Dynamics Trajectories Research Articles

A qualitative dynamic analysis of the relationship between tourism and human development

This study analyzes the dynamic relationship between tourism and human development in a sample of 123 countries between 1995–2019 using a symbolic time series methodological analysis, with the number of international tourist arrivals per capita as the tourism measurement variable and the Human Development Index as the development measurement variable. The objective was to determine if a higher level of tourism specialization is related to a higher level of economic development. The definition of economic regime is used and the concept of the distance between the dynamic trajectories of the different countries analyzed is introduced to create a minimum spanning tree. In this way, groups of countries are identified that display similar behavior in terms of tourism specialization and levels of human development. The results suggest that countries with a high level of tourism specialization have a higher level of development as compared to those in which tourism has a lower specific weight. However, the largest group of countries identified is characterized by low levels of tourism specialization and economic development, which appears to translate into a poverty trap. Therefore, policies related to tourism activity expansion should be created since higher tourism levels have been linked to higher levels of human development. In the case of less developed countries, however, these projects should be financed by international organizations so that these countries can escape the poverty trap in which they are currently found.

Reviewing approaches employed in Arabic chatbots

<p>The field of chatbots has witnessed a remarkable evolution in recent years, marked by a transition from simplistic rule-based structures to sophisticated systems employing advanced natural language processing (NLP) techniques. While most languages benefit from NLP support, the majority of chatbot research and development has been conducted in English, leaving a notable scarcity of comparable works in Arabic. This scarcity is attributed to the myriad challenges posed by the linguistically intricate nature of Arabic, encompassing orthographic variations and diverse dialects. This study systematically reviews articles that represent implementations of Arabic chatbots, revealing a discernible shift from rule-based frameworks to the predominant adoption of machine learning (ML) and deep learning (DL) methods. The results highlight the dynamic trajectory of chatbot technology, with a notable emphasis on the pivotal role of DL, as evidenced by a significant peak in 2023. Looking forward, the study anticipates a more sophisticated future for chatbot development, driven by ongoing advancements in artificial intelligence (AI) and NLP, offering valuable insights into the current state of Arabic chatbot research and laying the foundation for continued exploration in this evolving and dynamic field.</p>

Subphenotypes of platelet count trajectories in sepsis from multi-center ICU data

Although thrombocytopenia on admission to the ICU is associated with increased in-hospital mortality in septic patients, the role of longitudinally measured platelet counts, which are dynamically changing, is unclear. We aimed to identify patterns of dynamic platelet count trajectories and evaluate their association with outcomes and thrombocytopenia in septic patients. We tested the longitudinal platelet trajectory patterns of sepsis patients within the first four days of ICU admission in the MIMIC-IV database and their association with 28-day mortality, and independently validated our findings in the eICU-CRD database. Statistical methods used included multivariate regression, propensity score analysis, doubly robust estimation, gradient boosting model, and inverse probability weighting to ensure the robustness of our findings. A total of 22,866 septic patients were included in our study. The trajectory analysis categorizes patients into ascending (AS), stable (ST), or descending (DS) patterns. The risk of 28-day mortality was increased in the DS patients (OR = 2.464, 95%CI 1.895–3.203, p < 0.001) and ST patients (OR = 1.302, 95%CI 1.067–1.589, p = 0.009) compared to AS patients. The AS patients had lower ICU length of stay (2.36 vs. 4.32, p < 0.001) and 28-day maximum SOFA scores (5.00 vs. 6.00, p < 0.001) than the DS patients, but had more ventilator-free days within 28 days than the DS group (26.00 vs. 24.00, p < 0.001). The mediating effect of thrombocytopenia was significant (p < 0.001 for the average causal mediation effect (ACME)). Longitudinal platelet trajectory was associated with risk-adjusted 28-day mortality among patients with sepsis and was proportionally mediated through thrombocytopenia.

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Unsupervised learning of progress coordinates during weighted ensemble simulations: Application to millisecond protein folding.

Our method identifies outliers in a latent space model of the system's sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate a millisecond protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model. Results revealed that our ″on-the-fly″ DL of outliers enhanced the efficiency of WE by >3-fold in estimating the folding rate constant. Our efforts are a significant step forward in the unsupervised learning of slow coordinates during rare event sampling.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Mapping the developmental structure of stereotyped and individual-unique behavioral spaces in C. elegans

Developmental patterns of behavior are variably organized in time and among different individuals. However, long-term behavioral diversity was previously studied using pre-defined behavioral parameters, representing a limited fraction of the full individuality structure. Here, we continuously extract ∼1.2 billion body postures of ∼2,200 single C.elegans individuals throughout their full development time to create a complete developmental atlas of stereotyped and individual-unique behavioral spaces. Unsupervised inference of low-dimensional movement modes of each single individual identifies a dynamic developmental trajectory of stereotyped behavioral spaces and exposes unique behavioral trajectories of individuals that deviate from the stereotyped patterns. Moreover, classification of behavioral spaces within tens of neuromodulatory and environmentally perturbed populations shows plasticity in the temporal structures of stereotyped behavior and individuality. These results present a comprehensive atlas of continuous behavioral dynamics across development time and a general framework for unsupervised dissection of shared and unique developmental signatures of behavior.

Spatiotemporal transcriptomic mapping of regenerative inflammation in skeletal muscle reveals a dynamic multilayered tissue architecture.

Tissue regeneration is orchestrated by macrophages that clear damaged cells and promote regenerative inflammation. How macrophages spatially adapt and diversify their functions to support the architectural requirements of actively regenerating tissue remains unknown. In this study, we reconstructed the dynamic trajectories of myeloid cells isolated from acutely injured and early stage dystrophic muscles. We identified divergent subsets of monocytes/macrophages and DCs and validated markers (e.g., glycoprotein NMB [GPNMB]) and transcriptional regulators associated with defined functional states. In dystrophic muscle, specialized repair-associated subsets exhibited distinct macrophage diversity and reduced DC heterogeneity. Integrating spatial transcriptomics analyses with immunofluorescence uncovered the ordered distribution of subpopulations and multilayered regenerative inflammation zones (RIZs) where distinct macrophage subsets are organized in functional zones around damaged myofibers supporting all phases of regeneration. Importantly, intermittent glucocorticoid treatment disrupted the RIZs. Our findings suggest that macrophage subtypes mediated the development of the highly ordered architecture of regenerative tissues, unveiling the principles of the structured yet dynamic nature of regenerative inflammation supporting effective tissue repair.

Determination of Hydrogen Bonds in GROMACS: A New Implementation to Overcome Memory Limitation.

This work introduces a new faster implementation of the hydrogen bond network (complex arrangement of hydrogen bonds between or within molecules) search algorithm in biomacromolecules and their environment. Existing implementation of such an algorithm in GROMACS [Abraham et al. GROMACS 2024.2 Manual. 2024.] has limitations in the analysis of large structures and trajectories. The new implementation, in the form of a native GROMACS trajectory analysis module, allows for quick analysis of molecular dynamics trajectories without restrictions, thus overcoming the limitations of the original algorithm. The application of the developed method enabled the acquisition and analysis of hydrogen bond networks in the studied defensin-like protein Pentadiplandra brazzeana, as well as the study of hydrogen bond occupancies between the protein's residues and water molecules. The data obtained using the new implementation coincided with the experimental data.

Determining how oxygen legacy affects trajectories of soil denitrifier community dynamics and N2O emissions

Denitrification – a key process in the global nitrogen cycle and main source of the greenhouse gas N2O – is intricately controlled by O2. While the transition from aerobic respiration to denitrification is well-studied, our understanding of denitrifier communities’ responses to cyclic oxic/anoxic shifts, prevalent in natural and engineered systems, is limited. Here, agricultural soil is exposed to repeated cycles of long or short anoxic spells (LA; SA) or constant oxic conditions (Ox). Surprisingly, denitrification and N2O reduction rates are three times greater in Ox than in LA and SA during a final anoxic incubation, despite comparable bacterial biomass and denitrification gene abundances. Metatranscriptomics indicate that LA favors canonical denitrifiers carrying nosZ clade I. Ox instead favors nosZ clade II-carrying partial- or non-denitrifiers, suggesting efficient partnering of the reduction steps among organisms. SA has the slowest denitrification progression and highest accumulation of intermediates, indicating less functional coordination. The findings demonstrate how adaptations of denitrifier communities to varying O2 conditions are tightly linked to the duration of anoxic episodes, emphasizing the importance of knowing an environment’s O2 legacy for accurately predicting N2O emissions originating from denitrification.

The Evolutionary Trajectory and Developmental Dynamics of Intergovernmental Cooperative Environmental Governance from the Perspective of Historical Institutionalism

The Evolutionary Trajectory and Developmental Dynamics of Intergovernmental Cooperative Environmental Governance from the Perspective of Historical Institutionalism

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction.

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF3: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF3: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

Influence of wild ungulates on the secondary succession of an Apennine silver fir forest after windthrow

Windstorms are natural factors in vegetation dynamics, but their intensity is increasing and undermines the forest resilience. Post-event interventions depend on forest types and management purposes. To promote mixed stands that are less susceptible to windthrow than monocultures, natural tree regeneration is usually recommended. However, wild ungulates attracted by the increased food supply in the new clearings can influence the secondary succession and slow down vegetation recovery. The impact of ungulate pressure on secondary successions in blowdown areas is still poorly known, especially in Mediterranean areas, which is particularly vulnerable to climate stressors. We investigated this topic using a monospecific silver fir (Abies alba) forest of artificial origin in the Apennines as a model system. The forest was left to natural succession after a severe windthrow in 2015, offering the opportunity to establish an ungulate exclosure experiment to analyse vegetation changes over six years. For the whole plant community, cover and height, α-diversity (species richness, Shannon and evenness indexes), and species composition were recorded yearly, together with tree seedling density, in open and fenced plots at increasing distances from the forest edge. Ecological and functional traits (proportion of light-demanding and forest specialists, endo-/epi-zoochorous species), and life-forms of the community samples were also analysed. Overall, we found that ungulates significantly influenced the dynamic trajectories of secondary succession. The effects on plant cover and diversity were negative and increased with distance from the forest edge. The presence of ungulates favoured generalist species and endozoochorous taxa. However, distance from the forest edge strongly reduced the latter effect. The average density of tree seedlings was overall high (ca. 10,000/ha), but it was strongly reduced by ungulates for three deciduous species and A. alba, the most browsed species. Natural recolonization after windthrow in Apennine pure silver fir stands may favour the formation of mixed, less susceptible forests, but the current ungulate pressure slows down this process, especially in the areas furthest from the forest edge.

Online dual robot–human collaboration trajectory generation by convex optimization

For dynamic collision-free trajectory planning in dual-robot and human collaborative tasks, this paper develops an online dual-robot Mutual Collision Avoidance (MCA) scheme based on convex optimization. A novel convex optimization formulation model, named Disciplined Convex programming by Shifting reference paths (DCS), is proposed for solving the single-robot trajectory optimization problem. Furthermore, a new dual-robot trajectory convex optimization algorithm is presented for online adjustment of the dual-robot trajectories according to the collaborative task priority. The overall pipeline, named DCS-MCA, generates collision-free and time-optimal dual-robot trajectories, while prioritizing the task accessibility of the high-priority robot. Simulation experiments demonstrate that DCS exhibits comparable performance to the current state-of-the-art single-robot motion planner, while the DCS-MCA outperforms common algorithms by up to 30% in time optimality for dual-robot collaborative tasks. The feasibility and dynamic performance of the proposed approach are further validated in a real collaborative cell, illustrating its suitability for collaborative dual-robot tasks in moderately dynamic environments.

School placement trajectories of students with special educational needs—A longitudinal analysis of administrative data

AbstractSchool transfers may be particularly challenging for students with special educational needs (SEN), and may negatively impact academic and social outcomes for these students. In countries with multiple placement options for students with SEN, although transfers between different types of placements are plausible, studies on this phenomenon are rare. The current study investigates individual placement trajectories over 11 years of compulsory education, using administrative data from a Swiss canton (i.e. federal state) about students with intensive SEN support. We employed sequence analysis to describe individual characteristics of placement trajectories (type of placements attended, number and timing of transfers), and to identify typical placement trajectories. Findings indicate that students with SEN experience frequent placement transfers, however, high variability between individual placement trajectories exists. Four clusters representing typical placement trajectories emerged. The clusters differed in the placements predominately attended by students, the number of transfers experienced, as well as in the distribution of students' age, gender and first language. These differences may reflect variations between clusters in the type and severity of students' SEN. Our study results underscore the importance of understanding the dynamics of placement trajectories for students with SEN, contributing valuable insights into the complexities of their educational journeys.

Association between early blood glucose dynamic trajectory and mortality for critically ill patients with heart failure: Insights from real-world data

AimsThis study endeavors to explore the ramifications of early dynamic blood glucose (BG) trajectories within the initial 48 h of intensive care unit (ICU) admission on mortality among critically ill heart failure (HF) patients. MethodsThe study employed a retrospective observational design, analyzing dynamic BG data of HF patients from the Medical Information Mart for Intensive Care IV database. The BG trajectory subphenotypes were identified using the hierarchical clustering based on the dynamic time-warping algorithm. The primary outcome of the study was 28-day mortality, with secondary outcomes including 180-day and 1-year mortality. ResultsWe screened a total of 21,098 HF patients and finally 15,092 patients were included in the study. Our results identified three distinct BG trajectory subphenotypes: increasing (n = 3503), stabilizing (n = 6250), and decreasing (n = 5339). The increasing subphenotype was associated with the highest mortality risk at 28 days, 180 days, and 1 year. The stabilizing and decreasing subphenotypes showed significantly lower mortality risks across all time points, with hazard ratios ranging from 0.85 to 0.88 (P<0.05 for all). Sensitivity analyses confirmed the robustness of these findings after adjusting for various covariates. ConclusionsIncreasing BG trajectory within 48 h of admission is significantly associated with higher mortality in patients with HF. It is necessary to devote greater attention to the early BG dynamic changes in HF patients to optimize clinical BG management and enhance patient prognosis.

Proteomic and Phosphoproteomic Analyses during Plant Regeneration Initiation in Cotton (Gossypium hirsutum L.).

Somatic embryogenesis (SE) is a biotechnological tool used to generate new individuals and is the preferred method for rapid plant regeneration. However, the molecular basis underlying somatic cell regeneration through SE is not yet fully understood, particularly regarding interactions between the proteome and post-translational modifications. Here, we performed association analysis of high-throughput proteomics and phosphoproteomics in three representative samples (non-embryogenic calli, NEC; primary embryogenic calli, PEC; globular embryos, GE) during the initiation of plant regeneration in cotton, a pioneer crop for genetic biotechnology applications. Our results showed that protein accumulation is positively regulated by phosphorylation during SE, as revealed by correlation analyses. Of the 1418 proteins that were differentially accumulated in the proteome and the 1106 phosphoproteins that were differentially regulated in the phosphoproteome, 115 proteins with 229 phosphorylation sites overlapped (co-differential). Furthermore, seven dynamic trajectory patterns of differentially accumulated proteins (DAPs) and the correlated differentially regulated phosphoproteins (DRPPs) pairs with enrichment features were observed. During the initiation of plant regeneration, functional enrichment analysis revealed that the overlapping proteins (DAPs-DRPPs) were considerably enriched in cellular nitrogen metabolism, spliceosome formation, and reproductive structure development. Moreover, 198 DRPPs (387 phosphorylation sites) were specifically regulated at the phosphorylation level and showed four patterns of stage-enriched phosphorylation susceptibility. Furthermore, enrichment annotation analysis revealed that these phosphoproteins were significantly enriched in endosomal transport and nucleus organization processes. During embryogenic differentiation, we identified five DAPs-DRPPs with significantly enriched characteristic patterns. These proteins may play essential roles in transcriptional regulation and signaling events that initiate plant regeneration through protein accumulation and/or phosphorylation modification. This study enriched the understanding of key proteins and their correlated phosphorylation patterns during plant regeneration, and also provided a reference for improving plant regeneration efficiency.

Benchmarking machine learning interatomic potentials via phonon anharmonicity

Abstract Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.

Robust optimization and assessment of dynamic trajectory and mixed-beam arc radiotherapy: a preliminary study

Objective. Dynamic trajectory radiotherapy (DTRT) and dynamic mixed-beam arc therapy (DYMBARC) exploit non-coplanarity and, for DYMBARC, simultaneously optimized photon and electron beams. Margin concepts to account for set-up uncertainties during delivery are ill-defined for electron fields. We develop robust optimization for DTRT&DYMBARC and compare dosimetric plan quality and robustness for both techniques and both optimization strategies for four cases. Approach. Cases for different treatment sites and clinical target volume (CTV) to planning target volume (PTV) margins, m , were investigated. Dynamic gantry-table-collimator photon paths were optimized to minimize PTV/organ-at-risk (OAR) overlap in beam’s-eye-view and minimize potential photon multileaf collimator (MLC) travel. For DYMBARC plans, non-isocentric partial electron arcs or static fields with shortened source-to-surface distance (80 cm) were added. Direct aperture optimization (DAO) was used to simultaneously optimize MLC-based intensity modulation for both photon and electron beams yielding deliverable PTV-based DTRT&DYMBARC plans. Robust-optimized plans used the same paths/arcs/fields. DAO with stochastic programming was used for set-up uncertainties with equal weights in all translational directions and magnitude δ such that m= 0.7δ . Robust analysis considered random errors in all directions with or without an additional systematic error in the worst 3D direction for the adjacent OARs. Main results. Electron contribution was 7%–41% of target dose depending on the case and optimization strategy for DYMBARC. All techniques achieved similar CTV coverage in the nominal (no error) scenario. OAR sparing was overall better in the DYMBARC plans than in the DTRT plans and DYMBARC plans were generally more robust to the considered uncertainties. OAR sparing was better in the PTV-based than in robust-optimized plans for OARs abutting or overlapping with the target volume, but more affected by uncertainties. Significance. Better plan robustness can be achieved with robust optimization than with margins. Combining electron arcs/fields with non-coplanar photon trajectories further improves robustness and OAR sparing.

Model of interaction between product and process innovative potential of a manufacturing enterprise

The published article proposes an economic and mathematical model of the dynamics of development of a multifactor production enterprise, taking into account the interaction of product and process innovation potentials. The output of the enterprise under consideration is ensured by the Cobb-Douglas production multiplicative function, the parameters of which depend on the levels of product innovation potential and process innovation potential. A system of differential equations for the balance of an enterprise has been established regarding the volumes of all its resources and the volumes of its product and process innovation potentials. To manage the processes of gradual introduction of innovative potentials into production, special indicator functions are introduced into the system of differential equations of the model, which determine the time intervals for the deployment of innovative potentials. The maximum values of the volumes of enterprise resources, the maximum values of the volumes of innovative potentials and the maximum value of the volume of revenue have been calculated. The numerical solution of the system of differential equations of the developed model makes it possible to obtain dynamic trajectories of enterprise development, on the basis of which, by controlling indicator functions, it is possible to construct various scenarios for the operation of the enterprise.