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This study aims to investigate the adoption of the United Nations’ Sustainable Development Goals (SDGs) as the new performance perspective in cities. It also aims to understand how accounting for SDGs begins in city administrations by following Power’s (2015) fourfold development schema composed of policy object formation, object elaboration, activity orchestration and practice stabilization.

Focusing on a network of cities coordinated by the Finnish local government association, we analyzed the six largest cities in Finland employing a holistic multiple case study strategy. Our data consisted of Voluntary Local Reviews (VLRs), city strategies, budget plans, financial statements, as well as results of participant observations and semi-structured interviews with key individuals involved in accounting for SDGs.

We unveiled the SDG framework as an interpretive scheme through which cities glocalized sustainable development as a novel, simultaneously global and local, performance object. Integration of the new accounts in city management is necessary for these accounts to take life in steering the actions. By creating meaningful alignment and the ability to impact managerial practices, SDGs and VLRs have the potential to influence local actions. Our results indicate further institutionalization progress of sustainability as a performance object through SDG-focused work.

While prior research has focused mainly on general factors influencing the integration of the sustainability agenda, this study provides a novel perspective by capturing the process and demonstrating empirically how new accounts on SDGs are introduced and deployed in the strategic planning and management of local governments.

This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.

The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.

The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.

The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

The involvement of clients in service encounters and service development has become a central principle for contemporary health and social care organizations. However, in day-to-day work settings, the shift toward client involvement is still in progress. We examined how health and social care professionals, together with clients and managers, co-develop their conceptions of client involvement and search for practical ways in which to implement these in organizational service processes.

The empirical case of this study was a developmental intervention, the client involvement workshop, conducted in a Finnish municipal social and welfare center. The cultural-historical activity theory (CHAT) framework was used to analyze the development of client involvement ideas and the modes of interaction during the intervention.

Analysis of the collective discussion revealed that the conceptions of client involvement developed through two interconnected object-orientations: Enabling client involvement in service encounters and promoting client involvement in the service system. The predominant mode of interaction in the collective discussion was that of “coordination.” The clients' perspective and contributions were central aspects in the turning points from coordination to cooperation; professionals crossed organizational boundaries, and together with clients, constructed a new client involvement-based object. This suggests that client participation plays an important role in the development of services.

The CHAT-based examination of the modes of interaction clarifies the potential of co-developing client-involvement-based services and highlights the importance of clients' participation in co-development.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

Proper platform pose is important for the mobile manipulator to accomplish dexterous manipulation tasks efficiently and safely, and the evaluation criterion to qualify manipulation performance is critical to support the pose decision process. This paper aims to present a comprehensive index to evaluate the manipulator’s operation performance from various aspects.

In this research, a criterion called hybrid manipulability (HM) is proposed to assess the performance of the manipulator’s operation, considering crucial factors such as joint limits, obstacle avoidance and stability. The determination of the optimal platform pose is achieved by selecting the pose that maximizes the HM within the feasible inverse reachability map associated with the target object.

A self-built mobile manipulator is adopted as the experimental platform, and the feasibility of the proposed method is experimentally verified in the context of object-grasping tasks both in simulation and practice.

The proposed HM extends upon the conventional notion of manipulability by incorporating additional factors, including the manipulator’s joint limits, the obstacle avoidance situation during the operation and the manipulation stability when grasping the target object. The manipulator can achieve enhanced stability during grasping when positioned in the pose determined by the HM.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

Authenticity has been studied from a variety of disciplinary perspectives, leading to a rich but confused literature. This study, a review, aims to compare the psychology and sociology/tourism definitions of authenticity to clarify the concept. From a psychological perspective, authenticity is a mental appraisal of an object or experience as valued leading to feelings and summative judgements (such as satisfaction or perceived value). In objective authenticity, a person values the object due to belief in an expert’s opinion, constructive authenticity relies on socially constructed values, while existential authenticity is based on one’s self-identity. The resultant achievement of a valued goal, such as seeing a valued object, leads to feelings of pleasure. Sociological definitions are similar but based on different theoretical antecedent causes of constructed and existential authenticity. The paper further discusses the use of theory in tourism and the project to develop tourism as a discipline. This project is considered unlikely to be successful and in turn, as argued, it is more useful to apply theory from other disciplines in a multidisciplinary manner. The results emphasise that it is necessary for tourism researchers to understand the origins and development of the concepts they use and their various definitions.

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.

Circular economy advocates innovations that upcycle wastes in the food supply chain to generate high added-value materials. These innovations are not only disruptive and green but also they are often initiated by startups, leading to the emergence of novel open-loop supply chains connecting actors in food and non-food sectors. While earlier research has highlighted the need to seek legitimacy for disruptive innovations to survive and grow, little is known about how these innovations occur and evolve across sectors. This paper aims to elaborate on this mechanism by exploring the function of the circular economy as a boundary object to facilitate legitimacy-seeking strategies.

An exploratory multiple-case research design is adopted and features food waste innovation projects with multi-tier supply chains consisting of a food producer, a startup and a buying firm. The study is investigated from the legitimacy and boundary object lenses.

The findings proposed a framework for the role of a boundary object in enabling legitimacy-seeking strategies for novel food waste innovations. First, the interpretative flexibility of the circular economy affords actors symbolic resources to conduct manipulation strategy to achieve cognitive legitimacy. Second, small-scale work arrangements enable creation strategy for the new supply chain to harness moral legitimacy. Finally, pragmatic legitimacy is granted via diffusion strategy enabled by scalable work arrangements.

This paper provides novel insights into the emergence of food waste innovation from a multi-tier supply chain perspective. It also highlights the key role of the boundary object in the legitimacy-seeking process.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.