This article focuses on measuring the impact of artificial intelligence (AI) on the peace and security agenda, taking stock of recent initiatives and progress in this area. While there is a keen awareness of the fact that AI can be weaponized to become a tool of power politics and military competition, there is comparatively less systematic attention paid to what technology can do for peace. While it is important to address risk mitigation, equal space should be given to thinking about how to harness the peace potential of AI on a large scale. This study follows a series of publications that aim to assess the impact of technological innovation on peace, also referred to as PeaceTech, Global PeaceTech, peace innovation, or digital peacebuilding. The first section provides an overview of the debate on the impact of AI on peace and conflict. The second section examines conceptual frameworks and measures of the impact of AI on peace and conflict. The third section looks at the risks to peace and conflict posed by the use of AI and possible governance measures to mitigate them. The fourth section provides examples of AI-enabled initiatives that are having a positive impact on peace, providing a compass for public and private investment. The conclusion offers policy recommendations to advance the AI for peace agenda.

This study explored how collaborative writing, an often-used instructional strategy in second language (L2) learning, intersects with large-group dynamics, and investigated their potential impact on the quality of writing outcomes in an online distance learning course. Using a mixed-methods approach, the research scrutinized intra-group interaction processes in two large groups undertaking a computer-assisted language learning writing assignment and evaluated the impact of these interaction processes on their writing products. Data from discussions in both a public online forum and a private social communication platform (WeChat) were collected, systematically coded, and analysed quantitatively and qualitatively based on language functions. Data collection also included an assessment of the written products and follow-up group interviews. The findings indicate distinct interaction patterns between high-performing and low-performing groups, characterised by an expert/participant pattern and a dominant/passive pattern, respectively. Additionally, insights from the interviews shed light on these interaction patterns and the potential impact on student learning outcomes. The study suggests practical implications, highlighting the importance of task design in promoting high levels of collaborative knowledge construction to enhance students’ writing skills and L2 language learning in large-group settings.

This study creates a virtual space for language learning using a user-customizable metaverse platform and explores its potential for EFL learning. To this end, a virtual learning space, grounded in constructivist learning principles – contextualized learning, active learning, and collaborative learning – was created on a 2D metaverse platform. The metaverse was designed as a simulated deserted island for enjoyable and playful learning, allowing the students to actively explore, discover, and interact as they look for clues to escape the island. For educational application, 29 Korean middle school students participated in a two-hour activity. Data included screen recordings of student activities, student surveys, and interviews with the students and teachers. The findings showed that, as an EFL learning space of playful constructivism, the metaverse had great potential to embed contextualized learning and served as a medium for active learning that positively affected student interest and motivation. The results confirmed that the team-based approach combined with a game-like metaverse fostered student collaboration. Overall, the study showcased how language instructors can make use of a customizable metaverse for L2 learning and how a virtual space may serve as an arena for learner-centered instruction.

This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

Controlling the landing position of a spinning ball is difficult when using a table tennis robot. A complete physical model requires the factoring in of aerodynamic elements and object collisions, and inaccurate environmental coefficients would increase the landing position error. This study proposed a landing position control method based on a cascade neural network (CNN) that consists of forward and recurrent neural networks (RNNs). The forward NNs are used to estimate the velocity of the outgoing ball according to the velocity and acceleration of the incoming ball captured by cameras and the desired velocity of the outgoing ball. The RNN is employed to reverse-predict ball displacement based on the state of the incoming ball, desired landing point, and ball flight duration. The experiments verified that the method proposed in this study achieved control of differently spinning balls more effectively than the locally weighted regression (LWR)-based model did. The success rate of the CNN at two of six desired landing points was 25.9% and 32.9% higher, respectively, compared with use of the LWR-based model.

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. Machine learning has previously been used to reliably “screen” articles for review – that is, identify relevant articles based on reviewers’ inclusion criteria. The application of machine learning techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We, therefore, set out to develop a series of tools that would assist in the profiling and analysis of 1952 publications on the theme of “outcomes-based contracting.” Tools were developed for the following tasks: assigning publications into “policy area” categories; identifying and extracting key information for evidence mapping, such as organizations, laws, and geographical information; connecting the evidence base to an existing dataset on the same topic; and identifying subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of machine learning techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Beyond this, our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While machine learning techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analyzing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

Multi-player pursuit-evasion games are crucial for addressing the maneuver decision problem arising in the cooperative control of multi-agent systems. This paper presents a cooperative defense strategy involving cooperation and confrontation among the target, attacker, and multiple defenders based on location information only. The primary objective of the attacker is to capture the target while avoiding being captured by multiple defenders. Meanwhile, the target is confined to a restricted area and can only move within its boundaries. The proposed cooperative defense strategy aims to prevent the attacker from capturing the target while minimizing the time required to neutralize the threat. Therefore, the multiple defenders are classified into two categories: the primary defender and the auxiliary defenders. The primary defender is to prevent the attacker from approaching the target by predicting the intention of the attacker. On the other hand, the auxiliary defenders adopt a surround-shrink-capture strategy to reduce the time consumption to capture the attacker. Numerical simulations have been conducted to validate the effectiveness of the proposed strategy.

Due to the flexibility obtained through both materials and structures, soft robots have wide potential applications in complicated internal and external environments. This paper presents a new soft crawling robot with multiple locomotion patterns that integrate inchworm motion and various turning motions. First, the conceptual design of the proposed robot is presented by introducing thick-panel origami into the synthesis of a crawling robot, resulting in a Waterbomb-structure-inspired hybrid mechanism. Second, all locomotion patterns of the robot are precisely described and analyzed by screw theory in an algebraic manner, which include inchworm motion, restricted planar motion, quantitative turning motion, and marginal exploration motion. Then, the output motion parameter for each locomotion pattern is analytically modeled as a function of the robotic dimensional parameters, and the robot can thus be designed and controlled in a customized way for the expected output motion. Finally, the theoretical analysis and derivations are validated by simulation and physical prototype building, which lay the foundations for the design and manufacture of small-scale soft crawling robots with precise output motions in a complex planar environment.

Carbon neutrality cannot be achieved without different economic sectors, individuals and households, and the government making serious efforts. Green finance in different forms including environmental, social and governance investment and carbon emissions trading are used to measure the reduction in carbon emissions and place a monetary value on them. However, because of inconsistencies or even manipulation in the monitoring/measurement, reporting and verification (MRV) of air quality and carbon emissions data, the effectiveness of green finance has been largely compromised. Environmental MRV is a technology-based engineering task, which is also heavily impacted by institutional design and professionalism. This commentary will draw upon principal–agent theory and the practical arrangements of environmental MRV to discuss why professionalism is badly needed and how to bridge the missing link for achieving carbon neutrality and sustainability transitions.

We study computational aspects of repulsive Gibbs point processes, which are probabilistic models of interacting particles in a finite-volume region of space. We introduce an approach for reducing a Gibbs point process to the hard-core model, a well-studied discrete spin system. Given an instance of such a point process, our reduction generates a random graph drawn from a natural geometric model. We show that the partition function of a hard-core model on graphs generated by the geometric model concentrates around the partition function of the Gibbs point process. Our reduction allows us to use a broad range of algorithms developed for the hard-core model to sample from the Gibbs point process and approximate its partition function. This is, to the extent of our knowledge, the first approach that deals with pair potentials of unbounded range.

Let $r$ be any positive integer. We prove that for every sufficiently large $k$ there exists a $k$-chromatic vertex-critical graph $G$ such that $\chi (G-R)=k$ for every set $R \subseteq E(G)$ with $|R|\le r$. This partially solves a problem posed by Erdős in 1985, who asked whether the above statement holds for $k \ge 4$.

This note presents a historical survey of informal semantics that are associated with logic programming under answer set semantics. We review these in uniform terms and align them with two paradigms: Answer Set Programming and ASP-Prolog — two prominent Knowledge Representation and Reasoning Paradigms in Artificial Intelligence.

We use Stein’s method to obtain distributional approximations of subgraph counts in the uniform attachment model or random directed acyclic graph; we provide also estimates of rates of convergence. In particular, we give uni- and multi-variate Poisson approximations to the counts of cycles and normal approximations to the counts of unicyclic subgraphs; we also give a partial result for the counts of trees. We further find a class of multicyclic graphs whose subgraph counts are a.s. bounded as $n\to \infty$.

Soft robotics is rapidly advancing, particularly in medical device applications. A particular miniaturized manipulator design that offers high dexterity, multiple degrees-of-freedom, and better lateral force rendering than competing designs, has great potential for minimally invasive surgery. However, it faces challenges such as the tendency to suddenly and unpredictably deviate in bending plane orientation at higher pressures. In this work, we identified the cause of this deviation as the buckling of the partition wall and proposed design alternatives along with their manufacturing process to address the problem without compromising the original design features. In both simulation and experiment, the novel design managed to achieve a better bending performance in terms of stiffness and reduced deviation of the bending plane. We also developed an artificial neural network-based inverse kinematics model to further improve the performance of the prototype during vectorization. This approach yielded mean absolute errors in orientation of the bending plane below $5^{\circ }$.

Urban co-creation is an approach to urban design that actively involves stakeholders and end-users in the design process. As designers increasingly use digital tools to manage design information, stakeholders and residents may find it difficult to participate, resulting in a lack of engagement. The emergence of metaverse technologies offers a crucial opportunity to employ user-friendly and collaborative tools, enabling more effective participation. In the study presented in this article, a custom-designed digital game with virtual reality environment was used to facilitate a series of co-creation workshops. The study focused on changes in participants’ experience by comparing baseline and endline survey results against the design outputs. It employed a holistic framework considering four dimensions: game design, participatory experience, learning outcomes and co-creation results. The findings indicate that the digitally gamified approach helped enhance participation and knowledge sharing, and even though game design ratings varied, the use of video games motivated engagement, particularly in an intergenerational context. The co-creation workshop design documented in this article offers new methods to enhance community engagement in urban design. Especially during digital transformation, it opens renewed discussions on balancing traditional output-driven approaches with more participant-centric methods and design objectives.

For given positive integers $r\ge 3$, $n$ and $e\le \binom{n}{2}$, the famous Erdős–Rademacher problem asks for the minimum number of $r$-cliques in a graph with $n$ vertices and $e$ edges. A conjecture of Lovász and Simonovits from the 1970s states that, for every $r\ge 3$, if $n$ is sufficiently large then, for every $e\le \binom{n}{2}$, at least one extremal graph can be obtained from a complete partite graph by adding a triangle-free graph into one part.

In this note, we explicitly write the minimum number of $r$-cliques predicted by the above conjecture. Also, we describe what we believe to be the set of extremal graphs for any $r\ge 4$ and all large $n$, amending the previous conjecture of Pikhurko and Razborov.

Cooperative behavior constitutes a key aspect of human society and non-human animal systems, but explaining how cooperation evolves represents a major scientific challenge. It is now well established that social network structure plays a central role for the viability of cooperation. However, not much is known about the importance of the positions of cooperators in the networks for the evolution of cooperation. Here, we investigate how the spread of cooperation is affected by correlations between cooperativeness and individual social connectedness (such that cooperators occupy well-connected network positions). Using simulation models, we find that these correlations enhance cooperation in standard scale-free networks but not in standard Poisson networks. In contrast, when degree assortativity is increased such that individuals cluster with others of similar social connectedness, we find that Poisson networks can maintain high levels of cooperation, which can even exceed those of scale-free networks. We show that this is due to dynamics where bridge areas between social clusters act as barriers to the spread of defection. We also find that this positive effect on cooperation is sensitive to the presence of Trojan horses (defectors placed within cooperator clusters), which allow defection to invade. The results provide new knowledge about the conditions under which cooperation may evolve, and are also relevant to consider in regard to the design of cooperation studies.

The aim of this study was to contribute to the field of computer-assisted language learning (CALL) by investigating the individualization of intentional vocabulary learning. A total of 118 Japanese-speaking university students studied 20 low-frequency English words using flashcard software over two learning sessions. The participants practiced retrieval of vocabulary under different learning schedules, with short or long time intervals between encounters of the same word in each learning session: Short–Short, Short–Long, Long–Short, and Long–Long. Two individual difference measures – learning efficiency and language aptitude – were examined as predictors of long-term second language (L2) vocabulary retention. Learning efficiency was operationalized as the number of trials needed to reach a learning criterion in each session, whereas a component of aptitude (rote memory ability) was measured by a subtest of Language Aptitude Battery for the Japanese. Multiple regression and dominance analyses were conducted to evaluate the relative importance of learning efficiency and language aptitude in predicting delayed vocabulary posttest scores. The results revealed that learning efficiency in the second learning session was the strongest predictor of vocabulary retention. Language aptitude, however, did not significantly predict vocabulary retention. Moreover, the predictive power of learning efficiency increased when the data were analyzed within each learning schedule, underscoring the need to assess learners’ abilities under specific learning conditions for optimizing their computer-assisted learning performance. These findings not only inform the development of more effective, individualized CALL systems for L2 acquisition but also emphasize the importance of gauging individuals’ abilities such as learning efficiency in a more flexible, context-sensitive manner.