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In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The role of chief executive officer (CEO) personal characteristics in shaping corporate policies has attracted increasing academic attention in the past two decades. In this review, the authors synthesize extant research on CEO attributes by reviewing 232 articles published in 29 journals from the accounting, finance and management literature. This review provides an overview of existing findings, highlights current trends and interdisciplinary differences in research approaches and identifies potential avenues for future research.

To review the literature on CEO attributes, the authors manually collected peer-reviewed articles in accounting, finance and management journals from 2000 to 2021. The authors conducted in-depth analysis of each paper and manually recorded the theories, data sources, country of study, study period, measures of CEO attributes and dependent variables. This procedure helped the authors group the selected articles into themes and sub-themes. The authors compared the findings in various disciplines and provided direction for future research.

The authors highlight the role of CEO personal attributes in influencing corporate decision-making and firm outcomes. The authors categorize studies of CEO traits into three main research themes: (1) demographic attributes and experience (including age, gender, culture, experience, education); (2) CEO interactions with others (social and political networks) and (3) underlying attributes (including personality, values and ideology). The evidence shows that CEO characteristics significantly affect a wide range of specific corporate policies that serve as mechanisms through which individual CEOs determine firm success and performance.

CEO selection is one of the most crucial decisions made by corporations. The study findings provide valuable insights to corporate executives, boards, investors and practitioners into how CEOs’ personal characteristics can impact future firm decisions and outcomes that can, in turn, inform the high-stake process of CEO recruitment and selection. The study findings have significant practical implications for corporations, such as contributing to executive training programs, to assist executives and directors attain a greater level of self-awareness.

Building on the theoretical foundation of upper echelons theory, the authors offer an integrated theoretical framework to consolidate existing empirical research on the impacts of CEO personal attributes on firm outcomes across accounting and finance (A&F) and management literature. The study findings provide a roadmap for scholars to bridge the interdisciplinary divide between A&F and management research. The authors advocate a more holistic and multifaceted approach to examining CEOs, each of whom embodies a myriad of personal characteristics that comprise their unique identity. The study findings encourage future researchers to expand the investigation of the boundary conditions that magnify or moderate the impacts of CEO idiosyncrasies.

The lack of studies about the relevance of disaster awareness factors and disaster evacuation as a part of disaster responses especially for fire cases in an academic environment in Indonesia has triggered this study to explore the disaster awareness factors and evacuation experiment without emergency alarm for case study students in the classroom. The relevance of disaster awareness factors in transforming into practical action and decision in a disaster evacuation need to be examined to study the relevance of both phases in disaster.

This research conducted a quantitative approach by studying questionnaires from 162 respondents collectively divided into five groups to examine the student disaster awareness factors randomly from those groups. The qualitative approach was implemented through the evacuation experiments that were conducted twice to analyze the disaster evacuation performance. The analysis for the relevance is conducted by comparing the result of the questionnaire study and the evacuation experiment.

According to the questionnaire study, generally, the students are highly confident with their hazard knowledge in disaster awareness except that half of them are doubtful about appropriate steps in a disaster. The experiment without explosive sound showed that they have slower responses in the critical moment of evacuation. The response in the experiments showed relevance with several disaster awareness factors

This study has explored the relevance of disaster awareness factors with disaster response in a campus building. In the part of reducing risk during fire disaster, this research shows the importance of social interaction and hazard knowledge during the disaster.

The improvement of disaster evacuation procedures and training in a campus building is mandatory to reduce disaster risk based on the relevance of disaster awareness factors and disaster response in this study.

This study measures the relevance of disaster awareness factors performance of the students by comparing it to their actions and decisions in an experimental setting of fire building. The disaster awareness factor performance was measured by a questionnaire survey while the experiments were deployed to observe the performance of their actions and decisions during evacuation as part of the disaster response phase.

Over the decades, simultaneous localization and mapping (SLAM) techniques have been extensively researched and applied in robotic mapping. In complex environments, SLAM systems using a single sensor, such as a camera or light detection and ranging (LiDAR), often cannot meet the accuracy and map consistency requirements. This study aims to propose a tightly-coupled LiDAR-inertial SLAM system, which aims to achieve higher accuracy and map consistency for robotic mapping in complex environments.

This paper presents TC-Mapper, a tightly coupled LiDAR-inertial SLAM system based on LIO-SAM. The authors introduce the normal distribution-based loop closure detection method to the original one (i.e. the radius search-based method), which can enhance the accuracy and map consistency for robotic mapping. To further suppress map drift in complex environments, this paper incorporates a gravity factor into the original factor graph. In addition, TC-Mapper introduces incremental voxels (iVox) as the point cloud spatial data structure.

Extensive experiments in public and self-collected data sets demonstrate that TC-Mapper has high accuracy and map consistency.

TC-Mapper has two types of loop closure detections: the normal distribution-based method for correcting large drifts and the radius search-based method for fine-stitching, which can achieve higher accuracy and map consistency. The authors introduce iVox as the point cloud spatial data structure, which strives to attain a balance between precision and efficiency to the greatest extent feasible.

The purpose of this paper is to investigate the impact of managerial overconfidence on corporate financing decision and the moderating effect of government ownership on the relationship between managerial overconfidence and corporate financing decision.

Pooled OLS, fixed effect models (FEM), and Tobit regressions are employed to examine the relationship between managerial overconfidence, government ownership and corporate financing decision of publicly listed companies in Malaysia for the period of 2002-2011.

The authors conclude that: first, CEO overconfidence is significantly and negatively related to corporate financing decision; second, a higher degree of managerial overconfidence would result in lower leverage in GLCs, whereas the effect does not significantly exist in NGLCs; third, a larger ownership of government in a firm will reduce the negative effect of managerial overconfidence on corporate financing decision; fourth, the moderating effect of government ownership on the association between managerial overconfidence and corporate financing decision in GLCs is more effective than NGLCs; and fifth, government intervention plays its role as moderating effect on the relationship between managerial overconfidence and corporate financing decision in firms with lower ownership concentration but not in firms with high ownership concentration (more or equal than 50 percent).

The finding implies that the moderating effect of government ownership on the association between managerial overconfidence and corporate financing decision in GLCs is more effective than NGLCs.

The authors make the first attempt to test the moderating effect of government ownership on the relationship between ownership concentration and corporate financing decision.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

This paper aims to study the working principle of the capacitive pressure sensor and explore the distribution of pressure acting on the surface of the capacitor. Herein, a kind of high sensitivity capacitive pressure sensor was prepared by overlaying carbon fibers (CFs) on the surfaces of the thermoplastic elastomer (TPE), the TPE with high elasticity is a dielectric elastomer for the sensor and the CFs with excellent electrical conductivity were designed as the conductor.

Due to the excellent mechanical properties and electrical conductivity of CFs, it was designed as the conductor layer for the TPE/CFs capacitive pressure sensor via laminating CFs on the surfaces of the columnar TPE. Then, a ‘#' type structure of the capacitive pressure sensor was designed and fabricated.

The ‘#' type of capacitive pressure sensor of TPE/CFs composite was obtained in high sensitivity with a gauge factor of 2.77. Furthermore, the change of gauge factor values of the sensor under 10 per cent of applied strains was repeated for 1,000 cycles, indicating its outstanding sensing stability. Moreover, the ‘#' type capacitive pressure sensor of TPE/CFs was consisted of several capacitor arrays via laminating CFs, which could detect the distribution of pressure.

The TPE/CFs capacitive pressure sensor was easily fabricated with high sensitivity and quick responsiveness, which is desirably applied in wearable electronics, robots, medical devices, etc.

The outcome of this study will help to fabricate capacitive pressure sensors with high sensitivity and outstanding sensing stability.

Collaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively.

A collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity incident management are proposed.

Use case depicting how the collaborative-based national cybersecurity incident management would function within a typical information and communication technology ecosystem is illustrated. The proposed collaborative approach is evaluated based on the performances of an experimental cyber-incident management system against two multistage attack scenarios. The results show that the proposed approach is more reliable compared to the existing ones based on descriptive statistics.

The approach produces better incident impact scores and rankings than standard tools. The approach reduces the total response costs by 8.33% and false positive rate by 97.20% for the first attack scenario, while it reduces the total response costs by 26.67% and false positive rate by 78.83% for the second attack scenario.

The purpose of this paper is to explore determining factors that account for variation in public satisfaction with the local police in Saskatoon, Saskatchewan, Canada.

An integrated method was used to gather the data for this study, including official survey data conducted by Insightrix, and interviews with citizens in Saskatoon.

This research found that demographic factors including age, race (in this study, Aboriginal status in particular), education, and income, perception of neighborhood safety, citizen-police interaction, and learning about crime from news media all have impact on public attitudes toward the police, to different degrees. The gap or distance between the police and the Aboriginal community was highlighted as a major factor.

Further research should be done to compare statistical patterns in other same-level cities in Canada.

This paper indicates that Saskatoon Police Service in the future should provide a more structured avenue for citizen participation in establishing safe neighborhoods, more structured cultural sensitivity training, and create a wider channel through which community residents with various social backgrounds can demand some measure of accountability for police work in their area.

The paper is of value to law enforcement policy-makers and academic researchers with interest in policing and police-community relationship.

The purpose of this paper is to identify the factors influencing investment decisions in mobile services for profitablity and to become a global leader in mobile services sector.

A two-stage methodology is followed. In the first stage, factors are identified from literature, and are validated with telecommunication domain experts using the t-test. In the second stage, interpretive structural modeling (ISM) is used to understand the complex interrelationships among various factors. Further, MICMAC analysis is performed to analyze the indirect relationships and their effect on different factors by stabilizing the rank based on driving and dependence power. Based on MICMAC analysis, four clusters are identified to aid the policy- and decision-makers.

The major contribution of this research is imposing directions and dominance of various factors to make informed decision-making for investment in mobile services to meet the upcoming demand for mobile services in Indian telecommunication sector.

The applicability of these research findings is limited to emerging telecommunication market.

This paper forms the basis for identifying various factors that act as the driving force for the Indian telecommunication operators to pay special attention toward mobile services, with telecommunication data analytics and developing context-aware services. This paper will aid policy-makers in the government, managers in telecommunication companies and other stakeholders such as content providers, channel partners and application developers to take a lead role in developing appropriate mobile services to meet local needs of Indian users. It will help in developing strategies to collaborate and motivate other stakeholders, including device manufacturers to understand and work collaboratively to become world leader in mobile services.

This paper provides a framework for understanding the various factors that encourage telecommunication companies to establish and invest in mobile services and setup a separate vertical in their organization with a focus on mobile services to meet the future demands of Indian market. Appropriate utilization of telecommunication data analytics, personalization of services, customization in local languages and support for convergent services would encourage adoption of mobile services.