex is associated with cognition and dual tasking: a cross-sectional preliminary study between mild cognitive impairment and healthy controls

BackgroundIndividuals with mild cognitive impairment (MCI) exhibit poorer performance in cognition and dual-task paradigm, while the related cortical thickness and surface area alterations remains unclear. Methods: Thirty participants with MCI and thirty healthy controls (HC) were recruited. Magnetic resonance imaging, cognitive assessments and dual-task Timed Up and Go test (DT-TUG) were performed to assess cerebral cortical thickness and surface area, cognitive functions, and dual-task cost (DTC) of the execution time in TUG. Spearman correlations were conducted to assess the relationships between the cognitive, TUG performance with the cortical morphological measures. Results: MCI participants performed worse in the Montreal cognitive assessment (MoCA), WAIS Digit Span, TMT and the modified Posner peripheral cuing task. Their execution time on the DT-TUG was also prolonged. WAIS Digit Span Backwards was correlated with DT-TUG in HC group. A significant between-group difference was observed in the surface area of the left SPC. The cortical thickness of this brain region was positively correlated with the total scores and attention subdomain of MoCA in HC group. The cortical thickness and the surface area were correlated with the time of DT-TUG in HC group only. Conclusions: Individuals with MCI demonstrated declines in both cognitive function and dual-task walking performance. This study provides further evidence of surface-based structural differences in the left SPC in individuals with and without MCI, and supports the role of the left SPC in cognition and dual-task walking.

TBM Performance Prediction Using Statistical and Machine Learning Algorithms: Case Study of Slurry TBM Used in Mumbai Metro Rail Tunnel Line–3 (UGC-03), India

ABSTRACT Estimating the performance of a tunnel boring machine (TBM) is vital for enhancing mining productivity and reducing tunnelling operations-related risks. Penetration rate (PR) is the most prominent TBM performance indicator, which majorly influences the project completion time, contractual activities, and overall cost of the project. Prediction of PR considering varying geotechnical parameters needs to be addressed. This study comprises 235 data points collected from a 3.572 km long twin-tunnel bored using two refurbished articulated single-shield slurry TBM, employed in a metro project in package-03, (UGC- 03) of Mumbai metro rail corporation line-3 (MMRCL-3), in Mumbai, India. The PR of the TBM is predicted using; uniaxial compressive strength (UCS), point load test (PLT (I50)), rock mass rating (RMR), rock quality designation (RQD), porosity (n), and Brazilian tensile strength (BTS) as the input parameters considering two statistical approaches i.e., multivariate linear regression (MVLR) and multivariate non-linear regression (MVNLR) and five machine learning (ML) algorithms i.e., random forests (RF), K-nearest neighbour (KNN), XG-Boost, support vector regression (SVR) and artificial neural networks (ANN). A 90: 10 train: test split was used on the 235 data points. The model developed using XG-Boost regression predicted the most accurate PR with R2 = 0.7877 / RMSE = 0.2113, where RF was the second-best model and closest to XG-Boost with R2 = 0.7679 / RMSE = 0.2200. The developed models indicate a considerable relationship between the input parameters and PR, and PR can be predicted accurately.

Active flow control for bluff body under high Reynolds number turbulent flow conditions using deep reinforcement learning

This study employs deep reinforcement learning for active flow control in a turbulent flow field of high Reynolds numbers at Re = 274 000. That is, an agent is trained to obtain a control strategy that can reduce the drag of a cylinder while also minimizing the oscillations of the lift. Probes are placed only around the surface of the cylinder, and a proximal policy optimization (PPO) agent controls nine zero-net mass flux jets on the downstream side of the cylinder. The trained PPO agent effectively reduces drag by 29% and decreases lift oscillations by 18% of amplitude, with the control effect demonstrating good repeatability. Control tests of this agent within the Reynolds number range of Re = 260 000 to 288 000 show that the agent's control strategy possesses a certain degree of robustness, with very similar drag reduction effects under different Reynolds numbers. Analysis using power spectral energy reveals that the agent learns specific flow frequencies in the flow field and effectively suppresses low-frequency, large-scale structures. Graphically visualizing the policy, combined with pressure, vorticity, and turbulent kinetic energy contours, reveals the mechanism by which jets achieve drag reduction by influencing reattachment vortices. This study successfully implements robust active flow control in realistically significant high Reynolds number turbulent flows, minimizing time costs (using two-dimensional geometrical models and turbulence models) and maximally considering the feasibility of future experimental implementation.

A qualitative study of the home-based time burdens faced by persons affected by GI cancer.

822 Background: As cancer care is increasingly delivered at home, more tasks and responsibilities fall on patients and informal care partners. This is especially relevant for people with GI cancer who often have specific home-based care needs such as feeding, ostomy care, 5-fluorouracil chemotherapy, and complex medication management. Home-based time costs are undercounted in current measures of time toxicity that only include care received in formal healthcare settings. Methods: We conducted semi-structured interviews with patients with GI cancer and their care partners at a single tertiary cancer center in MN, USA from March-Oct 2023. Interviews explored cancer care tasks at home, associated time burdens, and how they compare to in-facility care. We analyzed interview transcripts using a grounded theory approach to identify themes. Results: We included 33 individuals (11 aged &gt;60y, 21 female, 25 white) including 15 patients (8 colorectal cancer, 8 ECOG 0) and 18 care partners (15 lived with the patient, 7 working at least part-time). We identified 5 themes (Table). Conclusions: This foundational work characterizes time burdens of home-based cancer care for persons affected by GI cancer. In addition to providing the first input on incorporating home-based time burdens into objective measures of time toxicity, we highlight the complexity of assessing time burden which is context and role-dependent. Theme Subtheme Quote Unexpected home-based care is time burdensome Challenge with home as a site of healthcare, difficult transition from hospital to home ‘’What they would do at the clinic behind the scenes she was doing on my kitchen table.” (Patient) Other burdens compound time burdens Logistic/administrative, emotional, symptom “The time spent on administrative things and coordinating [...] are more stressful than going to the clinic.” (Patient) Time burdens evolve over the disease course and differentially impact patients and care partners Cancer care constantly changes, discomfort with some tasks at home, less support provided if more competent, home-based care allows for connection “They’re in a season of remission and [...] we want to live as normal a life as we can. And then there are other seasons where it’s the absolute opposite.” (Care partner) Factors influencing the choice of home-based care Need to manage family duties, supportive care partners matter, value connection with nurses, decreased travel “There is a different cost when you start doing things at home, especially when you have small children because if they’re witnessing it, that’s emotional.” (Patient) Home-based care is generally perceived as less time-burdensome than similar cares provided in-facility Different opinions on whether home-based care should be included in time toxicity measure “If you’re just talking about video visits I wouldn't consider that time toxic. But if I’m going [to the clinic], I’ve pretty much lost the whole day.” (Patient)

An Interpretation of the Cumulative Impact of FASTag on the Reduction of Environmental Pollution and Traffic Delays at Toll Booths: A Case Study

Traffic congestion has been identified as a significant contributor to the environmental degradation and elevated fuel consumption. The causes of traffic delays are multifaceted, with toll booths positioned on highways being a notable factor. This study aims to analyze the impact of toll booths on traffic congestion, with a particular focus on the case study of the highway toll booth in question. The analysis will include a comparison of the delay times before and after the implementation of the FASTag system. The study will also undertake an economic evaluation to assess the cost implications of delay. The findings of this study indicate that congestion is more pronounced in the presence of manual toll booths compared to automated systems. The present study uses the 'Chakiya Toll Plaza on National Highway 27A, located between Motihari and Muzaffarpur, as a case study. This toll booth has been initially operated with a manual toll collection system, but has since transitioned to the FASTag implementation. A comprehensive analysis encompasses both traffic volume and delay studies, with a focus on their environmental and economic ramifications. The analysis reveals that for each Passenger Car Unit (PCU), the environmental cost is 8.3 rupees and the fuel cost is 15.34 rupees. The idle time cost is calculated as 8.34 rupees. The overall cost of the manual toll collection delay, including all the aforementioned factors, is found to be 52.3 rupees. However, the implementation of FASTag significantly reduces this cost to. 8.20 rupees.

A multi-scale feature fusion network based on semi-channel attention for seismic phase picking

In the field of seismic data processing, deep learning technologies have been widely used for seismic phase picking. However, it is difficult to take full advantage of the features extracted at different stages in existing models. In this paper, a multi-scale feature fusion network was proposed for seismic phase picking to address this problem. In the stage of feature extraction, semi-channel attention is introduced. It improves the representation ability of the model by efficiently utilizing the feature information extracted from the encoder. In the stage of decoding, a channel compression module is designed to reduce the number of feature channels. It improves the receptive field of channels. Additionally, a multi-feature fusion module is presented to integrate features at multiple scales. It reduces the loss of useful information and improves the accuracy of phase picking. The effectiveness of our network is validated on Stanford earthquake dataset, where the picking errors for phase picking are 2 ms. The parameter of our network is only 52,100. Compared with earthquake transformer, it has 42.1% fewer time costs to process 12,656 test samples on Graphics Processing Unit.

The Temporal and Financial Costs of Trauma Activation Wait Times.

Level 1 Trauma Centers alert hospital staff in advance of a trauma patient's arrival to allow time for trauma team assembly and preparedness. Excess staff wait times may result in reduced trauma center productivity and efficiency. The objective of this study was to explore the wait time expended by various hospital staff in anticipation of trauma patient arrivals and calculate cost and adequacy of preparation time. This prospective observational study recorded a sample of wait times for trauma team staff members at an urban Level 1 Trauma Center for 12months. We observed 288 trauma activations in total. We constructed a dataset of notification alerts, patient arrival times, staff arrival, and wait times, along with a qualitative staff assessment of time to prepare for the trauma patient's arrival. We applied detailed salary data to quantify the financial cost of Trauma Center staff wait time. When staff waited for a trauma patient's arrival, average wait times ranged from 4.27 to 10.67 minutes. This cost $139791.65 during calendar year 2023 at our hospital. Staff had enough time to arrive at trauma incidents 99.1% of the time. In 4.2% of cases (n = 12), staff had no advance notification of an incoming trauma patient. We find that a longer duration between the issuance of alerts and the actual arrival of trauma patients represents a direct financial cost attributable to lost productivity in addition to indirect and cascading effects on operational efficiency and patient care.

Multiple needle insertion attempts: insights from a US survey of patients and nurses.

Needle insertion, for example for venepuncture or intravenous (IV) cannulation, is a common intervention experienced by patients. However, up to half of venepuncture and IV cannulation procedures fail on the first attempt, resulting in further attempts. Multiple needle insertion attempts can lead to pain and other complications for patients and can take up staff time and increase equipment costs for hospitals, so it is important to ensure that needle insertion practices and outcomes are optimised. This article reports the results of a US survey that aimed to provide an insight into patients' and nurses' perspectives on and experiences of needle insertion. The results show that multiple needle insertion attempts are common and that associated complications have a detrimental effect on patients' satisfaction with the procedure. The results also suggest that patients and nurses are interested in alternatives to needle insertion, such as ultrasound guided IV cannulation or needle-free blood collection. Nurse leaders should be aware of these technologies and consider the implementation of sustainable initiatives to evolve practice.

Hamiltonian learning for 300 trapped ion qubits with long-range couplings.

Quantum simulators with hundreds of qubits and engineerable Hamiltonians have the potential to explore quantum many-body models that are intractable for classical computers. However, learning the simulated Hamiltonian, a prerequisite for any quantitative applications of a quantum simulator, remains an outstanding challenge due to the fast increasing time cost with the qubit number and the lack of high-fidelity universal gate operations in the noisy intermediate-scale quantum era. Here, we demonstrate the Hamiltonian learning of a two-dimensional ion trap quantum simulator with 300 qubits. We use global manipulations and single-qubit-resolved state detection to efficiently learn the all-to-all-coupled Ising model Hamiltonian, with the required quantum resources scaling at most linearly with the qubit number. We further demonstrate a physically guided learning scheme with the quantum sample complexity independent of system sizes by carefully fitting the anharmonic trap potential. Our work paves the way for wide applications of large-scale ion trap quantum simulators.

Action research at the BBC: Interrogating artificial intelligence with journalists to generate actionable insights for the newsroom

In this paper, we provide reflections from an embedded action research project undertaken at the UK’s largest public service broadcaster, the BBC, over a three-year period. It was aimed at eliciting research insights about the role and understanding of AI in news production and also intervening to engender change in the newsroom. We surface the messy realities of conducting this work, including the challenges to funding such long-term and resource-intensive research and the difficulties of measuring impacts. We include practical guidance to demystify the process of action-oriented research that strives for targeted change in news contexts, highlighting the need for researchers to cost in time for translational work and the importance of having ‘critical friends’ to hold them to account. Ultimately, we emphasise the value of action research in journalism studies and particularly at the nexus of news and technology. We argue for approaches that retain a critical perspective whilst closing the gap between theory, critique, and practice.

Passive control of wind turbine tower using nonlinear magnetic vibration absorber and eddy current damping

Horizontal axis wind turbines (HAWTs) are the most widely used means of producing clean wind energy. Giant wind turbines with high production capacities are prone to degradation through environmental loads like wind and earthquake which might result in shorter machine life time or higher maintenance costs and consequently increased energy price. Vibration control methods have proven to be effective in ensuring structural integrity and general safety of wind turbine towers (WTTs) as well as the blades. There are challenges associated with employing typical linear dampers that need to be addressed such as limited available space in the nacelle, practical feasibility issues, and narrow effective frequency bandwidth of linear dampers. In this paper a new method for passive vibration control of wind turbines is proposed and a novel bi-directional design of the damping device is presented which is based on magnetic forces and eddy current damping. The proposed device is described in details and its parameters are analytically evaluated. The effect of the damper on nacelle displacement due to wind load is examined numerically and the results are compared to a typical tuned mass damper in terms of effectiveness and required space inside the nacelle. The study reveals that the magnetic damper is a competitive device for vibration control that could be as effective as a TMD with less required space.