Abstract Background Mental health conditions in children include diagnoses and transdiagnostic characteristics A variety of factors such as child exposure to violence and neglect contribute to mental health conditions in children. However, less is known about how parental exposure to adverse childhood experiences (ACEs), such as domestic violence, neglect, and abuse, impacts their children. We aim to systematically synthesize available data on the association of parental ACEs with child mental health characteristics. Methods We conducted a systematic review and meta-analysis using searches across MEDLINE, Embase, CINAHL, APA PsycInfo, Scopus, and SciELO. Studies published up to September 2024 were included if they examined parental ACEs in relation to children's mental health. Meta-analyses used random effects models, conducted in STATA. Results The search yielded 6,270 articles (after removing duplicates), with 173 full-text reviewed, and 52 meeting inclusion criteria. Samples sizes ranged from 50 to 8,473 parent-child dyads (M = 764.89, SD = 1,446.95); median child age was 36 months at the time of outcome assessment. Studies were mainly from the U.S. and Canada. Parental ACEs were significantly associated with child mental health conditions (Beta: 0.01, 95% CI: 0.03, 0.05), particularly child behavioral dysregulation (Beta: 0.22, 95% CI 0.16, 0.28) and socio-emotional difficulties (Beta: 0.16, 95% CI: 0.00, 0.31). Parental ACEs were also linked to reduced child emotion regulation (Beta -0.07, 95% CI: -0.14, 0.00), and reduced child executive function (Beta -0.02, 95% CI: -0.06, 0.02). Conclusions Parental ACEs are associated with various offspring mental health conditions. Child mental health characteristics should be investigated with a transdiagnostic perspective focusing on transdiagnostic child mental health characteristics beyond diagnoses. More international studies investigating the association of parental ACEs on offspring's mental health conditions are needed.
Currently, the application of robotics technology in sports training and competitions is rapidly increasing. Traditional methods mainly rely on image or video data, neglecting the effective utilization of textual information. To address this issue, we propose: TL-CStrans Net: A vision robot for table tennis player action recognition driven via CS-Transformer. This is a multimodal approach that combines CS-Transformer, CLIP, and transfer learning techniques to effectively integrate visual and textual information. Firstly, we employ the CS-Transformer model as the neural computing backbone. By utilizing the CS-Transformer, we can effectively process visual information extracted from table tennis game scenes, enabling accurate stroke recognition. Then, we introduce the CLIP model, which combines computer vision and natural language processing. CLIP allows us to jointly learn representations of images and text, thereby aligning the visual and textual modalities. Finally, to reduce training and computational requirements, we leverage pre-trained CS-Transformer and CLIP models through transfer learning, which have already acquired knowledge from relevant domains, and apply them to table tennis stroke recognition tasks. Experimental results demonstrate the outstanding performance of TL-CStrans Net in table tennis stroke recognition. Our research is of significant importance in promoting the application of multimodal robotics technology in the field of sports and bridging the gap between neural computing, computer vision, and neuroscience.
This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as satellite attitude actuators in Earth's orbit and for long-term formation and attitude control. This study derives a unique, continuous, and power-optimal current solution via sequential convex programming and approximates it using a multilayer perceptron model. The effectiveness of our strategy was demonstrated through numerical simulations and experimental trials on the formation and attitude control.
Krzysztof Kowalczyk, Paweł Wachel, Cristian R. Rojas
This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error bounds. Both non-asymptotic analysis of the method and numerical simulation results are presented and discussed in the paper.
This paper demonstrates a disconnected ABM architecture that enables domain experts, and non-programmers to add qualitative insights into the ABM model without the intervention of the programmer. This role separation within the architecture allows policy-makers to systematically experiment with multiple policy interventions, different starting conditions, and visualizations to interrogate their ABM
Environmental noise causes enormous harm to human health. This research suggests an active noise control (ANC) system based on the cuckoo search (CS) algorithm to reduce indoor noise pollution. The indoor environment’s ANC is more complicated than the conventional linear ANC system used in cars, aircraft, and other confined spaces. The suggested system architecture considers noise from many directions in order to reduce the impact of external noise on persons within a given environment. The effective search strategy of the CS algorithm enhances the approach of updating filter coefficients based on the LMS/NLMS algorithm in the conventional ANC systems. The results from the simulations demonstrate that the suggested strategy successfully validates the hypothesis and provides significant noise reduction. Additionally, we developed the system’s hardware, which is based on a digital signal processor (DSP). The experimental results show that the proposed technology could perform well with respect to ANC.
Finite-time motion planning with collision avoidance is a challenging issue in multi-agent systems. This paper proposes a novel distributed controller based on a new Lyapunov barrier function which guarantees finite-time stability for multi-agent systems without collisions. First, the problem of finite-time motion planning of multi-agent systems is formulated. Then, a novel finite-time distributed controller is developed based on a Lyapunov barrier function. Finally, numerical simulations demonstrate the effectiveness of proposed method.
Multi-agent approach has become popular in computer science and technology. However, the conventional models of multi-agent and multicomponent systems implicitly or explicitly assume existence of absolute time or even do not include time in the set of defining parameters. At the same time, it is proved theoretically and validated experimentally that there are different times and time scales in a variety of real systems - physical, chemical, biological, social, informational, etc. Thus, the goal of this work is construction of a multi-agent multicomponent system models with concurrency of processes and diversity of actions. To achieve this goal, a mathematical system actor model is elaborated and its properties are studied.
The concept of emergence is a powerful concept to explain very complex behaviour by simple underling rules. Existing approaches of producing emergent collective behaviour have many limitations making them unable to account for the complexity we see in the real world. In this paper we propose a new dynamic, non-local, and time independent approach that uses a network like structure to implement the laws or the rules, where the mathematical equations representing the rules are converted to a series of switching decisions carried out by the network on the particles moving in the network. The proposed approach is used to generate patterns with different types of symmetry.
The influence of agents heterogeneity on the microscopic characteristics of pedestrian flow is studied via an evacuation simulation tool based on the Floor-Field model. The heterogeneity is introduced in agents velocity, aggressiveness, and sensitivity to occupation. The simulation results are compared to data gathered during an original experiment. The comparison shows that the heterogeneity in aggressiveness and sensitivity occupation enables to reproduce some microscopic aspects. The heterogeneity in velocity seems to be redundant.
We present a simple, yet realistic, agent-based model of an electricity market. The proposed model combines the spot and balancing markets with a resolution of one minute, which enables a more accurate depiction of the physical properties of the power grid. As a test, we compare the results obtained from our simulation to data from Nord Pool.
Solving a delegation graph for transitive votes is already a non-trivial task for many programmers. When extending the current main paradigm, where each voter can only appoint a single transitive delegation, to a system where each vote can be separated over multiple delegations, solving the delegation graph becomes even harder. This article presents a solution of an example graph, and a non-formal proof of why this algorithm works.
The design of punishment policies applied to specific domains linking agents actions to material penalties is an open research issue. The proposed framework applies principles of contract law to set penalties: expectation damages, opportunity cost, reliance damages, and party design remedies. In order to decide which remedy provides maximum welfare within an electronic market, a simulation environment called DEMCA (Designing Electronic Markets for Contractual Agents) was developed. Knowledge representation and the reasoning capabilities of the agents are based on an extended version of temporal defeasible logic.
Social choice theory is a theoretical framework for analysis of combining individual preferences, interests, or welfare to reach a collective decision or social welfare in some sense. We introduce a new criterion for social choice protocols called social disappointment. Social disappointment happens when the outcome of a voting system occurs for those alternatives which are at the end of at least half of individual preference profiles. Here we introduce some protocols that prevent social disappointment and prove an impossibility theorem based on this key concept.
This paper investigates the problem of protesting crowd simulation. It considers CROCADILE, an agent based distillation system, for this purpose. A model of protesting crowd was determined and then a CROCADILE model of protesting crowd was engineered and demonstrated. We validated the model by using two scenarios where protesters are varied with different personalities. The results indicated that CROCADILE served well as the platform for protesting crowd modeling simulation
We show that any discrete opinion pooling procedure with positive weights can be asymptotically approximated by DeGroot's procedure whose communication digraph is a Hamiltonian cycle with loops. In this cycle, the weight of each arc (which is not a loop) is inversely proportional to the influence of the agent the arc leads to.