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Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Metal laser-powder-bed-fusion using laser-beam parts are particularly susceptible to contamination due to particles attached to the surface. This may compromise so-called technical cleanliness (e.g. in NASA RPTSTD-8070, ASTM G93, ISO 14952 or ISO 16232), which is important for many 3D-printed components, such as implants or liquid rocket engines. The purpose of the presented comparative study is to show how cleanliness is improved by design and different surface treatment methods.

Convex and concave test parts were designed, built and surface-treated by combinations of media blasting, electroless nickel plating and electrochemical polishing. After cleaning and analysing the technical cleanliness according to ASTM and ISO standards, effects on particle contamination, appearance, mass and dimensional accuracy are presented.

Contamination reduction factors are introduced for different particle sizes and surface treatment methods. Surface treatments were more effective for concave design features, however, the initial and resulting absolute particle contamination was higher. Results further indicate that there are trade-offs between cleanliness and other objectives in design. Design guidelines are introduced to solve conflicts in design when requirements for cleanliness exist.

This paper recommends designing parts and corresponding process chains for manufacturing simultaneously. Incorporating post-processing characteristics into the design phase is both feasible and essential. In the experimental study, electroless nickel plating in combination with prior glass bead blasting resulted in the lowest total remaining particle contamination. This process applied for cleanliness is a novelty, as well as a comparison between the different surface treatment methods.

The traditional VeoVa10-VAc copolymer latex, which prepared via the emulsion polymerization of the mixed monomers of VAc and VeoVa10, has the poor water resistance and thermal stability because of the migration of the conventional emulsifier molecules and the low bond energy of C-C bond. The purpose of this work is that the fluorinated monomer is used to modify the latex. The film of the resultant latex has the C-F bond with high bond energy and low surface energy, which can effectively improve the heat resistance and water resistance of the resultant film. In addition, the reactive emulsifier is used to replace the conventional emulsifier. The drawbacks of the conventional emulsifier molecules migrate and desorb can be avoided when the polymer latex is stored, thereby also improving the water resistance.

The modified VAc-VeoVa10 latex has been successfully synthesized via the semi-continuous seeded emulsion polymerization, which VAc and VeoVa10 is used as the main monomers and HFMA was used as the functional monomer. KPS and reactive surfactants of SE-10 were used as the initiator and emulsifier, respectively. The structure of resultant latex film was characterized by Fourier transform infrared spectroscopy (FTIR). The latex films were tested by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and contact angle (CA). The particle size and its distribution of the latex were measured by the nano particle size analyzer.

The factors that had an influence on the properties of the latex and the film were investigated in detail. The stability of the resultant latex is good. The average particles of the latex and its distribution are small and uniform, respectively. In comparison with the conventional latex film, the thermal stability and hydrophobicity of the resultant latex film are improved obviously.

The resultant latex can be used in both the waterborne interior and exterior wall coatings, pickering stabilized waterborne polymer dispersions, polymer powders, environmentally friendly polymer-modified waterproof mortar and other fields, which can be satisfied with the high demand of thermal stability and hydrophobicity.

The modification of poly (VAc-VeoVa10) by reactive surfactant and fluorinated monomer is seldom reported. In this study, the fluorinated poly (VAC-VeoVa) latex is prepared via the reactive surfactants, which VAc and VeoVa10 is used as the main monomers and hexafluorobutyl methacrylate is used as the functional monomer. Potassium persulfate (KPS) and allyl nonyl phenoxy propyl alcohol polyoxyethylene ether ammonium sulfate are used as the initiator and emulsifier, respectively.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

This paper aims to present an innovative reconfigurable series-fed microstrip antenna using radiofrequency positive intrinsic negative (RF PIN) diodes for cognitive S-band and C-band satellite communications. The antenna can dynamically reconfigure its frequency, polarization and radiation pattern to meet diverse application needs.

The design involves a reconfigurable four-element microstrip antenna using FR4 substrate and copper patches. RF PIN diodes enable dynamic frequency, polarization and radiation pattern reconfiguration. Simulations and optimizations are performed using CST and HFSS, using techniques like the Nelder-Mead algorithm, particle swarm optimization, covariance matrix adaptation and trust region framework. An antenna prototype is also fabricated to validate the simulations.

The proposed antenna demonstrates significant reconfigurability: it switches between S-band (2.45 GHz, 2.52 GHz) and C-band (5.55 GHz, 5.59 GHz) with bandwidths of 120 MHz and 550 MHz, respectively. It transitions between circular and linear polarization in the S-band and modifies the radiation pattern by 45 degrees, providing an alternative radiation direction in the C-band. The antenna achieves a maximum gain of 5.95 dBi at 2.52 GHz and 93% efficiency at 5.55 GHz. Simulated results closely match those from the fabricated prototype, confirming the design’s validity.

The innovative use of RF PIN diodes enables comprehensive reconfigurability in frequency, polarization and radiation patterns within a single microstrip antenna, meeting the demands of S-band and C-band satellite communications. This study demonstrates superior performance, significant gains and efficiencies across various reconfiguration modes, validated by rigorous simulation and practical fabrication. The simple structural design further distinguishes this study from others in the field.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

Divers were contracted to carry out a detailed baseline survey which will form the Environmental Impact Assessment. This report presents information about the biodiversity of Cawsand Bay and the impact of installing a subsea tidal energy module. Subsequently, this addresses some of the SDG14 targets: 14.5, conserve coastal and marine areas; 14.7, increase the economic benefits from the sustainable use of marine resources to small island developing states and less developed countries; and 14.8, increase scientific knowledge, research and technology for ocean health. Contracted from November to December 2021 over a four-week period, five SCUBA divers conducted baseline transects over regular intervals of five meters at Cawsand Bay in each cardinal direction. Water and sediment samples were analysed to better understand the habitat and benthos at Cawsand Bay. Sediment samples established the biotope by identifying the benthos: sublittoral seagrass beds (SS.SMp.SSgr.Zmar). The data also revealed Zostera marina, commonly known as eelgrass (seagrass), is the most abundant species in the area, resulting in a high oxygen content within the water samples. In turn, this helps establish an environment capable of sustaining high levels of biodiversity for this time of year and is a more efficient support ecosystem.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

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.