Dynamic Interaction Research Articles

The enigmatic origin of two dormant BH binaries: Gaia BH1 and Gaia BH2

Abstract The two systems, namely, Gaia BH1 and Gaia BH2, that have been confirmed as dormant (i.e., no X-ray emission detected) black hole (BH) - low-mass star binaries in the latest Gaia mission data release (DR3) are intriguing in the context of their formation and evolution. Both systems consist of $\sim 9\, \mathrm{{\rm M}_{\odot }}$ BH and $\sim 1\, \mathrm{{\rm M}_{\odot }}$ star orbiting each other on a wide, eccentric orbit (e ∼ 0.5). We argue that formation of such Gaia BH-like systems through the isolated binary evolution (IBE) channel, under the standard common envelope assumptions, and from dynamical interactions in young massive and open clusters are equally probable, and that the formation rate of such binaries is of the order of $10^{-7}\, \mathrm{{\rm M}_{\odot }}^{-1}$ for both channels. We estimate that, according to our models, there are at most ∼900 detectable Gaia BH-like binaries in the Milky Way thin disc. What plays an important role in formation of Gaia BH-like systems via the IBE channel is the mutual position of the natal kick velocity vector and the binary angular momentum vector. We find that natal kicks with a median magnitude of ∼40 km/s are preferred for the formation of Gaia BH1-like binaries. Approximately 94% of those binaries are formed with the BH spin misaligned to the orbital axis by less than 40○. Gaia BH2-like binaries form if the low velocity natal kick (of median magnitude ∼20 km/s) is directed within 15○ about the orbital plane. In addition to natal kick, we also discuss the influence of tidal interaction and the adopted common envelope λce parameter prescription on the evolution of Gaia BH-like binaries. We follow the subsequent evolution of the binaries, once formed as Gaia BH1 and Gaia BH2 systems, to investigate their connection with the low-mass X-ray binary population.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall revealed through multi-experiment iEEG replication

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience network (SN), default mode network (DMN), and frontoparietal network (FPN), provides a framework for understanding these interactions. We analyzed intracranial electroencephalography (EEG) recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting state, highlighting the AI’s task-specific role in coordinating large-scale network interactions. This pattern persisted across externally driven memory encoding and internally governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI’s unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI’s critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Beyond formal politics: the epistemic facet of business power

Abstract This article introduces “epistemic business power” as a distinct facet of corporate influence. Epistemic business power refers to the strategic communication of firms to influence the perceptions and beliefs of policymakers, experts, media, and the public regarding which issues, goals, norms, methods, and instruments to be politically considered. Rather than influencing decision-making and policy formulation directly, this form of power enables firms to intermediate the anticipatory dynamics of structural forms of business power and shape issue salience as well as the definition and recognition of topics as political problems. The article explores the scope conditions of epistemic business power, detailing the interaction of material aspects, corporate communication and issue salience dynamics. Based on quantitative and qualitative content as well as reception analyses, ownership data and interviews, a case study of the world’s largest asset manager BlackRock illustrates how today’s finance capital can use epistemic channels with regard to fundamental questions about the role of state capacity and private enterprise in capitalist democracies. By foregrounding the political nature of corporate public relations, the conceptual endeavor and its empirical illustration contribute to a deeper understanding of corporate power and its multifaceted role in shaping politics in capitalist democracies.

Tumor microenvironment remodeling after neoadjuvant chemoradiotherapy in local advanced rectal cancer revealed by single-cell RNA sequencing

BackgroundThe use of neoadjuvant chemoradiotherapy (neoCRT) followed by surgery has markedly enhanced the quality of survival in patients suffering from local advanced rectal cancer (LARC). Enhancing this treatment requires a deep understanding of its underlying mechanism. The heterogeneous nature of the tumor microenvironment (TME) significantly impacts therapeutic responses, presenting complex therapeutic challenges.MethodsIn this comprehensive study, we explored the intricate cellular and molecular shifts within the TME of LARC after neoCRT administration. Using single-cell transcriptomic analysis, we meticulously examined 32,417 cells sourced from six samples, each representing different tumor regression grades (TRG: 0 versus 2). This detailed analysis enabled us to characterize the various cell subpopulations, encompassing epithelial cells, lymphocytes, myeloid cells, endothelial cells, and fibroblasts. Additionally, we identified their marker genes for deconvolution calculation in the READ cohort of the TCGA project. And we obtain their marker genes for deconvolution calculation in the READ cohort of the TCGA project.ResultsThrough cluster analysis and pathway comparisons of malignant tumor cells, we discerned that samples with poor tumor regression exhibit enhanced metabolic versatility and adaptability, enabling them to counteract the impacts of both radiotherapy and chemotherapy. Interestingly, within the TRG2 cohort, we observed a predominant immunosuppressive state in the TME, characterized by the activation of CD4 + regulatory T cells, maintained CD8 + T cell functionality, and a heightened M1 to M2 macrophage ratio. Moreover, the differing outcomes of neoCRT were reflected in the varying interaction dynamics between macrophages (M1 and M2) and CD4+/CD8 + T cells. Furthermore, our data reveal that neoCRT intricately modulates fibroblasts and endothelial cells, primarily through the extracellular matrix remodeling pathway, which orchestrates tumor angiogenesis. All changes were validated through immunofluorescence staining on intraoperative samples before and after treatment. To summarize, our investigation presents a comprehensive exploration of the cellular and molecular metamorphoses within the TME post-neoCRT.ConclusionsBy unveiling the sophisticated interaction between the multifaceted cells within the TME and their respective reactions to neoCRT, we establish a robust platform for ensuing future investigations. This study paves the way for novel therapeutic strategies that leverage these insights to bolster the efficacy of neoCRT in managing LARC.

Towards a System Dynamics Framework for Human–Machine Learning Decisions: A Case Study of New York Citi Bike

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

Abstract A031: ECM architecture drives distinct evolutionary patterns of tumor immune escape and elimination in breast cancer

Abstract Cancer progression poses a clinical hurdle due to tumor heterogeneity that contributes to treatment resistance and immune evasion. While the adaptive immune system can effectively eliminate cancer in certain instances, tumor escape remains challenging. The immune microenvironment plays an important role in this process. One unanswered question is the role of ECM geometry, also known as Tumor Associated Collagen Signatures (TACS), and its effects on tumor immune recognition. In solid malignancies, ECM geometry has been associated with disease stage and T cell infiltration. Specifically, empirically observed ECM topologies are frequently categorized based on fiber arrangement: random fibers (TACS1), circumferentially aligned fibers (TACS2), and radially arranged fibers (TACS3). Even though a clear negative correlation between TACS and patient survival has been established, the specific roles and extent of TACS in T cell-driven cancer evolution remain uncharacterized. The precise mechanisms underlying how TACS influences cell movement are still not fully elucidated, with divergent opinions on whether and how the ECM mediates immune cell infiltration. At present, we currently lack a physical model relating the impact of TACS on the spatial co-evolution between an adaptive immune repertoire and a heterogeneous population of evading cancer cells. Here, we developed the EVO-ACT (EVOlutionary Agent-based Cancer T cell interaction) model, which is based on the Gillespie algorithm, to study the effects of TACS on tumor evolution and dynamical tumor-T cell interactions. Our analysis of tumor-T cell interactions across three TACS types reveals distinct migration patterns, with TACS3 showing the highest efficiency, TACS1 intermediate, and TACS2 the lowest. TACS impacts T cells more than tumors, with stronger chemokine gradients aiding T cell infiltration. Our model suggests that despite TACS2's limited efficiency, it cannot fully impede T cell infiltration. Varied migration efficiencies lead to diverse T cell infiltration patterns and immunoediting levels. TACS3 shows a benefit to immune recognition and higher survival, contrary to lower survival in TACS3 patients observed clinically in breast cancer. Our results indicate that only after introducing phenotypic changes, such as Epithelial-Mesenchymal Transition (EMT), and noting that EMT occurs exclusively after the TACS2-TACS3 progression, can our findings successfully explain the clinically observed trends. Considering mesenchymal tumor cells' ability to upregulate PD-1/PD-L1, we explore TACS-specific tumor evolution's impact on PD-1/PD-L1 inhibitor responses and patient survival. We observe higher tumor escape rates and lower survival in TACS3 compared to TACS2. Thus, TACS3 alone does not account for lower survival in breast cancer; TACS3-associated late-stage cancer byproducts like EMT and elevated checkpoint expression significantly decrease survival. Citation Format: Yijia Fan, Jason Tomas George. ECM architecture drives distinct evolutionary patterns of tumor immune escape and elimination in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A031.

Bayesian Network Analysis: Assessing and Restoring Ecological Vulnerability in the Shaanxi Section of the Qinling-Daba Mountains Under Global Warming Influences

Human activities, especially industrial production and urbanization, have significantly affected vegetation cover, water resource cycles, climate change, and biodiversity in the Qinling-Daba Mountain region and its surrounding areas. These activities contribute to complex and lasting impacts on ecological vulnerability. The Qinling Mountain region exhibits a complex interaction with human activities. The current research on the ecological vulnerability of the Qinling Mountain region primarily focuses on spatial distribution and the driving factors. This study innovatively applies the VSD assessment and Bayesian networks to systematically evaluate and simulate the ecological vulnerability of the study area over the past 20 years, which indicates that the integration of the VSD model with the Bayesian network model enables the simulation of dynamic relationships and interactions among various factors within the study areas, providing a more accurate assessment and prediction of ecosystem responses to diverse changes from a dynamic perspective. The key findings are as follows. (1) Areas of potential and slight vulnerability are concentrated in the Qinling-Daba mountainous regions. Over the past 20 years, areas of extreme and high vulnerability have significantly decreased, while areas of potential vulnerability and slight vulnerability have increased. (2) The key factors impacting ecological vulnerability during this period included industrial water use, SO2 emissions, industrial wastewater, and ecological water use. (3) Areas primarily hindering the transition to potential vulnerability are concentrated in well-developed small urban regions within basins. Furthermore, natural factors like altitude and temperature, which cannot be artificially regulated, are the major impediments to future ecological restoration. Therefore, this paper recommends natural restoration strategies based on environmental protection and governance strategies that prioritize green development as complementary measures. The discoveries of the paper provide a novel analytical method for the study of ecological vulnerability in mountainous areas, offering valuable insights for enhancing the accuracy of ecological risk prediction, fostering the integration of interdisciplinary research, and optimizing environmental governance and protection strategies.

Abstract A029: Integrated modeling of renal cancer bone metastasis to illuminate tumor progression and therapy response

Abstract Bone metastatic clear cell renal cell carcinoma (BM ccRCC) is a persistent clinical challenge and a source of significant morbidity and lethality for up to 40% of metastatic patients. Despite the recent approval of life-prolonging agents (antiangiogenic agents, immunotherapy) patients with BM ccRCC will eventually progress, due to emerging resistance. Bidirectional communication between tumor cells and bone stroma has emerged as a key determinant of disease progression and recurrence. However, a major challenge in addressing the black box of therapy response in bone is the lack of efficient and informative systems that allow us to analyze the heterotypic stromal-epithelial interaction, further complicated by the anatomical inaccessibility of bone. For these reasons, the evolution of the metastatic niche and its implications in therapy response remain elusive. In order to improve clinical results, it is critical to establish biologically informed pre-clinical models to provide a mechanistic understanding of tumor progression in bone and treatment outcomes. To this purpose, we have established clinically relevant models of ccRCC bone metastasis that span in vivo and ex vivo multiphoton microscopy (iMPM and eMPM), tissue engineered bone window systems, and computational oncology. iMPM displays both sensitivity and time-resolution to identify dynamic interactions between ccRCC cells and bone 3D adaptive niches, which support therapy response and resistance. However, iMPM in bone is limited due to its cortical thickness, increased light scattering, and complex topology. As a novel alternative, we created a tissue-engineered bone construct (TEBC) that, after direct implantation of cancer cells, is combined with an adjacent skin window, allowing for non- destructive intravital examination of tumor growth. To flank these in vivo dynamic analyses, we established a pipeline for ex vivo extraction of topological information related to the molecular and cellular niches involved in tumor progression and response to treatment (eMPM). This method allows reconstruction of whole lesions, including zones more distant from the bone interface, paired with an extensive panel of molecular markers. To further investigate the effects of a broad multi-parameter space on tumor regression or persistence upon treatment, in silico, we developed an in vivo-inspired agent-based model (ABM) of ccRCC in bone. A computational approach combined to ad hoc biological experimentation can refine the experimental design, test clinically relevant hypotheses (including impact of treatments on disease progression) and predict scenarios that guide biological testing towards more successful outcomes. Consequently, by integrating these approaches, we are currently studying the progression of the bone metastatic niche and its response to therapy, with the ultimate goal of overcoming therapy failure. Citation Format: Sergio Barrios, Luca Marsilio, Stefan Maksimovic, Matthew Campbell, Stefano Casarin, Eleonora Dondossola. Integrated modeling of renal cancer bone metastasis to illuminate tumor progression and therapy response [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A029.

Modeling and Nonlinear Dynamic Characteristics Analysis of Fault Bearing Time-Varying Stiffness-Flexible Rotor Coupling System

There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study, the interaction between the time-varying stiffness of the rolling bearing and the transient response of the flexible rotor is considered. The Newmark-β integral method is used to solve the dynamic equation, and the relationship between the time-varying characteristics of bearing stiffness and load and the dynamic characteristics of the rotor is studied. The relationship between bearing stiffness and vibration strength is analyzed, and the influence of damage size on the time domain signal energy of the disc is analyzed. The results show that the model established in this paper can accurately reflect the dynamic interaction between the bearing and the rotor. With the extension of the bearing damage, the dynamic stiffness of the bearing attenuates, the intensity of the excitation force increases, and the vibration is transmitted to the disc, which affects the motion stability and vibration response of the disc.

A processable and recyclable gelatin/carboxymethyl chitosan hydrogel electrolyte for high performance flexible zinc-ion batteries

Hydrogels are currently under extensive research as flexible quasi-solid electrolytes for zinc-ion batteries. However, the non-degradability and non-recyclability of hydrogel electrolytes pose significant issues, leading to resource wastage and plastic pollution. Moreover, the increasing needs of hydrogel electrolyte with various shapes to meet individual requirements of next-generation flexible battery raise significant challenges. In this study, we introduce the Hofmeister effect and multiple non-covalent interactions to fabricate an eco-friendly and recyclable multifunctional hydrogel electrolyte using biomass natural polymers. This is achieved by simply soaking primary hydrogel networks of carboxymethyl chitosan (CMCS) and gelatin in zinc sulfate (ZnSO4) aqueous solutions, abbreviated as GCZ-x. The GCZ-x hydrogels exhibit both stiffness and toughness due to the formation of crystalline domains and ionic crosslinks. The dynamic physical interactions and temperature and pH responsiveness of the GCZ-x hydrogels enable them excellent processability and recyclability. The zinc-ion battery with GCZ-x hydrogel as electrolyte exhibits high specific capacity and superior cyclic performance (2200 h at 0.1 A g−1) without the formation of zinc dendrites. The GCZ-x electrolyte is sustainable with high recycling rate (above 80 %). It is envisaged that the GCZ-x hydrogel electrolyte will initiate new prosperity of green zinc-ion batteries in energy-efficient and environmentally sustainable ways.

Prediction of SHP2-E76K binding sites based on molecular dynamics simulation and Markov algorithm

SHP2-E76K, a mutant encoded by the PTPN11 gene, was associated with various solid tumors, such as lung cancer, glioblastoma, and intellectual disability. SHP2-E76K has become potential drug targets, while there was no effective inhibitor against the mutant currently. At present, the crystal complex structure of SHP099 with SHP2-E76K has been reported in the RCSB PDB protein data bank, however, the dynamic structure of SHP099 binding to the active center of SHP2-E76K protein was still lacking. Therefore, this study used molecular dynamics simulation and Markov model to characterize the kinetics of the inhibitor SHP099 with SHP2-E76K enzyme and to determine the active binding site, which would give a hint of a vital enzyme-substrate interaction in atomistic detail that proposed the potential to be applied for the discovery of more effective SHP2-E76K inhibitors and, in broader terms, dynamic protein-drug interactions.

Revealing mechanisms of processing defect mitigation in laser powder bed fusion via shaped beams using high-speed X-ray imaging

The laser powder bed fusion (LPBF) process utilizing a focused Gaussian-shaped beam faces challenges, including pore formation, melt pool fluctuation and liquid spattering. While beam shaping technology has been explored as a potential approach for defect mitigation, the beam-matter interaction dynamics during melting with shaped beams remain unclear. Here, we report the direct observation of ring-shaped beam-matter interaction dynamics, including pore formation, melt pool fluctuation and liquid spattering, and unveil defect mitigation mechanisms in ring-shaped beam laser powder bed fusion process. We find that, by spatially manipulating incident laser rays, the ring-shaped beam controls keyhole morphology, thereby managing the distribution of the reflected rays. This manipulation can effectively eliminate the formation of an unstable cavity at the keyhole tip, stabilizing the keyhole and mitigating keyhole pores. This enhanced keyhole stability effectively reduces the melt pool fluctuation, the formation of liquid breakup induced spatters and liquid droplet colliding induced large spatters in the laser powder bed fusion process. Additionally, the high-energy forefront of the ring-shaped beam effectively melts the powder bed, reducing agglomeration liquid spatter in the laser powder bed fusion process. The discovered defect mitigation mechanisms may guide the design of beam shaping strategies for simultaneously increasing the quality and productivity of metal additive manufacturing.

Optimizing alkalinity control in recirculating aquaculture systems (RAS): A dynamic modelling approach

Maintaining good water quality is essential for successful fish production in land-based recirculating aquaculture systems (RAS). Numerous interdependent factors influence water quality parameters, making it difficult to evaluate which operational strategies are most favorable. Mathematical models and model simulations have proven to be powerful tools to evaluate how RAS design and operation are linked to RAS dynamics, but these models rarely implement pH and carbonate species as dynamic variables. Here, we present a dynamic model for RAS (dynRAS) that combines rates of TAN (total ammonia nitrogen) removal, fish growth, and CO2 and TAN excretion, with a reaction model for pH and the carbonate system formulated based on the law of mass action. A novel aspect of our approach is the incorporation of a dosage system modelled by a Hill-function, enabling the exploration of diverse dosing strategies for pH and alkalinity management. The model was validated based on empirical data from a pilot scale RAS system operated with a feeding regime involving 12 h of feeding per day. We found that model simulations could be used to accurately predict diurnal cycling patterns in RAS water quality parameters. Furthermore, we made use of simulations to assess how diurnal cycling varies with changing pH and alkalinity levels. Model results emphasize the complexity of pH and alkalinity control in RAS in relation to overall water quality management. Based on our simulations, we argue that what should be considered as optimal pH and alkalinity in RAS depends on the state of the system. Accordingly, optimal pH and alkalinity thresholds may vary between different RAS units and between different time points of a rearing cycle. More generally, we demonstrate how modelling and model simulations can be an effective way of getting insights into the dynamics of complex RAS interactions and provide a valuable tool to efficiently explore effects of different operational strategies on water quality parameters.

Lipid droplet targeting of ABHD5 and PNPLA3 I148M is required to promote liver steatosis.

The storage and release of triacylglycerol (TAG) in lipid droplets (LDs) is regulated by dynamic protein interactions. α/β hydrolase domain-containing protein 5 (ABHD5; also known as CGI-58) is a membrane/LD bound protein that functions as a co-activator of Patatin Like Phospholipase Domain Containing 2 (PNPLA2; also known as Adipose triglyceride lipase, ATGL) the rate-limiting enzyme for TAG hydrolysis. The dysregulation of TAG hydrolysis is involved in various metabolic diseases such as metabolic dysfunction-associated steatotic liver disease (MASLD). We previously demonstrated that ABHD5 interacted with PNPLA3, a closely related family member to PNPLA2. Importantly, a common missense variant in PNPLA3 (I148M) is the greatest genetic risk factor for MASLD. PNPLA3 148M functions to sequester ABHD5 and prevent co-activation of PNPLA2, which has implications for initiating MASLD; however, the exact mechanisms involved are not understood. Here we demonstrate that LD targeting of both ABHD5 and PNPLA3 I148M is required for the interaction. Molecular modeling demonstrates important resides in the C-terminus of PNPLA3 for LD binding and fluorescence cross-correlation spectroscopy demonstrates that PNPLA3 I148M greater associates with ABHD5 than WT PNPLA3. Moreover, the C-terminus of PNPLA3 is sufficient for functional targeting of PNPLAs to LD and the interaction with ABHD5. In addition, ABHD5 is a general binding partner of LD-bound PNPLAs. Finally, PNPLA3 I148M targeting to LD is required to promote steatosis in vitro and in the liver. Overall results suggest that PNPLA3 I148M is a gain of function mutation and that the interaction with ABHD5 on LD is required to promote liver steatosis.

Human-Nature Boundaries: Cultural Heritage, Landscape Ecology, and Healing in the Kumano Kodo

Abstract: The Kumano Kodo, an ancient pilgrimage route network in the Kii Peninsula of Japan, serves as a unique case study in the intersection of cultural heritage, landscape ecology, and human-nature relationships. This paper explores the dynamic interactions between human interventions and natural processes along the Kumano Kodo, highlighting how landscape ecology principles illustrate the blurred boundaries between nature and culture. The rewilding of the human-imposed structures, coupled with the restorative experiences of modern pilgrims, reinforces the notion of coexistence rather than dominance over nature. The findings support Turners theory of landscape dynamics and Mortons assertion that the human-nature dichotomy is artificial, showing how human interventions and natural processes coalesce in a living landscape. The study also applies Kaplans Attention Restoration Theory to demonstrate the psychological benefits of engaging with cultural landscapes. However, the research is limited by its focus on a specific demographic of pilgrims and calls for more diverse methodologies in future studies. Broader data collection will offer deeper insights into the long-term psychological effects and cultural significance of such heritage sites.

Study on the Interactions Between Urbanization, Tourism, and the Ecological Environment in Underdeveloped Regions: PVAR Model Analysis Based on Data from 32 Chinese Cities

Clarifying the dynamic relationships between urbanization, tourism development, and the ecological environment in underdeveloped regions can assist local governments in optimizing industrial upgrades and environmental management through effective policy planning, despite resource constraints, thereby promoting high-quality urban development. This study, based on data from 32 cities in northwestern China, constructs a multi-dimensional evaluation system incorporating demographic, economic, social, and environmental indicators. Using a PVAR model, the long-term dynamic interactions between urbanization, tourism, and the ecological environment are explored. The results indicate that, although in the initial stages, the three subsystems can support each other, in the long term, tourism has a positive impact on urbanization and the ecological environment, whereas both urbanization and the ecological environment negatively impact tourism. The interaction effects between urbanization, tourism, and the ecological environment are weak, with contribution rates below 20%, failing to create a synergistic development. The primary reason for this issue is that urbanization development policies lag behind the needs of tourism and ecological environment development. Policy recommendations are provided, including optimizing cross-sector collaboration strategies, enhancing urbanization quality, and prioritizing environmental protection, to foster coordinated development among the three.

The Effect of Foreign Language Enjoyment on Students' Foreign Language Learning

Abstract: Globalization has prompted educators to focus on enhancing students' foreign language proficiency rather than merely improving language test scores. Foreign Language Enjoyment (FLE) is widely recognized as a active motivational factor that can significantly enhance language performance. After integrating Positive Psychology into Language Acquisition, researchers has shown an increasing interest in FLE. Recent studies underscore the role of FLE as a valuable element in the process and outcomes of foreign language learning. This article investigates the impact of FLE on different aspects of foreign language acquisition, the pathways through which FLE influences learning, and the outcomes of such effects. The research method adopted is literature analysis. The findings of this study reveal that FLE impacts the learning process by fostering direct affective motivation, enhancing self-confidence, promoting both cognitive and affective engagement, and facilitating dynamic interactions at both individual and group levels. Additionally, FLE influences learning outcomes by mitigating the interference of negative emotions, improving language proficiency, and establishing mediating variables that connect other psychological factors to foreign language performance. These insights highlight the importance of creating positive learning environments and experiences, which can significantly enhance students' FLE and engagement, thereby improving the effectiveness of foreign learning and overall language performance.

Dynamic Interaction between Energy Consumption, CO2 Emissions and Economic Growth in Malaysian Industrial Sector: An Aggregate and Disaggregate Analysis

Dynamic Interaction between Energy Consumption, CO2 Emissions and Economic Growth in Malaysian Industrial Sector: An Aggregate and Disaggregate Analysis

The slow clock is the master

AbstractWe investigate the synchronization and stability of mutually coupled oscillators through small impacts and explore both master–master and master–slave synchrony. The synchronization behaviour between two oscillators, one operating at a frequency $$ \omega $$ ω and the other at a frequency near a multiple of the first one, $$ n \omega $$ n ω where $$ n $$ n is an integer greater than 1, is investigated. It is observed that such systems tend to exhibit a consistent pattern of master–slave relationship. This phenomenon is geometrically elucidated by considering the interaction dynamics: while the multiple bursts of the faster oscillator tend to cancel each other out upon impacting the slower oscillator, the single burst of the slower oscillator on the faster has a secular effect. We propose that this synchronization pattern, arising from perturbative impacts, is prevalent across various physical systems. Further experimental validation of these findings could contribute to a deeper understanding of synchronization phenomena and their implications in natural and technological domains.

A scoping review of interaction dynamics in minimally verbal autistic individuals.

Interaction dynamics provide information about how social interactions unfold over time and have implications for communication development. Characterizing social interaction in autistic people who are minimally verbal (MV) has the potential to illuminate mechanisms of change in communication development and intervention. The purpose of this scoping review was to investigate the current evidence characterizing interaction dynamics in MV autistic individuals, methods used to measure interaction dynamics in this population, and opportunities for future research. Articles were included if participants were diagnosed with autism, considered MV, if interaction occurred with a human communication partner during live in-person interaction, and if variables were derived by measuring the relationship between behaviors in both partners. The seven articles included in this review demonstrate that limited research describes interaction dynamics in this population, and that behavioral coding measures can be leveraged to assess constructs such as turn-taking, social contingency, and balance in social interactions. While there is some evidence describing how MV autistic individuals and their communication partners construct reciprocal interaction, there is variability in how interaction dynamics are measured and limited evidence describing individual differences. Recommendations for future research are discussed.