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Three-dimensional (3D) printed parts usually have poor surface quality due to layer manufacturing’s “stair casing/stair-stepping”. So post-processing is typically needed to enhance its capabilities to be used in closed tolerance applications. This study aims to examine abrasive flow finishing for 3D printed polylactic acid (PLA) parts.

A new eco-friendly abrasive flow machining media (EFAFM) was developed, using paper pulp as a base material, waste vegetable oil as a liquid synthesizer and natural additives such as glycine to finish 3D printed parts. Characterization of the media was conducted through thermogravimetric analysis and Fourier transform infrared spectroscopy. PLA crescent prism parts were produced via fused deposition modelling (FDM) and finished using AFM, with experiments designed using central composite design (CCD). The impact of process parameters, including media viscosity, extrusion pressure, layer thickness and finishing time, on percentage improvement in surface roughness (%ΔRa) and material removal rate were analysed. Artificial neural network (ANN) and improved grey wolf optimizer (IGWO) were used for data modelling and optimization, respectively.

The abrasive media developed was effective for finishing FDM printed parts using AFM, with SEM images and 3D surface profile showing a significant improvement in surface topography. Optimal solutions were obtained using the ANN-IGWO approach. EFAFM was found to be a promising method for improving finishing quality on FDM 3D printed parts.

The present study is focused on finishing FDM printed crescent prism parts using AFM. Future research may be done on more complex shapes and could explore the impact of different materials, such as thermoplastics and composites for different applications. Also, implication of other techniques, such as chemical vapour smoothing, mechanical polishing may be explored.

In the biomedical field, the use of 3D printing has revolutionized the way in which medical devices, implants and prosthetics are designed and manufactured. The biodegradable and biocompatible properties of PLA make it an ideal material for use in biomedical applications, such as the fabrication of surgical guides, dental models and tissue engineering scaffolds. The ability to finish PLA 3D printed parts using AFM can improve their biocompatibility, making them more suitable for use in the human body. The improved surface quality of 3D printed parts can also facilitate their sterilization, which is critical in the biomedical field.

The use of eco-friendly abrasive flow finishing for 3D printed parts can have a positive impact on the environment by reducing waste and promoting sustainable manufacturing practices. Additionally, it can improve the quality and functionality of 3D printed products, leading to better performance and longer lifespans. This can have broader economic and societal benefits.

This AFM media constituents are paper pulp, waste vegetable oil, silicon carbide as abrasive and the mixture of “Aloe Barbadensis Mill” – “Cyamopsis Tetragonoloba” powder and glycine. This media was then used to finish 3D printed PLA crescent prism parts. The study also used an IGWO to optimize experimental data that had been modelled using an ANN.

This study examined participating teachers' expressions about teaching and learning when implementing lesson study (LS) about communication as a special didactic tool in mathematics; it also investigated their experience with LS. The initializing phase was characterized by letting the teachers become familiar with LS as a model for their professional development (PD). It also provided an opportunity for the participants to acquire common understanding of their starting point.

An adapted version of LS was used as a model for teachers' PD. The methods for data collection were a semi-structured interview and discussions with the teachers. From a teaching team in school year 1, two class teachers participated. The data obtained were qualitative and subjected to a thematic analysis. The teachers participated together in the different discussions during the study. All the discussions were audio-recorded.

During the discussions, the teachers raised some critical points: how to gain students' attention during lesson reviews; how to make follow-ups of the students' understanding of lesson content; how to plan and factors that could have been changed in education that could assist in all students' progress.

Both the initializing phase and the concept of special didactics have not received full attention in research. This study highlights the importance of capturing the teachers' attained competence toward understanding what is needed for future competence concerning communication as a special didactic tool in mathematics.

We present the concept and first experiences for a Web‐based ecological knowledge management system ILMAX. It manages experience knowledge resulting from an ecological long‐term investigation of the regeneration process of the stream Ilm (Thuringia, Germany). The tools used are iZone, a Web‐based content management system with an emphasis on information retrieval and feedback, and D3, a tool kit for building knowledge‐based systems and for managing case bases. The difficult initializing phase of knowledge management systems was done by a “knowledge champion” gathering and structuring various kinds of text documents, data sheets and unpublished knowledge of domain experts.

The purpose of this paper is to propose and examine the new user support in university network.

The new user support is realized by use of DACS (Destination Addressing Control System) Scheme which manages a whole network system through communication control on a client computer. This DACS Scheme has been studied conventionally by authors.

The new user support is more simplified than the normal user support in the network based on TCP/IP.

As a future research, the authors will implement the system for realizing DACS Scheme and apply that system to practical network. Then, the new user support proposed in this paper will be realized in practical network.

The functions needed for the new user support are confirmed. Some functions was confirmed in the past research. The other function is confirmed in the section of experimental results.

In the network introducing DACS Scheme, the setting of the client does not need to be changed at the time of changing a network system. In addition, annoying communication becomes very simplified. The first point is that the client computer which transmits annoying communication is specified simply. The second point is shown as follows. Because the influence to others is prevented by blocking a communication port of the client computer, time margin for the cause specification of annoying communication and the coping with it is generated effectively.

Wireless sensor network (WSN) and mobile crowd sensing (MCS) technologies face some challenges, especially when deployed in a large environment such as a smart city environment. WSN faces network latency, packets delivery and limited lifetime due to the nature of the used constrained internet of things small devices and low power network. On the other hand, most of the current applications that adapt MCS technology use 3G or long term evalution network to collect data and send them directly to the server. This leads to higher battery and bandwidth consumption and higher data cost.

This paper proposes a hybrid routing protocol based on the routing protocol (RPL) protocol that combines the two wireless sensing technologies (WSN and MCS) and allows the integration between them. The aim is to use MCS nodes in an opportunistic way to support static WSN nodes to enhance the performance.

The evaluation of the proposed protocol was conducted in a static WSN to study the impact of the integration on the WSN performance. The results reveal a good enhancement on packet delivery ratio (17% more), end-to-end delay (50% less) and power consumption (25% less) compared with native RPL (without MCS integration).

The authors believe that the hybrid-RPL protocol can be useful for sensing and data collection purposes, especially in urban areas and smart city contexts.

Many devices needed electrical power to work, thus, major energy carriers such as oil and gas were used to generate electrical power via converter mechanisms and special technologies. The microturbine is a developed technology that is remarkable for its relatively high performance and ability to use several types of fuels. Microturbines are economically feasible because of the production of combined heat and power and small-scale applicability. This study aims to investigate microturbine technology development to support modern energy access in a developing country (i.e. Iran).

This paper presents a technology foresight methodology to create plausible futures of microturbine development in Iran when the country faces different driving forces and uncertainties. On other hand, the paper deals with a theoretical question: how to select appropriate foresight methodology? A procedure is proposed, which equipped the research team to select appropriate method combinations based on Popper’s diamond. Finally, the selected methodology includes defining focal issues environmental scanning and patent analysis aimed at developing five plausible scenarios for microturbine development future in Iran and creating shared visions among policymakers.

This paper proposed a series of scenarios on the path to developing microturbine technology. The scenario development logic in a participatory way contains a common four-quarter technique that attempts to depict scenarios based on two critical uncertainties inclusive energy price and technology obsolescence, which will shape the future. Also, a scenario is presented to describe a wild card that can disturb the desired futures. Such materials help decision-makers to policies under plausible conditions that guarantee a robust policy basket.

The originality of this paper can be studied based on two aspects, first, the methodology that provides a systematic method selections procedure in an emerging complex technology development program. Second, from the practical aspect, this paper is one of the very first attempts to manage the microturbine technology development program. Then, results are used to feed the policy-making process in Iran.

To improve the quality of the additive manufacturing (AM) products, it is necessary to estimate surface roughness distribution in advance. Although surface roughness estimation has been previously studied, factors leading to the creation of a rough surface and a comprehensive test for model validation have not been adequately investigated. Therefore, this paper aims to establish a robust model using empirical data based on optimized artificial neural networks (ANNs) to estimate the surface roughness distribution in fused deposition modelling parts. Accordingly, process parameters such as time, cost and quality should be optimized in the process planning stage.

Process parameters were selected via a literature review of surface roughness estimation modelling by analytical and empirical methods, and then a specific test part was fabricated to provide a complete evaluation of the proposed model. The ANN structure was optimized by trial and error method and evolutionary algorithms. A novel methodology based on the combination of the intelligent algorithms including the ANN, linked to the particle swarm optimization (PSO) and imperialist competitive algorithm (ICA), was developed. The PSOICA algorithm was implemented to increase the capability of the ANN to perform much faster and converge more precisely to favorable results. The performances of the ANN models were compared to the most well-known analytical models at build angle intervals of equal size. The most effective process variable was found by sensitivity analysis. The validity of proposed model was studied comprehensively where different truncheon parts and medical case studies including molar tooth, skull, femur and a custom-made hip stem were built.

This paper presents several improvements in surface roughness distribution modelling including a more suitable method for process parameter selection according to the design criteria and improvements in the overall surface roughness of parts as compared to analytical methods. The optimized ANN based on the proposed advanced algorithm (PSOICA) represents precise estimation and faster convergence. The validity assessment confirms that the proposed methodology performs better in varied conditions and complex shapes.

This research fills an important gap in surface roughness distribution estimation modelling by using a test part designed for that purpose and optimized ANN models which uses purely empirical data. The novel PSOICA combination enhances the ability of the ANN to perform more accurately and quickly. The advantage in using actual surface roughness values is that all factors resulting in the creation of a rough surface are included, which is impossible if other methods are used.

The purpose of this paper is to search for the critical-scenarios of autonomous vehicles (AVs) quickly and comprehensively, which is essential for verification and validation (V&V).

The author adopted the index F1 to quantitative critical-scenarios' coverage of the search space and proposed the improved particle swarm optimization (IPSO) to enhance exploration ability for higher coverage. Compared with the particle swarm optimization (PSO), there were three improvements. In the initial phase, the Latin hypercube sampling method was introduced for a uniform distribution of particles. In the iteration phase, the neighborhood operator was adapted to explore more modals with the particles divided into groups. In the convergence phase, the convergence judgment and restart strategy were used to explore the search space by avoiding local convergence. Compared with the Monte Carlo method (MC) and PSO, experiments on the artificial function and critical-scenarios search were carried out to verify the efficiency and the application effect of the method.

Results show that IPSO can search for multimodal critical-scenarios comprehensively, with a stricter threshold and fewer samples in the experiment on critical-scenario search, the coverage of IPSO is 14% higher than PSO and 40% higher than MC.

The critical-scenarios' coverage of the search space is firstly quantified by the index F1, and the proposed method has higher search efficiency and coverage for the critical-scenarios search of AVs, which shows application potential for V&V.

Medians of a graph have many applications in engineering. Optimal locations for facility centers, distribution of centers and domain decomposition for parallel computation are a few examples of such applications. In this paper, a new ant system (AS) algorithm based on the idea of using two sets of ants, named active and passive ants is proposed for the problem of finding k‐medians of a weighted graph or the facility location problem on a network.

The structure of the algorithm is derived from two known heuristics; namely, rank‐based AS and max‐min ant system with some adjustments in pheromone updating and locating the ants on the graph nodes. The algorithms are designed with and without a local search.

An efficient algorithm for location finding, and the novel application of an ant colony system can be considered as the main contribution of this paper.

Combining two different tools; namely, graph theory and AS algorithm results in an efficient and accurate method for location finding. The results are compared to those of another algorithm based on the theory of graphs.

A new algorithm for reducing the profile and root‐mean‐square wavefront of sparse matrices with a symmetric structure is presented. Our numerical experiments show an overall better performance than the widely used reverse Cuthill‐McKee, Gibbs‐King and Sloan algorithms. The new algorithm is fast, simple and useful in engineering analysis where it can be employed to derive efficient orderings for both profile and frontal solution schemes.