Oral Roberts University

ABSTRACT Systems thinking emerged as a transdisciplinary movement that sought to bridge the sciences, humanities and practices of change. Today, it spans many fields but risks losing coherence as its core ideas are repeatedly rediscovered and reapplied without deepening their theoretical roots. This paper draws on the experiences of the cybersystemic community under the roof of the International Federation for Systems Research (IFSR) and a scoping review of three journals: Systems Research and Behavioural Science , Systems Practice and Action Research and the Journal of Awareness‐Based Systems Change . It maps 10 clusters of contemporary systems research that emerged from 2020 to 2025. The analysis reveals wide application yet limited synthesis, suggesting that while the field is vibrant, it struggles to integrate its philosophical foundations. Using Jürgen Renn's concept of epistemic networks, the paper reinterprets systems research as a living ecology of ontological, epistemological and axiological commitments. Rather than seeking unity through control, the next phase of the field can nurture coherence through relationship, recognising and realising the field of systems research in and for itself. The IFSR 2026 agenda explores the conditions for the possibility of cultivating shared spaces of learning, encouraging plural ways of knowing, and regenerating the epistemic commons that sustain systems inquiry as a dynamic, reflective and life‐affirming practice.

Machine learned potentials based on artificial neural networks are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One main criticism regarding neural network potentials is that their inferred energy is less interpretable than in traditional approaches, which use simpler and more transparent functional forms. Here we address this problem by extending tools recently proposed in the nascent field of explainable artificial intelligence to coarse-grained potentials based on graph neural networks. With these tools, neural network potentials can be practically decomposed into n-body interactions, providing a human understandable interpretation without compromising predictive power. We demonstrate the approach on three different coarse-grained systems including two fluids (methane and water) and the protein NTL9. The obtained interpretations suggest that well-trained neural network potentials learn physical interactions, which are consistent with fundamental principles.

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency.

Challenges in social responsiveness and social communicative behaviors are often observed in autistic children. It is imperative to develop effective treatment methods to enhance social communication and reciprocity in autistic children. This study examines the efficacy of two particular treatment methods to improve social communication including Reciprocal Communication Training (RCT) and Reciprocal Communication Training through Music (RCTM). Ten autistic children participated in this study and engaged in musical and nonmusical interventions that addressed greeting, receptive communication, imitation, initiation, and emotional congruence with facial expression, emotion identification, and emotional attunement. To analyze the impact of these interventions, the study included dependent samples t-tests to explore the differences in reciprocal communicative behaviors of autistic children between RCT and RCTM. A paired t-test analysis indicated that there were significant differences between RCT and RCTM on greeting, imitating behavior, initiating behavior, and emotional (happy vs. sad) congruence. The results indicated that participants who underwent RCTM demonstrated enhanced reciprocal communicative skills, particularly evident in the participants' improved greeting and imitation behaviors. This improvement was observed across both early and late intervention stages. Moreover, the study suggests that RCTM had a positive influence on various aspects of reciprocal and affective communication, optimizing the effects of music to create a sensory-rich environment for enhanced engagement. RCTM emerges as a promising method for fostering social communication skills in autistic children, offering potential benefits for their educational and therapeutic outcomes.

Abstract This article investigates United States Pentecostal women’s expectations and experiences of motherhood and depression. For Pentecostal women, motherhood within marriage is an ideal state, yet that ideal depends on a broader sociocultural understanding of motherhood that is child-centered, expert-guided, emotionally absorbing, labor-intensive, and financially demanding. Researchers increasingly link this model to adverse mental health outcomes in women, including depression. While Pentecostal experiences of depression have been explored to some extent, little research focuses specifically on how these experiences intersect with motherhood. This article seeks to contribute to the gap by using Grounded Theory to investigate how Pentecostals living in the United States experience depression in relation to motherhood. The findings reveal that the participants’ depression is tied to unmet expectations of motherhood, participants’ lived experiences of parenting, and a perceived lack of Spirit empowerment. While many participants were mothers, the study also included women who were not mothers but aspired to motherhood. The study, therefore, also highlights how the cultural and theological discourse of motherhood affects both groups. Overall, the research sheds light on the intricate relationship between motherhood, expectations, depression, and Pentecostal beliefs, emphasizing the influence of sociocultural contexts and spiritual factors in shaping women’s mental health experiences.

The cell’s mechanical environment is a fundamental determinant of its activity. Ostensibly, the cellular response is dependent on interactions between extracellular matrix deformations and the cell adhesome. Low-intensity vibration (LIV) induces sinusoidal mechanical accelerations that stimulate mesenchymal stem cell (MSC) anabolism despite generating minimal matrix strain. In this study, we tested the hypothesis that accelerations of less than 1g cause nuclear motions relative to the cell membrane in adherent cells, resulting in elevated stresses in the cytoskeleton that promote transcriptional activity. Coupling a piezoelectric vibration platform with real-time microscopy, we applied a 0.7g, 90Hz LIV signal that oscillates the cell with displacements of up to ±11 μm. Live-cell tracking revealed that the sinusoidal vibrations caused the nucleus to move ±1.27 μm (17% of total displacement) out of phase with the cell membrane. Disruption of the LINC complex, which mechanically couples the nucleoskeleton to the cytoskeleton, doubled the magnitude of this relative motion, indicating that the nucleo-cytoskeletal configuration plays a major role in regulating nuclear motion. Consistent with a previously reported increase in nuclear stiffness caused by LIV, machine-learning-based image segmentation of confocal micrographs showed that LIV increased both apical and basal F-actin fiber numbers, generating a denser, more branched actin network near the nucleus. Following six 20 min bouts of LIV applied to MSC, RNA sequencing identified 372 differentially expressed genes. Upregulated gene sets were linked to F-actin assembly and focal adhesion pathways. Finite element simulations showed that nuclear stresses increased by LIV up to 18% were associated with nuclei flattening and a 30–50% increase in actin-generated forces. These findings demonstrate that low-intensity accelerations, independent of matrix strain, can directly activate a response of the nucleus, leading to cytoskeletal reorganization and heightened nuclear stresses. Thus, even very small oscillatory mechanical signals can markedly influence cell outcomes, establishing a mechanosensing pathway independent of extracellular strains.