Assumption Of Balance Research Articles

Evaluation of Population Projections for Serbia 2011-2041 after the 2022 Census Results

Following the 2022 Population Census, the first assessment of Serbia's official population projections based on data from the 2011 Census and other vital statistics and migration sources has become feasible. According to the Census results, the population of Serbia in 2022 is approximately 2.3% below the projected values for that year, according to the closest projection variant – the constant variant. This paper explores the differences in terms of projected and realized values of fertility, mortality, and migration balance. Hypotheses regarding the total fertility rate and the number of live births were largely confirmed. However, a notable deviation was observed in mortality rates, primarily attributed to the COVID-19 pandemic period, during which life expectancy decreased by nearly two years. Apart from the pandemic's effect, the deviation between projected and realized population values is also due to the assumption of a positive migration balance for the period 2011-2022. Contrary to this optimistic assumption, the vital statistics method reveals negative external migration during this period.

Parameterization of Vertical Turbulent Transport in the Inner Core of Tropical Cyclones and Its Impact on Storm Intensification. Part II: Understanding TC Intensification in a Generalized Sawyer–Eliassen Diagnostic Framework

Abstract An analytical method for diagnosing the interaction between the primary and secondary circulations of a tropical cyclone (TC) and vortex intensification is developed. It includes a diagnostic equation describing the mean secondary circulation of a TC in an unbalanced framework by including the radial eddy forcing in the analytical system. It is an extension of the Sawyer–Eliassen equation (SEE) developed from the strict gradient-wind balance. This generalized SEE (GSEE) remediates some of the limitations of SEE and can be used to diagnose both balanced and unbalanced dynamical processes during the TC evolution. Using GSEE, this study investigates how the tangential and radial eddy forcing affects the TC intensification simulated by the Hurricane Weather Research and Forecasting Model (HWRF) with differently parameterized turbulent mixing. The diagnostic results show that the supergradient component of radial eddy forcing contributes positively to the acceleration of the peak tangential wind, whereas the subgradient component of the radial eddy forcing tends to lower the height of peak tangential wind. The relative importance of negative and positive effects of tangential eddy forcing on TC intensification varies depending on the details of turbulence parameterization. For a turbulent kinetic energy (TKE) scheme used in this study, a large sloping curvature of mixing length in the low troposphere causes the tangential eddy forcing to produce a net positive tangential wind tendency near the location of the peak tangential wind. In contrast, a small sloping curvature of mixing length generates a net negative tangential wind tendency at the peak tangential wind. Significance Statement The interaction between the primary and secondary circulations of a tropical cyclone (TC) plays a key role in TC evolution. Historically, the secondary circulation induced by turbulence and convection is often described by a so-called Sawyer–Eliassen equation (SEE). While SEE has provided much insight into the TC dynamics in the past, the assumption of gradient-wind balance used by SEE prevents it from understanding TC unbalanced dynamics. To remediate the limitation, we extended the analytical framework into the unbalanced regime by including radial eddy forcing in the analytical system and derived a generalized SEE (GSEE). Using GSEE, this study investigates how tangential and radial eddy forcing affects TC intensification. The result highlights the importance of multiple roles that turbulence plays in the intensification of TCs.

A path-dependent stress-strain model of FRP-confined circular concrete columns derived from an energy balance approach

It is generally believed that the use of fibre-reinforced polymer (FRP) confinement can effectively improve the strength and ductility of concrete. Passive confinement is one of the key features of the behavior of FRP-confined concrete. Analysis-oriented models for passively confined concrete have been established on the base of the active confinement model by many researchers. Among them, a stress path-independent assumption was made for those passively confined concrete. However, in recent years, numerous researchers have found that the influence of stress path on the stress-strain relationship of confined concrete cannot be neglected. Therefore, in order to further understand and simulate the uniaxial behavior of FRP-confined concrete, this paper presents a stress path-dependent model of FRP-confined concrete based on an energy balance approach in which the extra work done by the active confining pressure in the stress path-independent model is considered. Firstly, experimental data comprising 341 sets of FRP jacket confined concrete tests and 28 sets of concrete-filled FRP tube (CFFT) tests were collected. Subsequently, the stress paths of the two types of specimens were identified based on the properties of FRP materials, and then the energy balance assumption was introduced, and finally a new stress path-dependent model was established using the energy balance approach. By comparing the predictions of the proposed model with collected experimental data and those of several existing models, it was observed that the proposed model can more accurately predict the ultimate stress of FRP-confined concrete and exhibit good agreement with the experimental stress-strain curves.

Leveraging GANs data augmentation for imbalanced medical image classification

Machine learning algorithms have been widely applied and researched in the field of medical image classification. Most of the current algorithms are designed based on the assumption of data balance. However, in practical applications, medical data often face the problem of imbalanced data. The traditional data augmentation approaches can balance the original data, but they are unable to generate more effective features. Generative Adversarial Networks (GANs) can effectively perform sample augmentation. However, GAN often encounter issues such as intra-class mode collapse and generating noisy samples when faced with imbalanced data. This study proposes a GAN model called IBGAN that focuses on generating intra-class sparse samples and class boundary samples. Specifically, IBGAN addresses the issue of imbalanced data through two stages. In the first stage, the Isolation Forest (iForest) algorithm and boundary sample detection algorithm are designed to identify sparse region samples and boundary samples within the intra-class data. This enables the GAN model to effectively focus on generating such samples during training. The second stage involves the refinement process of the samples. We propose a sample evaluation method based on Support Vector Data Description (SVDD) to filter out noisy data in the generated samples, ensuring the quality of the generated data. We conducted extensive and in-depth experiments on five real-world datasets. The experimental results demonstrate that IBGAN can generate high-quality and diverse augmented samples, which contribute to the improvement of classifier performance. We compared our proposed method with seven baseline methods, including traditional approaches and classic GAN models. The experimental results, including the visualization of the generated data and the evaluation of classification performance, consistently show that our proposed method achieves more competitive results.

Feasibility and acceptability for LION, a fully remote, randomized clinical trial within the VA for light therapy to improve sleep in Veterans with and without TBI: An MTBI2 sponsored protocol.

Sleep-wake disturbances frequently present in Veterans with mild traumatic brain injury (mTBI). These TBI-related sleep impairments confer significant burden and commonly exacerbate other functional impairments. Therapies to improve sleep following mTBI are limited and studies in Veterans are even more scarce. In our previous pilot work, morning bright light therapy (MBLT) was found to be a feasible behavioral sleep intervention in Veterans with a history of mTBI; however, this was single-arm, open-label, and non-randomized, and therefore was not intended to establish efficacy. The present study, LION (light vs ion therapy) extends this preliminary work as a fully powered, sham-controlled, participant-masked randomized controlled trial (NCT03968874), implemented as fully remote within the VA (target n=120 complete). Randomization at 2:1 allocation ratio to: 1) active: MBLT (n=80), and 2) sham: deactivated negative ion generator (n=40); each with identical engagement parameters (60-min duration; within 2-hrs of waking; daily over 28-day duration). Participant masking via deception balanced expectancy assumptions across arms. Outcome measures were assessed following a 14-day baseline (pre-intervention), following 28-days of device engagement (post-intervention), and 28-days after the post-intervention assessment (follow-up). Primary outcomes were sleep measures, including continuous wrist-based actigraphy, self-report, and daily sleep dairy entries. Secondary/exploratory outcomes included cognition, mood, quality of life, circadian rhythm via dim light melatonin onset, and biofluid-based biomarkers. Participant drop out occurred in <10% of those enrolled, incomplete/missing data was present in <15% of key outcome variables, and overall fidelity adherence to the intervention was >85%, collectively establishing feasibility and acceptability for MBLT in Veterans with mTBI.

Nature of the diffuse emission sources in the H i supershell in the galaxy IC 1613

ABSTRACT We present a study of the nearby low-metallicity dwarf galaxy IC 1613, focusing on the search for massive stars and related feedback processes, as well as for faint supernova remnants (SNR) in late stages of evolution. We obtained the deepest images of IC 1613 in the narrow-band H α, He ii and [S ii] emission lines and new long-slit spectroscopy observations using several facilities (6-m BTA, 2.5m SAI MSU, and 150RTT telescopes), in combination with the multiwavelength archival data from MUSE/VLT, VLA, XMM–Newton, and Swift/XRT. Our deep narrow-band photometry identifies several faint shells in the galaxy, and we further investigate their physical characteristics with the new long-slit spectroscopy observations and the archival multiwavelength data. Based on energy balance calculations and assumptions about their possible nature, we propose that one of the shells is a possible remnant of a supernova explosion. We study five out of eight Wolf–Rayet (WR) star candidates previously published for this galaxy using the He ii emission line mapping, MUSE/VLT archival spectra, and new long-slit spectra. Our analysis discards the considered WR candidates and finds no new ones. We found P Cyg profiles in H α line in two stars, which we classify as Luminous Blue Variable (LBV) star candidates. Overall, the galaxy IC 1613 may have a lower rate of WR star formation than previously suggested.

A neoclassical validation of balanced and unbalanced rotations on EAST H-mode discharges

Predicting residual stress (RS) contribution to intrinsic rotation is one of the major challenges in the study of momentum transport in tokamaks. One efficient experimental means of quantifying RS torque magnitude is to generate radially-flat and near-zero rotation profiles, termed ‘balanced rotations’ in this work, using counter-Ip Neutral Beam Injections (NBIs) to effectively cancel the torques from co-Ip NBIs. One remaining question, however, is on whether or not the attained velocity profile is well zeroed and flat enough so that the predicted RS torques based on perfect balance assumption can be used for further studies such as fitting of diffusive and convective coefficients to match with experiments. This article presents a neoclassical means of validating the attained balanced and unbalanced rotations at EAST to consequently validate the predicted RS torque profiles, using TRANSP/NUBEAM and a recently-developed neoclassical rotation/transport code TransROTA (Bae et al 2024 Comput. Phys. Commun. 296 108992). Both balanced and unbalanced EAST H-mode discharges are analyzed to find that the suggested neoclassical validation methodology successfully validates attained balanced rotations. It also finds that neoclassical gyroviscous (NGV) torque serves as the balance-breaking mechanism for axisymmetric plasmas and the balance-breakings start from the core and propagate towards the edge. This work also suggests the possibility of using the neoclassical methodology to find locally-balanced rotations at an elevated velocity range near ∼20 km s−1, which implies possible scaling of RS torques up to a certain elevated velocity range.

Robust semi-supervised classification for imbalanced and incomplete data

Self-training semi-supervised classification has grown in popularity as a research topic. However, when faced with several challenges including outliers, imbalanced class, and incomplete data in reality, the traditional self-training semi-supervised methods might adversely damage the classification accuracy. In this research, we develop a two-step robust semi-supervised self-training classification algorithm that works with imbalanced and incomplete data. The proposed method varies from traditional self-training semi-supervised methods in three major ways: (1) The method in this paper does not necessitate the balance and complete assumption in traditional semi-supervised self-training methods, since it can complete and rebalance the dataset simultaneously. (2) This method is compatible with many classifiers, so it can handle multi-classification and non-linear classification cases. (3) The classifier in this paper is resistant to outliers during semi-supervised classification. Furthermore, several numerical simulations were performed in this research to illustrate the quality of our method to synthesized data, as well as multiple experiments to demonstrate our method superior classification performance on various real datasets.

Brazilian Water Security Threatened by Climate Change and Human Behavior

AbstractWater scarcity is a growing concern globally, with climate change and increasing population exacerbating the issue. Here, we introduce a new framework for assessing water availability in 708 Brazilian catchments that considers the effect of CO2 concentrations on potential evapotranspiration, uses CMIP6 bias‐corrected climate change simulations, and presumes an open water balance assumption, while considering the human‐aspect by incorporating water demand projections. We note an average reduction of water security in 81% of the analyzed catchments by 2100. Among these catchments, 37% presented a reduction of future water availability, while 63% undergo a worse scenario due to an increase in human water use, which highlights the role of the human aspect in water security assessment. Our study shows important aspects for both advancing future water availability studies and for drawing a picture of the impacts of changes in climate and water use on Brazilian future water security that may be useful for water resources management practices and advancing hydrologic studies.

Bipartite synchronization of stochastic Markov jump coupled systems via completely aperiodically intermittent discrete-time state observation control

This paper investigates the bipartite synchronization of stochastic Markov jump coupled systems (SMJCSs) on signed graphs via completely aperiodically intermittent discrete-time state observation control (CAIDC). It is the first time that CAIDC, as a new hybrid control, is designed. And it removes the constraints of quasi-periodicity compared with intermittent discrete-time state observation control. A structurally balanced signed network model is constructed where agents can be separated into two subgroups to describe signed networks mathematically and present the competing relationship. Based on the assumption of structural balance, the main results are obtained for bipartite synchronization of SMJCSs utilizing graph theory and Lyapunov method. As an application, coupled Chua’s circuits with Markov jump are elaborated and several numerical simulations are presented to verify the feasibility and effectiveness of the proposed results.

Generalized gradient structures for measure-valued population dynamics and their large-population limit

We consider the forward Kolmogorov equation corresponding to measure-valued processes stemming from a class of interacting particle systems in population dynamics, including variations of the Bolker–Pacala–Dieckmann-Law model. Under the assumption of detailed balance, we provide a rigorous generalized gradient structure, incorporating the fluxes arising from the birth and death of the particles. Moreover, in the large population limit, we show convergence of the forward Kolmogorov equation to a Liouville equation, which is a transport equation associated with the mean-field limit of the underlying process. In addition, we show convergence of the corresponding gradient structures in the sense of Energy-Dissipation Principles, from which we establish a propagation of chaos result for the particle system and derive a generalized gradient-flow formulation for the mean-field limit.

An active learning-based incremental deep-broad learning algorithm for unbalanced time series prediction

Time series are a kind of streaming data, which are chaotic and sequential. As real-world time series data are often not available at once and drift with time growth, Incremental Learning (IL) is well suited for Time Series Prediction (TSP). Most previous incremental TSP algorithms are limited by the assumption of data balance. However, real-world time series data are often unbalanced, with long-tailed distribution and other characteristics resulting in the failure of IL algorithms. In this paper, a balanced-driven Active Learning (AL) strategy is proposed to deal with data imbalance problems in IL processes. What’s more, by integrating the advantages of Deep Learning (DL) and the Broad Learning System (BLS), a novel Deep-Broad Learning (DeepBL) network with its incremental learning algorithm is proposed. The proposed Active Learning-based Incremental Deep-Broad Learning (AI_DeepBL) algorithm is applied to real-world univariate and multivariate time series datasets and achieves superior performance compared with classical and state-of-the-art TSP algorithms.

Quasi-elastodynamic Processes Involved in the Interaction between Solar Wind and Magnetosphere

The interaction between the solar wind and the magnetosphere is one of the most important research subjects in the fields of astrophysics and space physics. For more than half a century, based on the pressure balance assumption between the solar wind and the magnetosphere and considering other important factors, such as the interplanetary magnetic field and magnetic reconnection process, the dynamic processes at the magnetopause have been extensively analyzed. However, the responses of magnetopause to the solar wind dynamic pressure variations are still complicated to understand. Here, we show that the interaction between the solar wind and the magnetosphere can be regarded as a quasi-elastodynamic process. The driving frequency of the solar wind is determined as a crucial reason for the phase difference between solar wind dynamic pressure variations and magnetopause standoff distance. The low-pass filter effect and oscillation properties of the magnetopause can also be well explained by the forced damped vibrations. Moreover, the quasi-elastodynamic processes predict deformations at the magnetopause, which resemble the magnetopause surface wave. Finally, a three-dimensional time-dependent magnetopause model is constructed and verified by observation. Based on 12,242 magnetopause crossing events, it is found that the new model reveals ∼9.7% better prediction accuracy than the widely used time-independent model. These results can also shed light on our understanding of the solar-wind–magnetopause interaction for other planets.

Estimation of Turbulent Proton and Electron Heating Rates via Landau Damping Constrained by Parker Solar Probe Observations

The heating of ions and electrons due to turbulent dissipation plays a crucial role in the thermodynamics of the solar wind and other plasma environments. Using magnetic field and thermal plasma observations from the first two perihelia of the Parker Solar Probe, we model the relative heating rates as a function of the radial distance, magnetic spectra, and plasma conditions, enabling us to better characterize the thermodynamics of the inner heliosphere. We employ the Howes et al. steady-state cascade model, which considers the behavior of turbulent, low-frequency, wavevector-anisotropic, critically balanced Alfvénic fluctuations that dissipate via Landau damping to determine proton-to-electron heating rates Q p /Q e . We distinguish ion cyclotron frequency circularly polarized waves from low-frequency turbulence and constrain the cascade model using spectra constructed from the latter. We find that the model accurately describes the observed energy spectrum from over 39.4% of the intervals from Encounters 1 and 2, indicating the possibility for Landau damping to heat the young solar wind. The ability of the model to describe the observed turbulent spectra increases with the ratio of thermal-to-magnetic pressure, β p , indicating that the model contains the necessary physics at higher β p . We estimate high magnitudes for the Kolmogorov constant which is inversely proportional to the nonlinear energy cascade rate. We verify the expected strong dependency of Q p /Q e on β p and the consistency of the critical balance assumption.

A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful achievements and significantly benefited industry practices. However, most methods are developed based on the assumption of data balance, which is incompatible with engineering scenarios. First, the normal state accounts for the majority of the equipment’s lifespan; second, the probability of various faults varies, both of which result in an imbalance in the data. The consequence of data imbalance in intelligent fault diagnosis methods has attracted extensive attention from the research community, and a significant number of papers have been published. Nevertheless, a comprehensive review of achievements in this field is still missing, and the research perspectives have not been thoroughly investigated. To end this, we review and discuss all the research achievements in fault diagnosis under data imbalance in this paper, based on our best knowledge. First, the existing imbalanced learning methods are classified into three categories: data processing methods, model construction methods, and training optimization methods. Then, the three methodologies are introduced and discussed in detail: the data processing method is to optimize the inputs of the intelligent fault diagnosis model so that the imbalance rate of the sample set involved in training is reduced; the model construction method is to design the structure and the features of the intelligent fault diagnosis model so that the model itself is resistant to the effects of imbalance; the training optimization method is an optimisation of the training process for intelligent fault diagnosis models, raising the importance of the minority class in the training. Finally, this paper summarizes the prospects of the imbalanced learning problem in intelligent fault diagnosis, discusses the possible solutions, and provides some recommendations.

An online continual object detector on VHR remote sensing images with class imbalance

It is a great challenge for traditional offline detectors to learn from continuous data streams, remember previous tasks and adapt to new-coming tasks in dynamic environments. To meet the challenge, online continual learning has recently attracted increasing attention, while the overwhelming majority of works focus only on classification with a balanced class distribution assumption. In this paper, we propose a replay-based approach called an online continual object detector (OCOD) for very-high-resolution (VHR) remote sensing images. First, we find that rehearsal imbalance is ubiquitous, and has more important impact on experimental results than class imbalance, which is contrary to the situation of offline learning (due to the limited memory). Here, rehearsal imbalance refers to significant difference among the number of images pertaining to various classes. Second, entropy is used to measure the degree of rehearsal imbalance in the memory, and an entropy reservoir sampling (ERS) strategy is proposed to maintain rehearsal balance in the online memory. Finally, a rehearsal-balancing priority assignment network (RBPAN) is proposed to adaptively select images from the memory for a rehearsal-balancing replay procedure. The experimental results obtained on three publicly available VHR satellite images from the NWPU VHR-10, DIOR and DOTA datasets, highlight the effectiveness and practicality of developed method.

Seebeck effect in a nanometer-scale dot in a Si nanowire observed with electron counting statistics

We performed electron counting statistics to measure the thermoelectric effect in a nanometer-scale silicon dot. To separate the 100-nm-long dot from a silicon nanowire, we used an electrostatically created 30-nm-long energy barrier. By measuring current through a nearby sensor, we observed the random motion of single electrons between the dot and silicon nanowire. The statistics of single-electron motion provides us with information on temperature and voltage at the dot. Under the detailed balance assumption, we determined the temperature difference and the Seebeck voltage between the dot and silicon nanowire. The validity of our analysis was confirmed by observing the energy-barrier height dependence of the Seebeck coefficient. Furthermore, by counting the electrons leaving the dot, a minute output power on the order of sub-zeptowatt from the dot to the silicon nanowire was estimated.

The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long‐Term Streamflow Components

AbstractUnderstanding how streamflow and its components, baseflow and quickflow, vary spatially according to climate and landscape characteristics is fundamental for dealing with different water‐related issues. Analytical formulations have been proposed to investigate their long‐term behavior and additional influencing factors, suggesting that they are mainly controlled by the aridity index (). Nevertheless, these studies assume the catchment as a closed water balance system, neglecting inter‐catchment groundwater flow (IGF). This simplification makes the analysis of the long‐term streamflow components and their main control mechanisms challenging, given that many catchments cannot be considered as closed hydrologic entities. Here, we assessed the controls of the mean‐annual streamflow components and their behavior under an open water balance assumption, using observed data of 734 Brazilian catchments with diverse hydroclimatic conditions. Our results indicate that indeed streamflow components are primarily controlled by at the mean annual timescale. The consideration of an open water‐balance significantly improved the performance of aridity‐based functional forms to describe streamflow components while also elucidating how catchments' attributes may influence streamflow behavior. Land cover, groundwater, climate seasonality, and topographic attributes appeared as the main control mechanisms besides aridity. Overall, our study provides new insights into the main control mechanism of the streamflow behavior at the long‐term scale, while shedding light on the importance of the open water‐balance assumption for model development and water resources management.

Constraints on horizonless objects after the EHT observation of Sagittarius A*

The images of Sagittarius A* recently released by the Event Horizon Telescope collaboration have been accompanied [ Astrophys. J. Lett. 930 (2022) L17]by an analysis of the constraints on the possible absence of a trapping horizon, i.e. on the possibility that the object at the center of our galaxy is an ultra-compact object with a surface re-emitting incident radiation. Using the observed image size and the broadband spectrum of Sgr A*, it is claimed that the radius of any surface, in which incident radiation is re-emitted thermally, is strongly bounded from above by these latest observations. Herein, we discuss how the reported constraint relies on the extremely strong assumption of perfect balance in the energy exchange between the accretion disk and the central object, and show that this is violated whenever the surface is endowed with any non-zero absorption coefficient. We derive the upper-bound constraints that can be cast on the radius and dimensionless absorption coefficient of the surface. We show that the conclusions of the analysis presented by the EHT collaboration hold only for unnaturally small values of the absorption coefficient (i.e. much lower than 10-14), and thus have to be significantly revised in scenarios with physical significance.

Unsteady heat transfer in a multilayer composite cylinder containing porous media

The influence of heating equipment with low thermal conductivity, such as vacuum drying equipment and electric heating atomization systems, on the temperature distribution of heated objects is vital for structure design. The transient temperature of small heating equipment is difficult to obtain, particularly when the geometric size of the equipment is less than 0.1 m. Based on Fourier’s law, a heat transfer model for a multilayer composite cylinder with porous media is established to solve this problem. The heat transfer of the porous media is simplified by the local heat balance assumption. An experiment is designed to validate the reliability of the model. The results of this study indicated that the temperature difference between the inner and outer parts of the composite cylinder with thermal conductivity is noticeable when heated by a centralized heat source. When the temperature of the centralized heat source is approximately 190 °C, the temperature difference between the inner and outer parts of the porous medium layer in the composite cylinder is approximately 50 °C. Moreover, when the temperature of the outer part of the porous medium layer in the composite cylinder is approximately 50 ℃, the temperature increased by approximately 1 °C for every 10 % decrease in the porosity. The centralized heat source cannot be used to uniformly heat a material with low heat transfer performance. Increasing the equivalent thermal conductivity of porous media materials or changing the heating structure can effectively improve this problem. Furthermore, the temperature distribution of the composite cylinder can be easily controlled by adjusting the temperatures of the surrounding and centralized heat sources. This study proposes a simple and accurate unsteady temperature prediction method for composite cylinders containing porous media. The temperature of the heating structure and the surrounding environment of the composite cylinder are obtained through simple experiments, and a theoretical model is used to predict the transient temperature of the composite cylinder during the heating process. Moreover, the influence of various factors affecting the heat source and heated object on the unsteady temperature of the composite cylinder with porous media is clarified. This new technology can solve the difficulty in measuring the temperature field of heating structures and heated objects in small heating equipment. This work can aid in the heat source design of small heating equipment.