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This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

This study assessed the technology readiness of library commons in selected universities in Ghana from the perspectives of both clients and service providers.

A sample survey research design was used to carry out the study, and the study consisted of quantitative and qualitative approaches. The data were collected from 157 of the 430 commons’ users and six library directors/commons librarians, using a questionnaire and an interview schedule respectively. The quantitative data were analysed using descriptive statistics and inferential analysis with SPSS Ver. 22, and structural equation modelling with AMOS Ver. 23.

The study found significant relationships between the availability of learning resources and users' experience, use and satisfaction with the commons. Additionally, there were significant relationships between the readiness of the commons, users' perceptions and their satisfaction levels. The library/commons managers identified key preparedness issues, including inadequate planning and poor space allocation. Furthermore, the study revealed negative feelings among library/commons managers, such as insecurity, resistance and low confidence.

The academic libraries in Ghana should evaluate their commons following the findings of this study, and use other readiness implementation tools to identify and address the gaps in their implementation of the commons.

This study was based on the opinions of the users and operators of the commons and focused primarily on technology-related factors. However, it is essential to evaluate other readiness factors, such as change and services, and institutional factors, using frameworks such as Hess and Ostrom’s (2005) Institutional Development Framework, to provide complementary results that can guide better implementation of the commons and improve the quality of services.

The purpose is to investigate whether Brazilian e-commerce nonusers all have the same reasons not to purchase online or whether different behavior patterns might lead them to cluster in different groups.

This study carried out cluster analyses on a large sample (N = 9,065) from a nationwide survey on the use of information and communication technology in Brazil.

Three clusters of e-commerce nonusers were identified: the first cluster is quite reluctant; the second is characterized by disbelief in e-commerce; and the last cluster includes members who must see a product to believe it. Overall, nonusers have different reasons not to shop online, but they also share some similarities in this regard. Furthermore, socioeconomic factors do not seem to affect their behavior. The findings suggest that merchants’ failure to attract customers’ attention and tangibility are the major barriers to e-commerce use.

Even though nonusers have different reasons not to shop online, the key pattern that emerges is the value of tangibility for these individuals, which is a barrier present in all three clusters. This suggests that current marketing strategies and advertisements are ineffective to reach these consumers. Vendors should therefore try different approaches.

The findings contribute to the information systems (IS) literature by bringing a new perspective to the understanding of e-commerce rejection in addition to having managerial implications that involve strategies to attract potential users based on their specificities.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.

With the rise of mandating climate-related disclosures (CRD), this paper aims to investigate how energy and agriculture markets are exposed to climate disclosure risk.

Using the multivariable simultaneous quantile regression and data from 1 January 2017 to 29 February 2024, the authors examine daily and monthly responses of energy and agriculture markets to climate disclosure risk, energy risk, market sentiment, geopolitical risk and economic policy risk. The sample covers the global market, Australia, Canada, European Union (EU), Hong Kong, Japan, New Zealand, Singapore, the UK and the USA.

The results show that climate disclosure risk creates both positive and negative shocks in the energy and agriculture markets, and the impacts are asymmetric across quantiles in different economies. The higher the climate disclosure risk, the greater impact of crude oil future on the energy sector in North America (Canada and the USA) and Europe (EU and the UK), but no greater effects in Asia Pacific (Australia, New Zealand and Singapore). The agriculture sector can hedge against economic policy and geopolitical risks, but it is highly exposed to climate disclosure and energy risks.

This study timely contributes to the modest literature on the asymmetric effects of climate disclosure risk on the energy and agriculture markets at the global and national levels. The findings offer practical implications for policymakers and investment practitioners in understanding financial effects of mandating CRD to diversify risks depending upon market conditions and policy uncertainty.

This research aims to investigate the feasibility of imperfect produce use in the on-site foodservice management industry.

Study 1 explored experts’ perspectives via expert interviews on the use of imperfect produce in on-site foodservice operations, acceptability, and willingness to choose imperfect produce. Study 2, a sensory discrimination test, was performed with 100 consumers.

Study 1 yielded seven themes with managerial recommendations: appearance perception, customer value perception, operational difficulties, concerns for the world, food safety concerns, corporate advantages, and implementation. Study 2 found that participants were willing to choose menu items prepared with imperfect produce over those prepared with perfect produce. The ability to taste differences depended on the type of produce; participants could not taste differences between tangerines but could taste differences between apples and pears.

The findings of this study suggested that the psychological barriers of imperfect produce impact managers’ perceptions and decision-making processes. It is recommended that operators communicate with consumers about imperfect produce, its waste reduction benefits, and its quality.

This study shows that taste perception could be influenced by how imperfectness is described. Thus, appropriate marketing strategies could improve consumer acceptance of imperfect produce.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

Although it is a relatively recent conceptualization of malevolent personality, the dark triad (DT) has been widely researched and shown to be responsible for increases in physical violence, controlling behavior, short-term mating preferences and poor relationship quality. This study aims to investigate whether DT traits predict acceptance toward intimate partner violence (IPV) in the general population, addressing a gap in the literature regarding predictors of harmful attitudes toward romantic relationships.

In total, 150 adults aged 18–74 (76% women) completed two self-report questionnaires: the short DT and the IPV attitude scale-revised.

A series of hierarchical multiple regression analyses were conducted, with gender as predictor in the first models and DT traits added as predictors in second models. The results showed that male participants were more accepting of IPV than female participants. Over and above the contribution of gender, psychopathy and Machiavellianism positively predicted overall IPV acceptance, but narcissism did not. Psychopathy and Machiavellianism positively predicted acceptance of psychological abuse, and psychopathy positively predicted acceptance of controlling behaviors. Narcissism did not predict any facet of IPV acceptance.

To the best of the authors’ knowledge, as the first study to explore the roles of DT traits in acceptance of IPV behaviors, the results contribute to the understanding of how these traits may predispose individuals to harmful intimate partner behaviors. These findings can inform IPV prevention efforts to aid in the early identification of individuals who hold maladaptive beliefs surrounding romantic relationships.