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Workpiece sorting is a key link in industrial production lines. The vision-based workpiece sorting system is non-contact and widely applicable. The detection and recognition of workpieces are the key technologies of the workpiece sorting system. To introduce deep learning algorithms into workpiece detection and improve detection accuracy, this paper aims to propose a workpiece detection algorithm based on the single-shot multi-box detector (SSD).

Propose a multi-feature fused SSD network for fast workpiece detection. First, the multi-view CAD rendering images of the workpiece are used as deep learning data sets. Second, the visual geometry group network was trained for workpiece recognition to identify the category of the workpiece. Third, this study designs a multi-level feature fusion method to improve the detection accuracy of SSD (especially for small objects); specifically, a feature fusion module is added, which uses “element-wise sum” and “concatenation operation” to combine the information of shallow features and deep features.

Experimental results show that the actual workpiece detection accuracy of the method can reach 96% and the speed can reach 41 frames per second. Compared with the original SSD, the method improves the accuracy by 7% and improves the detection performance of small objects.

This paper innovatively introduces the SSD detection algorithm into workpiece detection in industrial scenarios and improves it. A feature fusion module has been added to combine the information of shallow features and deep features. The multi-feature fused SSD network proves the feasibility and practicality of introducing deep learning algorithms into workpiece sorting.

The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time.

To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms.

The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment.

This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.

The purpose of this paper is to analyse the characteristics of environmental protection behaviours of community residents from the perspective of system dynamics.

Documentation method is used to collect the factors and environmental improvement intervention strategies that influence the environmental protection behaviours of community residents from the top journals. To analyse the characteristics of the environmental protection behaviours of community residents which are nonlinear and dynamic, system dynamics (SD) are drawn. The initial value for simulation is obtained from the investigation report of some communities in China by questionnaires method.

The simulation shows that a process of continuous and interactional feedback is formed among environmental improvement intervention strategies, living context, environmental behavioural intention and environmental protection behaviours. These influences affect how environmental protection behaviour is affected by frequency of community environmental publicity, frequency of community participation regulation and local government investment, all of which are significant and decrease successively. Living context and environmental behavioural intention, which are intervened by the three strategies, can promote community residents’ environmental protection behaviours and reduce the degree of environmental pollution in communities.

The goal of reducing the community environmental pollution may be achieved, because the community environmental management departments can use this system dynamics approach to intervene the environmental protection behaviours of community residents.

The nonlinear and dynamic interactions among influencing factors are illustrated for an in-depth understanding of the environmental protection behaviours of community residents in this study. In addition, the initial parameter value and correlation coefficients are obtained for simulation through the questionnaire survey given to Chinese community residents to avoid the subjectivity of system dynamics.

This paper aims to identify industry drivers and priorities that influence decision-making towards adopting environmentally sustainable (ES) features in stadiums. An “Environmentally Sustainable Stadium (ESS) Process Model” is proposed to track ES features throughout their life cycle, guiding planning, designing, construction, operation and management.

Data were collected through 20 online semi-structured interviews with experts from sustainability, built environment, stadium management, mega-event planning and management and researchers. The experts’ project experiences spanned across Oceania, North America, South America, Asia and Europe. The data were recorded and transcribed through Teams and analysed using NVivo 11 application software.

Stadium’s ES features predominantly focus on energy, waste and materials management with some emphasis on carbon emission reductions and renewable energy sources. Emerging focus areas include flexible designs, audience (or fans) and community engagement, circular economy principles and integration with nature. Key drivers for adopting ES features include legislation and other sector-based requirements, competitive advantages and stakeholder pressures. ES feature success depends on owner support, budget, partnerships, expertise and opportunities. Major barriers include stakeholder diversity, infrastructure limitations and limited stadium-specific requirements.

This paper proposes a six-step “ESS Process Model” to support the stadium industry to holistically integrate ES features, from the initial decision-making to the implementation, ongoing improvement and stakeholder engagement. The model helps stakeholders to identify opportunities, navigate challenges and achieve continual improvement.

The ESS process model is a novel approach to integrate ES features in stadiums, through enhancing collaboration among stakeholders and overcoming challenges in choosing, implementing and maintaining ES features.

The purpose of this study is twofold. First, this study elaborates an integrative conceptual framework of tourism destination competitive productivity (TDCP) by blending established destination competitiveness frameworks, the competitive productivity (CP) framework and studies pertaining to big data and big data analytics (BDA) within destination management information systems and smart tourism destinations. Second, this study examines the drivers of TDCP in the context of the ongoing 4th industrial revolution by conceptualizing the destination business intelligence unit (DBIU) as a platform able to create sustained destination business intelligence under the guise of BDA, useful to support destination managers to achieve the tourism destination’s economic objectives.

In this work, the authors leverage both extant literature (under the guise of research on CP, tourism destination competitiveness [TDC] and destination management information systems) and empirical work (in the form of interviews and field work involving destination managers and chief executive officers of destination management organizations and convention bureaus, as well as secondary data) to elaborate, develop and present an integrative conceptual framework of TDCP.

The integrative conceptual framework of TDCP elaborated has been found helpful by a number of destination managers trying to understand how to effectively and efficiently manage and market a tourism destination in today’s fast-paced, digital and hypercompetitive environment. While DBIUs are at different stages of implementation, often as part of broader smart destination initiatives, it appears that they are increasingly fulfilling the purpose of creating sustained destination business intelligence by means of BDA to help tourism destinations achieve their economic goals.

This work bears several practical implications for tourism policymakers, destination managers and marketers, technology developers, as well as tourism and hospitality firms and practitioners. Tourism policymakers could embed TDCP into tourism and economic policies, and destination managers and marketers might build and make use of platforms such as the proposed DBIU. Technology developers need to understand that designing destination management information systems in general and more specifically DBIUs requires an in-depth analysis of the stakeholders that are going to contribute, share, control and use BDA.

To the best of the authors’ knowledge, this study constitutes the first attempt to integrate the CP, TDC and destination management information systems research streams to elaborate an integrative conceptual framework of TDCP. Second, the authors contribute to the Industry 4.0 research stream by examining the drivers of tourism destination CP in the context of the ongoing 4th industrial revolution. Third, the authors contribute to the destination management information systems research stream by introducing and conceptualizing the DBIU and the related sustained destination business intelligence.

Proposes a new real estate valuation methodology and presents the architecture for a decision support system for real estate analysis based on Geographic Information Systems (GIS) techniques integrated with fuzzy theory and spatial analysis.

The proposed information system architecture/problem‐solving methodology uses GIS technology integrated with two approaches: fuzzy logic and spatial analysis. The steps required in the proposed methodology are: database design and implementation; criteria and rules; system design; and implementation. The components/modules included in the proposed methodology are: requirement and definition analysis; data production; topology; integrated database; visualization; variables; quantification; valuation; and implementation.

The applicability of the system is evaluated via a case study in estimation of house sale prices. The proposed system/methodology was used in order to valuate property values in one municipality of Attica in Greece. The estimation, market analysis, forecasting and management of property values are of great importance and a prerequisite for real estate development.

The proposed methodology is innovative, easy to implement and has a vast theoretical background. Following the methodology/architecture, a prototype information system is presented in order to move from theory to practice. The value of the paper is the combination of new technology assessments and GIS tools, integrated with fuzzy theory and spatial analysis.

The purpose of this study is to perform bibliometric analysis to systematically and comprehensively examine the current landscape of digital knowledge, integration and performance in the transformation of sustainability accounting, reporting and assurance.

This research uses a systematic literature review, following the Scientific Procedures and Rationales for Systematic Literature Review protocol and uses various bibliometric and performance analytical methods. These include annual scientific production analysis, journal analysis, keyword cooccurrence analysis, keyword clustering, knowledge gap analysis and future research direction identification to evaluate the existing literature thoroughly.

The analysis reveals significant insights into the transformative impact of digital technologies on sustainability practices. Annual scientific production and journal analyses highlight key contributors to the adoption of digital technologies in sustainability accounting, reporting and assurance. Keyword cooccurrence analyses have identified key themes in sustainability accounting, reporting and assurance, highlighting the transformative role of digital technologies such as artificial intelligence (AI), blockchain, Internet of Things (IoT) and big data. These technologies enhance corporate accountability, transparency and sustainability by automating processes and improving data accuracy. The integration of these technologies supports environmental, social and governance (ESG) reporting, circular economy initiatives and strategic decision-making, fostering economic, social and environmental sustainability. Cluster-by-coupling analyses delve into nine broader revealing that IoT improves ESG report accuracy, eXtensible Business Reporting Language structures ESG data and AI enhances life cycle assessments and reporting authenticity. In addition, digital transformation impacts environmental performance, big data optimizes resource use and edge computing improves eco-efficiency. Furthermore, this study identifies avenues for future research to advance the understanding and implementation of digital technology in sustainability accounting, reporting and assurance practices.

Academically, this research enriches the understanding of how digital technologies shape sustainability practices and identifies gaps in digital knowledge and integration. Practically, it provides actionable insights for organizations to improve sustainability reporting and performance by effectively leveraging these technologies. Policy-wise, the findings advocate for frameworks supporting the effective implementation of these technologies, ensuring alignment with global sustainability goals.

This study offers a detailed analysis of the performance and intellectual framework of research on implementing digital technology in sustainability accounting, reporting and assurance. It highlights the evolving research landscape and emphasizes the need for further investigation into how emerging technologies can be leveraged to achieve sustainability goals.