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Quark–Gluon plasma driven by the strong force is subject to the conservativeness of the baryon number, net electric charge, strangeness, etc. However, the fluctuations around their mean values at specific temperatures and chemical potentials can provide viable signals for the production of Quark–Gluon plasma. These fluctuations can be captured theoretically as moments of different orders in the expansion of pressure or the thermodynamic potential of the system under concern. Here, we look for possible explanations in the methodologies used for capturing them by using the framework of the Polyakov–Nambu–Jona-Lasinio (PNJL) model under the 2 + 1 flavor consideration with mean-field approximation. The various quantities thus explored can act to signify meaningfully near the phase transitions. Justifications are also made for some of the quantities capable of serving necessarily under experimental scenarios. Additionally, variations in certain quantities are also made for the different collision energies explored in the high-energy experiments. Rectification of the quantitative accuracy, especially in the low-temperature hadronic sector, is of prime concern, and it is also addressed. It was found that most of the observables stay in close proximity with the existing lattice QCD results at the continuum limit, with some artifacts still remaining, especially in the strange sector, which needs further attention. [ABSTRACT FROM AUTHOR]

Introduction: Lebrikizumab is a novel monoclonal antibody with established efficacy in patients with moderate-to-severe atopic dermatitis (AD) in multiple Phase 3 trials. One of the ultimate treatment goals for patients with moderate-to-severe AD is to achieve stable disease control without concern for planning future life events. Methods: In ADvocate1 and ADvocate2, lebrikizumab-treated patients meeting the protocol-defined response criteria at Week 16 were re-randomized 2:2:1 to receive lebrikizumab every 2 weeks (Q2W), lebrikizumab every 4 weeks (Q4W), or placebo Q2W (lebrikizumab withdrawal) for 36 additional weeks. In this post hoc analysis, we evaluated the proportions of patients with no or minimal fluctuations of efficacy during the 36-week maintenance period and plotted individual patient trajectories. We defined no or minimal fluctuations as achieving and maintaining the defined endpoint (≥ 75% improvement in the Eczema Area and Severity Index [EASI 75], ≥ 90% improvement in EASI, Pruritus Numeric Rating Scale [NRS] ≥ 4-point improvement, or Pruritus NRS ≥ 3-point improvement) for ≥ 80% of the study visits. If patients used rescue medication, discontinued treatment, or transferred to the escape arm, data collected at or after the event were imputed as non-response. Results: The proportions of lebrikizumab responders who maintained EASI 75 with no or minimal fluctuations were 70.8% (lebrikizumab Q2W), 71.2% (lebrikizumab Q4W), and 60.0% (lebrikizumab withdrawal). Of the patients with baseline Pruritus NRS ≥ 4 and who achieved ≥ 4-point improvement at Week 16, 66.1% (lebrikizumab Q2W), 62.7% (lebrikizumab Q4W), and 55.2% (lebrikizumab withdrawal) maintained ≥ 4-point Pruritus NRS improvement with no or minimal fluctuations. Conclusions: Patients who met the response criteria at Week 16 and continued treatment with lebrikizumab Q2W or Q4W demonstrated a stable response with no or minimal fluctuations of efficacy in measures of skin and itch up to Week 52. Clinical Trial Registration: NCT04146363 (ADvocate1) and NCT04178967 (ADvocate2). Plain Language Summary: Atopic dermatitis, also known as atopic eczema (or just eczema), is a common skin disease that causes itchy, dry skin. Patients with eczema are often unsure of when disease flares will happen, even while receiving treatment. In two global studies, ADvocate1 and ADvocate2, lebrikizumab improved the signs and symptoms of moderate-to-severe eczema after 16 weeks of treatment. Most of these patients also saw improvement up to 52 weeks. We wanted to know if patients continued to feel better between Week 16 and Week 52. Patients who responded to lebrikizumab after 16 weeks were given lebrikizumab every 2 weeks, lebrikizumab every 4 weeks, or placebo every 2 weeks. We tested how many patients experienced stable response to therapy, which we said was maintaining the same level of improvement on skin signs and itch symptoms for at least 80% of study visits from Week 16 to Week 52. In patients treated with lebrikizumab every 2 weeks or every 4 weeks, we saw that about seven of every ten patients maintained a stable response in skin improvement and about six of every ten patients maintained stable response in itch symptoms. In patients who stopped lebrikizumab therapy, six out of every ten patients maintained a stable skin improvement and more than five of every ten patients maintained a stable improvement in itch symptoms. In ADvocate1 and ADvocate2, most lebrikizumab-treated patients showed a stable response over time on skin and itch with dosing every 2 weeks or every 4 weeks. [ABSTRACT FROM AUTHOR]

Background: Children's cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address. Method: The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a 'burst' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms. Discussion: CODEC's unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC's longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal 'burst' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children. Trial registration: ClinicalTrials.gov - NCT06330090 [ABSTRACT FROM AUTHOR]

We consider a dense aggregate of elastic, frictional particles isotropically compressed and next uniaxial strained at constant pressure. We show how failure can be predicted if fluctuations in the kinematics of contacting particles are introduced. We focus on the second order work and the possibility that at some stressed states it becomes negative under proper perturbations. Our analysis involves both a theoretical model and numerical simulations based upon the distinct element method (DEM). The theoretical model deals with contacting particles with incremental relative displacements that deviate from the average deformation in order to ensure their equilibrium. Because of this, the macroscopic stiffness tensor of the aggregate, that relates increments in stress with increments in strain, does not have the major symmetry. Consequently, in the hardening regime, we predict stressed states in which the second order work vanishes. The model seems transparent, and it makes clear and illustrative the role played by the fluctuations introduced in the kinematics of contacting particles in relation to the vanishing of second order work in an aggregate of compressed particles. The comparison with numerical simulations data supports the model. Statistical representation of the aggregate: conditional average. [ABSTRACT FROM AUTHOR]

Abstract Introduction Lebrikizumab is a novel monoclonal antibody with established efficacy in patients with moderate-to-severe atopic dermatitis (AD) in multiple Phase 3 trials. One of the ultimate treatment goals for patients with moderate-to-severe AD is to achieve stable disease control without concern for planning future life events. Methods In ADvocate1 and ADvocate2, lebrikizumab-treated patients meeting the protocol-defined response criteria at Week 16 were re-randomized 2:2:1 to receive lebrikizumab every 2 weeks (Q2W), lebrikizumab every 4 weeks (Q4W), or placebo Q2W (lebrikizumab withdrawal) for 36 additional weeks. In this post hoc analysis, we evaluated the proportions of patients with no or minimal fluctuations of efficacy during the 36-week maintenance period and plotted individual patient trajectories. We defined no or minimal fluctuations as achieving and maintaining the defined endpoint (≥ 75% improvement in the Eczema Area and Severity Index [EASI 75], ≥ 90% improvement in EASI, Pruritus Numeric Rating Scale [NRS] ≥ 4-point improvement, or Pruritus NRS ≥ 3-point improvement) for ≥ 80% of the study visits. If patients used rescue medication, discontinued treatment, or transferred to the escape arm, data collected at or after the event were imputed as non-response. Results The proportions of lebrikizumab responders who maintained EASI 75 with no or minimal fluctuations were 70.8% (lebrikizumab Q2W), 71.2% (lebrikizumab Q4W), and 60.0% (lebrikizumab withdrawal). Of the patients with baseline Pruritus NRS ≥ 4 and who achieved ≥ 4-point improvement at Week 16, 66.1% (lebrikizumab Q2W), 62.7% (lebrikizumab Q4W), and 55.2% (lebrikizumab withdrawal) maintained ≥ 4-point Pruritus NRS improvement with no or minimal fluctuations. Conclusions Patients who met the response criteria at Week 16 and continued treatment with lebrikizumab Q2W or Q4W demonstrated a stable response with no or minimal fluctuations of efficacy in measures of skin and itch up to Week 52. Clinical Trial Registration NCT04146363 (ADvocate1) and NCT04178967 (ADvocate2).

Abstract Background Children’s cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address. Method The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a ‘burst’ week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms. Discussion CODEC’s unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC’s longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal ‘burst’ design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children. Trial registration ClinicalTrials.gov - NCT06330090

The relevance of the topic presented in the article is determined primarily by the current foreign policy situation, namely the hybrid war unleashed by the collective West against the Russian Federation, a significant component of which are the so-called nonkinetic wars: informational, organizational, mental, cognitive, etc. In the article, the author focuses specifically on cognitive wars, which are the least studied in the social sciences. Hence, the main purpose of the article is to consider possible methodological foundations for the analysis of cognitive wars in the context of the high turbulence of the world order and the growing dynamics of digital transformations in the economy, politics, and social spheres. As the core of such a methodology, the author suggests considering social synergetics, the key postulates of which are openness, non-linearity of self-development, and the nonequilibrium nature of systems of various natures, which are most consistent with the current context of public relations. Special attention is paid to the role of the system of spiritual and moral values, or the social value basis, in ensuring the stability of the political system and effective counteraction to cognitive aggression. The author revealed its main functions, such as ensuring communicative effectiveness, overcoming the social atomization of modern society, and ensuring the cognitive stability of society by increasing resistance to cognitive and mental aggression.

The study aims to analyze the availability of renewable freshwater resources and their spatiotemporal distribution in the Eastern Region of Kosovo (ERK). As a crucible resource for agricultural, industrial, and commercial activities, water scarcity will lead to water shortages. Kosovo is a landlocked country, and ERK is the most important region for industrial activities (coal, mines) and agricultural use (plains). Located in continental climate conditions, surrounded by middle to high-altitude mountains, the region does not have a favorable spatio-temporal distribution of freshwaters. The determination of the water resources is based on the amount of surface runoff, which being divided per capita, gives indications of the water resources of a region (catchment). For our aims, main river discharge and population statistics are used to analyze the spatiotemporal distribution and availability of renewable freshwater in ERK. Falkenmark Freshwater Indicator with 1.483 m³/capita/year shows that ERK lies under water scarcity, and existing water reservoirs offer low security for water withdrawals, and further reservoirs would prevent water scarcity in the region.

An analysis of the features of measurements and correction of phase distortions in optical waves propagating in the atmosphere at various levels of turbulence was performed. It is shown that with increasing intensity fluctuations, the limiting capabilities of phase correction decrease, and the phase of an optical wave that has passed through a turbulence layer consists of two components: potential and vortex. It was found that even in the region of weak fluctuations there is an overlap of spectral filtering functions for intensity and phase fluctuations. Areas of turbulence inhomogeneities have been identified that will have mutual influence and negatively affect the operation of the phase meter. It is noted that correlation functions, both phase and intensity, are less susceptible to this compared to structural functions. The results of experimental studies on the reconstruction of the wavefront of laser radiation distorted by atmospheric turbulence using a Shack–Hartmann wavefront sensor during vignetting and central screening of the entrance pupil in the optical system are presented. Studies have been carried out on the propagation of laser radiation along a horizontal atmospheric path for various levels of turbulence. The results are analyzed in terms of Zernike polynomials. [ABSTRACT FROM AUTHOR]

Struts of a superconducting YBa2Cu3Oy (YBCO) foam prepared by the infiltration growth method on the base of commercial polyurethane foams were extracted from the bulk, and thoroughly characterized concerning the microstructure and the magnetoresistance, measured by the four-point technique. Optical microscopy, electron microscopy, electron backscatter diffraction and atomic force microscopy observations indicate a unique microstructure of the foam struts which shows a large amount of tiny Y2BaCuO5 (Y-211) particles (with diameters between 50 and 100 nm) being enclosed in channel-like grain boundaries between the YBCO grains and a one-of-a-kind surface of the struts covered with Ba3Cu5Oy-particles. The resistance data obtained at temperatures in the range 4.2 K ≤ T ≤ 150 K (applied magnetic fields ranging from 0 to 7 T) were analyzed in the framework of the fluctuation-induced conductivity (FIC) approach using the models of Aslamazov–Larkin (AL) and Lawrence–Doniach (LD). The resulting FIC curves reveal the presence of five distinct fluctuation regimes, namely, the short-wave (SWF), one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and critical (CR) fluctuation domains. The analysis of the FIC data enable the coherence length in the direction of the c-axis at zero-temperature ( ξ c (0) ), the irreversibility field ( B irr ), the upper critical magnetic field ( B c 2 ), the critical current density at T = 0 K ( J c (0) ) and several other parameters describing the the material's superconducting properties to be determined. The present data reveal that the minuscule Y-211 particles found along the YBCO grain boundaries alter the excess conductivity and the fluctuation behavior as compared to conventional YBCO samples, leading to a quite high value for J c (0) for a sample with a non-optimized pinning landscape. [ABSTRACT FROM AUTHOR]

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum "geometric information variability". We validate our methodology through numerical experimentation on the Ornstein–Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein–Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control. [ABSTRACT FROM AUTHOR]

Abstract 2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.

2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies. [ABSTRACT FROM AUTHOR]

"Chases and Escapes" is a classical mathematical problem. Recently, we proposed a simple extension, called "Group Chase and Escape," where one group chases another. This extension bridges the traditional problem with the current interest in studying collective motion among animals, insects, and cars. In this presentation, I will introduce our fundamental model and explore its intricate emergent behaviors. In our model, each chaser approaches the nearest escapee, while each escapee moves away from its closest chaser. Interestingly, despite the absence of communication within each group, we observe the formation of aggregate patterns. Furthermore, the effectiveness of capture varies as we adjust the ratio of chasers to escapees, which can be attributed to a group effect. I will delve into how these behaviors manifest in relation to various parameters, such as densities. Moreover, we have explored different expansions of this basic model. First, we introduced fluctuations, where players now make errors in their step directions with a certain probability. We found that a moderate level of fluctuations improves the efficiency of catching. Second, we incorporated a delay in the chasers' reactions to catch their targets. This distance-dependent reaction delay can lead to highly complex behaviors. Additionally, I will provide an overview of other groups' extensions of the model and the latest developments in this field. [ABSTRACT FROM AUTHOR]

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice. [ABSTRACT FROM AUTHOR]

Why do fractals appear in so many domains of science? What is the physical principle that generates them? While it is true that fractals naturally appear in many physical systems, it has so far been impossible to derive them from first physical principles. However, a proposed interpretation could shed light on the inherent principle behind the creation of fractals. This is the multiscale thermodynamic perspective, which states that an increase in external energy could initiate energy transport mechanisms that facilitate the dissipation or release of excess energy at different scales. Within this framework, it is revealed that power law patterns, and to a lesser extent, fractals, can emerge as a geometric manifestation to dissipate energy in response to external forces. In this context, the exponent of these power law patterns (thermodynamic fractal dimension D ) serves as an indicator of the balance between entropy production at small and large scales. Thus, when a system is more efficient at releasing excess energy at the microscopic (macroscopic) level, D tends to increase (decrease). While this principle, known as Principium luxuriæ, may sound promising for describing both multiscale and complex systems, there is still uncertainty about its true applicability. Thus, this work explores different physical, astrophysical, sociological, and biological systems to attempt to describe and interpret them through the lens of the Principium luxuriæ. The analyzed physical systems correspond to emergent behaviors, chaos theory, and turbulence. To a lesser extent, the cosmic evolution of the universe and geomorphology are examined. Biological systems such as the geometry of human organs, aging, human brain development and cognition, moral evolution, Natural Selection, and biological death are also analyzed. It is found that these systems can be reinterpreted and described through the thermodynamic fractal dimension. Therefore, it is proposed that the physical principle that could be behind the creation of fractals is the Principium luxuriæ, which can be defined as "Systems that interact with each other can trigger responses at multiple scales as a manner to dissipate the excess energy that comes from this interaction". That is why this framework has the potential to uncover new discoveries in various fields. For example, it is suggested that the reduction in D in the universe could generate emergent behavior and the proliferation of complexity in numerous fields or the reinterpretation of Natural Selection. [ABSTRACT FROM AUTHOR]

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This study aims to analyze fluctuations and disparity in broiler and carcass prices before, during, and after COVID-19 pandemic in five broiler-producing regions in Indonesia, including North Sumatra, West Java, Central Java, East Java, and South Sulawesi. Weekly data series were used to analyze fluctuations and disparity before (2017–February 2020), during (March 2020–2022), and after (January–June 2023) COVID-19 pandemic. Furthermore, Coefficient of variation (CV) and convergence models were also used during the analysis. The results showed that broiler price fluctuations in the five study areas were categorized as “medium” before and during pandemic. After pandemic, the majority of the areas were in the “low” category, except for South Sulawesi province, where CV was 10.02%. Carcass price fluctuations were categorized as “low” and “moderate” before and during COVID-19 pandemic. After the viral outbreak, all the provinces investigated in this study were classified as “low”. This indicated that the variability in carcass price decreased across all provinces after pandemic. The coefficient value of β1 was less than 1 indicating that broiler and carcass price did not show significant disparity between regions at all periods. The primary drivers of fluctuations and disparity were related to production and availability factors, and the government played a role in maintaining stability in producing areas.

The irradiation variations caused by cloud changes can cause rapid power fluctuations in large photovoltaic (PV) plants. The increased PV power share of the grid adversely affects the quality of power and the reliability of the power supply. Energy storage systems (ESSs)are often used to mitigate power fluctuations in the grid through various control algorithms. These algorithms create an ESS power reference that opposes the variations of the PV and reduces them to an acceptable value. Despite their everyday use, there have been few performance comparisons among the various methods, especially battery status, and the battery can operate in several systems with various smoothing methods. The paper aims to analyze the ramp-rate and step-rate control methods for smoothing solar PV fluctuations based on the irradiation profiles in a DC microgrid (MG) environment. The battery in the system operates as a continuous energy application, and a supercapacitor (SC) is used to remove transients in the battery system. The combined battery-SC coordinated system improves the performance of the DC microgrid system. The battery cycle analysis and depth are conducted using the well-established Coulomb counting technique. MATLAB/Simulink validates the simulation results, and the OP-5700 HIL test bench conducts real-time results.

Quark–Gluon plasma driven by the strong force is subject to the conservativeness of the baryon number, net electric charge, strangeness, etc. However, the fluctuations around their mean values at specific temperatures and chemical potentials can provide viable signals for the production of Quark–Gluon plasma. These fluctuations can be captured theoretically as moments of different orders in the expansion of pressure or the thermodynamic potential of the system under concern. Here, we look for possible explanations in the methodologies used for capturing them by using the framework of the Polyakov–Nambu–Jona-Lasinio (PNJL) model under the 2 + 1 flavor consideration with mean-field approximation. The various quantities thus explored can act to signify meaningfully near the phase transitions. Justifications are also made for some of the quantities capable of serving necessarily under experimental scenarios. Additionally, variations in certain quantities are also made for the different collision energies explored in the high-energy experiments. Rectification of the quantitative accuracy, especially in the low-temperature hadronic sector, is of prime concern, and it is also addressed. It was found that most of the observables stay in close proximity with the existing lattice QCD results at the continuum limit, with some artifacts still remaining, especially in the strange sector, which needs further attention.

Visual working memory (VWM) plays an important role during visual search, with some theories suggesting an equivalence between working memory representations and guidance from attentional templates. However, recent work has shown that participants can also use ‘negative templates’, the foreknowledge of distractor-features stored in VWM, to guide attention away from distractors during visual search. These negative templates must also be represented in working memory, but the question remains whether the quality of the working memory representations underlying negative and positive templates are similar, in spite of their opposite impacts on attention. In this study, participants (N = 33) engaged in a visual search task for a shape-defined target after receiving a positive cue (target color), negative cue (distractor color) or neutral cue (non-informative). In 20% of the trials, a color-wheel probe was presented instead of a search array to measure the quality of the cue representation stored in VWM. Our results revealed that participants were more likely to guess in response to neutral cues than negative cues. Yet, the comparison between positive and negative cues showed no significant differences. However, we found no difference in memory precision for the three cue types. More interestingly, the more the VWM quality is boosted by the negative cue, the greater the ability to guide attention away from distractors. Such a pattern of results might map to recent evidence of between-individuals differences in utilization of negative cues. These findings highlight the distinction between attentional templates and simple maintenance in working memory.

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting. [ABSTRACT FROM AUTHOR]

The entropy and charge distributions have been calculated for a simple model of polyelectrolytes attached to the surface of DNA using a field-theoretic method that includes fluctuations to the lowest one-loop order beyond mean-field theory. Experiments have revealed correlation-driven behavior of DNA in charged solutions, including charge inversion and condensation. In our model, the condensed polyelectrolytes are taken to be doubly charged dimers of length comparable to the distance between sites along the phosphate chains. Within this lattice gas model, each adsorption site is assumed to have either a vacancy or a positively charged dimer attached with the dimer oriented either parallel or perpendicular to the double-helix DNA chain. We find that the inclusion of the fluctuation terms decreases the entropy by ∼50% in the weak-binding regime. There, the bound dimer concentration is low because the dimers are repelled from the DNA molecule, which competes with the chemical potential driving them from the solution to the DNA surface. Surprisingly, this decrease in entropy due to correlations is so significant that it overcompensates for the entropy increase at the mean-field level, so that the total entropy is even lower than in the absence of interactions between lattice sites. As a bonus, we present a transparent exposition of the methods used that could be useful to students and others wishing to use this formulation to extend this calculation to more realistic models. [ABSTRACT FROM AUTHOR]

Assessing a potential resurgence of an epidemic outbreak with certainty is as important as it is challenging. The low number of infectious individuals after a long regression, and the randomness associated with it, makes it difficult to ascertain whether the infectious population is growing or just fluctuating. We have developed an approach to compute confidence intervals for the switching time from decay to growth and to compute the corresponding multiple-location aggregated quantities over a region to increase the precision of the determination. We estimated the aggregate prevalence over time for Europe and the northeast United States to characterize the COVID-19 second surge in these regions during year 2020. We find a starting date as early as 3 July (95% confidence interval (CI): 1-6 July) for Europe and 19 August (95% CI: 16-23 August) for the northeast United States; subsequent infectious populations that, as of 31 December, have always increased or remained stagnant; and the resurgences being the collective effect of each overall region with no location, either country or state, dominating the regional dynamics by itself.

An analysis of the features of measurements and correction of phase distortions in optical waves propagating in the atmosphere at various levels of turbulence was performed. It is shown that with increasing intensity fluctuations, the limiting capabilities of phase correction decrease, and the phase of an optical wave that has passed through a turbulence layer consists of two components: potential and vortex. It was found that even in the region of weak fluctuations there is an overlap of spectral filtering functions for intensity and phase fluctuations. Areas of turbulence inhomogeneities have been identified that will have mutual influence and negatively affect the operation of the phase meter. It is noted that correlation functions, both phase and intensity, are less susceptible to this compared to structural functions. The results of experimental studies on the reconstruction of the wavefront of laser radiation distorted by atmospheric turbulence using a Shack–Hartmann wavefront sensor during vignetting and central screening of the entrance pupil in the optical system are presented. Studies have been carried out on the propagation of laser radiation along a horizontal atmospheric path for various levels of turbulence. The results are analyzed in terms of Zernike polynomials.

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum “geometric information variability”. We validate our methodology through numerical experimentation on the Ornstein–Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein–Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control.

Struts of a superconducting YBa2Cu3Oy (YBCO) foam prepared by the infiltration growth method on the base of commercial polyurethane foams were extracted from the bulk, and thoroughly characterized concerning the microstructure and the magnetoresistance, measured by the four-point technique. Optical microscopy, electron microscopy, electron backscatter diffraction and atomic force microscopy observations indicate a unique microstructure of the foam struts which shows a large amount of tiny Y2BaCuO5 (Y-211) particles (with diameters between 50 and 100 nm) being enclosed in channel-like grain boundaries between the YBCO grains and a one-of-a-kind surface of the struts covered with Ba3Cu5Oy-particles. The resistance data obtained at temperatures in the range 4.2 K ≤T≤ 150 K (applied magnetic fields ranging from 0 to 7 T) were analyzed in the framework of the fluctuation-induced conductivity (FIC) approach using the models of Aslamazov–Larkin (AL) and Lawrence–Doniach (LD). The resulting FIC curves reveal the presence of five distinct fluctuation regimes, namely, the short-wave (SWF), one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and critical (CR) fluctuation domains. The analysis of the FIC data enable the coherence length in the direction of the c-axis at zero-temperature (ξc(0)), the irreversibility field (Birr), the upper critical magnetic field (Bc2), the critical current density at T= 0 K (Jc(0)) and several other parameters describing the the material’s superconducting properties to be determined. The present data reveal that the minuscule Y-211 particles found along the YBCO grain boundaries alter the excess conductivity and the fluctuation behavior as compared to conventional YBCO samples, leading to a quite high value for Jc(0) for a sample with a non-optimized pinning landscape.

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann-Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60-100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.

Abstract In modern power systems, the impact of random power sources and loads on power systems is increasing, resulting in threatened system frequency security. However, traditional methods are often not comprehensive in modelling randomness, and there is no analytical method to assess the frequency response of power systems under uncertain power fluctuations. Here, the power fluctuation in a power system is regarded as an interval random quantity. An analytical prediction method of the power system frequency deviation under uncertain power fluctuation is proposed. Through further deduction, the allowable range of power fluctuation under the premise of secure frequency deviation can also be defined, which provides a reference for frequency security and accident prevention in power systems. Numerical simulations with the model of the East China Power Grid demonstrate that the proposed analytical method can delimit the response interval of system frequency deviation well, and the backstepping calculation can also delimit the allowable range of power fluctuation to ensure the secure operation of the system.

The function of social media and it's awareness and information can play a significant role in the changes in stock prices and investors' decision making. Fluctuation is an important measure of financial performance that reflects uncertainty or risk. In view of this, the present study investigated and analyzed the social media and the fluctuations of the Tehran Stock Market during the period of April 2011 to August 2021 using auto-regression model. The results show that the instantaneous reaction functions of the stock returns of active companies in the Tehran stock market to the changes in the social media, the number of shares of the companies, the infected and the dead of covid-19 were positive until the third and fourth periods, and after the mentioned periods of shock entered into the explanatory variables did not have an effect on the fluctuation of the companies' returns. Also, the results of the analysis of variance showed the high effects of the number of shares of the companies in the changes of the efficiency of the companies and the effect of social media in explaining very slight changes in fluctuations have been observed

This paper describes the Climate Toolbox—a set of utilities for assessing and comparing real internal climates in terms of hazards they pose to museum collections. The Toolbox is a Microsoft® Excel® workbook with complex VBA codes optimised to process large datasets efficiently and quickly. The Toolbox takes user-provided climate data (up to 8000 sets of temperature, T, and relative humidity, rh, data) and characterises the climates in terms of critical parameters for stresses and mechanical damage, risk and magnitude of mould, relative permanence compared to (selectable) reference specifications, the proportion of data lying within and without selectable specification ranges, and the proportion of rh data swings exceeding a given magnitude. The interface is easily customisable, allowing users to input desired specification ranges, insert opening and closing times (to allow for different temperature specifications for periods when a space is open and occupied to when it is closed and empty), selectively change material critical strain parameters, and adjust the cycle periods for stress analysis. Results are summarised in a range of different graphical and tabular outputs and can be processed further to compare and rank spaces for their suitability to house different collections.

Raised bogs of the Baltic type are unique because of their geomorphologic structure and hydrologic conditions. There are about 80 bogs of this type in Poland. All are affected by human impact, and their water circulation patterns are disrupted by artificial outflows. The aim of the study was to show the effects of human impact on water circulation patterns in raised bog on an example of the Wielkie Bagno peat bog. The main work consisted of field studies, during which measurements of groundwater fluctuations, flows at main outflows, and hydrographic mapping were performed. Data on precipitation and evaporation were also obtained from state institutions. Research has shown that fluctuations in groundwater levels in the Wielkie Bagno bog average 78 cm per year (hydrological years 2018–2019) and the bog is in poor condition. This is mainly due to climate change, which manifests itself in too little precipitation in summer and an increase in evaporation, a too dense drainage network causing the lowering of the groundwater level in the peat bog, and the close proximity of a peat mine causing significant drainage of water from the examined peat bog, as well as too large fluctuations of groundwater during the year and in individual months. In some areas, a near-surface layer of the bog was also detected, about 30 cm thick, which is a sign of bog desiccation (especially in summer). Water balance data were used to show that the quantity of water available in the bog is determined by atmospheric precipitation, which is too small in the warm period. As a consequence, there are months with a negative climatic balance. It was also observed that factors such as physical location in different parts of the bog and the degree of degradation also affect water circulation patterns in the study area. At the moment, the most important task in the peat bog is to raise the groundwater level, reduce its fluctuations, and reduce human pressure on the bog. [ABSTRACT FROM AUTHOR]

We propose a method of cooling nuclear spin systems of solid-state nanostructures by applying a time-dependent magnetic field synchronized with spin fluctuations. Optical spin noise spectroscopy is considered a method of fluctuation control. Depending on the mutual orientation of the oscillating magnetic field and the probe light beam, cooling might be either provided by dynamic spin polarization in an external static field or result from population transfer between spin levels without build-up of a net magnetic moment ("true cooling"). [ABSTRACT FROM AUTHOR]

We study the swarming behavior of hydrodynamic alignment. Alignment reflects steering toward a weighted average heading. We consider the class of so-called p-alignment hydrodynamics, based on 2p-Laplacians and weighted by a general family of symmetric communication kernels. The main new aspect here is the long-time emergence behavior for a general class of pressure tensors without a closure assumption, beyond the mere requirement that they form an energy dissipative process. We refer to such pressure laws as "entropic", and prove the flocking of p-alignment hydrodynamics, driven by singular kernels with a general class of entropic pressure tensors. These results indicate the rigidity of alignment in driving long-time flocking behavior despite the lack of thermodynamic closure. [ABSTRACT FROM AUTHOR]

The preliminary STAR data for proton cumulants for central collisions at s=7.7GeV component proton multiplicity distribution. We show that this two-component distribution is statistics friendly in that factorial cumulants of surprisingly high orders may be extracted with a relatively small number of events. As a consequence the two-component model can be tested and verified right now with the presently available STAR data from the first phase of the RHIC beam energy scan.

The preliminary STAR data for proton cumulants for central collisions at s=7.7GeV component proton multiplicity distribution. We show that this two-component distribution is statistics friendly in that factorial cumulants of surprisingly high orders may be extracted with a relatively small number of events. As a consequence the two-component model can be tested and verified right now with the presently available STAR data from the first phase of the RHIC beam energy scan.

Chicken eggs are a staple food with a high enough animal protein content, but the price is quite affordable for all levels of society. This causes the consumption of chicken eggs to be relatively high compared to other animal protein sources, but currently, the amount of production and consumption by the public is not comparable; besides that, the price offered is constantly fluctuating. This study aims to determine price fluctuations and price movement patterns of broiler chicken eggs in Bengkulu Province. The data used is secondary data. The research method uses quantitative descriptive analysis. The research results show that. The risk of fluctuations in the price of broiler eggs in Bengkulu Province has a slightly higher risk for prices at the producer level when compared to prices at the consumer level. The development pattern of broiler egg prices in Bengkulu Province generally shows a positive direction. Fluctuations in the price of broiler eggs occur in specific periods. These fluctuations occur because they coincide with religious holidays such as Ramadan, Eid al-Fitr, Christmas and New Year, the influence of the Russia-Ukraine war, and economic problems due to the Covid-19 pandemic.

Exchange rate is an important part of financial markets. Our analysis finds that the fluctuations of exchange rates have several obvious features, such as spikes, thick tails, fluctuation aggregations and asymmetry. Based on this, we build novel GARCH class model by introducing a jumping process to describe the dynamics of their fluctuations. Our empirical results show that the models with jump factors can better characterize the agglomeration and thick tail characteristics of these return fluctuations than the models without jump factors. In particular, the model with double exponential jumps can fully handle and capture the fluctuation characteristics of the returns. Our findings will be useful for individuals and governments to predict exchange rate fluctuations, provide reference for the effective management of exchange rate risk in China, and further improve the financial risk management mechanism.

Ghost imaging (GI) has been received more and more attention in past thirty years. However, it has some limitations in the resolution. In this article, we propose a scheme to improve the resolution of GI by a new imaging information, i.e. the coefficient of skewness coming from the fluctuations of the second-order correlation function, and put forward an enhanced GI protocol exploiting those fluctuations. We demonstrate experimentally that the additional information obtained from the new information makes it possible to improve resolution compared to the traditional GI scheme.

This report summarizes the presentations and discussions during the Rapid Reaction Task Force “Dynamics of critical fluctuations: Theory – phenomenology – heavy-ion collisions”, which was organized by the ExtreMe Matter Institute EMMI and held at GSI, Darmstadt, Germany in April 2019. We address the current understanding of the dynamics of critical fluctuations in QCD and their measurement in heavy-ion collision experiments. In addition, we outline what might be learned from studying correlations in other physical systems, such as cold atomic gases.

This report summarizes the presentations and discussions during the Rapid Reaction Task Force “Dynamics of critical fluctuations: Theory – phenomenology – heavy-ion collisions”, which was organized by the ExtreMe Matter Institute EMMI and held at GSI, Darmstadt, Germany in April 2019. We address the current understanding of the dynamics of critical fluctuations in QCD and their measurement in heavy-ion collision experiments. In addition, we outline what might be learned from studying correlations in other physical systems, such as cold atomic gases.

Groundwater, found beneath the Earth’s surface in saturated zones of soil, sediment, and rock, plays a crucial role in sustaining ecosystems and supporting human activities like agriculture and industry. Monitoring and managing groundwater resources are crucial for sustainable use. As of the latest update in January 2022, Chennai, a city in southern India, has been grappling with water scarcity issues. The city has faced recurrent water shortages due to various factors, including rapid urbanization, inadequate infrastructure, depleting groundwater levels, and irregular rainfall patterns. Chengalpattu district in Tamil Nadu, India, is known for its diverse geographical features, incorporating urban and rural landscapes, and is significant for agriculture and water resource management. This study focuses on predicting variations in groundwater levels in open wells at different locations in the Chengalpattu district, assessing the effectiveness of various machine-learning models. This paper utilizes the ARIMA model provided by the stats models library. This library is widely employed for statistical modelling and hypothesis testing in Python, and encompasses a range of tools for time series analysis, including the ARIMA model. In this context. ARIMA models are employed for predicting future depth which focus on depicting autocorrelations in the data. Additionally, a package consolidating various models, including Seasonal Naïve (a straightforward forecasting method for seasonal data that serves as a reliable benchmark by relying on the observation from the same period a season ago), was incorporated in this study.

Why do fractals appear in so many domains of science? What is the physical principle that generates them? While it is true that fractals naturally appear in many physical systems, it has so far been impossible to derive them from first physical principles. However, a proposed interpretation could shed light on the inherent principle behind the creation of fractals. This is the multiscale thermodynamic perspective, which states that an increase in external energy could initiate energy transport mechanisms that facilitate the dissipation or release of excess energy at different scales. Within this framework, it is revealed that power law patterns, and to a lesser extent, fractals, can emerge as a geometric manifestation to dissipate energy in response to external forces. In this context, the exponent of these power law patterns (thermodynamic fractal dimension D) serves as an indicator of the balance between entropy production at small and large scales. Thus, when a system is more efficient at releasing excess energy at the microscopic (macroscopic) level, D tends to increase (decrease). While this principle, known as Principium luxuriæ, may sound promising for describing both multiscale and complex systems, there is still uncertainty about its true applicability. Thus, this work explores different physical, astrophysical, sociological, and biological systems to attempt to describe and interpret them through the lens of the Principium luxuriæ. The analyzed physical systems correspond to emergent behaviors, chaos theory, and turbulence. To a lesser extent, the cosmic evolution of the universe and geomorphology are examined. Biological systems such as the geometry of human organs, aging, human brain development and cognition, moral evolution, Natural Selection, and biological death are also analyzed. It is found that these systems can be reinterpreted and described through the thermodynamic fractal dimension. Therefore, it is proposed that the physical principle that could be behind the creation of fractals is the Principium luxuriæ, which can be defined as “Systems that interact with each other can trigger responses at multiple scales as a manner to dissipate the excess energy that comes from this interaction”. That is why this framework has the potential to uncover new discoveries in various fields. For example, it is suggested that the reduction in D in the universe could generate emergent behavior and the proliferation of complexity in numerous fields or the reinterpretation of Natural Selection.

Stabilization of food prices is still a strategic issue in Indonesia. This is because most Indonesians use their income mostly for food. Various publications reported that Indonesian food expenditure is approximately half of their income. Therefore, maintaining price stability, including beef and chicken meat price, is a significant policy to design. However, the continuous difficulty in predicting how beef and chicken meat prices behave is another reason for the importance of analyzing the price behavior of these two commodities. This research aims to respond to these problems by analyzing price fluctuations and price trends in beef and chicken meat commodities in Indonesia. The data used is monthly beef and chicken meat prices in Indonesia from January 2018 until December 2022, or 60 observations. Analysis of price fluctuations (instability) and trend analysis is applied. The results show that commodity chicken meat has a higher instability (fluctuation) than beef price. Both beef and chicken meat trend show a quadratic pattern, with an increasing trend for beef and a decreasing trend for chicken meat.

In modern power systems, the impact of random power sources and loads on power systems is increasing, resulting in threatened system frequency security. However, traditional methods are often not comprehensive in modelling randomness, and there is no analytical method to assess the frequency response of power systems under uncertain power fluctuations. Here, the power fluctuation in a power system is regarded as an interval random quantity. An analytical prediction method of the power system frequency deviation under uncertain power fluctuation is proposed. Through further deduction, the allowable range of power fluctuation under the premise of secure frequency deviation can also be defined, which provides a reference for frequency security and accident prevention in power systems. Numerical simulations with the model of the East China Power Grid demonstrate that the proposed analytical method can delimit the response interval of system frequency deviation well, and the backstepping calculation can also delimit the allowable range of power fluctuation to ensure the secure operation of the system. [ABSTRACT FROM AUTHOR]

We study the effect of time-fluctuating social influences on the formation of polarization and consensus in a three-party community consisting of two types of voters ("leftists" and "rightists") holding extreme opinions, and moderate agents acting as "centrists". The former are incompatible and do not interact, while centrists hold an intermediate opinion and can interact with extreme voters. When a centrist and a leftist/rightist interact, they can become either both centrists or both leftists/rightists. The population eventually either reaches consensus with one of the three opinions, or a polarization state consisting of a frozen mixture of leftists and rightists. As a main novelty, here agents interact subject to time-fluctuating external influences favouring in turn the spread of leftist and rightist opinions, or the rise of centrism. The fate of the population is determined under various scenarios, and it is shown how the rate of change of external influences can drastically affect the polarization and consensus probabilities, as well as the mean time to reach the final state. [ABSTRACT FROM AUTHOR]