Feedback Mechanism Research Articles

Passive Haptic Gloves with Embedded Sensors for Immersive Virtual Reality Training

Considering the increasing demand for the integration of haptic feedback into virtual reality (VR) devices, we studied and developed a multi-sensor and actuator array for real-time perception in virtual reality for training first responders. The sensors respond to users’ interactions in the VR world and actuate the response in the real world to create realistic sensory perception [1–2]. Though several haptic device applications have been created [3], very few of them have investigated the integration of an all-in-one sensory feedback mechanism. This paper presents a novel haptic feedback glove that helps with virtual reality training applications by utilizing all-in-one sensory feedback of heat, and cold. We investigated heat and cold sensing by incorporating peltiers and microheaters into the multi-functional haptic glove to generate the radiation of heat and cold. This version of the glove functions on three components: a microcontroller (ESP32), thermocouple modules that work as sensor elements (-55°C to 125°C), polyimide heater plate films, and micro Peltiers (12V max at 12W max) that work as actuators. These three modules are responsible for creating thermal haptic feedback, which communicates with the virtual environment world and the local environment to create the sensation of heat and cold, as shown in Fig. 1(a). When the user touches a hot or cold object in virtual reality, the hand tracking recognizes the element triggering the actuators, which are controlled by the sensor to regulate the temperature. We utilized Unreal Engine 5 (UE5) and two frameworks to enable serial and Bluetooth data communication between UE5 and the microcontroller for the high-risk training simulation. Based on Fig. 2 (a), after carefully testing, the distance between the data transfer start and maximum temperature output was about a full arm’s length in real life. This could be translated to 2 to 3 feet in distance. In the case of Fig. 2(b), we see multiple negative voltages applied to the Peltier device, producing the following equation:(1).In eq.1, A is the ambient temperature, T is the temperature of the object, D is the max distance of when the user starts feeling the object’s temperature, and t is the temperature of the finger because each Peltier is individually controlled. The heating ramping rate was 4 seconds from room temperature to 60°C and for cooling, the ramping time was 8 seconds from room temperature to 10°C. The currently developed glove demonstrates for the first time the integration of heat and cold sensing for VR applications. The developed prototype glove will be used for implementing training in VR for safety officers, military, and first responders. Figure 1

Detangling Ion Transport and Lithium Dendritic Growth Using Stimulated Raman Scattering Microscopy

Ion transport is one of the fundamental processes in batteries, directly impacting energy density, power density, and cycling lifetime. However, ion concentration in electrolyte is usually low (0.01 M to 1 M), while ion diffusion coefficient is extremely high (10^-6 cm^2/s), making characterization of this process extremely difficult. Conventional characterization techniques such as transmission electron microscopy, nuclear magnetic resonance, and synchrotron radiation have encountered significant challenges on this topic, leaving ion transport process inside working batteries largely unknown.Stimulated Raman Scattering microscopy (SRS) is a recently emerging imaging technique, offering a speed approximately 10^8 times faster than conventional Raman spectrometers. It provides extremely high concentration, time, and spatial resolution, along with high selectivity and non-invasiveness. In our study, SRS was employed for the first time to dynamically image ion depletion within batteries and the growth of lithium dendrites in situ, providing visualization and quantitative analysis of ion distribution in liquid electrolytes. A three-stage lithium deposition process is uncovered, corresponding to no depletion, partial depletion, and full depletion of lithium ions. Further analysis reveals a feedback mechanism between the lithium dendrite growth and heterogeneity of local ionic concentration on lithium metal electrode, which can be effectively suppressed by artificial solid electrolyte interphase.We further explored lithium dendrite growth in PEO solid polymer electrolyte, but found unexpected opposite behaviors in solid polymer electrolytes: concentration polarization can induce phase transformation in a PEO electrolyte, forming a new PEO-rich but salt-/plasticizer-poor phase at the lithium/electrolyte interface, as unveiled by SRS. The new phase has a significantly higher Young’s modulus (1–3 GPa) than a bulk polymer electrolyte (<1 MPa). We hereby propose a design rule for PEO electrolytes: their compositions should be near the boundary between single-phase and two-phase regions in the phase diagram so that the applied current can induce the formation of a mechanically rigid PEO-rich phase to suppress lithium whiskers.This study revealed the growth mechanism of lithium dendrites and proposed solutions to suppress metallic lithium dendrites. SRS offers unprecedented ability over conventional characterization tools in studying reaction mechanisms, and is going to play a crucial role in research fields such as batteries, supercapacitors, and catalysis in the future.

Doping- and capacitor-less 1T-DRAM cell using reconfigurable feedback mechanism

In this paper, we propose a doping- and capacitor-less 1T-DRAM cell, which achieved virtual doping by leveraging charge plasma and bias-induced electrostatic doping (bias-ED) techniques in a 5 nm-thick intrinsic silicon body, thereby eliminating doping processes. Platinum was in contact with the drain, while aluminum was in contact with the source, enabling virtual doping of the silicon body into ap*-i-n* configuration via the charge-plasma technique. Two coupled polarity gates and one control gate are positioned above the intrinsic channel region. The intrinsic channel region is virtually doped through the bias-ED by applying voltages to the gates, forming potential wells inside the channel. The voltage applied to the two coupled polarity gates determines whether the device operates in thep- orn-channel mode, whereas the control gate governs the flow of charge carriers. Charge carriers are stored and released in the potential wells inside the channel by adjusting the gate, effectively replacing the capacitor. In this device, the placement of polarity gates on either side of the control gate enables the observation of the reconfigurable characteristics. Moreover, the proposed device utilizes a feedback mechanism, enabling excellent memory characteristics such as a high on/off current ratio of ∼109, steep switching behavior of ∼0.2µV dec-1, short write time of 10 ns, long hold retention of over 100 s, and long read retention of over 600 s.

Ocean crustal veins record dynamic interplay between plate-cooling-induced cracking and ocean chemistry

As ocean crust traverses away from spreading ridges, low-temperature hydrothermal minerals fill cracks to form veins, transforming the physical and chemical properties of ocean crust whilst also modifying the composition of seawater. Vein width and frequency observations compiled from the International Ocean Discovery Program (IODP) South Atlantic Transect (∼31°S) and previous scientific ocean drilling holes show that vein width distributions progressively broaden and observed strain (Σmveins/mcore) increases with crustal age, whereas vein densities (#veins/mcore) remain approximately constant. Elemental mapping and textural observations illuminate multiple precipitation and fracturing episodes that continue as the ocean crust ages. This challenges the existing notion that ocean crustal veins are passively filled; rather, they are dynamic features of ocean crust aging. These data, combined with thermal strain modelling, indicate a positive feedback mechanism where cooling of the ocean plate induces cracking and the reactivation of pre-existing veins, ultimately resulting in further cooling. Waning of this feedback provides a mechanism for the termination of the global average heat flow anomaly. Sites with total vein dilation greater than expected for their age correspond with crustal formation during periods of high atmospheric CO2. The amount of vein material thus reflects the changing balance between ocean plate cooling, ocean chemistry, and the age of the ocean crust. Our results demonstrate that ocean crust endures as an active geochemical reservoir for tens of millions of years after formation.

A novel hypoxia-induced lncRNA, SZT2-AS1, boosts HCC progression by mediating HIF heterodimerization and histone trimethylation under a hypoxic microenvironment.

Hypoxic microenvironment plays a critical role in solid tumor growth, metastasis and angiogenesis. Hypoxia-inducible factors (HIFs), which are canonical transcription factors in response to hypoxia, are stabilized under hypoxia and coordinate the process of hypoxia-induced gene expression, leading to cancer progression. Increasing evidence has uncovered that long noncoding RNAs (lncRNAs), which are closely associated with cancer, play crucial roles in hypoxia-mediated HCC progression, while the mechanisms are largely unknown. Here, we identified SZT2-AS1 as a novel lncRNA in HCC, which was induced by hypoxia in a HIF-1-dependent manner and promoted HCC growth, metastasis and angiogenesis both in vitro and in vivo. And SZT2-AS1 also mediated the hypoxia-induced HCC progression. Clinical data indicated that SZT2-AS1 level was substantially increased in HCC and closely associated with poor clinical outcomes, acting as an independent prognostic predictor. Mechanistically, SZT2-AS1 recruited HIF-1α and HIF-1β to form the HIF-1 heterodimer, and it was required for the occupancy of HIF-1 to hypoxia response elements (HREs) and HIF target gene transcription. In addition, SZT2-AS1 was required for hypoxia-induced histone trimethylation (H3K4me3 and H3K36me3) at HREs. Through recruiting methyltransferase SMYD2, SZT2-AS1 promoted trimethylation of H3K4 and H3K36 in HCC cells. Taken together, our results uncovered a lncRNA-involved positive feedback mechanism under hypoxia and established the clinical value of SZT2-AS1 in prognosis and as a potential therapeutic target in HCC.

Research on the Design and Promotion of Macau City Brand Image

With the deepening of globalization, the shaping and promotion of Macao's city brand has become increasingly important in enhancing international recognition, attracting external investment and tourists, and enriching the local economic structure. This paper focuses on the design and promotion of Macao city brand image, and discusses its challenges and strategies in cross-cultural communication, the role of government, and the evaluation and feedback mechanism of brand promotion. The paper analyzes the difficulties encountered in the cross-cultural communication of Macao brand image and puts forward the localization promotion strategies for different markets. The need for government leadership and policy support in brand promotion is also discussed in detail and the importance of establishing evaluation indicators and user feedback mechanisms to ensure the effectiveness and continuous improvement of brand promotion is proposed.

Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics

Mesozooplankton are critical components of marine ecosystems, acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations. They play pivotal roles in the pelagic food web and export production, affecting the biogeochemical cycling of carbon and nutrients. Therefore, accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies. However, modeling these dynamics remains challenging due to the complex interplay among physical, chemical, and biological factors, and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models. Graph neural network (GNN) models offer a promising approach to forecast multivariate features and define correlations among input variables. The high interpretive power of GNNs provides deep insights into the structural relationships among variables, serving as a connection matrix in deep learning algorithms. However, there is insufficient understanding of how interactions between input variables affect model outputs during training. Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species. We find that forecasting accuracy is closely related to interactions within ecosystem dynamics. Notably, increasing the number of nodes does not always enhance model performance; closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing. Therefore, we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest. These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.

Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective

The privacy-sensitive nature of medical image data is often bounded by strict data sharing regulations that necessitate the need for novel modeling and analysis techniques. Federated learning (FL) enables multiple medical institutions to collectively train a deep neural network without sharing sensitive patient information. In addition, FL uses its collaborative approach to address challenges related to the scarcity and non-uniform distribution of heterogeneous medical domain data. Nevertheless, the data-opaque nature and distributed setup make FL susceptible to data poisoning attacks. There are diverse FL data poisoning attacks for classification models on natural image data in the literature. But their primary focus is on the impact of the attack and they do not consider the attack budget and attack visibility. The attack budget is essential for adversaries to optimize resource utilization in real-world scenarios, which determines the number of manipulations or perturbations they can apply. Simultaneously, attack visibility is crucial to ensure covert execution, allowing attackers to achieve their objectives without triggering detection mechanisms. Generally, an attacker’s aim is to create maximum attack impact with minimal resources and low visibility. So, considering these three entities can effectively comprehend the adversary’s perspective in designing an attack for real-world scenarios. Further, data poisoning attacks on medical images are challenging compared to natural images due to the subjective nature of medical data. Hence, we develop an attack with a low budget, low visibility, and high impact for medical image classification in FL. We propose a federated learning attention guided minimal attack (FL-AGMA), that uses class attention maps to identify specific medical image regions for perturbation. We introduce image distortion degree (IDD) as a metric to assess the attack budget. Also, we develop a feedback mechanism to regulate the attack coefficient for low attack visibility. Later, we optimize the attack budget by adaptively changing the IDD based on attack visibility. We extensively evaluate three large-scale datasets, namely, Covid-chestxray, Camelyon17, and HAM10000, covering three different data modalities. We observe that our FL-AGMA method has resulted in 44.49% less test accuracy with only 24% of IDD attack budget and lower attack visibility compared to the other attacks.

Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective

This paper is concerned with sensor fault diagnosis problems for multi-axis carving machine systems (MACMSs) with repetitive machining tasks. A novel fault diagnosis method that combines the multi-feature fusion technology and Gaussian mixture hidden Markov models (GMHMMs) is proposed, which is inspired by a data- and model-driven collaborative perspective. With fault-sensitive features first extracted from both the time domain and time–frequency domain, the composite health index (CHI) is constructed to facilitate the understanding of the time-varying evolution. Then, GMHMMs are established to characterize the probabilistic relationship between the hidden states and CHI. To achieve high-precision fault classification, a well-designed global objective function is adopted to dynamically optimize both the CHI construction and classifier model training in a closed-loop feedback mechanism. Specifically, the fusion coefficients with range and equality constraints are integrated as part of the model parameters into the global optimization objective function, thereby reducing the search range and improving convergence speed. Besides, the well-trained GMHMMs interact with each other to capture the correlation information between different faults, and are utilized for online fault diagnosis. Finally, experiments are conducted on a self-developed multi-axis carving machine platform. The results exhibit outstanding performance in comparison with existing methods, particularly attaining a diagnostic accuracy of 95.37%.

Extracts from Tartary Buckwheat Sprouts Restricts Oxidative Injury Induced by Hydrogen Peroxide in HepG2 by Upregulating the Redox System.

Oxidative stress, which results from an overproduction of reactive oxygen species (ROS), can cause damage that may contribute to a range of metabolic disorders. Antioxidants are considered to upregulate the activity of antioxidant enzymes, which are crucial for eliminating excess ROS and safeguarding the body against oxidative stress-induced damage. In the present study, the effect of polyphenol extracts from tartary buckwheat sprouts (TBSE) on the redox system of HepG2-cell-induced oxidative injury by hydrogen peroxide were investigated for evaluating the protective effect and mechanism of tartary buckwheat sprouts (TBS). The results revealed that TBSE that had sprouted for a period of 10 days possessed six predominant phenolic compounds, ranked from the most abundant to the least: chlorogenic acid, syringic acid, caffeic acid, rutin, ferulic acid, and quercetin. TBSE could successfully inhibit H2O2-induced ROS overproduction, restore and balance the mitochondrial membrane potential, while also significantly increasing cellular antioxidant activity (CAA) and the expression of protective enzymes such as SOD, CAT, and GST. More interestingly, treating HepG2 cells with TBSE triggered the translocation of Nrf2 to the nucleus, accompanied by a negative feedback mechanism involving Keap1. Therefore, it regulated the downstream production of antioxidant enzymes, including NQO1 and HO-1. Overall, this finding suggested that TBSE could restore the redox state of H2O2-resistant HepG2 cells, indicating TBSE protected cells from H2O2-induced oxidative stress significantly. Beneficial resistance and effects on redox balance were attributed to activation of Nrf2. Present work revealed the potential health benefits of TBS and provided a test basis for developing functional food of TBS.

Towards ecosystem-based techniques for tipping point detection.

An ecosystem shifts to an alternative stable state when a threshold of accumulated pressure (i.e. direct impact of environmental change or human activities) is exceeded. Detecting this threshold in empirical data remains a challenge because ecosystems are governed by complex interlinkages and feedback loops between their components and pressures. In addition, multiple feedback mechanisms exist that can make an ecosystem resilient to state shifts. Therefore, unless a broad ecological perspective is used to detect state shifts, it remains questionable to what extent current detection methods really capture ecosystem state shifts and whether inferences made from smaller scale analyses can be implemented into ecosystem management. We reviewed the techniques currently used for retrospective detection of state shifts detection from empirical data. We show that most techniques are not suitable for taking a broad ecosystem perspective because approximately 85% do not combine intervariable non-linear relationships and high-dimensional data from multiple ecosystem variables, but rather tend to focus on one subsystem of the ecosystem. Thus, our perception of state shifts may be limited by methods that are often used on smaller data sets, unrepresentative of whole ecosystems. By reviewing the characteristics, advantages, and limitations of the current techniques, we identify methods that provide the potential to incorporate a broad ecosystem-based approach. We therefore provide perspectives into developing techniques better suited for detecting ecosystem state shifts that incorporate intervariable interactions and high-dimensionality data.

The presence of clouds lowers climate sensitivity in the MPI-ESM1.2 climate model

Abstract. Clouds affect the sensitivity of the climate system by changing their distribution, height, and optical properties under climate change. Although the precise magnitude remains uncertain, the direct cloud response to an external forcing is known to be destabilising. Additionally, clouds have a masking effect on CO2 forcing and can influence other feedback mechanisms such as the surface albedo feedback. To understand the overall impact of clouds, we compute how much the equilibrium climate sensitivity (ECS) to a doubling of CO2 changes when clouds are made transparent to radiation in an Earth system model (MPI-ESM1.2, the Max Planck Institute for Meteorology Earth System Model version 1.2). In practice, to stabilise the model climate at near-preindustrial temperatures, the solar constant was reduced by 8.8 %. Our experiments reveal that clouds exert a stabilising influence on the model, with a clear-sky ECS of 4.29 K, which is higher than the corresponding full-sky ECS of 2.84 K, contrasting with their direct destabilising effect. Detailed partial radiative perturbation diagnostics show that beyond directly amplifying warming by themselves, clouds also strengthen the negative lapse rate and positive water vapour feedbacks, while strongly damping the positive albedo feedback. These findings highlight the complex role of clouds in modulating climate sensitivity.

Contrasting impact of single-year and multi-year El Niño on the Pacific-North American teleconnection pattern

Abstract Distinct Pacific-North American (PNA) teleconnection patterns and their associated climate impacts in North America have been observed during single-year El Niño events, as well as during both the first and second years of multi-year El Niño events. The research highlights the critical role of PNA-related North Pacific circulation anomalies, which shift northwestward during multi-year events and reach their peak intensity in the second year. Multiple dynamical diagnostic methods are employed to elucidate the reasons behind the diverse subtropical circulation responses. Differences in the anomalous tropical heat sources influence the formation of Rossby wave sources through adjustments in divergent wind anomalies, subsequently modulating the position and intensity of the PNA patterns. Additionally, variations in the meridional range of sea surface temperature anomalies affect the edge of tropospheric temperature through moist-adiabatic adjustment, leading to distinct subtropical jet stream responses. This, in turn, modifies the position of North Pacific circulation anomalies through the advection of anomalous kinetic energy. Furthermore, synoptic-scale transient eddies act as a feedback mechanism, helping to maintain and intensify these diverse atmospheric anomalies.

Clouds and precipitation in the initial phase of marine cold-air outbreaks as observed by airborne remote sensing

Abstract. Marine cold-air outbreaks (MCAOs) strongly affect the Arctic water cycle and, thus, climate through large-scale air mass transformations. The description of air mass transformations is still challenging, partly because previous observations do not resolve fine scales, particularly for the initial development of an MCAO, and due to a lack of information about the thermodynamical evolution starting over sea ice and continuing over open ocean and associated cloud microphysical properties. Therefore, we focus on the crucial initial development within the first 200 km over open water for two case studies in April 2022 during the HALO-(AC)3 campaign (named after the High Altitude and Long Range Research Aircraft and Transregional Collaborative Research Centre ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes and Feedback Mechanisms (AC)3). The two events, just 3 d apart, belong to a particularly long-lasting MCAO and occurred under relatively similar thermodynamic conditions. Even though both events were stronger than the climatological 75th percentile of that period, the first event was characterized by colder air masses from the central Arctic which led to an MCAO index twice as high compared to that of the second event. The evolution and structure were assessed by flight legs crossing the Fram Strait multiple times at the same location, sampling perpendicularly to the cloud streets. Airborne remote sensing and in situ measurements were used to build statistical descriptions of the boundary layer, dynamics, clouds, and precipitation. For this purpose, we established a novel approach based solely on radar reflectivity measurements to detect roll circulation that forms cloud streets. The two cases exhibit different properties of clouds, riming, and roll circulations, though the width of the roll circulation is similar. For the stronger event, cloud tops are higher; more liquid-topped clouds exist; the liquid water path, mean radar reflectivity, precipitation rate, and precipitation occurrence have increased; and riming is active. The variability in rime mass has the same horizontal scale as the roll circulation, implying the importance of roll circulation on cloud microphysics and precipitation. Boundary layer and cloud properties evolve with distance over open water, as seen by, e.g., cloud top height rising. In general, cloud streets form after traveling 15 km over open water. After 20 km, this formation enhances cloud cover to just below 100 %. After around 30 km, precipitation forms, though for the weaker event, the development of precipitation is shifted to larger distances. Within our analysis, we developed statistical descriptions of various parameters (i) within the roll circulation and (ii) as a function of distance over open water. These detailed cloud metrics are particularly well suited for the evaluation of cloud-resolving models close to the sea ice edge to evaluate their representation of dynamics and microphysics.

Adapting to My User, Engaging with My Robot: An Adaptive Affective Architecture for a Social Assistive Robot

Affective feedback from social robots is a useful technique for communicating to people whether they are interacting “well” with the robot or not. However, some users, such as people with physical or cognitive difficulties, may not be able to interact in all the desired ways. In these cases, affective feedback from the robot could be excessively negative—an “unhappy” robot, leading to an unrewarding experience for the user. This article presents a motivation-based architecture for an autonomous multimodal social robot, that incorporates an affective feedback mechanism which generates an affective state by combining the internal needs of the robot and the social interaction quality. The balance between these two factors can dynamically change, allowing the robot to adapt its affective feedback to the user's interaction style and capabilities. We have implemented this architecture in a simulation and in a MiRo social robot, and report experiments examining the behavior of the system in interactions with different experimental user profiles. The results show that the adaptive mechanism allows the robot to change its affective feedback to give more positive encouragement to users than in non-adaptive cases.

Correlation analysis of failure risk factors in automated container port logistics systems from a resilience perspective

This paper aims to explore the correlations among failure risk factors of automated container port logistics systems from a resilience perspective. Based on the Wuli-Shili-Renli (WSR) theory and a multi-dimensional resilience framework, five key categories of risk factors affecting system resilience are identified: port infrastructure, port logistics operation organization, port personnel, port operational environment, and port emergency response and management. Through expert interviews, system component analysis, and a review of related literature across these five dimensions, 36 specific risk factors were identified to establish an initial set of system failure risk factors. Subsequently, a dual-factor screening method combining grey relational analysis and rank correlation was applied to simplify the initial set, constructing a system failure risk factor index system from a resilience perspective. Finally, a comprehensive model combining the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method and the Total Adversarial Interpretation Structure Model (TAISM) was proposed. This model identifies and quantifies the complex correlations among system failure risk factors from a resilience perspective, revealing their relative importance and their impact on system resilience. The results indicate that system resilience is primarily influenced by internal factors such as the smoothness of collection and distribution operations, infrastructure service capacity, and the stability of technical support systems. External factors, such as the prevention capability for major emergency threats and the improvement of government supervision and coordination feedback mechanisms, also significantly impact system resilience. The complex interactions among key factors have a profound effect on system resilience, underscoring the need to enhance coordination and management measures to strengthen overall system resilience. This study provides a theoretical foundation for developing targeted strategies to enhance the resilience of automated container port logistics systems.

A thermodynamic framework for nonequilibrium self-assembly and force morphology tradeoffs in branched actin networks.

Branched actin networks are involved in a variety of cellular processes, most notably the formation of lamellipodia in the leading edge of the cell. These systems adapt to varying loads through force dependent assembly rates that allow the network density and material properties to be modulated. Recent experimental work has described growth and force feedback mechanisms in these systems. Here, we consider the role played by energy dissipation in determining the kind of growth-force-morphology curves obtained in experiments. We construct a minimal model of the branched actin network self assembly process incorporating some of the established mechanisms. Our minimal analytically tractable model is able to reproduce experimental trends in density and growth rate. Further, we show how these trends depend crucially on entropy dissipation and change quantitatively if the entropy dissipation is parametrically set to values corresponding to a quasistatic state. Finally, we also identify the potential energy costs of adaptive behavior by branched actin networks, using insights from our minimal models. We suggest that the dissipative cost in the system beyond what is necessary to move the load may be necessary to maintain an adaptive steady state. Our results hence show how constraints from stochastic thermodynamics and non-equilibrium thermodynamics may bound or constrain the structures that result in such force generating processes.

Modelling of the Ukrainian labor market: system dynamics approach

The purpose of the article is to develop a System Dynamics model of the functioning and development of the Ukrainian labor market, which can subsequently be used to simulate various government decisions and policies. The Stella Architect software is used to construct the model, allowing to provide insights into the current state of the labor market and forecasts future trends, helping identify potential improvements.The explanatory System Dynamics Model effectively demonstrates the complex interrelationships that influence the dynamics of the Ukrainian labor market. The identified balancing loops highlight key factors such as retirement, business closures, production levels, and the formation of new businesses, all of which shape labor supply and demand. Each loop illustrates how changes in one economic aspect can impact others, underscoring the interconnectedness of labor dynamics, economic growth, and business development. Furthermore, the developed causal loop diagrams emphasize the cyclical nature of these interactions, where increases in labor supply can result in higher wages and GDP, leading to further investment and business creation. This model offers valuable insights into the feedback mechanisms that may stabilize or destabilize the labor market, particularly in response to external shocks or policy interventions. In the future, the developed model can be used to test various policies for major elements of the model and to simulate its behavior.

Unraveling the Dynamics of e-Government Digitization, Penetration and User Experience: A Case Study of Greek Municipalities

This study examines the user experience (UX) of Greek municipal websites, the first in-depth look at local e-Government services in Greece. Using Lighthouse for technical performance and an Adapted UX Audit, it explores factors beyond digital skills affecting citizen adoption of these platforms. Findings reveal widespread deficiencies, with no municipality scoring above 55%, highlighting significant challenges in usability, functionality, and user satisfaction. Critical issues in Technical Performance and Missing Features, both scoring below 50%, suggest that inherent design flaws hinder user acceptance. The study emphasizes the need for improvements in feedback mechanisms, multilingual support, and accessible help sections to better serve diverse populations. Addressing these gaps could enhance e-democracy by fostering more inclusive, user-friendly services. The results advocate for a strategic overhaul of municipal websites to increase engagement, improve e-Government effectiveness, and strengthen democratic participation. Limitations and recommendations for future research and policy-making are also provided.

Response and adaptation of the transcriptional heat shock response to pressure.

The molecular mechanisms underlying pressure adaptation remain largely unexplored, despite their significance for understanding biological adaptation and improving sterilization methods in the food and beverage industry. The heat shock response leads to a global stabilization of the proteome. Prior research suggested that the heat shock regulon may exhibit a transcriptional response to high-pressure stress. In this study, we investigated the pressure-dependent heat shock response in E. coli strains using plasmid-borne green fluorescent protein (GFP) promoter fusions and fluorescence fluctuation microscopy. We quantitatively confirm that key heat shock genes-rpoH, rpoE, dnaK, and groEL - are transcriptionally upregulated following pressure shock in both piezosensitive Escherichia coli and a more piezotolerant laboratory-evolved strain, AN62. Our quantitative imaging results provide the first single cell resolution measurements for both the heat shock and pressure shock transcriptional responses, revealing not only the magnitude of the responses, but also the biological variance involved. Moreover, our results demonstrate distinct responses in the pressure-adapted strain. Specifically, PgroEL is upregulated more than PdnaK in AN62, while the reverse is true in the parental strain. Furthermore, unlike in the parental strain, the pressure-induced upregulation of PrpoE is highly stochastic in strain AN62, consistent with a strong feedback mechanism and suggesting that RpoE could act as a pressure sensor. Despite its capacity to grow at pressures up to 62 MPa, the AN62 genome shows minimal mutations, with notable single nucleotide substitutions in genes of the transcriptionally important b subunit of RNA polymerase and the Rho terminator. In particular, the mutation in RNAP is one of a cluster of mutations known to confer rifampicin resistance to E. coli via modification of RNAP pausing and termination efficiency. The observed differences in the pressure and heat shock responses between the parental MG1655 strain and the pressure-adapted strain AN62 could arise in part from functional differences in their RNAP molecules.