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In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range.

The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances.

The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally.

The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.

This research explored how COVID-19 affects Shenzhen high school students' reading behavior and preference and their parents' attitudes toward reading during the lockdown.

This research adopted a qualitative approach to conduct one-on-one semi-structured interviews with parents of a boarding high school in Shenzhen, China. Thirteen parents were recruited through a purposeful sampling method, and NVivo12 software was used to analyze the results with a theme-based approach guided by the 5E instructional model.

The results revealed the effectiveness and problems of high school students' use of electronic resources and discovered changes in the reading behavior of high school students and their parents' attitudes during COVID-19.

There are few studies specifically on the reading behavior of boarding students from a parental view, especially in Asia. This research can fill the gaps in related research during COVID-19.

Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.

This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.

Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.

The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

This study aims to chart the impact of technological advancements on tourism from the post–Second World War era to the present and forecast their influence until 2050. It assesses how technologies have reshaped travel experiences and operations, with a focus on upcoming innovations such as the metaverse, Web 3.0 and AI, and their implications for sustainable and ethical tourism.

This study uses a hybrid approach, combining historical analysis and future projections. It analyzes archival data, industry reports and academic literature.

This study identifies crucial technological milestones that have significantly impacted tourism, including the rise of commercial aviation, the internet and AI. Future trends suggest emerging technologies will further transform the sector. Challenges in sustainability, ethics and inclusivity are highlighted as critical considerations for future development.

This paper offers a unique longitudinal perspective on technology’s influence on tourism, bridging past trends with future projections.

本研究采取混合方法, 融合历史分析与未来趋势预测。研究分析了丰富的档案数据、行业报告以及学术文献。

旨在勾勒从二战后至今技术进步对旅游业的影响, 并展望至2050年的潜在影响。本研究着重评估技术如何重塑旅游体验和运作, 特别是对元宇宙、网络3.0和人工智能等即将到来的创新技术及其对可持续和伦理旅游的意义。

识别了旅游业中关键的技术里程碑, 包括商业航空、互联网和人工智能的崛起。研究指出, 未来趋势显示新兴技术将继续深刻改变旅游业。同时强调, 可持续性、伦理和包容性是未来发展中不可忽视的关键要素。

本文从独特的纵向视角出发, 深入探讨了技术对旅游业的历史与未来影响, 将过去发展趋势与未来展望紧密结合。

Este estudio emplea un enfoque híbrido que combina el análisis histórico y las proyecciones de futuro. Analiza datos de archivo, informes del sector y bibliografía académica.

La investigación pretende trazar el impacto de los avances tecnológicos en el turismo desde la era posterior a la Segunda Guerra Mundial hasta la actualidad y prever su influencia hasta 2050. Evalúa cómo las tecnologías han reconfigurado las experiencias y las operaciones de viaje, centrándose en las próximas innovaciones como el Metaverso, la Web 3.0 y la IA, y sus implicaciones para un turismo sostenible y ético.

El estudio identifica hitos tecnológicos cruciales que han tenido un impacto significativo en el turismo, como el auge de la aviación comercial, Internet y la IA. Las tendencias futuras sugieren que las tecnologías emergentes transformarán aún más el sector. Los retos en sostenibilidad, ética e inclusividad se destacan como consideraciones críticas para el desarrollo futuro.

Este artículo ofrece una perspectiva longitudinal única sobre la influencia de la tecnología en el turismo, tendiendo un puente entre las tendencias pasadas y las proyecciones futuras.

The primary aim is to identify and examine the critical success factors (CSFs) in FinTech that influence social responsibility (SR). This study aims to differentiate between internal factors (e.g. risk management, innovation, regulatory compliance, technology integration) and external factors (e.g. market conditions and regulatory changes) and offer insights into effective FinTech practices that enhance SR performance.

A comprehensive literature review using the Web of Science database analyzed 143 relevant articles. This review categorizes CSFs and assesses how FinTech practices impact SR.

The findings indicate that strategic FinTech practices, such as innovation in financial services, effective data management and stakeholder collaboration, are crucial for enhancing operational efficiency, fostering sustainability and improving responsiveness to social and environmental needs. This study also highlights current trends in FinTech and SR, emphasizing the integration of FinTech into broader SR strategies.

This study provides valuable insights for practitioners on how to leverage FinTech to enhance SR, including strategies for integrating innovative technologies and improving stakeholder engagement.

By advancing the understanding of how FinTech can contribute to SR, this research highlights the potential for FinTech to drive positive social and environmental outcomes, fostering greater corporate responsibility and sustainability.

This research provides a novel bibliometric analysis of FinTech’s role in advancing SR, offering a comprehensive overview of the CSFs and emerging trends in this field.

Previous studies have identified concerns and anxiety in individuals who are without their mobile phones, which is known as nomophobia, an acronym for “no mobile phone phobia.” However, there is currently limited evidence of qualitative research that explores nomophobia. The purpose of this study is to explore personal experiences and individual meanings associated with mobile phone use and nomophobia.

To address this gap, the present study employed a Heideggerian phenomenological approach to investigate the issue. Ten college students, who have medium to high nomophobia were selected as respondents. Semi-structured interviews were conducted to collect data, which were then transcribed verbatim and analyzed using reflexive thematic analysis.

A deep understanding of the psychological dynamics, experiences, meanings, perceptions and beliefs of individuals regarding smartphone use can only be explored through a qualitative approach that presents personal statements of individuals through in-depth semi-structured interviews. This study contributes to a deep understanding of the psychological dynamic of nomophobia.

This study has limitations that must be carefully considered. First, the respondents came from a university with a sample size of 10 people, which may not fully represent the phenomenon of nomophobia among students due to differences in cultural and academic backgrounds. However, we addressed this limitation by selecting only students who scored moderate to high levels of nomophobia. Second, the researchers acknowledge that the sample used in the study may not be fully representative of the broader target population or a larger group, as previously mentioned. Therefore, generalizing the findings of this research must be done carefully, being cautious and thoughtful to avoid hasty conclusions.

In the functional context, it is recommended for individuals to be more mindful of their smartphone usage and strive to strike a balance between utilizing their device's capabilities for productive purposes and avoiding excessive dependency that may lead to distractions or information overload. This includes limiting excessive smartphone usage for entertainment purposes, restricting aimless and irrelevant Internet browsing and implementing effective time management when using smartphones. For smartphone users from the ontological context, the advice is to critically evaluate their digital presence and ensure that their online activities align with their true values and self-perception, promoting responsible and ethical smartphone use. Engaging in fasting or break sessions by completely turning off the smartphone at specific times and utilizing that time for activities such as reading books, writing and engaging in spiritual practices, or exercising is also advisable. In the anthropomorphic context, individuals are advised to reflect on the potential emotional dependence on their smartphones and consider establishing healthy boundaries to avoid excessive reliance on these devices to fulfill emotional needs. One practical recommendation is to engage in self-reflection, where individuals take some time to ponder their smartphone usage patterns and emotional attachment to the device. They should ask themselves why they feel the need to constantly check their phone and how it impacts their emotions and overall well-being.

This study contributes to a deep understanding of the psychological dynamics of nomophobia by exploring the experiences, meanings, perceptions, and beliefs of individuals regarding smartphone use.

This study longitudinally investigated the predictors and mediators of adolescent smartphone addiction by examining the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3, as well as the separate and sequential role of adolescent self-esteem and depression at T2 as mediating factors.

This study used a hierarchical regression and the PROCESS macro (Model 6) to investigate research model by collecting 3,904 parent-adolescent pairs. Panel data were collected from three waves of the Korean Children and Youth Panel Survey (KCYPS).

First, the result showed that parental smartphone addiction at T1 significantly and positively predicted adolescent smartphone addiction at T3. Second, the serial mediation analysis revealed that the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3 was mediated by adolescent self-esteem and depression at T2 independently and serially.

The findings enhance our comprehension of the impact of parental smartphone addiction, adolescent self-esteem and depression, on adolescent smartphone addiction.

This study aims to emphasize green energy-driven solutions to address environmental sustainability issues, particularly to promote the uptake of electric vehicles (EVs). This study intends to investigate user adoption of EVs as the existing predicament of converting car owners to EV buyers, demanding a push to create a facilitating environment for EV uptake.

A survey-based quantitative study involving 330 car owners and potential buyers was conducted involving four predictors, i.e. financial benefits, social influence, charging infrastructure and range consciousness. Environmental concerns and socio-demographic factors such as age, family income and gender were considered as moderators between these predictors and EV adoption intention. Partial least square structural equation modelling was used to analyse the proposed relationships.

The findings indicated that financial benefits (ß = 0.169, t = 3.930), social influence (ß = 0.099, t = 2.605), range consciousness (ß = 0.239, t = 3.983) and charging infrastructure (ß = 0.142, t = 4.8) significantly impact EV adoption. Family income was the most significant moderator with a large effect size (F square = 0.224), followed by environmental concern (F square = 0.182) and age (F square = 0.042) having a medium moderation effect and, subsequently, gender (F square = 0.010) as a mild moderator.

By analysing environmental concerns as a moderator, this study fosters a novel understanding of how environmental concerns impact EV adoption, which has not been explored. Additionally, the empirical assessment of the socio-economic and socio-demographic factors of EV adoption helps to offer a consumer perspective to the government and policymakers in undertaking initiatives to promote EV adoption.