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There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

Various designs of corrugated webs include trapezoidal, sinusoidal, triangular and rectangular profiles. The increasing use of curved plates has prompted the creation of I-sections made of steel with a corrugated web design. This study aims to examine the effectiveness of an I-beam steel section that features a perforated-triangular web profile.

In the current study, finite element analysis was conducted on corrugated-perforated steel I-sections using ANSYS software. The study focused on inspecting the design of the perforations, including their shape (circle, square, hexagon, diamond and octagon), size of perforations (80 mm, 100 mm and 120 mm) and layout (the position of web perforation), as well as examining the geometric properties of the section in term of bending, lateral torsional buckling, torsion and shear behavior.

The study revealed that perforations with diamond, circle and hexagon shapes exhibit good performance, whereas the square shape performs poorly. Moreover, the steel section’s performance decreases with an increase in perforation size, regardless of loading conditions. In addition, the shape of the web perforations can also influence its stress distribution. For example, diamond-shaped perforations have been found to perform better than square-shaped perforations in terms of stress distribution and overall performance. This was because of their ability to distribute stress more evenly and provide greater support to the surrounding material. The diagonal alignment of the diamond shape aligns with principal stress directions, allowing for efficient load transfer and reduced stress concentrations. Additionally, diamond-shaped perforations offer a larger effective area, better shear transfer and improved strain redistribution, resulting in enhanced structural integrity and increased load-carrying capacity.

Hence, the presence of lateral-torsional buckling and torsional loading conditions significantly impacts the performance of corrugated-perforated steel I-sections.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

Bionic flapping-wing aerial vehicles (FWAVs) mimic natural flyers to generate the lift and thrust, such as birds, bats and insects. As an important component of the FWAVs, the flapping wings are crucial for the flight performance. The aim of this paper is to study the effects of different wings on aerodynamic performance.

Inspired by the wings structure of birds, the authors design four cambered wings to analyze the effect of airfoils on the FWAVs aerodynamic performance. The authors design the motor-driven mechanism of flapping wings, and realize the control of flapping frequency. Combined with the wind tunnel equipment, the authors build the FWAVs force test platform to test the static and dynamic aerodynamic performance of different flapping wings under the state variables of flapping frequency, wind speed and inclined angle.

The results show that the aerodynamic performance of flapping wing with a camber of 20 mm is the best. Compared with flat wing, the average lift can be improved by 59.5%.

Different from the traditional flat wing design of FWAVs, different cambered flapping wings are given in this paper. The influence of airfoils on aerodynamic performance of FWAVs is analyzed and the optimal flapping wing is obtained.

To improve the corrosion resistance of magnesium alloys, the construction of protective coatings is necessary to extend the service life of Mg-based materials.

SiO₂ nanoparticles modified by dodecyltrimethoxysilane (DTMS) were added to the PP and a superhydrophobic Mg(OH)₂/PP-60mSiO₂ composite coating was fabricated on the surface of AZ31 magnesium alloy via the hydrothermal method and subsequently the immersion treatment.

Hydrophilic SiO₂ nanoparticles become hydrophobic after modified by DTMS, showing a higher dispersibility in xylene. By incorporating modified SiO₂ nanoparticles into the composite PP coating, the hydrophobicity of the layer was enhanced, resulting in a contact angle of 166.3° and a sliding angle of 3.4°. It also improved the water repellency and durability of the coating. Furthermore, the intermediate layer of Mg(OH)₂ significantly strengthened the bond between the PP layer and the substrate. The Mg(OH)₂/PP-60mSiO₂ composite coating significantly enhances the corrosion resistance of the magnesium alloy by effectively blocking the infiltration of the corrosion anions during corrosion. The corrosion current density of the Mg(OH)₂/PP-60mSiO₂ composite coating is approximately 8.23 × 10^–9 A·cm^-2, which can achieve a magnitude three times lower than its substrate, making it a promising surface modification for the Mg alloy.

The composite coating effectively and durably enhances the corrosion resistance of magnesium alloys.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

This study aims to investigate the factors influencing team innovation from the perspective of strategic leaders. The study draws from the leader–member exchange (LMX) theory to propose that the quality of exchange the leaders perceive with the team members may provide a useful cue to identify the key elements and processes that may help drive team innovation.

A qualitative study using a hybrid approach was used, and a thematic analysis was performed. The data were based on 25 interviews collected from strategic leaders using the long interview technique.

The findings revealed themes and factors influencing innovation orientation among leaders and team members. Five themes were identified, namely modeling leadership behavior, autonomy and psychological safety for teams, organizational structure and technology, innovation and the decision-making process and innovation during times of uncertainty.

Because of the purposefully chosen sample of only leaders who were involved in the innovation process, the research results may lack generalizability. Therefore, researchers are encouraged to corroborate the finding using a sample of teams involved in the innovation process.

A conceptual model is proposed with guidance for implementing innovation decisions and strategies in practice.

While the strategic leadership and team innovation literature emphasizes the interaction between leaders and team members, research on how these interactions unfold is still nascent. This paper fulfills these needs from a strategic leader’s perspective.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.