Dynamic Structural Changes Research Articles

Soil bacterial community structure and co‐occurrence networks in response to the succession of secondary forests in subtropical regions

AbstractA large amount of secondary successional vegetation (subtropical evergreen broadleaved forests) has formed after the extensive deforestation and cultivation of coniferous forests in Wuyi Mountain, Southeast China. However, we lack a detailed understanding of the dynamic changes in microbial community structure and its co‐occurrence network during succession. The features that are responsible for alterations in the soil bacterial community remain poorly defined. Therefore, we compared the soil physicochemical properties, enzyme activities, and bacterial diversity among three different forests (a coniferous forest [CF], early stage of succession; a mixed conifer‐broadleaf forest [CBMF], middle stage of succession; an evergreen broadleaf forest [EBF]; late stage of succession). Results indicated that the contents of soil organic carbon and microbial biomass carbon (MBC) in CBMF were higher compared with those in CF. The alpha diversity indices were the highest in CBMF. Functional annotation of prokaryotic taxa predicted the relative abundances of chemoheterotrophy and aerobic chemoheterotrophy were lower in CF than in the other two forests. Bacterial network topology was affected by forest succession, with CBMF having more complex bacterial networks. Mantel's test indicated the soil nitrate nitrogen content (P = 0.03) was the most important property that shaped the bacterial community composition, bacterial diversity was influenced by soil pH (P = 0.026), and the contents of available phosphorus (P = 0.04) and MBC (P = 0.022) during the succession process. In conclusion, the succession of secondary forests promoted soil nutrient accumulation and improved bacterial diversity and network complexity.

A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework

The literature on opinion dynamics lacks consideration of the changing states of nodes during the process of opinion interaction, as observed in the literature on social networks. To address this gap, this paper proposes an adapted three-dimensional Deffuant–Weisbuch model based on the Susceptible Infected Recovered (SIR) model, which explores the impact of node state changes on opinion interaction. We incorporate the SIR model to describe changes in node state and improve the trust threshold by taking into account nodes’ emotional factor, cognitive ability, and state of the arts. The model is also modified using the opinion natural evolution coefficient and accounts for dynamic changes in social network structure. We validate our model with different network types and structures, and verify our results using the Louvain algorithm and index of centrality. Our model reveals that under different network types and structures, multiple opinion cliques are formed, and the opinions of ordinary nodes tend to converge around those of opinion leaders, indicating the guiding role of opinion leaders in shaping public opinion.

Dynamic Copper Site Redispersion through Atom Trapping in Zeolite Defects.

Single-site copper-based catalysts have shown remarkable activity and selectivity for a variety of reactions. However, deactivation by sintering in high-temperature reducing environments remains a challenge and often limits their use due to irreversible structural changes to the catalyst. Here, we report zeolite-based copper catalysts in which copper oxide agglomerates formed after reaction can be repeatedly redispersed back to single sites using an oxidative treatment in air at 550 °C. Under different environments, single-site copper in Cu-Zn-Y/deAlBeta undergoes dynamic changes in structure and oxidation state that can be tuned to promote the formation of key active sites while minimizing deactivation through Cu sintering. For example, single-site Cu2+ reduces to Cu1+ after catalyst pretreatment (270 °C, 101 kPa H2) and further to Cu0 nanoparticles under reaction conditions (270-350 °C, 7 kPa EtOH, 94 kPa H2) or accelerated aging (400-450 °C, 101 kPa H2). After regeneration at 550 °C in air, agglomerated CuO was dispersed back to single sites in the presence and absence of Zn and Y, which was verified by imaging, in situ spectroscopy, and catalytic rate measurements. Ab initio molecular dynamics simulations show that solvation of CuO monomers by water facilitates their transport through the zeolite pore, and condensation of the CuO monomer with a fully protonated silanol nest entraps copper and reforms the single-site structure. The capability of silanol nests to trap and stabilize copper single sites under oxidizing conditions could extend the use of single-site copper catalysts to a wider variety of reactions and allows for a simple regeneration strategy for copper single-site catalysts.

High‐Temperature X‐Ray Imaging with Transparent Ceramics Scintillators

AbstractScintillators with high spatial‐resolution and high‐stability are highly desirable for X‐ray imaging applications at unpredictable environments. To challenge state‐of‐the‐art technology, it is necessary to design high thermal stable scintillators with high‐resolution and high light yield. In this work, the zero‐thermal‐quenching Lu3Al5O12:Ce3+ (LuAG: Ce3+) transparent ceramics (TCs) is synthesized with high optical transparency. The thermoluminescence (TL) curves of the as‐obtained TCs demonstrate that the formation of the oxygen vacancies, acting as the trapping centers, can capture the carriers and then contribute to the enhancement of its thermals stability via the thermal compensation effect. The exceptional thermal‐stability of the TCs scintillator endows the monitoring of the dynamic changes of the internal structure of the objective samples without any ghosting effects during heating. In addition, given the ultra‐high uniform, transparency, and fast light decay of the TCs scintillator, the excellent spatial resolution (≈112 lp mm−1) of the X‐ray microscopic imaging is realized. These results undeniably demonstrate that the as‐obtained LuAG: Ce3+ TCs have great prospects for advancement in X‐ray detectors.

Electroencephalogram-Based Metastability in Mild Cognitive Impairment Alzheimer's Disease.

Background: In this study, we analyze metastability, a feature of brain dynamics in subjects experiencing mild cognitive impairment Alzheimer's disease (MCI-AD) under eyes open (EO) and eyes closed (EC) conditions. Alzheimer's disease (AD) is a critically prolonged brain disorder that interrupts neural synchronization and desynchronization. Thus, studying metastability under EO and EC conditions would help in understanding the cortical dynamics and its impact in early-stage AD. Methods: Metastability is investigated using three methods namely frequency variance analysis, Kuramoto order parameter, and through meta-state activation patterns. Frequency variance estimated from 21 electroencephalogram (EEG) channels was clustered into three regions namely anterior, central, and posterior to study the regional metastability analysis. Global metastability was assessed from Kuramoto order parameter and meta-state activation patterns by collating the 21 EEG channels. Results: Reduction in metastability was observed in central regions of MCI-AD subjects through the study of frequency variance analysis. There was a marked reduction in global metastability in the patient group under the resting EO condition. Reduction in meta-state activation properties such as temporal activation sequence complexity, modularity, and leap size in MCI-AD condition under the EO condition indicates an overall reduction in brain flexibility. Conclusion: Taken together, the study infers an underlying structural change in neuronal dynamics influencing the reduction of metastability under the MCI-AD condition. The study further revealed that this reduction in metastability is more pronounced in the EO condition.

Cerebral Perfusion Characteristics and Dynamic Brain Structural Changes in Stroke-Prone Renovascular Hypertensive Rats: A Preclinical Model for Cerebral Small Vessel Disease.

Hypertension is a leading cause of cerebral small vessel disease (CSVD) and vascular dementia in elderly individuals. We aimed to assess cerebral perfusion and dynamic changes in brain structure in stroke-prone renovascular hypertensive rats (RHRSPs) with different durations of hypertension and to investigate whether they have pathophysiological features similar to those of humans with CSVD.The RHRSP model was established using the two-kidney, two-clip (2k2c) method, and the Morris water maze (MWM) test, MRI, immunohistochemistry, and biochemical analysis were performed at multiple time points for up to six months following the 2k2c operation.Systolic blood pressure was significantly greater in the RHRSP group than in the sham-operated group at week 4 post-surgery and continued to increase over time, leading to cognitive decline by week 20. Arterial spin labeling revealed cerebral hypoperfusion in the RHRSP group at 8 weeks, accompanied by vascular remodeling and decreased vessel density. Diffusion tensor imaging and Luxol fast blue staining indicated that white matter disintegration and demyelination gradually progressed in the corpus callosum and that myelin basic protein levels decreased. Eight weeks after surgery, blood-brain barrier (BBB) leakage into the corpus callosum was observed. The albumin leakage area was negatively correlated with the myelin sheath area (r=-0.88, p<0.001). RNA-seq analysis revealed downregulation of most angiogenic genes and upregulation of antiangiogenic genes in the corpus callosum of RHRSPs 24 weeks after surgery.RHRSPs developed cerebral hypoperfusion, BBB disruption, spontaneous white matter damage, and cognitive impairment as the duration of hypertension increased. RHRSPs share behavioral and neuropathological characteristics with CSVD patients, making them suitable animal models for preclinical trials related to CSVD.

Soils in the current waste management system

This article is part of the dissertation research. In modern conditions of active construction, the volumes of excavated soil can amount to hundreds of thousands and millions of cubic meters, making up the bulk of waste generated in the process of engineering and economic activities. Such significant volumes of waste (in the form of soil) pose the challenge of making appropriate decisions about their subsequent disposal, reuse or disposal. To solve such issues, it is necessary to know the system of waste management, which is soil, and the current shortcomings in the principles of organization and functioning mechanisms of this system. The authors of the presented article analyzed the system for handling soils falling into the category of waste. In general, soils are complex multicomponent systems characterized by dynamic changes in composition, structure and properties. Taking into account the specifics of the object under study, the weakest aspects and controversial issues were identified in the analyzed regulatory and methodological materials. One of which is the very fact that soil is categorized as waste from a regulatory point of view. The article examines the contradictions in the definitions used in regulatory documents, the position of soils in the overall waste management system, as well as the procedure for establishing the hazard class of waste; Particular emphasis is placed on the need to use biotesting methods to confirm the V hazard class of waste. The article presents the generalized results of large-scale experimental studies by the team of authors, obtained when assessing the hazard class of waste soils, carried out for the purity of the experiment on soils under the conditions of modeling different in composition and level of pollution. Based on the results obtained, basic proposals have been formulated for improving approaches in the current system for handling soils that fall into the category of waste during engineering and economic, and, above all, engineering and construction activities.

Dynamic Structural Change of Plant Epidermal Cell Walls under Strain.

The molecular foundations of epidermal cell wall mechanics are critical for understanding structure-function relationships of primary cell walls in plants and facilitating the design of bioinspired materials. To uncover the molecular mechanisms regulating the high extensibility and strength of the cell wall, the onion epidermal wall is stretched uniaxially to various strains and cell wall structures from mesoscale to atomic scale are characterized. Upon longitudinal stretching to high strain, epidermal walls contract in the transverse direction, resulting in a reduced area. Atomic force microscopy shows that cellulose microfibrils exhibit orientation-dependent rearrangements at high strains: longitudinal microfibrils are straightened out and become highly ordered, while transverse microfibrils curve and kink. Small-angle X-ray scattering detects a 7.4nm spacing aligned along the stretch direction at high strain, which is attributed to distances between individual cellulose microfibrils. Furthermore, wide-angle X-ray scattering reveals a widening of (004) lattice spacing and contraction of (200) lattice spacing in longitudinally aligned cellulose microfibrils at high strain, which implies longitudinal stretching of the cellulose crystal. These findings provide molecular insights into the ability of the wall to bear additional load after yielding: the aggregation of longitudinal microfibrils impedes sliding and enables further stretching of the cellulose to bear increased loads.

Molecular Mechanisms Underlying Vascular Remodeling in Hypertension.

Hypertension, a common cardiovascular disease, is primarily characterized by vascular remodeling. Recent extensive research has led to significant progress in understanding its mechanisms. Traditionally, vascular remodeling has been described as a unidirectional process in which blood vessels undergo adaptive remodeling or maladaptive remodeling. Adaptive remodeling involves an increase in vessel diameter in response to increased blood flow, while maladaptive remodeling refers to the narrowing or thickening of blood vessels in response to pathological conditions. However, recent research has revealed that vascular remodeling is much more complex. It is now understood that vascular remodeling is a dynamic interplay between various cellular and molecular events. This interplay process involves different cell types, including endothelial cells, smooth muscle cells, and immune cells, as well as their interactions with the extracellular matrix. Through these interactions, blood vessels undergo intricate and dynamic changes in structure and function in response to various stimuli. Moreover, vascular remodeling involves various factors and mechanisms such as the renin-angiotensin-aldosterone system (RAS), oxidative stress, inflammation, the extracellular matrix (ECM), sympathetic nervous system (SNS) and mechanical stress that impact the arterial wall. These factors may lead to vascular and circulatory system diseases and are primary causes of long-term increases in systemic vascular resistance in hypertensive patients. Additionally, the presence of stem cells in adventitia, media, and intima of blood vessels plays a crucial role in vascular remodeling and disease development. In the future, research will focus on examining the underlying mechanisms contributing to hypertensive vascular remodeling to develop potential solutions for hypertension treatment. This review provides us with a fresh perspective on hypertension and vascular remodeling, undoubtedly sparking further research efforts aimed at uncovering more potent treatments and enhanced preventive and control measures for this disease.

Operando imaging in electrocatalysis: insights into microstructural materials design.

Electrocatalysis plays a pivotal role in renewable energy conversion and associated chemical production, enabling a variety of emerging sustainability technologies with societal impacts. Achieving marked improvement in electrocatalytic performance relies on a deep understanding of catalyst microstructures and catalytic mechanisms, with a particular emphasis on the detailed, spatiotemporally resolved characterizations of the underlying fundamental electrocatalytic processes. This fundamental need drives the development of operando imaging techniques, which improve the ability to detect dynamic structural changes in electrocatalysts and establish clear structure-performance relationships for morphologically complex, hierarchically structured catalytic materials. This review aims to highlight significant advancements in the application of operando imaging techniques to develop a deeper understanding of important heterogeneous electrocatalytic reactions critical for emerging sustainability technologies. We summarize the up-to-date key mechanistic insights regarding these reactions achieved through a range of operando imaging techniques, including electron microscopies, X-ray imaging techniques, scanning probe microscopies, and optical microscopies. We conclude by pointing out emerging directions and future prospects within the field of operando imaging in electrocatalysis.

Coupling and biological free-energy transduction processes as a bridge between physics and life: Molecular-level instantiation of Ervin Bauer’s pioneering concepts in biological thermodynamics

The nonequilibrium coupled processes of oxidation and ATP synthesis in the biological process of oxidative phosphorylation (OXPHOS) are fundamental to all life on our planet. These steady-state energy transduction processes ‒ coupled by proton and anion/counter-cation concentration gradients in the OXPHOS pathway ‒ generate ∼95 % of the ATP requirement of aerobic systems for cellular function. The rapid energy cycling and homeostasis of metabolites involved in this coupling are shown to be responsible for maintenance and regulation of stable nonequilibrium states, the latter first postulated in pioneering biothermodynamics work by Ervin Bauer between 1920 and 1935. How exactly does this occur? This is shown to be answered by molecular considerations arising from Nath’s torsional mechanism of ATP synthesis and two-ion theory of energy coupling developed in 25 years of research work on the subject. A fresh analysis of the biological thermodynamics of coupling that goes beyond the previous work of Stucki and others and shows how the system functions at the molecular level has been carried out. Thermodynamic parameters, such as the overall degree of coupling, q of the coupled system are evaluated for the state 4 to state 3 transition in animal mitochondria with succinate as substrate. The actual or operative P to O ratio, the efficiency of the coupled reactions, η, and the Gibbs energy dissipation, Φ have been calculated and shown to be in good agreement with experimental data. Novel mechanistic insights arising from the above have been discussed. A fourth law/principle of thermodynamics is formulated for a sub-class of physical and biological systems. The critical importance of constraints and time-varying boundary conditions for function and regulation is discussed in detail. Dynamic internal structural changes essential for torsional energy storage within the γ-subunit in a single molecule of the FOF1-ATP synthase and its transduction have been highlighted. These results provide a molecular-level instantiation of Ervin Bauer’s pioneering concepts in biological thermodynamics.

Topologically Engineered Strain Redistribution in Elastomeric Substrates for Dually Tunable Anisotropic Plasmomechanical Responses.

The prompt visual response is considered to be a highly intuitive tenet among sensors. Therefore, plasmomechanical strain sensors, which exhibit dynamic structural color changes, have recently been developed by using mechanical stimulus-based elastomeric substrates for wearable sensors. However, the reported plasmomechanical strain sensors either lack directional sensitivity or require complex signal processing and device design strategies to ensure anisotropic optical responses. To the best of our knowledge, there have been no reports on utilizing anisotropic mechanical substrates to obtain directional optical responses. Herein, we propose an anisotropic plasmomechanical sensor to distinguish between the applied force direction and the force magnitude. We employ a simple strain-engineered topological elastomer to mechanically transform closely packed metallic nanoparticles (NPs) into anisotropic directional rearrangements depending on the applied force direction. The proposed structure consists of a heterogeneous-modulus elastomer that exhibits a highly direction-dependent Poisson effect owing to the periodically line-patterned local strain redistribution occurring due to the same magnitude of applied external force. Consequently, the reorientation of the self-assembled gold (Au)-NP array manifests dual anisotropy, i.e., force- and polarization-direction-dependent plasmonic coupling. The cost-effectiveness and simple design of our proposed heterogeneous-modulus platform pave the way for numerous optical applications based on dynamic transformation and topological inhomogeneities.

A Review on China's Exports and Crowding-Out Effects

Export-led growth strategies have become the main paradigm for developing countries seeking to achieve higher economic growth. While exports are key to generating income, there are concerns regarding limitations in external demand. Specifically, China’s remarkable export performance has been at the center of debates that revolve around possible export displacement effects on other countries. This study reviews this literature on export-led growth and crowding-out effects induced by China’s growing exports on various countries and regions. Moreover, reflecting dynamic changes in export structure, lists of key products for Chinese exports are provided. While using the 4-digit level Harmonized System (HS) classification, the selection of the products is based on improvements in revealed comparative advantage (RCA) over the period from 2009 to 2018 and is specific to China’s top three sectors with the largest global market shares: textile, electronics and machinery. Additionally, lists of competitor, potential competitor and potential loser countries are identified in the markets for these specified key products. The findings demonstrate increased competitive pressures on more advanced countries in connection with the increased sophistication of Chinese exports.

Quantifying Disorder in a Protein by Mapping its Locally Correlated Structure and Kinetics.

Proteins under physiological conditions are inherently mobile and sample a vast array of structures. Consequently, the need arises, on the one hand, at a local level to determine the independent moving parts and their associated conformations and kinetics, and on the other hand, at a global level, to quantify the disorder in the full protein molecule. We present an approach that provides these quantities in the form of local kinetic network models, which are constructed by analyzing the molecular dynamics (MD) trajectories of the protein molecule. Entropies of independent parts of the molecule are quantified. The method outlined here, using the Trp-cage miniprotein prototype, offers a new tool to understand the dynamic structural changes that ultimately govern the functioning of a protein. The method is particularly suited to problems where there are subtle changes in the structure or dynamics at local levels, for example, due to ligand binding.

Modular rewritable Petri nets: An efficient model for dynamic distributed systems

Modern distributed systems are becoming pervasive and increasingly provided with adaptation, (self-)reconfiguration and mobility capability. On one side, to face the challenges of the highly dynamic environments where they are deployed. On the other side, to keep production/maintenance costs down. Therein lies the increasing demand for formal models encompassing all of these aspects (besides concurrency). Hardly any of the classical formalisms like Petri Nets, Automata, and Process Algebra, even though powerful, permits designers to easily specify dynamic structural changes to systems and evaluate their impact on system behaviour. That has led to several extensions of classical formal models (e.g., the Pi calculus or the Nets-within-Nets paradigm) rarely accompanied by suitable analysis techniques. A recent formalization of a class of Rewritable Place-Transition Nets (RwPT) in Maude has proved potentially convenient to specify dynamically reconfigurable systems. Concerning analogous proposals, the RwPT formalism provides more abstraction/flexibility in modelling and efficiency in analysis. Nevertheless, its ability to scale the size of distributed systems built of several similar (nested) components is limited.This paper presents a compositional approach to define large RwPT models in a typical algebraic way and to exploit the modular structure of models during the analysis: Symmetries are implicitly captured by composite node-labelling (reflecting the model's hierarchical structure) that is preserved by net rewrites. A distributed, gracefully degrading production system is used as a case study. Experimental evidence points out the dramatic impact of the approach against a non-modular one and the advantages over alternative techniques. Even if the emphasis is on state-space-based verification, the paper shows the convenience of combining it with structural analysis, which is typical of Petri nets as well. For that purpose, rewrite-rule abstractions are given in the form of guidelines.

Experimental Investigation and Damage Identification of High-Pile Wharf Framed Bents under Horizontal Impact Loads.

This study investigates damage characteristics, dynamic structural performance changes, and quantitative damage assessment of high-pile wharf framed bents exposed to horizontal impact loads. Through extensive testing of wharf framed bents under such loads, a damage identification approach based on stiffness, natural vibration period, and acceleration data derived from experiments is presented. The findings reveal that under horizontal impact loads, framed bents initially exhibit tensile damage and leaning piles, followed by short straight piles. Additionally, structural damage results in a reduced self-oscillation frequency and an increased amplitude decay rate. Both stiffness-based and cycle-based damage indicators effectively track the cumulative damage progression of the structure. However, the cycle-based damage indicators demonstrate superior stability and accuracy, while acceleration-based indicators precisely identify the moment of damage mutation. This research contributes to enhancing local components, implementing damage identification methods, and advancing health monitoring practices in high-pile wharf projects, aligning with the standards of scientific publications in the field.

On the hierarchical Bayesian modelling of frequency response functions

Structural health monitoring (SHM) strategies seek to evaluate, predict, and maintain structural integrity, to improve the safety and design service life of structures in operation. Many of these strategies involve monitoring changes in structural dynamics, as damage can affect modal properties and present as changes in the characteristics of the resonance peaks of the frequency response function (FRF). While recent advances have improved the safety and reliability of structures, a number of challenges remain, impeding the practical implementation and generalisation of these systems. Like damage, benign variations, such as those caused by changes in temperature or other environmental fluctuations, can affect dynamic properties, making it difficult to distinguish between damage and normal operating conditions. In addition, newly-deployed structures can have insufficient data to describe the normal operating conditions (i.e., data scarcity), which can impair the development of data-based prediction models. Another common challenge is data loss (i.e., data sparsity), which may result from transmission issues, sensor failure, a sample-rate mismatch between sensors, and other causes. Missing data in the time domain will result in decreased resolution in the frequency domain, which can impair dynamic characterisation.For situations that may benefit from information sharing among datasets, e.g., population-based SHM of similar structures, the hierarchical Bayesian approach provides a useful modelling structure. Hierarchical Bayesian models learn statistical distributions at the population (or parent) and the domain levels simultaneously, to bolster statistical strength among the parameters. As a result, variance is reduced among the parameter estimates, particularly when data are limited. In this paper, a combined probabilistic FRF model is developed for a small population of nominally-identical helicopter blades, using a hierarchical Bayesian structure, to support information transfer in the context of sparse data. The modelling approach is also demonstrated in a traditional SHM context, for a single helicopter blade exposed to varying temperatures, to show how the inclusion of physics-based knowledge can improve generalisation beyond the training data, in the context of scarce data. These models address critical challenges in SHM, by accommodating benign variations that present as differences in the underlying dynamics, while also considering (and utilising), the similarities among the domains.

Attention-guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.

Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic resonance imaging (sMRI) data from a single time point whereas neglecting the dynamic changes in brain structure over time. Current longitudinal studies inadequately explore disease evolution dynamics and are burdened by high computational complexity. This paper introduces a novel lightweight 3D convolutional neural network specifically designed to capture the evolution of brain diseases for modeling the progression of MCI. First, a longitudinal lesion feature selection strategy is proposed to extract core features from temporal data, facilitating the detection of subtle differences in brain structure between two time points. Next, to refine the model for a more concentrated emphasis on lesion features, a disease trend attention mechanism is introduced to learn the dependencies between overall disease trends and local variation features. Finally, disease prediction visualization techniques are employed to improve the interpretability of the final predictions. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of area under the curve (AUC), accuracy, specificity, precision, and F1 score. This study confirms the efficacy of our early diagnostic method, utilizing only two follow-up sMRI scans to predict the disease status of MCI patients 24 months later with an AUC of 79.03%.

ІТАЛІЙСЬКІ НЕОЛОГІЗМИ У КОНТЕКСТІ СОЦІАЛЬНИХ МЕРЕЖ: СОЦІОКУЛЬТУРНИЙ АСПЕКТ

The systematic and continuous study of new lexical units is intrinsically linked to the dynamic evolution and constant changes in the lexical structure of the language, as well as the integration and adaptation of neologisms into the language. The article examines and gives examples of neologisms of modern social networks, analyzes the peculiarities of their formation, focusing on the tendency of using new words and expressions in online communication, especially among young people. The profound impact of social networking on life of modern generations is described, the vocabulary disparities across different age groups is juxtaposed. The distribution and significance of Italian neologisms are analyzed, and their impact on language and culture is determined. The results of the study underscore not only the evolutionary trajectory of the Italian language but also its pivotal role in articulating sociocultural trends within modern society through the pervasive adoption of neologisms.

Photovoltage transient technique: an alternative approach to real-time analysis of membrane interface dynamic processes

&lt;sec&gt;The dynamic interactions between active molecules and the cell membrane play a crucial role in various fundamental biological processes. The photovoltage transient technique developed in recent years has provided an an &lt;i&gt;in situ&lt;/i&gt;, real-time, and non-invasive approach to studying dynamic processes at the membrane interface. In this technique, the photoelectric response of silicon wafers is used to generate charges and record voltage transient pulses in the charging and discharging process of phospholipid membranes. These pulses directly reflect the instantaneous structure and properties of the membrane. By analyzing the temporal evolution of voltage pulses, the dynamic changes in membrane structure induced by molecular actions can be elucidated. In particular, this technique provides valuable insights into the timing of transitions between different functional states. In this paper, the working principle, equipment setup, and data processing methods used in photovoltage transient analysis are comprehensively reviewed. Furthermore, the use of supported phospholipid bilayers as model cell membranes highlights recent progress of this technique in investigating the mechanisms underlying membrane interactions of active molecules such as surfactants, polymers, peptides, and nanoparticles. Finally, its advantages and limitations are evaluated, and its development prospects are discussed.&lt;/sec&gt;&lt;sec&gt;The photovoltage transient technique is initially used to analyze the charging and discharging curves, as well as the hydration process, of single- and multi-layered membranes composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) phospholipids. Previously, X-ray diffraction (XRD) and quartz crystal microbalance with dissipation (QCM-D) technology were commonly utilized for the real-time monitoring of the swelling process in phospholipid membranes, providing the information about changes in mass and thickness of Z-direction layers. In contrast, the photovoltage transient technique presents additional insights into the kinetics of the swelling process and timing of transitions between different stages. The present study demonstrates the effectiveness of photovoltage transient technique in real-time monitoring of membrane interface processes, specifically, it quantitatively measures the characteristic &lt;i&gt;τ&lt;/i&gt; value of DOPC phospholipid membrane, thereby enabling further development of quantitative analysis method for this technique. Then, the photovoltage transient technique, in conjunction with giant unilamellar vesicle (GUV) leakage assays, atomic force microscopy (AFM) and QCM-D, is employed to monitor the structural perturbation of surfactants (TTAB) and polymers (Brij35 and PVPk30) on the membranes. Specifically, Brij35 primarily undergoes an adsorption-accumulation-penetration process; whereas PVPk30 exhibits a dynamic equilibrium between molecular adsorption-desorption and/or membrane permeation-healing competing mechanisms. This disparity in membrane action processes elucidates the discrepancy observed in their cytotoxicity during live cell experiments. The ability of photovoltage transient technology to investigate the entire membrane as a research subject along with its high sensitivity enables it to capture fluctuations in data points that reflect the coexistence of competitive mechanisms. Furthermore, photovoltage monitoring reveals the occurrence of peptide-induced membrane permeabilization. The distinct mechanism of action on the membrane between melittin (as a representative antimicrobial peptide) and TAT (a typical cell penetrating peptide) is elucidated. Finally, the conductive carbon dots (CDs) induced phenomena of membrane overcharging and overdischarging, are likely to be attributed to charge transfer between the silicon substrate and the embedded conductive CDs.&lt;/sec&gt;