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Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

This study examined the effects of adopting dairy feed technology bundles on the milk production of smallholder dairy farmers.

The study was based on Multinomial Endogenous Switching Regression (MESR) to estimate the effects of the adoption of three feed technology bundles on milk production using data collected from 518 dairy farm households in West Java, Indonesia.

The findings indicated that adopting technology bundles had positive and robust effects on milk production, with gradual positive effects between non-adoption and the adoption of different bundles of technologies.

This study focused on the association between the adoption of feed technology bundles and milk production. However, further analysis of the causal links between the adoption of feed technologies and milk production as well as the inclusion of other outcomes in the analysis, such as production costs and risk mitigation, are required.

Most of the literature on agricultural technology adoption focuses on the adoption of individual technologies, crop farming and conservation practices. Therefore, this study examined the effects of the adoption of dairy feed technology bundles.

As a result of space debris problem, it is necessary to deorbit uncontrollable satellites or repair them to extend mission duration to avoid sending a new satellite. The purpose of this paper is to develop a docking mechanism that can be easily customized for different missions, providing on-orbit servicing for nanosatellites.

This research outlines a system and mechanism design for the docking phase of on-orbit servicing to nanosatellites. The umbrella-inspired mechanism is designed with utmost simplicity to minimize the likelihood of failure. CAD, structural analyse and mechanism analyse tools are used for designing and analysing the system. To ensure that the design attains the desired durability, numerous iterations are conducted. A three-dimensional (3D)-printed prototype is generated for mechanism verification in laboratory conditions.

The aimed mechanism is generated successfully. A 3D-printed prototype is assembled to verify the mechanism. Also, an equation for customis the presented design is generated for different mission requirements in the future.

The usage of the proposed design can help increase the lifespan of satellites.

The primary innovation in this study is the development of a docking mechanism featuring a movable platform to provide servicing for nanosatellites in orbit. The mechanism presented can be displaced without initiating the unfolding process. This provides a customizable coupling distance for different mission requirements. Therefore, the presented mechanism can serve both different types of satellites and more than one satellite on-orbit with a cost-effective design. Also, the presented design can be easily customized to enable adaptation for the different mission requirements in the future.

With the advancements in electric vertical take-off and landing (eVTOL) aircraft technology such as batteries, mechanisms, motors, configurations and so on, designers and engineers are encouraged to create unique and unconventional configurations of eVTOL aircraft to provide better capabilities and higher efficiencies to compete in the market. The box fan-in-split-wing tiltrotor eVTOL aircraft is an innovative design that aims to address the aerodynamic inefficiencies such as propeller effects in cruise and engine mounts drag that existed in traditional eVTOL aircraft designs such as vectored thrust, rotorcraft, lift + cruise and multi-copter configurations. This paper aims to propose a multi-disciplinary design process to conceptually design the box fan-in-split-wing Tiltrotor eVTOL aircraft.

An unconventional methodology was used to design the UAM aircraft, and the following parameters are considered: capable of vertical take-off and landing, highly aerodynamic with a high lift-to-drag ratio, low Cd₀ modern and appealing, rechargeable or battery swappable and feature to minimise or negate propeller drag. A heavy emphasis on improving performance and weight based on aerodynamics was enforced during the conceptual design phase. MAPLA and XFOIL were used to identify the aerodynamic properties of the aircraft.

Upon determining the key parameters and the mission requirements and objectives, a list of possible VTOL configurations was derived from theoretical and existing designs. The fan in the wing/split wing was selected, as it could stow the propellers. A tiltrotor configuration was selected because of its ability to reduce the total number of lift props/motors, reducing powerplant weight and improving aerodynamic efficiency. For the propulsion configuration, a battery–motor configuration with a hexa-rotor layout was chosen because of its ability to complement the planform of the aircraft, providing redundant motors in case of failure and because of its reliability, efficiency and lack of emissions. Coupled with the fan-in-wing / split wing concept, the box wing seamlessly combines all chosen configurations.

The box fan-in-split-wing Tiltrotor eVTOL aircraft aims to address the aerodynamic inefficiencies of earlier designs such as propeller effects in cruise and engine mounts drag. The potential benefits of this aircraft, such as increased range, endurance and payload capacity, make it an exciting prospect in the field of Urban Air Mobility.

This paper aims to explore the extent and characteristics of Canadian university reporting of and policy regarding greenhouse gas emissions from air travel. It identifies current approaches’ details and limits and recommends improvements.

The study developed questions and considerations for analysing reporting and policy, reviewed university documents and webpages and contacted university staff.

Roughly 20% of Canadian universities report flight emissions. Figures vary by factors of over 100 even when normalized or expressed as a percentage of institutional emissions. Inter-university differences in data collection and emissions calculation practices shape reporting. Canadian university air travel emissions cannot be meaningfully compared. Few universities have flight emissions reduction policies; those that do leave relevant decisions to individuals. These approaches do not respond adequately to the emissions reduction challenge.

This study is the first comprehensive survey of university flight emissions reporting for any country. Its original framework highlights calculation’s complexities. It recommends standardizing reporting process information disclosure, reporting flight emissions as a range and faculty leadership of emissions reduction efforts.

The ability to measure cognitive load in the workplace provides several opportunities to improve workplace learning. In recent years, virtual reality (VR) has seen an increase in use for training and learning applications due to improvements in technology and reduced costs. This study aims to focus on the use of simulation task load index (SIM-TLX), a recently developed self-reported measure of cognitive load for virtual environments to measure cognitive load while undertaking tasks in different environments.

The authors conducted a within-subject design experiment involving 14 participants engaged in digit-recall n-back tasks (1-back and 2-back) in two VR environments: a neutral grey environment and a realistic industrial ozone facility. Cognitive load was then assessed using the SIM-TLX.

The findings revealed higher task difficulty for the 2-back task due to higher mental demand. Furthermore, a notable interaction emerged between cognitive load and different virtual environments.

This study relied solely on an n-back task and SIM-TLX self-report measure to assess cognitive load. Future studies should consider including ecologically valid tasks and physiological measurement tools such as eye-tracking to measure cognitive load.

Identifying cognitive workload sources during VR tasks, especially in complex work environments, is considered beneficial to the application of VR training aimed at improving workplace learning.

This study provides unique insights into measuring cognitive load from various sources as defined by the SIM-TLX sub-scales to investigate the impact of simulated workplace environments.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

This study aims to find the characteristics of supercritical airfoil in helicopter rotor blades for hovering phase using numerical analysis and the validation using experimental results.

Using numerical analysis in the forward phase of the helicopter, supercritical airfoil is compared with the conventional airfoil for the aerodynamic performance. The multiple reference frame method is used to produce the results for rotational analysis. A grid independence test was carried out, and validation was obtained using benchmark values from NASA data.

From the analysis results, a supercritical airfoil in hovering flight analysis proved that the NASA SC rotor produces 25% at 5°, 26% at 12° and 32% better thrust at 8° of collective pitch than the HH02 rotor. Helicopter performance parameters are also calculated based on momentum theory. Theoretical calculations prove that the NASA SC rotor is better than the HH02 rotor. The results of helicopter performance prove that the NASA SC rotor provides better aerodynamic efficiency than the HH02 rotor.

The novelty of the paper is it proved the aerodynamic performance of supercritical airfoil is performing better than the HH02 airfoil. The results are validated with the experimental values and theoretical calculations from the momentum theory.

This study analyzes the role of cooperative behavior in facing the risk of encouraging innovative agricultural production projects by small-scale farmers in the context of farmers in developing countries.

A within-subjects field experiment was conducted with small-scale Colombian panela cane farmers. The authors used the collected data to run the regression analyses.

The results suggest that when small farmers can follow cooperative behavior by joining a group and pooling resources to face risk, they are more willing to invest in a novel and profitable alternative, albeit riskier. However, the possibility of cooperating with a group to invest in a novel production project depends on its expected risk level.

These results will help develop agricultural policies for sustainable development. Establishing informal networks for small-scale farmers to deal with unpredictable risks may aid in developing innovative systems.

Agriculture is highly vulnerable to climatic impacts, which, combined with the inherent risk of innovation, may reduce small farmers' willingness to adopt innovation. Cooperation appears to be a mechanism for pooling resources and facing risk.

Research has focused on experimentally testing the effect of cooperative behavior when facing risk. The authors contribute to the literature by demonstrating the impact of the ability of small-scale farmers in rural areas to collectively manage risk on investment in innovative projects.