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The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

This study analyzes the effect of the techniques of active teaching and learning as a way of delivery on the outcomes of quality learning. Focusing on the courses of architectural science taught in a nontraditional method using various active learning strategies, the study takes the case study of the course Building Illumination and Acoustics (BIA) delivered in the academic year 2019–2020 at the University of Sharjah (UoS)'s Architectural Engineering Department (AED).

Utilizing both quantitative and qualitative research approaches, the study applied a case study and survey as methods. A questionnaire was designed and performed to assess the level of students' satisfaction with the implemented active teaching method.

The vibrant learning setting made the students actively engaged and more motivated and enthusiastic. The active learning practices used, including employing senses as in sight and hearing, reasoning rationally and intuitively, reflecting and acting, working steadily and in fits and starts, creating mathematical models, visualizing and memorizing and drawing analogies, were efficient in boosting their ability to comprehend theoretical concepts more effectively. The delivery style effectively enhances quality learning when various active techniques are used pedagogically beyond being merely a utilitarian instrument to prepare novice students of architectural engineering to fulfill practical challenges.

This article focuses specifically on a theoretical, scientific non-studio course in a particular program of architectural engineering in a particular semester before the dramatic changes in styles of teaching delivery that happened due to the COVID-19 pandemic. Future research could further highlight its results by comparing them to statistical evidence of the development of the course, especially for the duration of online teaching during the pandemic and the hybrid teaching period after it.

This article contributes to the development of teaching and learning of architectural engineering in the local Emirati context by putting original theories of teaching into practice. This paper further contributes to the field of architectural pedagogy in terms of the effect of active learning in the architecture field in the non-studio courses in higher education in the United Arab Emirates.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

The study explains why Parliamentarians in the United Kingdom (UK) focused on accountability through data during the COVID-19 pandemic, as well as on how data could be used to improve the government’s response to the pandemic.

Understanding the implications of accountability for COVID-19 is crucial to understanding how governments should respond to future pandemics. This article provides an account of what a select committee in the UK thought were the essential elements of these accountability relationships. To do so, the authors use a neo-Roman concept of liberty to show how Parliamentary oversight of the pandemic for accountability was crucial to maintaining the liberty of citizens during the crisis and to identify what lessons need to be learnt for future crises.

The study shows that Parliamentarians were concerned that the UK government was not meeting its obligations to report openly about the COVID-19 pandemic to them. It shows that the government did make progress in reporting during the pandemic but further advancements need to be made in future for restrictions to be compatible with the protection of liberty.

The study extends the concept of neo-Roman liberty showing how it is relevant in an emergency situation and provides an account of why accountability is necessary for the preservation of liberty when the government uses emergency powers.

Governments and Parliaments need to think about how they preserve liberty during crises through enhanced accountability mechanisms and the publication of data.

The study extends previous work on liberty and calculation, providing a theorisation of the role of numbers in the protection of liberty.

The tourism industry is facing significant challenges in an ever-changing world marked by globalisation, digitalisation and societal shifts. The issues of overtourism and massification exacerbate concerns about sustainability and the industry's impact on the environment and local communities. These concerns arise as profit-driven ideologies overshadow the industry's original vocation to contribute to meaningful encounters, well-being and social justice. This chapter explores the cultivation of humaneness and conscience within tourism through education, knowledge and personal reflection. Drawing inspiration from Hannah Arendt's interpretation of Socrates' philosophy, it highlights the importance of critical thinking and a comprehensive understanding of the industry's role in shaping alternative futures. Tourism higher education plays a pivotal role in empowering students to become catalysts for systemic transformation. Furthermore, this chapter emphasises the value of embracing diverse viewpoints and engaging in meaningful encounters and dialogues with local communities and stakeholders to collaboratively imagine and implement sustainable practices. Only by dismantling entrenched habits through critical thinking and fostering collaboration can the tourism industry envision alternative trajectories towards a more conscientious and humane path forward.

This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.

Robots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

There is a gap in understanding with regards to the discrimination and prejudice experienced by gay entrepreneurs. To address this, an intersectional perspective is adopted to facilitate a better understanding of how lesbian and gay entrepreneurs may experience heterosexism.

This qualitative study uses semi-structured interviews to explore the experiences of 14 lesbian and gay entrepreneurs as they navigate homophobia and heterosexism.

The study contributes novel insights to the field of entrepreneurship, extending the study of lesbian and gay entrepreneurs to include gender and a fine-grained analysis of the experience of heterosexism. Its inclusion of an intersectional perspective of the lesbian-female entrepreneur expands the emerging body of literature examining intersectional identities of minority entrepreneurs.

The authors provide a more nuanced understanding of the impact of heterosexism on LGBT+ entrepreneurial activities. This is facilitated by the authors' adoption of an intersectional perspective which shows how the different axes of identity influenced gender identity performance in relation to the model of perceived neutrality in LGBT+ entrepreneurship. The authors also make an original contribution to minority stress literature through the authors' exploration of one facet of minority entrepreneurship, namely the impact of heterosexism on LGBT+ entrepreneurial activities.