Classical Time Research Articles

Pergamos. An unexplored fortified site in north-eastern Greece

The aim of this article is to present the first results of a project investigating the hitherto unexplored site of Pergamos, located on a hill known as Koules/Alonaki just to the south of modern Moustheni in the Pieria valley. The site has a long history, with the earliest traces of human activity dating to the Late Bronze Age and the first visible architectural remains—impressive and well-preserved fortifications built in a stack-work technique—originating in the Late Archaic period. Following this, surface finds testify to activity and habitation throughout the centuries until a substantial strengthening of the defensive walls during Late Roman or medieval times, before the site was abandoned at a currently unknown point. To achieve the aim the article begins with an introduction to the site and its surrounding landscape. Following this, the results of a survey of the visible remains are presented, focusing on the fortifications and two architectural fragments. The site is then discussed within its spatial and chronological context, suggesting that Pergamos was an important settlement in the area, exhibiting traits of a polis, in particular during Archaic and Classical times before it fell within the sphere of influence of the Roman colony Philippi.

Data-Supported Prediction of Surface Settlement Behavior on Opencast Mine Dumps Using Satellite-Based Radar Interferometry Observations

To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. This dual approach—leveraging advanced data analysis tools and precise modeling—yields valuable insights into spatial settlement behavior. In particular, classic time–settlement models are applied to the InSAR data through least square regression and Taylor approximation. The integration of both approaches enables the more robust, data-validated forecasts of key geotechnical indicators, such as the time to settlement stabilization and the expected maximum settlement over large areas. An application at a mine in central Germany illustrates the method by generating spatial predictions of the settlement behavior over more than 200 ha. In general, the results provide a comprehensive dataset for investigating other factors influencing the settlement behavior of opencast mine dumps.

The classical and modern trends of interaction between European philosophy and medicine

The interaction between European philosophy and medicine during historical periods of the New Time, the Enlightenment era and XX-XXI century is highlighted in the article. Special attention is paid to the influence of research in the world outlook and motivation basis of human activity, conducted by the main philosophers of the classical and modern time; on the formation of theoretical and axiological basis of modern medicine, as well as the concept of interrelation of human’s mental and physiological health. The main aspects of theoretical research in ontology (or metaphysics), gnoseology (epistemology), methodology, social philosophy and ethics connected to medicine theory and practice are also discussed in the article.

Modelling Service Times Using Some Beta-Based Compound Distribution

The design of the queueing model involves modelling the arrival and service processes of the system. Conventionally, the arrival process is assumed to follow Poisson while service times are assumed to be exponentially distributed. Other distributions such as Weibull, uniform, lognormal have been used to model service times however, generalized distributions have not been used in this regard. In recent times, attention have been shifted to generalised families of distributions including Beta generalized family of distributions which led to the development of Beta-based distributions. Distributions generated from a mixture of beta random variables are quite numerous in literature with little or no application to service times data. In this study, six Beta-based compound distributions - Beta-Log-logistic distribution (BLlogD), Beta-Weibull distribution (BWeiD), Beta-Lomax distribution (BLomD), Beta-exponential distribution (BExD), Beta-Gompertz distribution (BGomD) and Beta-log-normal distribution (BLnormD) - were compared with the classical service times model on four service times data sets. Maximum likelihood estimator was employed in estimating parameters of the selected models while Akaike Information Criteria (AIC), Consistent Akaike Information Criterion (CAIC), Bayesian Information Criterion (BIC) and Hannan Quin information criterion (HQIC) statistics were employed to select the best model. CDFs, PDFs and PP-plots were used to fit the data of the suggested models. Results from the study shows that Beta-Exponential distribution (BExpD) performed better for the datasets I (AIC=640.3, CAIC=640.5,BIC=648.1 and HQIC=643.4), Beta-weibull distribution (BWeiD) performed better for the data sets II and III (AIC=204.2, 2142.4,CAIC=204.2, 2142.7,BIC=212.8, 2154.9 and HQIC=207.6, 2147.5) while Beta-log-logistic distribution (BLlogD) performed better for the data sets IV (AIC=2275.3,CAIC=2275.5, BIC=2289.3 and HQIC=2280.9). Findings from this study revealed some useful Beta-based compound distributions which performed better than the conventional service time model. From the findings of this study, we recommend researchers to dive deep into queueing theory.

Topological transport of a classical droplet in a lattice of time

AbstractThouless charge pumps are quantum mechanical devices whose operation relies on topology. They provide the means for transporting quantum matter in space lattices with a single quantum precision. Contrasting space crystals that spontaneously break a continuous spatial translation symmetry and form crystals in space, time crystals have emerged as novel states of matter that organise into time lattices and spontaneously break a discrete time translation symmetry. The utility of Thouless pumps that enable topologically protected quantised transport of electrons and neutral atoms in spatial superlattices leads to the question if corresponding devices exist for time crystals. Here we show that topological pumps can be realised for time solids by transporting droplets of a liquid forward and backward in time lattices and we measure the topological index that characterises such pumping processes. By exploiting a synthetic time dimension, classical time crystals can circumvent the quantum tunnelling that underpins Thouless charge pumps. Our results establish topological pumping through time instead of space and pave the way for applications of time crystals.Key Points Periodically driven fluid droplets can be made to bounce persistently in a gravitational field and may undergo spontaneous time translation symmetry breaking forming classical time crystals. These classical time crystals can be pumped from one time site to another, and this pumping process is characterised by a topological index. Our observations establish a classical temporal counterpart to Thouless pumping of quantum particles in space lattices.

An augmented streamline upwind/Petrov-Galerkin method for the time-spectral convection-diffusion equation

Discretizing a solution in the spectral rather than time domain presents a significant advantage in solving transport problems encountered in fields like cardiorespiratory modeling, where the flow varies smoothly and periodically in time. To solve the system expressed in the frequency domain, one may rely on the classical time domain upwind techniques, such as the streamline upwind/Petrov-Galerkin (SUPG). While these classical methods successfully remove spurious oscillations in the solution in convection dominated flows, their accuracy deteriorates in a time-spectral setting as the element Womersley number approaches one. To overcome this limitation, this study introduces a new stabilized method, which we call augmented SUPG (ASU). The ASU is a consistent weighted residual method with two complex-valued stabilization parameters that act independently on the source and convective trial functions. Through a series of test cases, the superior accuracy of the ASU in comparison to four classical methods is shown across a wide range of flow conditions.

Some anthropological characteristics of ancient (according to fine art sources) and modern Greeks

Introduction. Greek sculpture and vase painting, despite the comprehensive study of specialists in many fields of science, can represent a source of information for historical and biological anthropology. The vase painting and sculpture of the ancient Greeks has been studied in the context of ethnic and biological continuity with the modern Greek population. Materials and methods. A total of 1020 vases (2379 people) and 174 reliefs (300 people) were studied. Two characteristics were studied – the development of tertiary hair growth (beard growth) and the nature of the head hair of ancient and modern Greeks. The material was collected from online sources and relevant literature, and processed according to anthropological methods. The composite portrait according to F. Galton method in digital format has been used to visualize morphological variability. Results and discussion. Modern Greeks (the middle total type of the population) are characterized by straight or lightly wavy head hair (middle score 1.41) and most often above middle or heavy beard growth (middle score 3.38). The proportion of straight and wavy hair in all vase painting samples is approximately the same, with wavy hair significantly predominant. The middle scores for hair nature and beard growth based on relief funeral images of ancient Greeks in our samples turned out to be significantly higher than for modern Greeks and amounted to: for nature of the hair – 2,62 points for classical time and 2,81 points for late classical and hellenistic times, for beard growth – 4,38 points and 4,30 points, respectively. Conclusion. Despite the change in artistic forms of conveying morphological features during the transition from archaic to classical times, in Greek vase painting and sculpture the representation of the heavy beard growth and the wavy nature of head hair was preserved, which, according to the authors, may be a real reflection of the typical morphological characteristics of the group.

Moyal deformation of the classical arrival time

The quantum time of arrival (TOA) problem requires the statistics of measured arrival times given only the initial state of a particle. Following the standard framework of quantum theory, the problem translates into finding an appropriate quantum image of the classical arrival time TC(q,p), usually in operator form T̂. In this paper, we consider the problem anew within the phase space formulation of quantum mechanics. The resulting quantum image is a real-valued and time-reversal symmetric function TM(q,p) in formal series of ℏ2 with the classical arrival time as the leading term. It is obtained directly from the Moyal bracket relation with the system Hamiltonian and is hence interpreted as a Moyal deformation of the classical TOA. We investigate its properties and discuss how it bypasses the known obstructions to quantization by showing the isomorphism between TM(q,p) and the rigged Hilbert space TOA operator constructed in Pablico and Galapon [Eur. Phys. J. Plus 138, 153 (2023)], which always satisfy the time-energy canonical commutation relation for arbitrary analytic potentials. We then examine TOA problems for a free particle and a quartic oscillator potential as examples.

Maximum Cardinality \(f\) -Matching in Time \(O(n^{2/3}m)\)

We present an algorithm that finds a maximum cardinality \(f\) -matching of a simple graph in time \(O(n^{2/3}m)\) . Here \(f:V\to\mathbb{N}\) is a given function and an \(f\) -matching is a subgraph wherein each vertex \(v\in V\) has degree \(\leq f(v)\) . This result generalizes a string of algorithms that concentrate on simple bipartite graphs. The bipartite case is based on the notion of level graph, introduced by Dinic for network flow. In general graphs this notion breaks down: Vertices no longer have unique levels, and there are too many levels to analyze the corresponding level graph like bipartite graphs ( \(\Theta(n^{2})\) vs. \(n\) ). We use “natural” levels to prove properties of shortest augmenting trails (e.g., formulas for trail length). We use “shortened” levels to derive the algorithm's time bound. The algorithm, unmodified, is also efficient on multigraphs, achieving time \(O(\min\{\sqrt{f(V)},n\} m)\) for \(f(V)=\sum_{v}f(v)\) . The special case \(f\equiv 1\) shows the algorithm duplicates the classic time bound for maximum cardinality matching, \(O(\sqrt{n} m)\) .

Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis

Accurate prediction of blood glucose level (BGL) has proven to be an effective way to help in type 1 diabetes management. The choice of input, along with the fundamental choice of model structure, is an existing challenge in BGL prediction. Investigating the performance of different data-driven time series forecasting approaches with different inputs for BGL prediction is beneficial in advancing BGL prediction performance. Limited work has been made in this regard, which has resulted in different conclusions. This paper performs a comprehensive investigation of different data-driven time series forecasting approaches using different inputs. To do so, BGL prediction is comparatively investigated from two perspectives; the model’s approach and the model’s input. First, we compare the performance of BGL prediction using different data-driven time series forecasting approaches, including classical time series forecasting, traditional machine learning, and deep neural networks. Secondly, for each prediction approach, univariate input, using BGL data only, is compared to a multivariate input, using data on carbohydrate intake, injected bolus insulin, and physical activity in addition to BGL data. The investigation is performed on two publicly available Ohio datasets. Regression-based and clinical-based metrics along with statistical analyses are performed for evaluation and comparison purposes. The outcomes show that the traditional machine learning model is the fastest model to train and has the best BGL prediction performance especially when using multivariate input. Also, results show that simply adding extra variables does not necessarily improve BGL prediction performance significantly, and data fusion approaches may be required to effectively leverage other variables’ information.

Forecasting of Turkey's Hazelnut Export Amounts According to Seasons with Dendritic Neuron Model Artificial Neural Network

It is seen that artificial neural networks have begun to be used extensively in the literature in solving the time series forecasting problem. In addition to artificial neural networks, classical forecasting methods can often be used to solve this problem. It is seen that classical forecasting methods give successful results for linear time series analysis. However, there is no linear relationship in many time series. Therefore, it can be thought that deep artificial neural networks, which contain more parameters but create more flexible non-linear model structures compared to classical time series forecasting methods, may enable the production of more successful forecasting methods. In this study, the problem of forecasting hazelnut export amounts according to seasons in Turkey with a dendritic neuron model artificial neural network is discussed. In this study, a training algorithm based on the particle swarm optimization algorithm is given for training the dendritic neuron model artificial neural network. The motivation of the study is to investigate Turkey's hazelnut export amounts according to seasons, using a dendritic neuron model artificial neural network. The performance of the proposed method has been compared with artificial neural networks used in the literature.

Emotion recognition from multichannel EEG signals based on low-rank subspace self-representation features

In recent years, emotion recognition based on electroencephalogram (EEG) has become the research focus in human–computer interaction (HCI), but deficiencies in EEG feature extraction and noise suppression are still challenging. In this paper, a novel robust low-rank subspace self-representation (RLSR) of EEG is developed for emotion recognition. Instead of using classical time–frequency EEG feature, the data-driven based EEG self-representation in low-rank subspace is extracted for emotion characterization. The Robust Principal Component Analysis (RPCA) is incorporated to separate the noise part in the process of solving self-representation. The accuracy and robustness of the result are improved because of the superior features and noise suppression. To fully exploit the effective knowledge of different EEG frequency bands, the Tucker decomposition based data dimensionality reduction is introduced. Experiments conducted on the public dataset DEAP reveal that the average accuracies of the proposed method can reach to 93.04 % and 93.13 % for binary classification of valence and arousal, respectively. The average accuracy reaches to 88.82 % of four-class classification.

Leapfrogging Sycamore: Harnessing 1432 GPUs for 7× faster quantum random circuit sampling

Abstract Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor network methods, has significantly reduced the classical simulation time and challenged the claim of the first-generation quantum advantage experiments. However, in terms of generating uncorrelated samples, time-to-solution, and energy consumption, previous classical simulation experiments still underperform the Sycamore processor. Here we report an energy-efficient classical simulation algorithm, using 1432 GPUs to simulate quantum random circuit sampling which generates uncorrelated samples with higher linear cross entropy score and is 7 times faster than the Sycamore 53 qubits experiment. We propose a post-processing algorithm to reduce the overall complexity, and integrate state-of-the-art high-performance general-purpose GPU to achieve two orders of lower energy consumption compared to previous works. Our work provides the first unambiguous experimental evidence to refute Sycamore’s claim of quantum advantage, and redefines the boundary of quantum computational advantage using random circuit sampling.

Faraday instability of viscous liquid films on a heated substrate with Maxwell–Cattaneo heat flux

Faraday instability of viscous liquid films with Maxwell–Cattaneo (MC) heat flux on an infinite, heated horizontal substrate subject to vertical time-varying periodic vibration is investigated theoretically. The MC effect means that the response of the heat flux to a temperature gradient obeys a relaxation time law rather than a classical Fourier time law. Applying the classic Floquet theory to linear analysis, a recursive relation is obtained. When considering the MC effect, a new phenomenon appears at a large wave number k. The neutral stability curves branch new tongues that turn left rather than right as before, but the tongues still move up and right as the wave number increases. Furthermore, typical harmonic (H) and subharmonic (SH) alternation behavior continues to exist. Interestingly, the first tongue of a branch is H or SH, implying that there is a transition following the branches. However, near the critical wave number kc of a branch, the SH and H almost overlap. As Cattaneo number C increases, the tongue-like unstable zones of branches become wider, and the critical wave number kc of the appeared branch becomes small. As the driving frequency ω decreases, the branch tongues become elongated and the critical wave number kc of the appeared branch becomes small.

Forecasting fish prices with an artificial neural network model during the tuna fraud

Agricultural price forecasting plays an important role in stabilising markets and ensuring food security. It provides insights for various stakeholders to optimise planting choices, allocate resources efficiently and mitigate potential risks. However, price forecasting during food safety incidents poses unique challenges. This study focused on a case of tuna fraud in Spain in 2017, which caused 105 people to fall ill and influenced consumer behaviour in fish purchases. To forecast fish prices during an incident of fraud, we used an artificial neural network model (ANN) based on the price of tuna and its substitutes, salmon and hake, as well as a communication index based on the number of posts regarding the tuna fraud from the social media platform X (formerly Twitter). ANN was compared with a threshold vector autoregressive model (TVAR), a classical time series econometric model that offers valuable insights into price dynamics. The results showed that, in the short term, TVAR offers a better price forecast for tuna and salmon, considering the impacts of the X platform. In the medium term, ANN outperformed TVAR. This study contributes to the ANN literature regarding agrifood price forecasting during food safety incidents.

Adaptive conservative time integration for unsteady compressible flow

Unsteady flow computations typically rely on time integration schemes which employ the same step size everywhere. However, in cases with strong variations of wave speed and/or spatial resolution, local stability criteria and truncation errors would allow for much larger time steps in parts of the domain. To exploit this potential of improving the computational efficiency, adaptive time-stepping methods have been developed. Recently, an adaptive conservative time integration (ACTI) scheme for explicit finite volume methods was devised. Opposed to previous methods, ACTI guarantees exact conservation and periodic synchronization. Both are achieved by splitting a global time step into 2L (L≥0 is a cell dependent level) sub-steps, whereas the order of cell updates is critical. It has been demonstrated for flows in fractured porous media and for the Euler equations that ACTI can reduce the computational cost dramatically while preserving second order accuracy in space and time. In this paper, the ACTI scheme is extended to the compressible Navier-Stokes-Fourier system, and special attention is required for the spatio-temporal discretization of the convective and viscous fluxes. Numerical studies of unsteady flows involving shock boundary layer interaction demonstrate that the same accuracy is achieved with the new compressible ACTI flow solver as with classical time integration, but at much lower cost. Moreover, since the method is explicit, it has the potential for efficient computations on multiple CPUs and GPUs.

A prediction model for chlorophyll concentration in seawater based on BorutaShap-MEMD-GRU

To solve the problem of the difficulty in selecting multi-parameter features of the ocean and the lack of power of traditional time-series prediction models in predicting ocean data, an ocean time-series prediction GRU model based on the Borutashap algorithm, and a hybrid multivariate empirical modal decomposition is proposed to predict multivariate in this paper. The feature selection of multi-feature ocean data is carried out by the Borutashap algorithm based on the XG-boost model, then the selected data are decomposed by multi-modal decomposition, and the data are reconstructed to get the high-frequency and low-frequency components, and the trend term components by Permutation Entropy, and finally the high-frequency and low-frequency components and the trend term are respectively brought into the GRU network and the prediction is summed up to get the final result. In this paper, the model’s effectiveness is verified by ablation experiments and compared with other feature selection models and classical time series prediction models, the results show that the model has a better prediction effect.

Good Lawmaking, Democracy, and Accountability

ABSTRACT In the present paper, we analyze good lawmaking under the general aspect of democracy and accountability. We present briefly the procedure of lawmaking and accountability under the direct democracy of ancient Athens during the Classical times (508–323 BC), giving one characteristic example, followed with a comparison with modern lawmaking under representative democracy. We argue that accountability in present-day democracies has three drawbacks, that are related to (i) the principal-agent problem, (ii) the fact that accountability is only periodic since elections are held every four years (iii) the information asymmetry between government and citizens, which makes difficult for citizens to understand the issues (regarding lawmaking) in depth or hold a government accountable for its actions and decisions on various matters. We explain how the ancient Athenian democrats dealt effectively with these institutional inconsistencies. We finally conclude by offering some proposals for improving the quality of democracy nowadays, inspired by the functioning of the Athenian-related institutions.

Computing the worst-case due dates violations with budget uncertainty

We study the problem of maximizing the violation of due dates when considering either the total violation, or the number of jobs that are tardy. We consider classical completion times and a variant useful in heuristics. The four problems arise when solving (exactly or heuristically) robust scheduling problems with release and due dates/deadlines and processing time uncertainty, and also routing problems with (soft) time windows and travel time uncertainty. We provide polynomial dynamic programming algorithms for the four problems.

Chaotic time series prediction based on physics-informed neural operator

This paper investigates the prediction of chaotic time series using physics-informed neural operator (PINO) with different driven methods, such as data-driven method, physics-driven method, and hybrid data-physics-driven method. Here, the chaotic time series are generated from two classical time delayed chaotic systems, including Mackey–Glass equation (MG) and Optoelectronic Oscillator (OEO). The simulation results from these two chaotic systems or experimental results from Optoelectronic Oscillator demonstrate that when there is enough data with good quality, it is possible to train the model solely using data-driven method. Conversely, when possessing complete mastery of the physical prior knowledge, it is also available to train the model solely using physics-driven method, indicating there is no need to prepare label datasets as in data-driven method. However, when dataset quality is poor, for example, contaminated by noise, and precise physical prior knowledge is not fully acquired, the hybrid method will have a better performance.