Abstract Abiotic, biotic, and spatial processes shape patterns of biodiversity in coastal ecosystems. In this study, we test how these processes influence eelgrass‐associated invertebrate diversity in a subarctic seascape. We sampled invertebrate assemblages in 12 meadows along 300 km of coastline in James Bay, Canada, and tested the relative contributions of abiotic conditions, spatial arrangement, and meadow attributes in explaining variation in invertebrate community composition, abundance, and diversity within and among meadows. We found that eelgrass meadows in James Bay support diverse invertebrate communities comprising at least 72 taxa. Greater invertebrate abundance was associated with higher epiphyte load and clearer water (lower turbidity). Invertebrate diversity increased with aboveground eelgrass biomass and epiphyte loads, and species richness increased in clearer water. Invertebrate communities in very salty and very fresh waters contributed the most to regional beta diversity. Variation in taxonomic composition across the seascape was notable; no species was observed at every site, and most taxa were observed at 3–5 sites. The most widely distributed taxa were the arctic amphipod Gammarus setosus and the bivalves Mytilus trossulus and Macoma balthica , each observed at 11 of the 12 sites. Variation in taxonomic composition among meadows was partially explained by meadow spatial arrangement and local abiotic conditions (salinity, fetch, temperature, and turbidity) but not meadow attributes such as eelgrass biomass or shoot size. We conclude that both local conditions and regional ecological processes such as spatially structured population dynamics and species interactions may be important for regional‐scale biodiversity patterns on this coastline.

The spread of infectious diseases across regions is a global phenomenon, often complicated by the concurrent influence of multiple diseases within the same time frame. This paper explores the dynamics of disease transmission between two population groups, each affected by a distinct disease. The model incorporates transmission parameters dependent on spatial variables, while population dynamics are examined over both time and spatial dimensions. This spatially sensitive approach to model parameters enhances the applicability of findings to real-world scenarios. Our study is framed within a reaction-diffusion, multi-SIR population dynamics model. Initially, we investigate the well-posedness of the proposed model's solution via semigroup theory. Steady states are determined, and leveraging the disease-free steady state and the next-generation operator, we derive the basic reproduction number. Furthermore, we analyze the stability properties of these steady states through eigenvalue problems in partial differential equations. Finally, numerical simulations are performed to validate the proposed model with spatially dependent data, underscoring its relevance to spatially structured disease dynamics.

The author declares no conflicts of interest.

This study investigates how green finance affects innovation in new energy enterprises amid China’s institutional shift toward high-quality development. By ex-ploiting a quasi-natural experiment of the 2017 Green Finance Reform and Innovation Pilot Zones and adopting the difference-in-differences method, we find that green finance reform has significantly promoted simultaneous improvement in the quantity and quality of green innovation in new energy enterprises. We validated this core finding through a series of robustness tests. The mechanism analysis shows that the ex-pansion and accessibility of long-term debt financing constitute a core transmission channel. Additionally, the policy strengthens its incentive effect on green innovation by increasing the proportion of R&D personnel and enhancing green innovation efficiency. The heterogeneity analysis indicates that the policy effect is more pronounced in regions with a higher level of financial development, private enterprises, and enterprises whose senior management teams lack an economic background. These findings highlight the socially embedded nature and heterogeneous effects of green finance and provide insights into China’s green transformation and the underlying structural dy-namics that shape policy outcomes.

Optimizing repetition rate and fluence for improved groove depth in femtosecond laser processing of silicon

The given study introduces a visual analytic and predictive modelling concept of interpreting financial narratives in digital media, which holds the problem of volatility-inducing sentiment, narrative bias, and informational overload of news, social sites, and corporate disclosures. The aim is to make a systematic comprehension of the impact of visual signals, linguistic structuring and time dynamics on market-relevant narratives and expectations. The suggested approach will combine multimodal visual analytics, natural language processing, and time-series prediction. Visual modules process charts, infographics, thumbnails and video frame trends, extract emphasis of trends, scale distortion, color semantics as well as attention cues whereas language models identify sentiment polarity, stance, uncertainty and causal framing. Such properties are combined on the basis of transformer-based schemata and matched with market indicators to acquire narrative and market correlations. Predictive components are models using probabilistic prediction to estimate the volatility and directional risk of short horizon in conditioned directions using narrative signals. On massive financial media datasets, assessments show a reality that more accurately extracts narratives and predicts risks with gains of up to 16 and 14 per cent on sentiment -return correspondence and false volatility alarms respectively over text-only controls. The interactive visual dashboards present elucidative insights, indicating persuasive visuals, words, and time changes, which propel the predictions. The results reveal that visual analytics, when paired together with predictive modelling, can create a robust transparent method of decoding financial stories that will in turn aid analysts, regulators and investors to make decisions in time and also make informed choices about literacy on media in the rapidly changing digital information ecosystems. It also allows cross-platform comparison, stress testing, and early warning in the face of uncertainty to the stakeholders worldwide.

SUMMARYThere is growing recognition in both the experimental and modeling literature of the importance of spatial structure to the dynamics of viral infections within the host. Aided by the evolution of computing power and motivated by recent biological insights, there has been an explosion of new, spatially explicit models for within-host viral dynamics in recent years. This development has only been accelerated in the wake of the COVID-19 pandemic. Spatially structured models offer improved biological realism and can account for dynamics that cannot be well-described by conventional, mean-field approaches. However, despite their growing popularity, spatially structured models of viral dynamics are underused in biological applications. One major obstacle to the wider application of such models is the huge variety in approaches taken, with little consensus as to which features should be included and how they should be implemented for a given biological context. Previous reviews of the field have focused on specific modeling frameworks or on models for particular viral species. Here, we instead apply a scoping review approach to the literature of spatially structured viral dynamics models as a whole to provide an exhaustive update of the state of the field. Our analysis is structured along two axes, methodology and viral species, in order to examine the breadth of techniques used and the requirements of different biological applications. We then discuss the contributions of mathematical and computational modeling to our understanding of key spatially structured aspects of viral dynamics and suggest key themes for future model development to improve robustness and biological utility.

Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic resistance. Here, we used machine learning-based virtual screening as a verification technique to find potential inhibitors possessing different chemical scaffolds, using structure-based drug design as a discovery platform. Methods: Four machine learning models, built based on chemical fingerprint data, were trained, and the best models were used for virtual screening of the ChEMBL library, which covers 153 active molecules. Molecular dynamics (MD) simulations of 200 ns were carried out for all three compounds in order to explain conformational changes, evaluate stability, and provide validation of the docking results. Post-simulation analyses include principal component analysis (PCA), bond analysis, free-energy landscape (FEL), dynamic cross-correlation matrix (DCCM), radial distribution function (RDF), salt-bridge identification, and secondary-structure profiling, etc. Results: For molecular docking, the screened compounds were used against the GuaB protein to achieve proper docked conformation. Upon visual examination of the best-docked compounds, three leads (lead-1, lead-2, and lead-3) were found to have better interaction with the GuaB protein in comparison to the control. The mean RMSD scores between the three leads and the control were between 2.54 and 2.89 Å. In addition, the three leads as well as the control were characterized for pharmacokinetic features. All three leads met Lipinski’s Rule 5 and were thus drug-like. PCA and FEL analyses showed that lead-2 exhibited improved conformational stability, identified as deeper energy minima, whereas RDF and DCCM analyses revealed that lead-2 and lead-3 exhibited strong local structuring and concerted dynamics. In addition, lead-2 displayed a very rich hydrogen-bonding network with a total of 460 frames possessing such interactions, which is the highest among the complexes investigated here. Based on entropy calculations and the maximum entropy method of gamma–gram, lead-1 proved to be the most stable one with the lowest binding free-energy. Conclusions: This study provides an integrated machine learning-based virtual screening pipeline for the identification of new scaffolds to moderate infections associated with AMR; however, in vitro validation is still required to assess the efficacy of such compounds.

This paper contextualizes Shao Yihui's recent works within the evolving framework of feminist film theory, examining how her cinematic practice constitutes both a theoretical intervention and a formal revolution. Through close analysis of Love Myth (2021) and Her Story (2024), this study demonstrates how Shao's directorial efforts fashion complex female subjectivities while systematically challenging patriarchal narrative conventions. Her achievement lies not in merely applying feminist theory to cinematic content, but in generating what might be termed an "immanent critique"working through innovation from within cinematic form itself, rather than external criticism that raises the problem of form. This paper explores in particular Shao's soundscape, narrative and spatial manipulation to emerge from the conception of space, structure and spatial dynamism. The "feminist formal language" that reconfigures the bond between characters within these films and their cinematic environment. More specifically, by comparing Shao's construction to the gender politics in box office hit animated film Ne Zha: The Demon Boy Churns the Sea , this discussion illustrates how the problems of patriarchal representation remain while highlighting Shao's unique role in the making of what can be interpreted as "feminine gaze" in the Chinese cinema of today. The paper examines how digital counterpublics are challenging what feminist discourse can look like in China's rapidly evolving media ecology.

This article presents the results of a comprehensive ontogenetic-population analysis of the adventive species Sinapis arvensis L., a significant component of the segetal flora in the agrocenoses of field crops in the Right-Bank Forest-Steppe of Ukraine. The research was conducted over a multi-year period (2013–2024) on private farms located in Odesa and Vinnytsia regions, which allowed for tracking the dynamics of population changes under various agricultural practices. This species was conditionally identified as a model object for further studies of invasion pro­cesses, due to its high segetal potential, confirmed by consistently high abundance indicators, significant projective cover, and high frequency of occurrence in all studied agrocenoses. These characteristics de­monstrate the successful adaptation of S. arvensis L. and its dominance in the agroecosystems of the re­gion. A detailed analysis of the age structure of S. arvensis L. populations revealed their high density and the formation of full-membered right-sided spectra of ontogenetic states. This means that populations include all age groups — from seedlings and juvenile forms to adult virile, generative, and senescent individuals. Such full-membership is a critical indicator of population stability and its ability for effective self-reproduction even under intensive anthropogenic pressure. The presence of all life cycle stages confirms the species’ existence in conditions of ecological and phytocenotic optimum. The application of the quan­titative «delta-omega» (Δ/ω) classification by Zhivotovsky allowed for establishing that S. arvensis L. populations primarily belonged to young and matur­ing types. However, long-term monitoring revealed significant dynamism in the ontogenetic structure of populations and its dependence on the type of agro­ cenosis: in the agrocenoses of winter cereals (winter wheat, winter barley) and oilseeds (winter rapeseed), a «rejuvenation» of populations was observed in the period 2017–2024. This is likely linked to the in­ tensification of agricultural practices, which elimi­nate older individuals but create conditions for the mass germination of seeds from the soil seed bank.In unflower and corn crops, S. arvensis L. populations maintained a young status, indicating consistently fa­vorable conditions for intensive population renewal in these cultures; сonversely, in sugar beet agrocenoses, a tendency for the population to transition to a mature stage was recorded. This may suggest the formation of more stable and self-reproducing communities, possibly due to the specificity of agricultural technologies or lower competitive pressure in these crops, allowing a larger number of individuals to reach reproduc­tive age. This observed dynamism in the ontogenetic structure of populations and its dependence on a complex of ecological, phytocenotic, and anthropo­genic factors underlines the high ecological plasticity of S. arvensis L., its exceptional competitiveness, and its ability for effective persistence in agrocenoses. The obtained results are of fundamental importance for understanding the bioecology of invasive species and serve as a scientific basis for developing effective integrated control systems for wild mustard, aimed at minimizing its negative impact on the productivity of agricultural crops under intensive farming conditions in Ukraine.

"Linguistic inflation" refers to the "devaluation" caused by the mismatch between linguistic form and meaning, representing a new trend in sociolinguistic research. Through quantitative analy-sis of data from mainstream domestic online platforms, this study reveals manifestations of lin-guistic inflation at lexical, syntactic, and symbolic levels, summarized as the generalization of in-timate terms, excessive stacking of modal particles, and the widespread use of emojis. The re-search finds that this phenomenon arises from the combined effects of social structure, psy-cho-logical needs, technological development, and linguistic dynamism. While it enhances emo-tional expression, it also leads to issues such as informational ambiguity and superficial social interac-tions. To address these challenges, strategies such as strengthening linguistic norms, promoting diversity, and regulating the online environment are proposed. The study aims to provide theo-retical support for the healthy development of language and to advance the depth of sociolin-guistic research.

Electrostatic interactions are fundamental to biomolecular structure, stability, and function. While these interactions are traditionally modeled using fixed-charge force fields, such approaches are not transferable among different molecular environments. Polarizable force fields, such as DRUDE, address this limitation by explicitly incorporating the polarization effect. However, their performance does not uniformly surpass that of nonpolarizable force fields since multiple factors such as bonded terms, dihedral correction maps, and solvent screening also modulate biomolecular dynamics. In this work, we study the Im7 protein to evaluate the structural and dynamic behaviors of nonpolarizable (CHARMM36m) and polarizable (DRUDE2019) force fields relative to NMR experiments. Our simulations show that DRUDE2019 better stabilizes α-helices than CHARMM36m, including shorter ones that contain helix-breaking residues. However, both force fields underestimate loop dynamics, particularly in the loop I region, mainly due to restricted dihedral angle sampling. Moreover, salt bridge analysis reveals that DRUDE2019 and CHARMM36m preferentially stabilize different salt bridges, driven by ionic interactions, charge screening by the environment, and neighboring residue flexibility Additionally, the latest DRUDE2019 variant, featuring updated NBFIX and NBTHOLE parameters for ion-protein interactions, demonstrated improved accuracy in modeling Na+-protein interactions. These findings are further supported by simulations of CBD1, a protein with a β-sheet and flexible loops, which exhibited similar trends of stable structured regions and restricted loop dynamics across both force fields. These findings highlight the need to balance bonded and nonbonded interactions along with dihedral correction maps while incorporating polarization effects to improve the accuracy of force fields to model protein structure and dynamics.

Offline goal-conditioned reinforcement learning methods have shown promise for reach-avoid tasks, where an agent must reach a target state while avoiding undesirable regions of the state space. Existing approaches typically encode avoid-region information into an augmented state space and cost function, which prevents flexible, dynamic specification of novel avoid-region information at evaluation time. They also rely heavily on well-designed reward and cost functions, limiting scalability to complex or poorly structured environments. We introduce RADT, a decision transformer model for offline, reward-free, goal-conditioned, avoid region-conditioned RL. RADT encodes goals and avoid regions directly as prompt tokens, allowing any number of avoid regions of arbitrary size to be specified at evaluation time. Using only suboptimal offline trajectories from a random policy, RADT learns reach-avoid behavior through a novel combination of goal and avoid-region hindsight relabeling. We benchmark RADT against 3 existing offline goal-conditioned RL models across 11 tasks, environments, and experimental settings. RADT generalizes in a zero-shot manner to out-of-distribution avoid region sizes and counts, outperforming baselines that require retraining. In one such zero-shot setting, RADT achieves 35.7% improvement in normalized cost over the best retrained baseline while maintaining high goal-reaching success. We apply RADT to cell reprogramming in biology, where it reduces visits to undesirable intermediate gene expression states during trajectories to desired target states, despite stochastic transitions and discrete, structured state dynamics.

A general TD-Q learning control approach for discrete-time Markov jump systems.

Digital transformation in the Micro, Small, and Medium Enterprises (MSMEs) sector or UMKM in Indonesian language, is part of the inclusive development agenda that encourages efficiency and open access to the financial system. One form of this transformation is the implementation of QRIS (Quick Response Code Indonesian Standard) which aims to simplify non-cash transactions in various sectors, including higher education. This article examines the dynamics of digital structuring through a case study of MSME actors in the canteen of the Faculty of Agriculture, University of Jember, who experienced a policy transition from a rental system to a profit-sharing system and were required to use QRIS in all transactions. The approach used was qualitative with a case study method, supported by in-depth interviews and observations, and analyzed using Anthony Giddens' Structuration Theory framework. The results of the study show that the implementation of digital policies did not run completely without obstacles; a number of technical obstacles were found such as the slow QRIS system, limited network infrastructure, and delays in disbursement of funds. However, MSMEs actors demonstrated their capacity as reflective agents who were able to respond to structures through resistance, adaptation, and negotiation. This study concludes that the success of digital structuring is highly dependent on the balance between structural strength and agent reflective power, as well as the need for inclusive and equitable structural support. As part of the Global South context, the implementation of QRIS must truly side with weak groups.

Intrinsic Noise in Structured Replicator Dynamics Modelling Time Delays

Recent work has emphasized the ubiquity of higher-order interactions in brain function. These interactions can be characterized as being either redundancy or synergy-dominated by applying tools from multivariate information theory. Though recent work has shown the importance of both synergistic and redundant interactions to brain function, their dynamic structure is still unknown. Here we analyze the moment-to-moment synergy and redundancy dominance of the fMRI BOLD signal during rest for 95 unrelated subjects to show that redundant and synergistic interactions have highly structured dynamics across many interaction sizes. The whole brain is strongly redundancy-dominated, with some subjects never experiencing a whole-brain synergistic moment. In small sets of brain regions, our analyses reveal that subsets which are redundancy dominated on average exhibit the most complex dynamic behavior as well as the most synergistic and most redundant time points. In accord with previous work, these regions frequently belong to a single coherent functional system, and our analysis reveals that they become synergistic when that functional system becomes momentarily disintegrated. Although larger subsets cannot be contained in a single functional network, similar patterns of instantaneous disintegration mark when they become synergistic. At all sizes of interaction, we find notable temporal structure of both synergy and redundancy-dominated interactions. We show that the interacting nodes change smoothly in time and have significant recurrence. Both of these properties make time-localized measures of synergy and redundancy highly relevant to future studies of behavior or cognition as time-resolved phenomena.

Review of the results of the study of genetic-demographic processes in the population of the three biggest megalopolises of Russia: Moscow, Saint Petersburg, Novosibirsk, and the capital of Republic of Belarus – Minsk is presented. By the survey and census data of the population of megalopolises, the main genetic-demographic parameters are calculated. Data on migration of population and parameters of marriage structure are provided. The analysis results of uneven settlement of ethnic groups and estimates of ethnic diversity, and, also, selected maps reflecting ethnic topography of the population of megalopolises are presented. In the male population of the studied megalopolises, peculiarities of distribution of haplogroups of Y-chromosome in connection of migration are revealed. In the population of megalopolises, necessity of developing genetic data bases and, also, reference data bases for the goals of medical genetics and criminalistics taking in consideration complexity and dynamism of population structure under action of genetic-demographic processes is justified.

This study explored genetic variation among and within Schistosoma japonicum, S. mekongi, and S. malayensis populations using two intronic regions of the taurocyamine kinase (TK) gene, the bridge intron (TkBridInt) and intron 6 of domain 1 (TkD1Int6). Both introns were successfully amplified across all species and confirmed as true intronic sequences. Sequence analysis of S. japonicum revealed high nucleotide polymorphism in both introns, with the population from Sorsogon (Philippines) exhibiting the highest genetic diversity. Haplotype network analysis indicated several region-specific haplotypes. Pairwise FST estimates demonstrated significant genetic differentiation among geographical populations, reflecting limited gene flow. The observed low intraspecific but clear interspecific genetic distances further support distinct population structure. Notably, S. malayensis was genetically closer to S. mekongi than to S. japonicum. These findings highlight the potential of intron sequences in detecting intra- and interspecific variation. The findings underscore the importance of applying these markers to more recently collected and geographically comprehensive samples to better elucidate host-associated genetic structuring and transmission dynamics of these zoonotic schistosomes.

Constructive feedback forms the backbone of effective public speaking coaching, bridging the gap between performance and potential. This article explores structured methods and relational dynamics that make feedback actionable, motivational, and sustainable. By analyzing models such as the Situation-Behavior-Impact (SBI) and BOOST frameworks, it demonstrates how specificity, balance, and empathy transform critique into learning. Practical tools including reflective questioning, journaling, video review, and peer assessment are discussed as mechanisms for fostering coachee ownership of progress. Case studies illustrate how structured feedback strengthens engagement, self-awareness, and measurable improvement. Ultimately, feedback is framed not merely as evaluation, but as a developmental process—one that reinforces confidence, accelerates growth, and sustains trust within the coaching relationship.