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To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results.

The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification.

A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments.

This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.

By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.

The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.

In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.

Distributed photovoltaic (DPV) projects generally have output risks, and the production effort of the supplier is often private information, so the buyer needs to design the optimal procurement contract to maximise its procurement utility.

Based on the principal-agent theory, we design optimal procurement contracts for DPV projects with fixed payments and incentive factors under three situations, i.e. symmetry information, asymmetry information without monitoring and asymmetry information with monitoring. We obtain the optimal production effort and expected utility of the supplier, the expected output and expected utility of the buyer and analyse the value of the information and monitoring.

The results show that under asymmetric information without monitoring, risk-averse suppliers need to take some risk due to output risk, which reduces the optimal production effort of the supplier and the expected output and expected utility of the buyer. Therefore, when the monitoring cost is below a certain threshold value, the buyer can introduce a procurement contract with monitoring to address the asymmetry information. In addition, under asymmetric information without monitoring, the buyer should choose a supplier with a low-risk aversion.

Considering the output risk of DPV projects, we study the optimal procurement contract design for the buyer under asymmetric information. The results provide some theoretical basis and management insights for the buyer to design optimal procurement contracts in different situations.

The global market for cloud gaming is growing rapidly. How gamers evaluate the service quality of this emerging form of cloud service has become a critical issue for both researchers and practitioners. Building on the literature on service quality and software as a service, this study develops and validates a gamer-centric measurement instrument for cloud gaming service quality.

A three-step measurement instrument development process, including item generation, scale development and instrument testing, was adopted to conceptualize and operationalize cloud gaming service quality.

Cloud gaming service quality consists of two second-order constructs of support service quality and technical service quality with seven first-order dimensions, namely rapport, responsiveness, reliability, compatibility, ubiquity, smoothness and comprehensiveness. The instrument exhibits desirable psychometric properties.

Practitioners can use this new measurement instrument to evaluate gamers' perceptions toward their service and to identify areas for improvement.

This study contributes to the service quality literature by utilizing qualitative and quantitative approaches to develop and validate a new measurement instrument of service quality in the context of cloud gaming and by identifying new dimensions (compatibility, ubiquity, smoothness and comprehensiveness) specific to it.

This study aims to analyse the antecedents and consequences of consumer participation behaviour in the hotel industry. It investigates how citizenship behaviour influences the link between participation and switching intention. It also investigates hotel innovativeness, brand identification and consumer value co-creation using the frameworks of innovation diffusion theory, social identity theory and social exchange theory. By combining different academic perspectives, the study provides a thorough grasp of the elements that influence consumer behaviour and loyalty to hospitality brands.

This study used a descriptive-analytical approach to survey 526 guests who stayed at five-star hotels affiliated with chains in Egypt. The hotels were chosen using stratified random sampling, which ensured representation across multiple segments. For practical reasons, convenience sampling techniques were used to pick research participants. To test the study hypotheses, structural equation modelling was used, which provided a strong statistical analysis to assess the correlations between variables and validate the suggested theoretical framework.

The research indicates that hotel innovativeness has a substantial impact on consumers’ brand identification and involvement behaviour, but not their propensity to move brands. The relationship between participation behaviours and intention to switch is mediated by consumer citizenship behaviour.

The cross-sectional study methodology may not address causal linkages or developmental difficulties because it only collected data at one point in time. A longer study period is required, and the sample group’s breadth is restricted to five-star hotel guests from Egypt’s chain-affiliated hotels.

This study offers a thorough methodology for assessing how hotel innovation influences consumer brand identification. It also looks at how consumer brand identification affects involvement and switching intentions. The study also investigates the moderating influence of consumer citizenship behaviour in the link between involvement and switching intentions.

This research paper aims to investigate the predictors and outcomes of immersive technology adoption in tourism.

PLS-SEM is used for data collected from tourists visiting the UAE and Egypt to examine predictors and consequences of adoption.

It is revealed that perceived ease of use, enjoyment, immersion, usefulness and attitude towards technology predict immersive technology adoption. It is also revealed that the adoption affects tourists’ perceived value and engagement, which, in turn, affects tourists’ satisfaction and loyalty.

The study has integrated a research model that combines both antecedents and consequences of immersive technology adoption where few empirical investigations were revealed to draw conclusions on this research area. Also, missing relations have been included and tested in the research model.

To cater to the senior tourist market, it is essential to comprehend the factors motivating and deterring them from international travel post-COVID-19. This study aims to focus on senior citizens’ destination choice intentions and aims to develop a model that prioritizes positive and negative factors leading to international travel destination choices. It uses push–pull factors, perceived travel risks (PTRs) and perceived travel constraints (PTCs).

Decision-making trial and evaluation laboratory (DEMATEL) and fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS) are two multi-criteria decision-making (MCDM) techniques used to identify connections between variables and determine their relative importance in the decision-making model.

DEMATEL found push and pull factors are “effects” while PTCs and PTRs are “causes” affecting senior citizens’ destination choices. Push factors and PTCs have a greater impact than pull factors and PTRs. Fuzzy TOPSIS highlighted “improving health and wellness” and “self-fulfillment and spirituality” as key push factors, “health safety and security quotient” as the most important pull factor, and “interpersonal constraints” as the most critical PTC. Finally, “health risks” is the top PTR.

This paper adds to the tourism literature by looking at the relationship between senior tourists’ motivation, PTRs and PTCs and showing how the subfactors affect their choice of destination rank. The data analysis techniques used in this study are also novel, having never been used before in senior tourism research. Finally, even though there is a lot of research on senior tourism, not much is known about how Indian senior tourists act. In light of this study’s findings, practical recommendations were offered to tourism stakeholders worldwide, interested in tapping into the market of Indian outbound senior tourists or repositioning product or destination offerings to take this promising market or similar markets into account.

为了成功迎合蓬勃发展的老年旅游市场, 了解激励和阻止老年人国际旅行的因素尤为重要, 尤其是在 COVID-19 之后。本研究侧重于老年人的目的地选择意向, 并基于推拉因素、感知旅行风险 (PTR) 和感知旅行限制 (PTC), 旨在开发影响老年人国际旅游目的地选择的积极和消极因素的模型。

决策试验和评估实验室 (DEMATEL), 和与理想解决方案相似度的模糊偏好顺序 (Fuzzy TOPSIS) 是两种多标准决策 (MCDM) 技术, 用于识别变量之间的联系并找出它们在决策模型中的相对重要性。

DEMATEL的结果表明, 推力和拉力因素是“影响”, 而感知旅行约束（PTC）和感知旅行风险（PTR）是影响老年人目的地选择意愿的因素中的“原因”。推动因素和 PTC 比拉动因素和 PTR 发挥更重要的作用。 Fuzzy TOPSIS分析结果表明, “改善健康”和“自我实现和精神”是推动因素下最重要的因素。此外, 目的地的“健康安全商数”是拉动因素中最重要的, “人际约束”是PTC中最重要的。最后, 研究结果表明, “健康风险”是 PTR 中最重要的。

本文通过评估旅游动机、PTR 和老年游客 PTC 之间的相互关系, 为现有的旅游文献做出了贡献。此外, 该研究展示了影响老年游客目的地选择意愿的因素中各个子因素的比较优先级。本研究中使用的数据分析技术也很新颖, 以前从未在老年人旅游研究中使用过。最后, 虽然对老年旅游有丰富的研究, 但印度老年旅游者的行为相对不为人知。研究结果向有兴趣进入印度出境老年游客市场或重新定位产品或目的地的全球旅游利益相关者提供了切实可行的建议, 以考虑这个有前景的市场或类似市场。

Para atender a un mercado turístico de la tercera edad, es esencial comprender los factores que les motivan y les disuaden de realizar viajes internacionales tras el COVID-19. Este estudio se centra en las intenciones de elección de destino de las personas mayores y pretende desarrollar un modelo que priorice los factores positivos y negativos que conducen a la elección de un destino de viaje internacional. Utiliza los factores push-pull, los riesgos de viaje percibidos (PTR) y las limitaciones de viaje percibidas (PTC).

Decision Making Trial and Evaluation Laboratory (DEMATEL) y Fuzzy Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) son dos técnicas de toma de decisiones multicriterio (MCDM) utilizadas para identificar las conexiones entre variables y determinar su importancia relativa en el modelo de toma de decisiones.

DEMATEL descubrió que los factores de empuje y atracción son “efectos,” mientras que las PTC y las PTR son “causas” que afectan a las elecciones de destino de las personas mayores. Los factores de empuje y los PTC tienen un mayor impacto que los factores de atracción y los PTR. El Fuzzy TOPSIS destacó la “mejora de la salud y el bienestar” y la “autorrealización y espiritualidad” como factores de empuje clave, el “cociente de seguridad y protección de la salud” como el factor de atracción más importante y las “limitaciones interpersonales” como el PTC más crítico. Por último, los “riesgos para la salud” son el principal PTR.

Este artículo se suma a la literatura turística al estudiar la relación entre la motivación de los turistas sénior, los PTR y los PTC y mostrar cómo afectan los subfactores a su elección del destino. Las técnicas de análisis de datos empleadas en este estudio también son novedosas, ya que nunca se habían utilizado en la investigación sobre el turismo senior. Por último, aunque existen muchas investigaciones sobre el turismo sénior, el comportamiento de los turistas de la tercera edad en la India es relativamente desconocido. A la luz de los resultados del estudio, se ofrecen recomendaciones prácticas a las partes interesadas en el turismo de todo el mundo, interesadas en aprovechar el mercado de los turistas senior indios emisores o en reposicionar la oferta de productos o destinos para tener en cuenta este prometedor mercado o mercados similares.

This study aims to investigate the effects of financial aid via zakat and the perceived learning–teaching process on the educational outcomes of undergraduate students in Malaysia.

Approximately 350 zakat recipients (mustahiq) were selected to evaluate their educational outcomes, measured by the learning process, student readiness and academic performance. Partial least squares (PLS) regression was used to test the selected samples and approve the hypothesis.

In accordance with the underlying theories, the results of the PLS regression highlighted several major findings: financial aid, through zakat indeed increases positive effects in the learning process; zakat aid is found to increase student readiness in the education process and academic performance of undergraduate students; and student readiness in the education process plays a significant role in mediating the effect between zakat aid and the learning process and academic performance. This study also demonstrates that the learning process is equally important for student readiness and academic performance.

Importantly, this study contributes novelty by exploring the impact of zakat in education, specifically the higher education sector, because previous studies have enormously discussed zakat as a poverty-mitigating topic. The findings of this study are essential for zakat stakeholders to understand the contribution of zakat to developing human capital, particularly post-COVID-19 in Malaysia.

To support digital humanities research more effectively and efficiently, this study develops a novel Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI) technology that would not only allow humanities scholars to look at the relationships between people but also has the potential for aiding digital humanities research by identifying latent relationships between people via relationships between people and organizations.

To verify the effectiveness of KGAT-PO, a counterbalanced design was applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO, respectively, to perform people relationship inquiry and to see if there were significant differences in the effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance between the two groups. Interviews and Lag Sequential Analysis were also used to observe research participants’ perceptions and behaviors.

The results show that the DHRP-BCMP with KGAT-PO could help research participants improve the effectiveness of exploring relationships between people, and the research participants showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that most research participants agreed that the KGAT-PO is a good starting point for exploring relationships between people and improves the effectiveness and efficiency of exploring people’s relationship networks.

The research’s limitations encompass challenges related to data quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-child, friend, teacher-student, relative, work, and others. These factors should be carefully considered to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities research.

The study’s practical implications encompass enhanced research efficiency, aiding humanities scholars in uncovering latent interpersonal relationships within historical texts with high technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and marketing, educational settings, and innovative research directions, ultimately contributing to data-driven insights in the field of digital humanities.

The research’s originality lies in creating a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and effectiveness of their research.