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A growing body of evidence points to the influence of location and allocation decisions on the structure of healthcare networks. The authors introduced a three-level hierarchical facility location model to minimize travel time in the healthcare system under uncertainty.

Most healthcare networks are hierarchical and, as a result, the linkage between their levels makes it difficult to specify the location of the facilities. In this article, the authors present a hybrid approach according to data envelopment analysis and robust programming to design a healthcare network. In the first phase, the efficiency of each potential location is calculated based on the non-radial range-adjusted measure considering desirable and undesirable outputs based on a number of criteria such as the target area's population, proximity to earthquake faults, quality of urban life, urban decrepitude, etc. The locations deemed suitable are then used as candidate locations in the mathematical model. In the second phase, based on the proposed robust optimization model, called light robustness, the location and allocation decisions are adopted.

The developed model is evaluated using an actual-world case study in District 1 of Tehran, Iran and relevant results and different sensitivity analyses were presented as well. When the percentage of referral parameters changes, the value of the robust model's objective function increases.

The contributions of this article are listed as follows: Considering desirable and undesirable criteria to selecting candidate locations, providing a robust programming model for building a service network and applying the developed model to an actual-world case study.

Modular construction is widely used for residential buildings of 4 to 8 storeys. In the context of open building systems, modular construction provides a systemised approach to design in which the benefits of prefabrication are maximised. There is demand to extend this form of construction to more than 12 storeys for residential buildings. This paper presents a review of modular technologies, and describes load tests and analysis on light steel modular walls that are used to justify the use of light steel technology to support higher loads.

For taller modular buildings, the effect of installation and geometric inaccuracies must be taken into account and it is proposed that maximum out of verticality of a vertical group of modules is 50mm relative to ground datum. Using these geometric tolerances, the notional horizontal force used to evaluate stability of a group of modules should be taken as a minimum of 1% of the applied vertical load on the modules. Robustness to accidental load effects is important in all high-rise buildings and it is proposed that the tie force in the connections between modules should be taken as not less than 30% of the total vertical load applied to the module in both horizontal directions.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

In large-scale environments and unstructured scenarios, the accuracy and robustness of traditional light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) algorithms are reduced, and the algorithms might even be completely ineffective. To overcome these problems, this study aims to propose a 3D LiDAR SLAM method for ground-based mobile robots, which uses a 3D LiDAR fusion inertial measurement unit (IMU) to establish an environment map and realize real-time localization.

First, we use a normal distributions transform (NDT) algorithm based on a local map with a corresponding motion prediction model for point cloud registration in the front-end. Next, point cloud features are tightly coupled with IMU angle constraints, ground constraints and gravity constraints for graph-based optimization in the back-end. Subsequently, the cumulative error is reduced by adding loop closure detection.

The algorithm is tested using a public data set containing indoor and outdoor scenarios. The results confirm that the proposed algorithm has high accuracy and robustness.

To improve the accuracy and robustness of SLAM, this method proposed in the paper introduced the NDT algorithm in the front-end and designed ground constraints and gravity constraints in the back-end. The proposed method has a satisfactory performance when applied to ground-based mobile robots in complex environments experiments.

To improve robustness of a dairy supply chain (SC) against corruption, the purpose of this paper is to propose a systemic model of a corruption impacted dairy SC, exposing relationships among SC operations, risks and the impact of corruption.

Cases from the dairy industry in New Zealand (NZ) are used for thematic analysis of interview data collected from participants at senior levels of NZ dairy firms. Based on these and other inputs from literature, a systemic model is built subsequently.

Mitigating certain risks can significantly alleviate the impact of corruption, an external factor, on supply chain performance (SCP). The causal loop diagram (CLD) developed here brings out the modifying effect of corruption on dairy risks and SCP.

The illustration of the CLD helps business managers better understand the interactions among risk variables and explains the systemic reasons for SC vulnerability.

This is the first paper to construct a holistic system to comprehensively reveal the interactions of supply chain risks (SCRs) and the impact of corruption. Also, by utilizing SCR interactions, this study indicates a pathway to mitigate the negative effects of corruption through improving dairy SC robustness.

This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.

Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity.

The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz.

Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.

Aims at developing vision‐based algorithms to improve efficiency and quality in agricultural applications. Two case studies are analyzed dealing with the harvest of radicchio and the post‐harvest of fennel, respectively.

Presents two visual algorithms, which are called the radicchio visual localization (RVL) and fennel visual identification (FVI). The RVL serves as a detection system of radicchio plants in the field for a robotic harvester. The FVI provides information to an automated cutting device to remove the parts of fennel unfit for the market, i.e. root and leaves. Laboratory and field experiments are described to validate our approach and asses the performance of our visual modules.

Both the visual systems presented showed to be effective in experimental trials, computational efficient, accurate, and robust to noises and lighting variations. Computer vision could be successfully adopted in the intelligent and automated production of fresh market vegetables to improve quality and efficiency.

Provides guidance in the development of vision‐based algorithms for agricultural applications.

Describes visual algorithms based on intelligent morphological and color filters which lends themselves very well to agricultural applications and allow robustness and real‐time performance.

The light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift phenomenon and large accumulated error are inevitable when using SLAM. The purpose of this study is to alleviate the accumulated error and drift phenomenon in the process of mapping.

A novel light detection and ranging SLAM system is introduced based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies conditions of loop-closed.

The proposed algorithm exhibits competitiveness compared with current approaches in terms of the accumulated error and drift distance. Further, supplementary to the place recognition process that is usually performed for loop detection, the authors introduce a novel dynamic constraint that takes into account the change in the direction of the robot throughout the total path trajectory between corresponding frames, which contributes to avoiding potential misidentifications and improving the efficiency.

The proposed system is based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies condition of loop-closed.

The purpose of this paper is to present the results of a survey study of the achievement of twenty‐first century skills in higher education.

The study employs a quantitative survey design.

The findings indicate that the basic scientific and technological skills of reading critically and writing persuasively as well as accessing and using information efficiently have been achieved to a great extent in the context of the study; whereas, mathematical and scientific skills and global awareness and cross‐cultural issues still need more attention. Variations in the level of mastery as well as gender differences in the achievement of certain skills clusters were also identified and discussed in light of the robustness of the theory of the “universal digital native”.

The results cannot be generalized into other contexts and the data were basically self‐reported and not corroborated by evidence from triangulated sources.

Effective dealing with the basic and technological skills should be continued; however, more attention should be given to the development of the skills in mathematics and the sciences. Likewise, the visual‐literacy skills and the levels of global awareness and cross‐cultural understanding and appreciation should be improved.

This exploratory study fills a knowledge gap and may set the stage for further research into the extent to which the twenty‐first century skills are being realized by institutions of higher learning given the scarcity or non‐existence of this research.

The purpose of this paper is to present a dual manipulator system for aloft hot-line assembly tasks of connection fittings in 110-kv intelligent substation, which is significant to the research on hot-line working robots.

This paper addresses the challenges of the task and presents a dual manipulator system which can overcome these challenges to realize the robotic assembly of connection fittings in narrow space without impacting the safe distance of both phase to phase and phase to ground. Two manipulators share a same global reference coordinate. The mission of Manipulator 1 is to position the fixed part of connection fittings and screw the bolts on it. Visual computing provides the approximately position for the end-effector of Manipulator 2, after which The Manipulator 2 carries the removable part of connection fittings to this position. Then, the assembly task could be completed with the posture of the Manipulator 2 adjusted following the guidance by force-position control.

The dual manipulator system can position the target under different illumination conditions and complete fast assembly of connect fittings in 110-kV substation. No strong arc discharge or surface erosion phenomenon has been observed.

This dual manipulator system will be particularly useful for the hot-line assembly of connection fittings in 110-kv intelligent substation, as well as some assembly tasks where uncertain target position and complex contact surface such as cylindrical hole is involved.

This study presents a dual manipulator system used by a field robot working in 110-kv intelligent substation. The system is able to achieve the connection fittings assembly task under energized simulation experimental system. Unlike other peg-in-hole assembly strategy, it does not require high stability of manipulator or plane contact surface around the hole.