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The purpose of this study is to present a highly stretchable and flexible strain sensor with simple and low cost of fabrication process and excellent dynamic characteristics, which make it suitable for human motion monitoring under large strain and high frequency.

The strain sensor was fabricated using the rubber/latex polymer as elastic carrier and single-walled carbon nanotubes (SWCNTs)/carbon black (CB) as a synergistic conductive network. The rubber/latex polymer was pre-treated in naphtha and then soaked in SWCNTs/CB/silicon rubber composite solution. The strain sensing and other performance of the sensor were measured and human motion tracking applications were tried.

These strain sensors based on aforementioned materials display high stretchability (500 per cent), excellent flexibility, fast response (approximately 45 ms), low creep (3.1 per cent at 100 per cent strain), temperature and humidity independence, superior stability and reproducibility during approximately 5,000 stretch/release cycles. Furthermore, the authors used these composites as human motion sensors, effectively monitoring joint motion, indicating that the stretchable strain sensor based on the rubber/latex polymer and the synergetic effects of mixed SWCNTs and CB could have promising applications in flexible and wearable devices for human motion tracking.

This paper presents a low-cost and a new type of strain sensor with excellent performance that can open up new fields of applications in flexible, stretchable and wearable electronics, especially in human motion tracking applications where very large strain should be accommodated by the strain sensor.

This study aims to present comprehensive nonlinear material modelling techniques and simulations of reinforced concrete (RC) beams subjected to short-term monotonic static load using the robust and reliable general-purpose finite element (FE) software ANSYS. A parametric study is carried out to analyse the flexural and ductility behaviour of RC beams under various influencing parameters.

To develop and validate the numerical FE models, a total of four experimentally tested simply supported RC beams are taken from the available literature and two beams are selected from each author. The concrete, steel reinforcements, bond-slip mechanism, loading and supporting plates are modelled using SOLID65, LINK180, COMBIN39 and SOLID185 elements, respectively. The validated models are then used to conduct parametric FE analysis to investigate the effect of concrete compressive strength, percentage of tensile reinforcement, compression reinforcement ratio, transverse shear reinforcement, bond-slip mechanism, concrete compressive stress-strain constitutive models, beam symmetry and varying overall depth of beam on the ultimate load-carrying capacity and ductility behaviour of RC beams.

The developed three-dimensional FE models can able to capture the load and midspan deflections at critical points, the accurate yield point of steel reinforcements, the formation of initial and progressive concrete crack patterns and the complete load-deflection curves of RC beams up to ultimate failure. From the numerical results, it can be concluded that the FE model considering the bond-slip effect with Thorenfeldt’s concrete compressive stress-strain model exhibits a better correlation with the experimental data.

The ultimate load and deflection results of validated FE models show a maximum deviation of less than 10% and 15%, respectively, as compared to the experimental results. The developed model is also capable of capturing concrete failure modes accurately. Overall, the FE analysis results were found quite acceptable and compared well with the experimental data at all loading stages. It is suggested that the proposed FE model is a practical and reliable tool for analyzing the flexural behaviour of RC members and can be used for performing parametric studies.

Projecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas. Conventional studies require the collection of people’s trajectory data through offline means, which is limited in terms of cost and data availability. The wide use of online social network (OSN) apps over smartphones has provided the opportunities of devising a lightweight approach of conducting the study using the online data of smartphone apps. This paper aims to reveal the relationship between the online social networks and the offline communities, as well as to project the population distribution by modeling geo-homophily in the online social networks.

In this paper, the authors propose the concept of geo-homophily in OSNs to determine how much the data of an OSN can help project the population distribution in a given division of geographical regions. Specifically, the authors establish a three-layered theoretic framework that first maps the online message diffusion among friends in the OSN to the offline population distribution over a given division of regions via a Dirichlet process and then projects the floating population across the regions.

By experiments over large-scale OSN data sets, the authors show that the proposed prediction models have a high prediction accuracy in characterizing the process of how the population distribution forms and how the floating population changes over time.

This paper tries to project population distribution by modeling geo-homophily in OSNs.

This study aims to clarify the relationship of friction material type and brake disc temperature through braking experiment.

The braking performances of resin materials (RM), semimetallic materials (SM) and copper-based powder metallurgy materials (PM) friction blocks mating with forged steel brake disc were examined based on TM-I-type reduced-scale inertial braking dynamometer. The brake disc surface temperature was recorded by infrared thermal camera during braking.

Experimental results indicate that the thermal wear resistance of three friction materials differs with mental content, resulting in the deviation of pad-disc system contact state during braking, thus forming different temperature distribution on the brake disc surface. The peak temperature on the disc face of RM (190°C) is 36.6% and 45.4% lower than that of PM (300°C) and SM (348°C) at 160 km/h. The maximum radial temperature deviation of PM (35°C) is approximately three times than that of RM (12°C) and 40% higher than that of SM (25°C) at 50 km/h, whereas the maximum temperature deviation of SM (97°C) is six times than that of RM (16°C) and 31% higher than that of PM (74°C) at 160 km/h.

The effect of friction material type on the disc surface temperature distribution is revealed, which provides a meaningful reference for the design of brake friction pairs and choice of brake pad materials.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

This paper aims to reveal the effects of the copper third body on different copper matrix friction materials with a novel experimental way called “exogenous powder.”

An accurate adding device of exogenous copper powder was designed to control the flow rate. The tribological properties with and without exogenous copper powder were investigated by a pin-on-disc tribometer during dry sliding.

Experimental results indicate that the Cu addition tends to increase the friction coefficient. For pure Cu material, the exogenous copper third body exhibits poor fluidity on the friction surface, causing serious adhesive wear on the friction interface. For the Cu 90% + 10% Gr material, the plasticity of exogenous copper powder may intensify the deformation of the third body of the surface, presenting layered accumulation distribution. For the pure Cu and Cu 95% + 5% SiO₂ material, the Cu addition makes the composition and density of the third body uneven in the direction of depth.

The role of the copper component on different materials is revealed from a new perspective, and the relationship between the third body structure and the friction properties is explored.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

The purpose of this paper is to provide a systematic method to perform analysis and test for vibration‐thermal strain behavior of plastic ball grid array (PBGA) assembly by considering thermal and vibration loading mode. Also to investigate the dynamic behavior of PBGA assembly by considering loading modes for design and reliability evaluation of PBGA packaging.

A PBGA assembly prototype with different structure and material parameters is designed and manufactured. Based on investigation of the structural and physical parameters of PBGA sample, the vibration‐thermal strain test is developed to measure the strain distribution at the surface of the BT (bismaleimide triazine) substrates and PCB (printed circuit board) surface under vibration‐thermal cycling loading such as random vibration and the temperature is changed from 0°C to 100°C.

The test results show that the loading modes have different impact on PCB, EMC and substrate, respectively. In the meantime, it is shown that the characteristics of the compound mode is not the linear accumulative result by single vibration mode and single thermal loading mode as forecasted. The nonlinear mechanism for these modes application is the future work for progress.

It is very difficult to set up a numerical approach to illustrate the validity of the testing approach because the complex loading modes and the complex structure of PBGA assembly. The research on an accurate mathematical model of the PBGA assembly prototype is a future work.

It implies a potential design characteristic for future application of PBGA assembly. It also builds a basis for future work for design and reliability evaluation of BGA package.

This paper fulfils useful information about the thermal‐vibration coupling dynamic behavior of PBGA assembly with different structure characteristics, materials parameters.

The purpose of this paper is to provide a critical synthesis of research conducted within the hospitality and tourism industries in response to the impact of the COVID-19 pandemic, identify key perspectives and themes relating to the recovery and resilience of the two sectors and put forward recommendations that help address organizational and consumer behavior changes produced by the pandemic.

This study adopted a critical reflection approach to identify, select and synthesize relevant research based on which recommendations are drawn.

This study offers a contemporary framework discussing three distinct themes that emerged from existing research regarding the impact of COVID-19 on the hospitality and tourism industries: management, marketing and consumer behavior.

This study offers operational, practical and actionable recommendations for organizations about how to adapt and recover from the impact of the COVID-19 pandemic by guiding the industry in sustaining long-term resilience.

This study provides a critical and current synthesis of selected literature and theory that discuss key implications of the COVID-19 pandemic for the recovery and resilience-building of the hospitality and tourism sectors.

With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.