Dynamic Fluctuations Research Articles

Experimental and optimization research of the rack thermal environment based on the dynamic server power

Dynamic fluctuations in server power consumption significantly impact the thermal environment within the rack and the efficiency of computer room air conditioners (CRACs), necessitating thorough analysis it for safe and effective thermal management. Drawing on field experiment, this study presents an overview of the range and characteristics of variations in rack power consumption within a data center, revealing that 74.3 % of racks display periodic changes in power consumption. The change cycle has a diurnal pattern, and the dynamic changes in rack power consumption depend on the number of servers, the amount of task processing, and the nature of the customers. Subsequently, we designed the rack thermal environment experimental platform to analyze the response characteristics of the rack thermal environment to server power consumption changes and the variation rules of the thermal environment with various server layouts. The results show that a shift in server power severely affects the rack outlet temperature and is accompanied by a specific delay phenomenon. The near heat source effect, thermal buoyancy, and top heat accumulation primarily affect and form the rack thermal environment. It is recommended to place the server in the middle of the rack and with a 1U or 2U gap or uniform arrangement to create a better thermal environment. Finally, we propose three methods for optimizing the rack's thermal environment. Compared to the use of blind plates, the installation of air inlet deflectors, rear baffles, and top outlets can collectively reduce the hot spot temperature by up to 1.3 °C, 2.1 °C, and 0.4 °C, with the supply heat index (SHI) optimized at 0.184, 0.142, and 0.13. The return temperature index (RTI) was optimized at 88.99 %, 88.76 %, and 92.5 %, respectively.

Data fusion integrated network forecasting scheme classifier (DFI-NFSC) via multi-layer perceptron decomposition architecture

The Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA, LSTM, and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway’s coverage zone.

Dynamical fluctuations of pions in pp collisions at different LHC energies: an in-depth analysis with factorial correlator

Dynamical fluctuations of pions in pp collisions at different LHC energies: an in-depth analysis with factorial correlator

ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions

Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was conducted in three distinct forested regions. Utilizing a deep learning model, the system analyzed and predicted daily carbon emissions data. The experiments demonstrated remarkable accuracy, with the model achieving a coefficient of determination (R2) of up to 0.94, 0.98, and 0.99 across datasets from all three regions, thereby justifying its use for forecasting carbon emission trends over the following months. Subsequently, the system employed natural language processing to assess the importance of various collected forest management strategies. Finally, the system fine-tuned these strategy combinations in response to the predicted carbon emission trends, ensuring flexibility and effectiveness in addressing the complex dynamics of carbon emission fluctuations.

Room-Temperature Solution Fluorescence Excitation Correlation Spectroscopy.

Single-molecule fluorescence spectroscopy is a powerful tool for investigating the physical properties of individual molecules, yet elucidating the fast fluctuation dynamics of freely diffusing single molecules in solution at room temperature, where a variety of chemical and biological processes occur, remains challenging. In this study, we report on fluorescence excitation correlation spectroscopy of room-temperature solutions, which enables the study of spontaneous fluctuation of the excitation spectrum with microsecond time resolution. By employing Fourier transform spectroscopy with broadband femtosecond pulses and time-correlated single-photon counting, we achieved fluorescence excitation spectroscopy of a room-temperature solution at the single-molecule level. Building upon this single-molecule measurement, we obtained an excitation wavelength-resolved fluorescence autocorrelation function in the microsecond to millisecond range, demonstrating the potential of this method to elucidate fast, spontaneous, time-dependent changes of excitation spectra in statistically equilibrated systems. With further development, this method will allow the study of spectral exchange associated with transitions between sub-ensembles of solution-phase molecules with unprecedented time resolution.

Exploring the diagnostic and prognostic significance of circulating tumor cells in stage II-IV colorectal cancer using a nano-based detection method.

Colorectal cancer (CRC) is a leading cause of cancer mortality globally, underscoring the urgency for a noninvasive and effective biomarker to enhance patient prognosis. Circulating tumor cells (CTCs), a potential marker for real-time tumor monitoring, are limited in clinical utility due to the low sensitivity of existing detection methods. Previously, we introduced a novel Nano-based CTCs detection method that relies on the electrical properties of cell surfaces, thus eliminating thereby obviating the need for specific molecular biomarkers. In this study, we employed this technique to evaluate the diagnostic and prognostic value of CTCs in stage II-IV CRC. A total of 194 participants were included, consisting of 136 CRC patients and 58 healthy individuals. The peripheral blood of the participants was collected, and CTCs enumeration was performed utilizing the Nano-based detection method that we newly developed. The Receiver Operating Characteristic (ROC) curve and multivariate Cox proportional-hazards analysis were employed to assess the effectiveness of CTCs for diagnosing CRC and predicting patient prognosis. The Nano-based method demonstrated an ability to differentiate CRC patients from healthy individuals with a sensitivity of 84.6% and a specificity of 94.8%. Furthermore, baseline CTCs levels were predictive of progression-free survival (PFS) in CRC patients, with lower levels associated with longer PFS compared to higher levels (4.5 months vs. 8.0 months at 15 CTCs/mL, p = 0.016; 4.4 months vs. 8.0 months at 20 CTCs/mL, p = 0.028). We also explored the dynamic changes in the number of CTCs after 1-5 cycles of chemotherapy. Patients with increasing CTCs levels typically experienced disease progression (PD), while those with decreasing levels often achieved a partial response (PR) or maintained stable disease (SD). These findings suggest that the dynamic fluctuations in CTCs counts are closely tied to the clinical course of the disease. Our study indicates the potential of Nano-based CTCs detection in diagnosing and predicting outcomes for patients with stage II-IV CRC.

Reducing dynamical fluctuations and enforcing self-averaging by opening many-body quantum systems

Reducing dynamical fluctuations and enforcing self-averaging by opening many-body quantum systems

Monitoring the dynamics of nanozeolite formation by combined in situ coherent small angle X-ray scattering techniques

Understanding the dynamics of zeolite formation is key to synthesising high-quality zeolitic materials with controllable properties, in order to develop more efficient and performant materials. X-ray photon correlation spectroscopy (XPCS) using coherent X-rays offers new possibilities for in situ observation of nano to micron-scale fluctuation dynamics during crystal growth. An in situ cell, which is capable of collecting time-resolved coherent X-ray scattering data under hydrothermal conditions has been developed and used to study, by in situ XPCS combined with small and wide angle X-ray scattering, zeolite formation and dynamics. Analysis of the results using two-time correlations enables to accurately identify the successive growth and crystallisation steps, revealing the dissolution process of the LTA topology during the SOD growth. This approach opens a powerful new avenue for studying the dynamics of nanomaterials formation, phase transitions and growth processes under in situ conditions that will enable profound insights into the nanoscale synthesis mechanisms.

Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for short. The PhyFill method employs a partial convolutional neural network technique to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous spatially daily soil moisture data on a global scale. Three validation strategies are employed: visual inspection through global pattern, simulated missing-region validation, and soil moisture validation with in situ measurements. The results indicated that the reconstructed soil moisture achieved a higher spatial coverage with satisfactory spatial continuity with neighbouring pixels. The simulated validation of the missing regions revealed that the averaged unbiased root mean square difference (ubRMSD) and correlation coefficient (R) were 0.01 m3/m3 and 0.99, respectively versus the gap filled SMAP product. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSD compared with the original SMAP soil moisture data (0.04 m3/m3vs. 0.04 m3/m3). The PhyFill method can generate globally continuous, high accurate soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.

Dynamical behavior of obligatory mutualistic-cheater interactions under the influence of white noise

Intraspecific mutualism is vital for ecosystems. Some interactions involve one species benefiting without reciprocating. Recent experiments suggest stable co-existence between mutualists and cheaters. This paper focuses on interactions between obligate mutualistic species and their cheaters, using a modified Lotka-Volterra model to analyze the negative impact of cheaters. We establish conditions for stability and verify a diffusive system's stability under spatial effects. Additionally, we study population dynamics fluctuations in the presence of Gaussian noise.

Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning

The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scale-specific models. To bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh–Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics. Training and testing data are generated by conducting mDPD and RP simulations for nonlinear bubble dynamics with initial bubble radii ranging from 0.1 to 1.5 micrometers. The results show that the trained composite neural operator model can accurately predict bubble dynamics across scales, with a 99% predictive accuracy for the time evolution of the bubble radius under varying external pressure while containing correct size-dependent stochastic fluctuations in microscale bubble growth dynamics. The composite neural operator is the first deep learning surrogate for multiscale bubble growth dynamics that can capture correct stochastic fluctuations in microscopic fluid phenomena, which sets a new direction for future research in multiscale fluid dynamics modeling.

Health, safety and environmental risk assessment tool applied to site selection for geological hydrogen storage in saline aquifers

Hydrogen (H2) emerges as a pivotal player in the transition to renewable energy sources. To address the seasonal fluctuations in energy dynamics, underground storage emerges as the most efficient approach, and saline aquifers within sedimentary basins as promising repositories for substantial H2 volumes. However, this potential storage solution remains relatively unexplored. Conducting a comprehensive health, safety, and environment (HSE) risk assessment is vital for the technological advancement and public acceptance of geological H2 storage sites. This study introduces a methodology designed to select and classify potential formations based on their HSE risks, offering a safety-oriented approach to identifying suitable storage sites. The proposed methodology underwent testing in two deep saline aquifers located in distinct geological contexts within the Iberian Peninsula. The aim of this study is to establish a foundation for further investigations and implementation of geological H2 storage sites while ensuring safety and adhering to environmental and health standards.

Inference on the Macroscopic Dynamics of Spiking Neurons.

The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying the interactions of neurons. Estimating parameters of this class of generative model allows one to predict the system's dynamics and responses under changing inputs and, indeed, changing parameters. We first assume a set of known state-space equationsand address the problem of inferring the lumped parameters from observed time series. Crucially, we consider this problem in the setting of bistability, random fluctuations in system dynamics, and partial observations, in which some states are hidden. To identify the most efficient estimation or inversion scheme in this particular system identification, we benchmark against state-of-the-art optimization and Bayesian estimation algorithms, highlighting their strengths and weaknesses. Additionally, we explore how well the statistical relationships between parameters are maintained across different scales. We found that deep neural density estimators outperform other algorithms in the inversion scheme, despite potentially resulting in overestimated uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. We then eschew the mean-field approximation and employ deep neural ODEs on spiking neurons, illustrating prediction of system dynamics and vector fields from microscopic states. Overall, this study affords an opportunity to predict brain dynamics and responses to various perturbations or pharmacological interventions using deep neural networks.

Computational Insights for Interactions between nsP2 and nsP3 of CHIKV and Hormones through DFT Computations and Molecular Dynamics Simulations.

The non-structural protein (nsP2 & nsP3) of the Chikungunya virus (CHIKV) is responsible for the transmission of viral infection. The main role of non-structural proteins are involved in the transcription process at an early stage of the infection. In this work, authors have studied the impact of nsP2 and nsP3 of CHIKV on hormones present in the human body using a computational approach. The ten hormones of chemical properties such as 4-Androsterone-2,17-dione, aldosterone, androsterone, corticosterone, cortisol, cortisone, estradiol, estrone, progesterone and testosterone were taken as a potency. From the molecular docking, the binding energy of the complexes is estimated, and cortisone was found to be the highest negative binding energy (-6.57 kcal/mol) with the nsP2 and corticosterone with the nsP3 (-6.47 kcal/mol). This is based on the interactions between hormones and nsP2/nsP3, which are types of noncovalent intermolecular interactions categorized into three types: electrostatic interactions, van der Waals (vdW) interactions, and hydrogen-bonding (H-bonding) interactions. To validate the docking results, additional molecular dynamics simulations and MM-GBSA methods were performed. The change in enthalpy, entropy, and free energy were calculated using MM-GBSA methods. The nsP2 and nsP3 of CHIKV interact strongly with the cortisone and corticosterone with free energy changes of -20.55 & -36.08 kcal/mol, respectively. Methods: The crystal structures of 3TKR and 3GPO proteins of nsP2 and nsP3 were extracted from the RCSB Protein Data Bank. Initially, unnecessary atoms like extra cations or anions and missing explicit hydrogen atoms were removed and added from the native domain of nsP2 and nsP3. The alignment of coordinated in the native domain was performed using Chimera and Notepad++ tools. The molecular docking of protein and ligand was performed usingAutoDock tool; it is essential for the prediction of the orientation of the ligand into the cavity of the target protein based on binding affinity. Based on thermodynamic parameters, MD Simulations were employed to calculate the change in binding free energies of various complexes followed by a change in enthalpy and entropy with time. According to MD production, the CPPTAJ and PTRAJ programs were used to analyse the trajectories, such as dynamic stability (RMSD), residual fluctuation (RMSF), compatibility, and hydrogen bonds of the newly formed complexes. After that, the Density Functional Theory (DFT) were used to calculate the electronic properties of selected molecules by Gaussian 16 on applying the B3LYP method with the 6-311G (d, p) basis set.

Single-file dynamics with general charge measures.

We study charge fluctuations in single-file dynamics with general charge measures. The exact finite-time distribution of charge fluctuations is obtained in terms of a dressing transformation acting on the finite-time distribution of particle fluctuations. The transformation is mapped to a simple substitution rule for corresponding full-counting statistics. By taking the asymptotics of the dressing transformation, we analyze typical and large scale charge fluctuations. Typical charge fluctuations in equilibrium states with vanishing mean charge are anomalous, while large charge fluctuations undergo first and second order dynamical phase transitions out of equilibrium.

Dynamic fluctuations of current and mass in nonequilibrium mass transport processes

We study steady-state dynamic fluctuations of current and mass in several variants of random average processes on a ring of L sites. These processes violate detailed balance in the bulk and have nontrivial spatial structures: their steady states are not described by the Boltzmann–Gibbs distribution and can have nonzero spatial correlations. Using a microscopic approach, we exactly calculate the second cumulants, or the variance, ⟨Qi2(T)⟩c and ⟨Qsub2(l,T)⟩c , of cumulative (time-integrated) currents up to time T across the ith bond and across a subsystem of size l (summed over bonds in the subsystem), respectively. We also calculate the (two-point) dynamic correlation function of the subsystem mass. In particular, we show that, for large L≫1 , the second cumulant ⟨Qi2(T)⟩c of the cumulative current up to time T across the ith bond grows linearly as ⟨Qi2⟩c∼T for initial times T∼O(1) , subdiffusively as ⟨Qi2⟩c∼T1/2 for intermediate times 1≪T≪L2 , and then again linearly as ⟨Qi2⟩c∼T for long times T≫L2 . The scaled cumulant liml→∞,T→∞⟨Qsub2(l,T)⟩c/2lT of current across the subsystem of size l and up to time T converges to the density-dependent particle mobility χ(ρ) when the large subsystem-size limit is taken first, followed by the large-time limit; when the limits are reversed, it simply vanishes. Remarkably, regardless of the dynamical rules, the scaled current cumulant D⟨Qi2(T)⟩c/2χL≡W(y) as a function of scaled time y=DT/L2 can be expressed in terms of a universal scaling function W(y) , where D is the bulk-diffusion coefficient; interestingly, the intermediate-time subdiffusive and long-time diffusive growths can be connected through a single scaling function W(y) . The power spectra for current and mass are also exactly characterized by the respective scaling functions. Furthermore, we provide a microscopic derivation of equilibrium-like Green–Kubo and Einstein relations that connect the steady-state current fluctuations to an ‘operational’ mobility (i.e. the response to an external force field) and mass fluctuation, respectively.

Developing Flexible Carbon Nitride Quantum Dots Decorated Polyaniline Nanocomposite Layer for a Non-Invasive Wearable Sweat Biosensor for Glucose Monitoring

Understanding dynamic biomarker fluctuations, particularly glucose, is vital for early diabetes therapy. Novel non-invasive wearable sweat biosensors are gaining appeal, enabling constant severity monitoring. Nevertheless, the key challenges are that multiplexed motions may result in undesirable metal cracking by the rigid electrocatalytic layer, leading to decreased sensitivity and stability. In this work, we designed carbon nitride quantum dots (CNQDs) decorated polyaniline (PANI) nanocomposite to realize flexible wearable biosensors with high detection accuracy. Upon the enhanced electron transfer by CNQDs, CNQDs/PANI nanocomposite offers greater sensitivity in H2O2 detection compared to pristine PANI, resulting in a sensitivity of 37.21 μA mM-1 cm-2. Therefore, after the glucose oxidase (GOx) immobilization, GOx/CNQDs/PANI-based biosensors is expected to show excellent performance for glucose detection in artificial sweat. Figure 1

Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

Abstract Parallel-phase processing enables rapid phase extraction from off-axis digital holograms. To achieve fast and accurate results, the phase reconstruction processes were parallelized using improved filter algorithms and optimized programming strategies. First, an adaptive filtering method based on the Chan-Vese (CV) model which better suits parallelism was designed to extract the +1 term spectrum. We selected suitable computer unified device architecture (CUDA) libraries according to the characteristics of the key phase reconstruction steps. Acceleration technologies, such as virtual memory and shared memory, were used to improve the computational efficiency. Furthermore, we combined an improved 4f optical imaging system with an embedded graphic processing unit (GPU) platform to design a low-cost phase reconstruction system for off-axis digital holography. To verify the feasibility of our method, the reconstructed quality of the CV filtering method was estimated, and the run times of phase retrieval on the central processing unit (CPU) and embedded GPU were compared for off-axis holograms with different pixel sizes. Additionally, the dynamic fluctuation phase maps of water droplet evaporation were retrieved to demonstrate the real-time capability of the method.

Dynamical large deviations for long-range interacting inhomogeneous systems without collective effects.

We consider the long-term evolution of a spatially inhomogeneous long-range interacting N-body system. Placing ourselves in the dynamically hot limit, i.e., assuming that the system only weakly amplifies perturbations, we derive a large deviation principle for the system's empirical angle-averaged distribution function. This result extends the classical ensemble-averaged kinetic theory given by the so-called inhomogeneous Landau equation, as it specifies the probability of typical and large dynamical fluctuations. We detail the main properties of the associated large deviation Hamiltonian, particularly focusing on how it complies with the system's conservation laws and possesses a gradient structure.

Orthogonal Persulfide Generation through Precision Tools Provides Insights into Mitochondrial Sulfane Sulfur.

The sulfane sulfur pool, comprised of persulfide (RS-SH) and polysulfide (RS-SnH) derived from hydrogen sulfide (H2S), has emerged as a major player in redox biochemistry. Mitochondria, besides energy generation, serve as significant cellular redox hubs, mediate stress response and cellular health. However, the effects of endogenous mitochondrial sulfane sulfur (MSS) remain largely uncharacterized as compared with their cytosolic counterparts, cytosolic sulfane sulfur (CSS). To investigate this, we designed a novel artificial substrate for mitochondrial 3-mercaptopyruvate sulfurtransferase (3-MST), a key enzyme involved in MSS biosynthesis. Using cells expressing a mitochondrion-localized persulfide biosensor, we demonstrate this tool's ability to selectively enhance MSS. While H2S was previously known to suppress human immunodeficiency virus (HIV-1), we found that MSS profoundly affected the HIV-1 life cycle, mediating viral reactivation from latency. Additionally, we provide evidence for the role of the host's mitochondrial redox state, membrane potential, apoptosis, and respiration rates in managing HIV-1 latency and reactivation. Together, dynamic fluctuations in the MSS pool have a significant and possibly conflicting effect on HIV-1 viral latency. The precision tools developed herein allow for orthogonal generation of persulfide within both mitochondria and the cytosol and will be useful in interrogating disease biology.