Search

Aiming at the problems of low energy efficiency and unstable operation in the optimal allocation of optical storage capacity in rural new energy microgrids, this paper proposes an optimization method based on two-layer multi-objective collaborative decision-making. First, an outer optimization objective function containing constraints on capacity allocation, line transmission security, charging and discharging power of the energy storage system, microgrid security, and power supply reliability was constructed, and an inner optimization objective function containing constraints on energy storage self-discharge correction and power balance was constructed. The quantum-behaved particle swarm optimization algorithm is used to solve the optimal solution set of the objective function, and the interactive multi-criteria decision-making method is used to select the compromise solution to realize efficient optimal allocation of optical storage capacity. The test results show that this method can obtain the Pareto optimal solution set of multi-objective functions in the model. Comprehensive prospect value calculation results are obtained according to each configuration scheme, and a compromise scheme is obtained. The total energy consumption, power deviation rate, light rejection rate, and load loss cost were significantly reduced, with maximum values of 90.5%, 2.03% and 114,700 yuan, respectively, and the load loss probability was lower than 2.2%. The results show that the proposed method can effectively improve the total energy consumption utilization of the microgrid, reduce the power deviation rate and light abandonment rate, and provide significant advantages in different lighting and load demand scenarios. [ABSTRACT FROM AUTHOR]

The study sought to determine the influence of collaborative leadership practices of the school heads to teachers' teaching performance and pupils' academic achievements. Descriptive-correlational research method was utilized and the Statistical tools used in the study were mean, standard deviation, frequency count, and percentages to analyze the extent of the collaborative leadership practices in terms of shared school governance, collaborative decision-making, and stakeholders' participation; teachers' teaching performance; and pupils' academic achievement. Pearson-Product Moment Correlation was utilized to ascertain the significant relationship between the level of teachers' teaching performance and the extent of collaborative leadership of school heads and the significant relationship between teachers' teaching performance and pupils' academic achievement. Findings revealed that collaborative leadership in terms of shared school governance, collaborative decision-making, and stakeholders' participation were practiced to the "Very High Extent" while majority of the teachers that is 60% had a "Very Satisfactory" teaching performance and majority of the pupils that is 57% had a "Very Satisfactory" academic achievements. Subsequently, it was also found out in the Statistical Treatment of data that collaborative leadership of school heads in terms of shared school governance, collaborative decision-making, and stakeholders' participation were "Significantly Correlated" or had "Significant Relationship" to teachers' teaching performance while teachers' teaching performance had "Significant Correlation" to pupils' academic achievements. As a summary, it was recommended that school heads must continue to practice the collaborative leadership to stimulate teachers improve teachers' teaching effectiveness and pupils to develop their academic achievements. [ABSTRACT FROM AUTHOR]

As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements. [ABSTRACT FROM AUTHOR]

In Canada, recent advances towards reconciliation have introduced new collaborations between Indigenous and non-Indigenous governments, including for species-at-risk recovery planning. During these collaborations, Indigenous Knowledge (IK) is often requested, however, clear expectations of what IK is being sought and how diverse knowledge systems will be woven to produce tangible benefits to species recovery are often limited. Here, we provide a case study of a two-stage process to identify and collect IK components that can aid whudzih (caribou) recovery planning. First, we surveyed non-Indigenous government professionals involved in caribou initiatives to specify what IK would benefit recovery planning. Responses were used to guide the development of semi-structured interview questions. Interviews were conducted with knowledge holders from Lhtako Dene, a Southern Dakelh Nation in British Columbia, Canada with historic socioecological ties to caribou. Responses of government professionals highlighted 24 topics for caribou recovery, and interviews with Lhtako Dene knowledge holders revealed strong linkages between ecological and social information types. In some cases, the IK requested was not available from knowledge holders. Collaborations for caribou recovery would benefit from clarity on expectations and outcomes of IK sharing. We suggest that structured processes that respectfully facilitate IK requests and collection become commonplace in species recovery planning.

Abstract Aiming at the problems of low energy efficiency and unstable operation in the optimal allocation of optical storage capacity in rural new energy microgrids, this paper proposes an optimization method based on two-layer multi-objective collaborative decision-making. First, an outer optimization objective function containing constraints on capacity allocation, line transmission security, charging and discharging power of the energy storage system, microgrid security, and power supply reliability was constructed, and an inner optimization objective function containing constraints on energy storage self-discharge correction and power balance was constructed. The quantum-behaved particle swarm optimization algorithm is used to solve the optimal solution set of the objective function, and the interactive multi-criteria decision-making method is used to select the compromise solution to realize efficient optimal allocation of optical storage capacity. The test results show that this method can obtain the Pareto optimal solution set of multi-objective functions in the model. Comprehensive prospect value calculation results are obtained according to each configuration scheme, and a compromise scheme is obtained. The total energy consumption, power deviation rate, light rejection rate, and load loss cost were significantly reduced, with maximum values of 90.5%, 2.03% and 114,700 yuan, respectively, and the load loss probability was lower than 2.2%. The results show that the proposed method can effectively improve the total energy consumption utilization of the microgrid, reduce the power deviation rate and light abandonment rate, and provide significant advantages in different lighting and load demand scenarios.

South Africa faces significant challenges concerning the scarcity and unequal distribution of water, energy and food resources. The Constitution of the Republic of South Africa, 1996 underscores the importance of social justice and mandates collaboration among all levels of government and state entities. To effectively address the interconnected nature of these resources, a collaborative governance approach is necessary. This article introduces the concept of 'Water-Energy-Food (WEF) communities', a concept not currently recognised in South African law, drawing inspiration from the European Union's Renewable Energy Directive (RES-Directive) of 2018 and the Electricity Market Directive (E-Directive) of 2019 that introduced the concept of 'energy communities'. WEF communities, operating at the local level, could integrate improved governance and decision-making in the water, energy, and food sectors, thereby advancing social justice in local communities. The article compares existing multi-actor governance instruments in South African law and uses the EU law as learning points for the establishment of WEF communities as opposed to energy communities that focus on one determinant only. Although the current South African multi-actor governance instruments do not explicitly address an integrated WEF governance approach, they may serve as a basis for proposing framework rules to establish WEF communities, highlighting their potential to achieve social justice goals in relation to their access to water, energy and food. The article proposes rules for the potential establishment of WEF communities, taking learning points from the EU directives as well as existing South African instruments. [ABSTRACT FROM AUTHOR]

Abstract The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or involvement, such that a human operator actions the final decision. Previously, we have shown that on average, humans assisted by a simulated AFRS (sAFRS) failed to reach the level of accuracy achieved by the same sAFRS alone, due to overturning the system’s correct decisions and/or failing to correct sAFRS errors. The aim of the current study was to investigate whether participants’ trust in automation was related to their performance on a one-to-one face matching task when assisted by a sAFRS. Participants (n = 160) completed a standard face matching task in two phases: an unassisted baseline phase, and an assisted phase where they were shown the identification decision (95% accurate) made by a sAFRS prior to submitting their own decision. While most participants improved with sAFRS assistance, those with greater relative trust in automation achieved larger gains in performance. However, the average aided performance of participants still failed to reach that of the sAFRS alone, regardless of trust status. Nonetheless, further analysis revealed a small sample of participants who achieved 100% accuracy when aided by the sAFRS. Our results speak to the importance of considering individual differences when selecting employees for roles requiring human–algorithm interaction, including identity verification tasks that incorporate facial recognition technologies.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or involvement, such that a human operator actions the final decision. Previously, we have shown that on average, humans assisted by a simulated AFRS (sAFRS) failed to reach the level of accuracy achieved by the same sAFRS alone, due to overturning the system's correct decisions and/or failing to correct sAFRS errors. The aim of the current study was to investigate whether participants' trust in automation was related to their performance on a one-to-one face matching task when assisted by a sAFRS. Participants (n = 160) completed a standard face matching task in two phases: an unassisted baseline phase, and an assisted phase where they were shown the identification decision (95% accurate) made by a sAFRS prior to submitting their own decision. While most participants improved with sAFRS assistance, those with greater relative trust in automation achieved larger gains in performance. However, the average aided performance of participants still failed to reach that of the sAFRS alone, regardless of trust status. Nonetheless, further analysis revealed a small sample of participants who achieved 100% accuracy when aided by the sAFRS. Our results speak to the importance of considering individual differences when selecting employees for roles requiring human–algorithm interaction, including identity verification tasks that incorporate facial recognition technologies. [ABSTRACT FROM AUTHOR]

Organizational silos pose a common challenge for many companies, as they create barriers to communication, coordination, and resource efficiency. Addressing these challenges necessitates successful negotiation, yet the realm of multi-level team negotiation remains understudied. This research employs a computational simulation model to explore the dynamics of two-level negotiation, encompassing interactions of individuals searching for an agreement within and between teams in the organization. Our model involves individuals and teams with conflicting opinions on mutual interest issues. Within the intra-team negotiation process, the model integrates loyalty-driven opinion adjustments and the influence of the collective opinions of team members on team decisions. Concurrently, the inter-team negotiation introduces parameters reflecting teams’ willingness to negotiate with each other, emphasizing their openness to opinion adjustments. Our findings highlight the importance of individual loyalty, the leader acceptance ratio, and team willingness to negotiate as pivotal factors for achieving successful negotiation. We shed light on the mechanisms involved in two-level negotiations, including both within a team and between teams. This contribution enriches the literature on organizational negotiation and team dynamics in the context of organizational conflict. Moreover, this study advances the field by developing a computational simulation model, laying the groundwork for future studies exploring the multi-level negotiation processes. The insights in this study can equip managers with strategies to foster a win-win mindset for improved team coordination. [ABSTRACT FROM AUTHOR]

This paper describes an empirical user study with 24 participants during collaborative decision-making at large wall displays. The main objective of the user study is to analyze combinations of mid-air pointing gestures with other gestures or gaze. Particularly, we investigate gesture sequences (having pointing gestures as an initiator gesture) and gaze-pointing gesture misalignments. Our results show that most pointing gestures are part of gesture sequences and more precise gestures lead to touch gestures on the wall display, likely because they are associated with precise concepts. Regarding combinations of pointing gestures and gaze, misalignments often happen when users touch the display to make a change and want to observe the effect of that change on another display. The analyses conducted as part of this study clarify which natural awareness cues are more frequent in face-to-face collaboration, so that appropriate choices can be made regarding the transmission of equivalent cues to a remote location.

As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements.

Environmental factors and electronic interference often disrupt communication between UAV swarms and ground control centers, requiring UAVs to complete missions autonomously in offline conditions. However, current coordination schemes for UAV swarms heavily depend on ground control, lacking robust mechanisms for offline task allocation and coordination, which compromises efficiency and security in disconnected settings. This limitation is especially critical for complex missions, such as rescue or attack operations, underscoring the need for a solution that ensures both mission continuity and communication security. To address these challenges, this paper proposes an offline task-coordination algorithm based on blockchain smart contracts. This algorithm integrates task allocation, resource scheduling, and coordination strategies directly into smart contracts, allowing UAV swarms to autonomously make decisions and coordinate tasks while offline. Experimental simulations confirm that the proposed algorithm effectively coordinates tasks and maintains communication security in offline states, significantly enhancing the swarm’s autonomous performance in complex, dynamic scenarios.

Acceptability analyses of place–based innovations provide crucial in-depth knowledge (e.g., perceptions and values on landscapes) for the social–ecological transformation of landscapes. However, previous acceptability analyses often neglected complex and ongoing processes. We argue that, for the design of a sustainability-oriented transformation and to address spatial and temporal dynamics in landscapes, an operational heuristic is needed; one that integrates acceptability analyses into an adaptive landscape co-design and management approach. Therefore, this conceptual–empirical paper introduces the concept of the 'acceptability and landscape design cycle' (ALDC), which is based on findings from various transdisciplinary innovation processes in the Spreewald region (Germany). It is composed of four iterative phases: (1) defining the preconditions for acceptability analysis, (2) conducting the acceptability analysis, (3) integrating the results into the landscape development strategy, and (4) re-designing and refining it. We illustrate the application of these phases using a case study of the cultural landscape in Spreewald. The paper provides practical implementation guidelines of the ALDC and contributes to a better understanding of the dynamics of acceptability decisions regarding the transformation processes of landscapes. Furthermore, it can advance the understanding of how co-evolution of socio-ecological systems occurs. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, engineering education is evolving rapidly, requiring new approaches to continuing engineering education (CEE). Traditional methods struggle to meet the demands of professionals looking to update their skills. This study presents an experimental proposal using the Multisensory Virtual Decision Making Center (MVDC) to improve CEE training creation. The MVDC creates collaborative and immersive virtual environments, enabling real-time participation and decision-making with a greater sense of presence and complete data visualization. The proposed experiment compares the effectiveness of MVDC sessions with traditional video call sessions in developing CEE courses. The research aims to evaluate improvements in the quality of decisions, participation, and user satisfaction in this collaborative process by streamlining the training planning process for continuing education. This study anticipates significant insights into integrating cutting-edge technologies such as MVDC for CEE, proposing it as a crucial tool for upskilling and reskilling. Expected outcomes include practical guidelines for implementing multisensory environments in CEE curriculum development, highlighting their potential to revolutionize the planning process. The research underscores the need for innovative approaches to meet the changing demands of the engineering workforce in the era of Industry 4.0. It lays the foundation for future studies that validate and expand these findings, paving the way for more effective and engaging CEE programs.

In addressing global pandemics, robust cooperation across nations, institutions, and individuals is paramount. However, navigating the complexities of individual versus collective interests, diverse group objectives, and varying societal norms and cultures makes fostering such cooperation challenging. This research delves deep into the dynamics of interpersonal cooperation during the COVID-19 pandemic in Canton Ticino, Switzerland, using an integrative approach that combines qualitative and experimental methodologies. Through a series of retrospective interviews and a lab-in-the-field experiment, we gained insights into the cooperation patterns of healthcare and manufacturing workers. Within healthcare, professionals grappled with escalating emergencies and deteriorating work conditions, resisting the “new normalcy” ushered in by the pandemic. Meanwhile, manufacturing workers adapted to the altered landscape, leveraging smart working strategies to carve out a fresh professional paradigm amidst novel challenges and opportunities. Across these contrasting narratives, the centrality of individual, institutional, and interpersonal factors in galvanizing cooperation was evident. Key drivers like established relational dynamics, mutual dependencies, and proactive leadership were particularly salient. Our experimental findings further reinforced some of these qualitative insights, underscoring the pivotal role of recognition and the detrimental effects of uncertainty on cooperative behaviors. While contextual and sample-related constraints exist, this study illuminates vital facets of cooperation during crises and lays the groundwork for future explorations into cooperative decision-making.

The German government appointed an advisory committee to negotiate the design of a structural change prompted by energy and climate policy. The Coal Exit Commission was assigned with this complex mandate on how to end coal-fired power generation. Against many expectations, the Commission, comprised of stakeholders with competing interests, achieved a consensus outlining the coal exit in Germany by 2038. Considering that the result of this compromise serves as the basis for a major policy change, it is legitimate to ask if the final recommendations of the Coal Exit Commission were balanced, reliable and socially robust, and if the Commissions' advice satisfies the need for quality standards in negotiated multi-source policy advice? This paper investigates the Commission in that regard, while testing a framework for hybrid multi-source stakeholder advisory committees. The research shows broad compatibility with the framework. Simultaneously, this paper argues for additional factors such as the careful reflection of the composition of a hybrid advisory group, a precise, clear-cut mandate, development of trust between group members, independence of the adviser from the appointing authority. Finally, while the Coal Exit Commission seems an interesting model to reach consensus in difficult societal issues, the paper critically assesses its limits. [ABSTRACT FROM AUTHOR]

Hard-bodied ticks have become a major concern in temperate regions because they transmit a variety of pathogens of medical significance. Ticks and pathogens interact with hosts in a complex social-ecological system (SES) that influences human exposure to tick-borne diseases (TBD). We argue that addressing the urgent public health threat posed by TBD requires an understanding of the integrated processes in the forest ecosystem that influence tick density and infection prevalence, transmission among ticks, animal hosts, and ultimately disease prevalence in humans. We argue that collaborative modeling of the human-tick SES is required to understand the system dynamics as well as move science toward policy action. Recent studies in human health have shown the importance of stakeholder participation in understanding the factors that contribute to human exposure to zoonotic diseases. We discuss how collaborative modeling can be applied to understand the impacts of forest management practices on ticks and TBD. We discuss the potential of collaborative modeling for encouraging participation of diverse stakeholders in discussing the implications of managing forest ticks in the absence of large-scale control policy. [ABSTRACT FROM AUTHOR]

AbstractAlthough collaborative decision-making with multiple stakeholders in supply chain has become an important research issue in recent years, current research on the mechanism/process, methods, and performance assessment of collaborative decision-making still lags behind the practical needs in supply chain management. This paper reviews the existing literature in the past decade on collaborative supply chain from the perspective of decision-making in four stages with a causal framework. This paper reveals a general retrospect of the main subjects developed and investigated with related applications of cases, which includes both theoretical assumptions and practical data. The findings indicate that most articles in this field are based on a direct relationship between collaboration and performance improvement but lack of a mechanism between them. Additionally, dynamic collaboration with members in the whole supply chain both horizontally and vertically has not been conducted by previous studies. This paper provides a new conceptual framework and future research directions for further study in supply chain collaboration.

In the case of airlines recovering from flight delays, resources are indirectly linked to each other through the flight plan, and it is difficult to ensure the overall feasibility and global optimization of the recovery solution if each decision‑making department considers the recovery of resources in its own area. This paper proposes a multi‑agent simulation method for flight recovery in order to investigate the decision models of the departments and their collaborative relationship in the airline flight recovery process. Firstly, the framework of flight recovery multi‑agent system is constructed based on the actual organizational structure of the airline. Then, the whole process of delay recovery is divided into three stages： Pre‑recovery, feasible solution negotiation and balanced solution negotiation, and a three‑stage collaborative decision‑making mechanism is constructed. Finally, the core decision model of each department agent is developed according to the recovery characteristics of different resources, and the multi‑agent auto‑negotiation model is established based on the collaborative decision mechanism. The simulation results show that the multi‑agent based collaborative decision‑making method can produce a complete delay recovery plan in 3.8 s for a flight schedule consisting of three aircrafts and twelve flights in one day, and it can balance the local interests of each decision maker to a certain extent while safeguarding the overall benefits of the airline. [ABSTRACT FROM AUTHOR]

Abstract To accommodate growing air traffic demand, the traffic complexity management plays a crucial role in the capacity improvement of future automated air traffic management. In this paper, an autonomous and collaborative trajectory planning (ACTP) method is presented to facilitate collaborative decision‐making and enhance flexibility of the traffic complexity management in the en route airspace. A nonlinear integer model is formulated for the conflict‐free autonomous trajectory planning by reorganizing the trajectories with the combination of speed adjustment, lateral rerouting and flight level allocation maneuvers. Within the framework of the air‐ground situation awareness sharing, the spatio‐temporal reachable space is defined to describe the feasible solution space under the constraints of aircraft performance and required time of arrival. In addition, both the system‐level and the individual‐level objectives, traffic complexity and flight efficiency, are considered in the model, which are measured through the metric based on linear dynamical systems and the deviation from the user‐preferred trajectory. To balance the optimality and computational efficiency in the multi‐aircraft trajectory planning, a multi‐aircraft clustered and collaborative optimization algorithm is proposed based on the hybrid distributed‐centralized control strategy. The experimental results of the scenario in Western China airspace is presented, which verify the effectiveness of the proposed ACTP method through the comparison of other methods. Furthermore, a multi‐criteria decision‐making problem is discussed to strike a better trade‐off between different objectives in the trajectory planning.

Abstract In complex product design, lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts. However, since complex products involve intensive multi-domain knowledge, preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain. In recent years, enormous challenges are involved in the design process, within the increasing complexity of preference. This article mainly proposes a novel decision-making method based on generalized abductive learning (G-ABL) to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively. The proposed G-ABL framework, containing three cores: classifier, abductive kernel, and abductive machine, supports preference integration from data and fuzzy knowledge. In particular, a subtle improvement is presented for WK-means based on the entropy weight method (EWM) to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant. Furthermore, fuzzy comprehensive evaluation (FCE) and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels. Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set. Finally, an engineering application is provided to verify the effectiveness of the proposed method, and the superiority of which is illustrated by comparative analysis.

We present a spatial-spectral decoupling framework (SDF) to improve the performance of hyperspectral image classification, it mainly contains three modules, including data preprocessing, feature representation, and collaborative decision-making. Specifically, the data preprocessing module based on band selection (BS) network can effectively emphasize useful spectral bands while suppressing redundant ones. Besides, the feature representation module is based on spatial-spectral decoupling (SD) network to avoid information confusion between the spatial and the spectral domains. In addition, the collaborative decision-making mechanism based on joint optimization can maintain the discriminative properties of different branches and enhance mutual facilitation among them. Finally, the experimental results validate the effectiveness and superiority of our SDF. [ABSTRACT FROM AUTHOR]

Military culture relies on hierarchy and obedience, which contradict the implementation and use of collaborative care models. In this commentary, a team of lived experience, clinical and research experts discuss, for the first time, cultural, communication and policy considerations for implementing collaborative care models in military mental healthcare settings.

Acceptability analyses of place–based innovations provide crucial in-depth knowledge (e.g., perceptions and values on landscapes) for the social–ecological transformation of landscapes. However, previous acceptability analyses often neglected complex and ongoing processes. We argue that, for the design of a sustainability-oriented transformation and to address spatial and temporal dynamics in landscapes, an operational heuristic is needed; one that integrates acceptability analyses into an adaptive landscape co-design and management approach. Therefore, this conceptual–empirical paper introduces the concept of the ‘acceptability and landscape design cycle’ (ALDC), which is based on findings from various transdisciplinary innovation processes in the Spreewald region (Germany). It is composed of four iterative phases: (1) defining the preconditions for acceptability analysis, (2) conducting the acceptability analysis, (3) integrating the results into the landscape development strategy, and (4) re-designing and refining it. We illustrate the application of these phases using a case study of the cultural landscape in Spreewald. The paper provides practical implementation guidelines of the ALDC and contributes to a better understanding of the dynamics of acceptability decisions regarding the transformation processes of landscapes. Furthermore, it can advance the understanding of how co-evolution of socio-ecological systems occurs.

Automated Facial Recognition Systems (AFRS) are used by governments, law enforcement agencies, and private businesses to verify the identity of individuals. Although previous research has compared the performance of AFRS and humans on tasks of one-to-one face matching, little is known about how effectively human operators can use these AFRS as decision-aids. Our aim was to investigate how the prior decision from an AFRS affects human performance on a face matching task, and to establish whether human oversight of AFRS decisions can lead to collaborative performance gains for the human-algorithm team. The identification decisions from our simulated AFRS were informed by the performance of a real, state-of-the-art, Deep Convolutional Neural Network (DCNN) AFRS on the same task. Across five pre-registered experiments, human operators used the decisions from highly accurate AFRS (.90%) to improve their own face matching performance compared with baseline (sensitivity gain: Cohen's d = 0.71-1.28; overall accuracy gain: d = 0.73-1.46). Yet, despite this improvement, AFRS-aided human performance consistently failed to reach the level that the AFRS achieved alone. Even when the AFRS erred only on the face pairs with the highest human accuracy (.89%), participants often failed to correct the system's errors, while also overruling many correct decisions, raising questions about the conditions under which human oversight might enhance AFRS operation. Overall, these data demonstrate that the human operator is a limiting factor in this simple model of human-AFRS teaming. These findings have implications for the "human-in-the-loop" approach to AFRS oversight in forensic face matching scenarios. [ABSTRACT FROM AUTHOR]

To accommodate growing air traffic demand, the traffic complexity management plays a crucial role in the capacity improvement of future automated air traffic management. In this paper, an autonomous and collaborative trajectory planning (ACTP) method is presented to facilitate collaborative decision‐making and enhance flexibility of the traffic complexity management in the en route airspace. A nonlinear integer model is formulated for the conflict‐free autonomous trajectory planning by reorganizing the trajectories with the combination of speed adjustment, lateral rerouting and flight level allocation maneuvers. Within the framework of the air‐ground situation awareness sharing, the spatio‐temporal reachable space is defined to describe the feasible solution space under the constraints of aircraft performance and required time of arrival. In addition, both the system‐level and the individual‐level objectives, traffic complexity and flight efficiency, are considered in the model, which are measured through the metric based on linear dynamical systems and the deviation from the user‐preferred trajectory. To balance the optimality and computational efficiency in the multi‐aircraft trajectory planning, a multi‐aircraft clustered and collaborative optimization algorithm is proposed based on the hybrid distributed‐centralized control strategy. The experimental results of the scenario in Western China airspace is presented, which verify the effectiveness of the proposed ACTP method through the comparison of other methods. Furthermore, a multi‐criteria decision‐making problem is discussed to strike a better trade‐off between different objectives in the trajectory planning. [ABSTRACT FROM AUTHOR]

This article explores how the analysis of inter-rater discourse can be used to support collective reflective practice in second language (L2) assessment. To demonstrate, a focused case of the discourse between two experienced language teachers as they negotiate assessment decisions on L2 written texts is presented. Of particular interest was the discourse surrounding the raters' most divergent assessment decisions, which in this case were those relating to Task Achievement. Thematic analysis indicated that rater discourse predominantly focused on explicit objective factors, primarily the L2 texts and the rating scale; however, rater discourse also focused on more subjective, rater-centred factors. The discourse surrounding these rater-centred factors was often central to the identification and resolution of rating disagreements. The paper argues that the subjective dimension of language assessment needs to be more directly and systematically reflected upon in language teaching contexts and that analysis of rater discourse, especially discourse focused on points of disagreement between raters, provides a valuable mechanism to facilitate this. [ABSTRACT FROM AUTHOR]

To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters. • A two-stage collaborative collision avoidance decision-making considering risk attitudes is proposed. • Collision avoidance behavior is extracted and formulated as prior knowledge. • Risk attitude probability is estimated by observation, reasoning and modification. • Collaborative mechanism is realized by the incomplete information static game. [ABSTRACT FROM AUTHOR]

The notions of a collaborative virtual power plant ecosystem (CVPP-E) and a cognitive household digital twin (CHDT) have been proposed as contributions to the efficient organization and management of households within renewable energy communities (RECs). CHDTs can be modeled as software agents that are designed to possess some cognitive capabilities, enabling them to make autonomous decisions on behalf of their human owners based on the value system of their physical twin. Due to their cognitive and decision-making capabilities, these agents can exhibit some behavioral attributes, such as engaging in diverse collaborative actions aimed at achieving some common goals. These behavioral attributes can be directed to the promotion of sustainable energy consumption in the ecosystem. Along this line, this work demonstrates various collaborative practices that include: (1) collaborative roles played by the CVPP manager such as (a) opportunity seeking and goal formulation, (b) goal proposition/invitation to form a coalition or virtual organization, and (c) formation and dissolution of coalitions; and (2) collaborative roles played by CHDTs which include (a) acceptance or decline of an invitation based on (i) delegation/non-delegation and (ii) value system compatibility/non-compatibility, and (b) the sharing of common resources. This study adopts a simulation technique that involves the integration of multiple simulation methods such as system dynamics, agent-based, and discrete event simulation techniques in a single simulation environment. The outcome of this study confirms the potential of adding cognitive capabilities to CHDTs and further shows that these agents could exhibit certain collaborative attributes, enabling them to become suitable as rational decision-making agents in households. [ABSTRACT FROM AUTHOR]

Contingency and Synchronization is an ongoing research project exploring the interplay between openness and predictability in computational art. Utilizing distributed multi-agent networks, the project investigates computational synchronization phenomena across installations and visualizations. Central to the project are the concepts of contingency and synchronization, providing a framework for understanding aesthetic nuances arising from computation, sound, site, and collaborative decision-making. This paper examines the project's conceptual and artistic impact. It explores the relationship between contingency and synchronization and addresses repercussions for concepts of listening, emergence, computation, space, and performance, and presents speculative theses outlining preliminary findings. [ABSTRACT FROM AUTHOR]