Time Slices Research Articles

Traversable AdS wormhole via non-local double trace or Janus deformation

We study (i) Janus deformations and (ii) non-local double trace deformations of a pair of CFTs, as two different ways to construct CFT duals of traversable AdS wormholes. First we construct a simple model of traversable wormholes by gluing two Poincaré AdS geometries and BTZ black holes and compute holographic two point functions and (pseudo) entanglement entropy. We point out that a Janus gravity solution describes a traversable wormhole when the deformation parameter takes imaginary values. On the other hand, we show that double trace deformations between two decoupled CFTs can reproduce two point functions of traversable AdS wormholes. By considering the case where the double trace deformation is given by a non-local TT¯ deformation, we analyze the dual gravity which implies emergence of wormholes. We present toy model of these deformed CFTs by using free scalars and obtain qualitative behaviors expected for them. We argue that the crucial difference between the two constructions is that a global time slice of wormhole is described by a pure state for Janus deformations, while it is a mixed state for the double trace deformations.

Estimation of heritabilities and genetic correlations by time slices using predictivity in large genomic models

Abstract Under genomic selection, genetic parameters may change rapidly from generation to generation. Unless genetic parameters used for a selection index are current, the expected genetic gain may be unrealistic, possibly with a decline for antagonistic traits. Existing methods for parameter estimation are computationally unfeasible with large genomic data. We present formulas for estimating heritabilities and genetic correlations applicable for large models with any number of genotyped individuals. Heritabilities are calculated by combining 2 formulas for genomic accuracies: one that relies on predictivity and another that depends on the number of independent chromosome segments. Genetic correlations are calculated from predictivities across traits. We simulated data including 2 traits for 240k genotyped and phenotyped animals in 6 generations, namely, production trait with an initial heritability of 0.4 and a fitness trait with a fixed heritability set at 0.1 in each generation. Only the first trait (production) was selected, whereas the second trait (fitness) was constructed so that its genetic correlation with the first trait declined by about 0.1 per generation. Calculations were for 3-generation windows, with the first 2 generations treated as a reference population. Compared with realized values, the estimated heritabilities were within 0.02. Genetic correlations were within 0.15 with predictivity of production phenotype by prediction for fitness and within 0.05 with predictivity of the fitness phenotype by prediction for production. The proposed formulas enable the estimation of heritabilities and genetic correlations by time slices for models in which predictivities can be calculated and genetic evaluation is feasible.

An Enhanced and Improved Half-Life Variable Quantum Time Round Robin (EImHLVQTRR) CPU Scheduling Algorithm

Study’s Excerpt:• An extended Improved Half-Life Variable Quantum Time Round Robin is proposed.• This will further improve the efficiency of the system.• The experimental analysis and simulation results show a promising better performance.• Most drawbacks, like starvation in existing algorithms, were also addressed.Full Abstract:The processing speed and powers of Computer systems is a function of Central Processing Unit (CPU) efficiency. Depending on the algorithm used to implement a particular system, the turnaround time, waiting time, response time, and number of context switch are responsible for reducing CPU idle time and, overall, slowing down computer system processing power. There are many existing scheduling algorithms; among them is the “Improved Half Life Variable Quantum Time with Mean Time Slice Round Robin (ImHLVQTRR) Algorithm,” which has been proposed to address the starvation problem – delay in access to the requested resources experienced in most of the earlier algorithms. This paper aims to enhance (the ImHLVQTRR) algorithm by modifying the time quantum (TQ), thereby improving system performance. To achieve this, a square root of the product of the processes average burst time and minimum burst time was computed to determine the TQ. The computed TQ is then used to execute RQ processes in an iterative manner within a specified period of time until the ready queue is empty. Overall, the experimental analysis shows that the proposed (EImHLVQTRR) algorithm performed better in terms of AWT of 371ms as against 390ms and 371ms, ATAT of 399ms as against 423ms and 399ms, ART of 71ms as against 236ms and 88ms and NCS of 91 as against 46 and 65; AWT of 326ms as against 350ms and 326ms, ATAT of 351ms as against 378ms and 351ms, ART of 51ms as against 153ms and 59ms and NCS of 77 as against 45 and 57 in both Zero and Non-Zero Arrival Time simulation results for the processes generated as shown in Figure 10 & 13 respectively. The experimental results also show some significant improvement as the ATAT of 22.6ms as against 27.2ms and 29.6ms, AWT of 13.4ms as against 18.0ms and 20.4ms, ART of 4.8ms as against 9.0ms and 11.8ms with equal number of context switch for Zero Arrival Time; ATAT of 30.6ms as against 32.2ms and 40.6ms, AWT of 18.6ms as against 20.2ms and 28.6ms, ART of 18.6ms as against 10.6ms and 11.2ms and NCS of 4 as against 6 and 7 for Non-Zero Arrival Time experimental summary results shown in Table 3 & 4.

A Bibliometric Analysis to Study the Evolution of Artificial Intelligence in Business Ethics

ABSTRACTThe contemporary world is witnessing the pervasive diffusion of artificial intelligence (AI) across diverse societal domains. Concurrently, the implementation of these technologies in numerous management areas raises novel and critical ethical considerations. Considering that AI is not a neutral technology, this study employs a bibliometric approach to conduct a systematic literature review, aiming to understand the evolution of this nascent field within management studies. Our analysis is limited to publications from 2016 onward, a year widely recognized as marking the onset of significant AI dissemination. By examining the recent years of diffusion and the evolution of this phenomenon, our work reveals a fragmented field. To investigate thematic evolution, we used the Cobo method, partitioning the corpus into three distinct time slices with cut‐off points at 2020 and 2022. The findings indicate that, despite the emergence of key themes and a substantial body of research exploring AI from both technical and philosophical standpoints, the field remains in its initial stages of development. This analysis underscores the increasing relevance of research in management science concerning the responsible and ethical application of AI technologies. Consequently, this area presents considerable opportunities for future management research employing diverse managerial and theoretical frameworks, particularly in relation to the antecedents of ethical AI adoption and the implications of AI for ethical leadership.

First observations of edge instabilities in strongly shaped negative triangularity plasmas on DIII-D

Abstract Throughout the 2023 DIII-D negative triangularity (NT) campaign, unexpected magnetic fluctuations indicative of an edge localized instability were observed in the majority of strongly shaped ( − 0.55 ⩽ δ ⩽ − 0.45 ) discharges. Moreover, these same fluctuations were found to be absent in weaker NT plasma shapes. Based on a database study, three separate mode behaviors are observed based primarily on the edge safety factor q 95 and normalized plasma beta β N . The main regime is the bursty state, in which short ( ∼ 5 ms) magnetic bursts occur every 5–10 ms. Each burst drives a small reduction in the edge electron temperature ( T e ) as measured by electron cyclotron emissions (ECE), which then rapidly recovers between the bursts. The quiescent regime appears to have governing mechanics similar to the bursty regime, but with multiple unique mode signatures observed in the magnetics. These additional modes greatly reduce the bursty nature of the plasma. Finally, a saturated state is observed in a number of discharges, particularly at early time slices. The saturated regime is often short-lived (maximum 500 ms), but can have a large impact on the dynamics of the plasma edge region, causing significant drops in electron temperature as observed in the ECE diagnostic. 2D ECE imaging (ECE-I) is utilized, showing large kink-like perturbations localized near the last closed flux surface that extend into the SOL indicative of an external kink/peeling behavior. Additionally, evidence of phase mixing and observed tearing-parity perturbations localized near the edge rational surfaces is observed in the quiescent regime plasmas. The various modes do not appear to have a large impact on global confinement but may play a role in regulating the transport of the NT edge and the observed edge gradients.

Hyperpolarized 129Xe diffusion-weighted MRI of the lung with 3D golden-angle radial sampling and keyhole reconstruction.

Hyperpolarized (HP) 129Xe multiple b-values diffusion-weighted imaging (DWI) facilitates the assessment of pulmonary morphology. However, conventional DWI method, such as 2D GRE DWI, is limited in its application due to the long acquisition time and relatively thick slice. To develop a method combining 3D golden-angle radial sampling with keyhole reconstruction (GRSK) for accelerating 129Xe multiple b-values DWI and obtaining thinner slice. For 3D GRSK DWI, 3D kooshball golden-angle radial sampling was used for image acquisition, with each spoke assigned to 1 of 4 b-values, and keyhole method was applied for reconstructing DW images under different b-values. 2D fully sampled GRE DWI and 3D GRSK DWI were obtained in five healthy young volunteers (HYV; 25 [24-26] years). Lung morphological parameter maps, including mean linear intercept (Lm) and surface-to-volume ratio (SVR), were generated using a cylinder model (CM) from the four b-values DW images, and apparent diffusion coefficient (ADC) maps were derived through mono-exponential fitting. Spearman correlation and Bland-Altman analysis were performed to compare Lm, SVR and ADC from 2D GRE and 3D GRSK DWI. In addition, 3D GRSK DWI was applied in five emphysema patients (66 [60-69] years) and five age-matched healthy controls (AMC; 59 [54-68] years), and Lm, SVR and ADC maps were also derived. Wilcoxon rank-sum test was utilized to contrast Lm, SVR and ADC from patients with those from AMC. DW images with an isotropic resolution (5mm) were obtained with 3D GRSK DWI within 11.4 s. In comparison, 2D GRE DWI acquired four slices with 30mm thickness in 15.9 s. For Lm, SVR and ADC from 2D GRE and 3D GRSK DWI, the Spearman correlation coefficients were 0.975, 0.900, and 1.000, with corresponding p-values of 0.005, 0.037, and<0.001, and the Bland-Altman analysis had biases of -3.19%, 1.44%, and -3.71%, respectively. Furthermore, Lm and ADC in patients were significantly higher (p=0.008 and p=0.008) than those in AMC, while SVR was notably reduced (p=0.008). The proposed method could obtain isotropic resolution DW images with four b-values within an 11.4 s breath-hold duration, mitigating the problems of long scan time and large slice thickness in conventional DWI.

Survival path model outperforms conventional static machine learning models in long-term dynamic prognosis prediction for patients with intermediate stage hepatocellular carcinoma.

Patients with intermediate stage hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment, and treatment planning. A novel machine learning model called survival path mapping (SP) model was developed, while its performance as compared with conventional machine learning models remains unknown. Between January 2007 and December 2018, the time-series data of 2644 intermediate stage HCC patients from four medical centers in China were reviewed and included. Static machine learning models by Gaussian Naive Bayes (GNB), support vector machine (SVM), and random forest (RF) for the prediction of survivorship were built based on data at initial admission. Longitudinal data divided into different time slices were utilized for the construction of the SP model. The time-dependent c-index was compared between models. The training set, internal testing set, and external testing set consisted of 1560, 670, and 414 HCC patients, respectively. The survival path model had superior or non-inferior performance in prognosis prediction compared to GNB and RF models since the 12th month after initial diagnosis in the training set and the external testing set. The survival path model had higher time-dependent c-index over all conventional ML models since the 6th month in the external testing cohort. In conclusion, the survival path model had superior performance in long-term dynamic prognosis prediction compared to conventional static machine learning models for intermediate stage HCC. The parameters of models are provided in the manuscript.

Certifying Quantum Temporal Correlation via Randomized Measurements: Theory and Experiment.

We consider the certification of temporal quantum correlations using the pseudo-density operator (PDO), an extension of the density matrix to the time domain, where negative eigenvalues are key indicators of temporal correlations. Conventional methods for detecting these correlations rely on PDO tomography, which often involves excessive redundant information and requires exponential resources. In this work, we develop an efficient protocol for temporal correlation detection by virtually preparing the PDO within a single time slice and estimating its second-order moments using randomized measurements. Through sample complexity analysis, we demonstrate that our protocol requires only a constant number of measurement bases, making it particularly advantageous for systems utilizing ensemble average measurements, as it maintains constant runtime complexity regardless of the number of qubits. We experimentally validate our protocol on a nuclear magnetic resonance platform, a typical thermodynamic quantum system, where the experimental results closely align with theoretical predictions, confirming the effectiveness of our protocol.

Correlated Photon Lidar Based on Time-Division Multiplexing

Single-photon lidar (SPL) exhibits high sensitivity, making it particularly suitable for detecting weak echoes over long distances. However, its susceptibility to background noise necessitates the implementation of advanced filtering techniques and complex algorithms, which can significantly increase system cost and complexity. To address these challenges, we propose a time-division-multiplexing-based correlated photon lidar system that employs a narrowband pulsed laser with stable time delays and variable pulse intensities, thereby establishing temporal and intensity correlations. This all-fiber solution not only simplifies the system architecture but also enhances operational efficiency. An adaptive cross-correlation method incorporating time slicing has been developed to extract histogram signals, enabling successful 1.5 km distance measurements under intense daytime noise conditions, using a 1 s accumulation time and a 20 mm receiving aperture. The experimental results demonstrate a 38% (from 1.11 to 1.52) improvement in the signal-to-noise ratio (SNR), thereby enhancing the system’s anti-noise capability, facilitating rapid detection, and reducing overall system costs.

Spatial and Temporal Variability of the Adult Human Height of Men and Women from Different Regions of Russia in the 1900s–2000s

The present article aimed to study the secular changes in the mean adult human height of males and females across different regions of Russia. Here we present the results of the meta-analysis of a comprehensive data set on the mean heights of males from the 1900–1910s to the 1990–2000s birth cohorts among 63 regions of the Russian Federation (in over 470 groups), and mean heights of females from the 1940–1950s to the 1990–2000s birth cohorts among 54 regions of the Russian Federation (in over 340 groups). The regional secular trends of mean height were illustrated for the adult population in seven federal districts in Russia. We analyzed anthropological data using a combined approach, in which the temporal aspects of variation in body height were studied through retrospective analysis, and the spatial aspects (regional differences in adult height over a long period) were studied using cross-population analysis. The positive secular trend of the adult body height of men and women throughout the 20th century can be observed both at the national and subnational levels. During the last century, adult height of males increased by 9,35 cm (from 166,33 cm to 175,68 cm) at a rate of 0,84 cm/decade. From the 1940–1950s to the present, women’s adult height increased by 3,75 cm (from 160,06 cm to 163,81 cm) at a rate of 0,78 cm/decade. In our study, we found regional trends of secular dynamics and differences in mean height in different time slices. At the early 20th century, men of the Far Eastern Federal District exhibited the highest mean height, and at the end of the observation period this distinction was held by the men of Moscow and Krasnodar Krai. In the middle of the last century, the highest female height was documented in Pskov Oblast and Volgograd Oblast, and in the end of the century — in the Krasnodar Krai and Altai Krai. Regional differences in the spatiotemporal variation in adult human height may be explained by socio-economic differentiation between the subjects of the Russian Federation.

Projections of Heavy Precipitation Characteristics Over the Greater Alpine Region Using a Kilometer–Scale Climate Model Ensemble

AbstractThis study presents a detailed analysis of the CORDEX‐FPS multi‐model ensemble of convection‐permitting climate simulations over the greater Alpine region. These simulations cover 10‐year time slices and were obtained by downscaling global climate model (GCM) projections, using regional climate models (RCMs) and kilometer‐scale convection‐permitting models (CPMs). Our analysis over the Alpine area agrees with previous studies in terms of projected summer precipitation changes for the end of the century, in particular regarding a decrease in mean precipitation and increases in hourly precipitation intensities. In addition, we assess projected changes over different subregions, provide analyses at monthly and seasonal basis for temporal aggregations ranging from 1 hr to 5 days, address different extreme precipitation indices, and present validation against an Alpine‐scale daily precipitation data set and an hourly precipitation product based on 3 Doppler radars. The evaluation reveals that CPMs show a refinement of spatial patterns, reduce the overestimation of precipitation frequency, and better capture intense precipitation characteristics. The improvements are especially apparent on the sub‐daily scale and in the summer season. Convection‐Permitting Model climate projections show an increase in precipitation intensity for all seasons and across all temporal aggregations in all regions, except for the Mediterranean in summer. The projections from different CPMs qualitatively agree, despite significant differences in the GCMs circulation changes, suggesting that the increase in heavy events is primarily due to thermodynamic effects. We also present a hypothesis explaining why projections of relative changes in hourly precipitation percentiles are similar between CPMs and RCMs, despite large biases in RCMs.

A three-parameter high-resolution 3D fast Radon Transform

Abstract When the underground medium exhibits anisotropy, the time slices of seismic data after normal moveout correction exhibit non-standard elliptical shapes. This complexity hinders the accurate representation by the traditional two-parameter 3D Radon transforms. To address this limitation, we introduced an additional parameter to describe ellipse rotation and integrated the frequency and curvature together, resulting in a frequency-independent transform operator, which can enhance both accuracy and computational efficiency of the algorithm. However, the algorithm suffers from low resolution in the transform domain, which limits its effectiveness in suppressing multiples with small moveouts. Existing high-resolution fast Radon transform algorithms typically adopt an iterative approach to obtain respective regularization terms for each frequency, which improves resolution but at the cost of sacrificing the frequency-independent advantage of the fast algorithm. This significantly increases computational costs. In response, we propose a novel three-parameter high-resolution 3D fast Radon transform algorithm based on the constraints derived from dominant frequency calculation. This algorithm utilizes calculation results from dominant frequency seismic data as the regularization term for all frequency calculations. By pre-computing high-resolution transform operator and its inversion, and calling them in all frequency calculations, the algorithm can effectively enhance the suppression of multiples with small moveouts while avoiding a significant increase in computational costs. To further enhance computational efficiency, we also implement a GPU-based strategy for the algorithm, which significantly improves the practicality of the approach. Both synthetic and real data tests have verified the feasibility and effectiveness of this new method.

Modelling the potential impact of climate change on the productivity of soybean in the Nigeria Savannas.

A well-calibrated and evaluated GROPGRO module of the Decision Support System for Agro-technological Transfer (DSSAT) was used to simulate productivity of soybean in northern Nigeria under climate change. Both historical (1990-2019) and projected climate scenarios from 5 general circulation models (GCMs) under two representative concentration pathways (RCP 4.5 and RCP 8.5) in the mid-century (2040-2069) and end of the century (2070-2099) periods were used. Depending on climate scenario, the minimum temperature is expected to rise by 1.7-4.4oC at Kano in the Sudan savanna (SS) agroecological zone (AEZ) and 1.4-4.0oC at Zaria in the northern Guinea savanna (NGS) AEZ, while maximum temperatures are projected to increase by 1.7-4.1oC in the SS and 1.3-3.6oC in the NGS. Seasonal average rainfall will increase by 4.8-14.5% in the SS and decrease by 2.6-3.8% in the NGS, relative to the baseline climate. The model predicted delaying trends for days to flowering and maturity for both varieties in all climate scenarios in the two AEZs. Despite the delay in flowering and increase in crop cycle length, climate change will result in grain yield reduction in most of the future scenarios. Across location, variety and time slice, the grain yield will decline by between 8.4 and 23.6% under RCP4.5 scenario, with much higher decline by between 28.7 and 51.4% under RCP 8.5 scenario. However, using the early maturing variety can reduce the adverse effects of climate change on grain yield. On average, the yield of the early-maturing TGX1835-10E is predicted to be 15.2% higher under RCP4.5 scenario and up to 21.7% under RCP8.5 than that of the medium-maturing TGX1951-3F for both centuries in the SS AEZ. In the NGS, the average yield of TGX1835-10E is predicted to be 9.0% and 7.5% higher than that of TGX1951-3F under RCP4.5 and RCP8.5 scenarios, respectively. Using early-maturing soybean varieties is a key management strategy to boost the resilience of soybean production in Nigeria's savannas under climate change conditions.

Characteristics of Sporadic E Layer Occurrence in a Global Chemistry‐Climate Model: A Comparison With COSMIC‐Derived Data

AbstractThis study presents an analysis of sporadic‐E (Es) structures within WACCM‐X (the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension), including electrodynamical transport of metallic ions. A set of selection criteria have been developed to identify Es layers in WACCM‐X output based on the total metal ion density in each model grid box. These criteria are used to create a climatology of Es, which is compared to Es occurrence rates derived from FORMOSAT/COSMIC‐1 (Constellation Observing System for Meteorology, Ionosphere, and Climate) radio‐occultation measurements. The novel identification algorithm analyses 2‐week time slices between altitudes of 90–150 km, with Es layer events identified where the three selection criteria are met. Distinct seasonal distributions in Es occurrence were observed that are consistent with previous studies, with peaks during summer and reduced frequencies during winter, alignment of Es with geomagnetic contours, and layers descending in altitude as a function of local time. While discrepancies exist between WACCM‐X and COSMIC data (WACCM‐X occurrence rates are a factor of ∼2 lower than COSMIC‐derived occurrence rates at mid‐latitudes), highlighting the ongoing challenges in modeling Es layers, this study enhances the modeling capabilities of sporadic Es and deepens our understanding of their formation; it establishes a basis for their enhanced integration into global climate models and facilitates further investigation of Es behavior under different atmospheric conditions, paving the way to improved prediction of the occurrence of Es.

Integrating Spatially‐Explicit and Genetic Analyses to Identify Conservation Priorities for Bufo eichwaldi (Amphibia: Anura) in the Hyrcanian Forest

Bufo eichwaldi (Amphibia: Anura) is a toad species endemic to the Hyrcanian forests of northern Iran and southeastern Azerbaijan, increasingly threatened by anthropogenic pressures, underscoring the urgent need for conservation measures. We assessed the genetic diversity, population structure, and potential distribution of B. eichwaldi using molecular analyses and Species Distribution Modeling in order to inform management plans for the species. We analyzed two mitochondrial gene fragments (16S rRNA and D‐loop) and one nuclear gene (Recombination activating gene 1), totaling 1865 bp, in 23 individuals from four populations. Our genetic analyses revealed high haplotype diversity (0.984) and significant genetic differentiation (FST) among populations, with the Azerbaijan population showing a genetic distance of 1.85%–2.04% from Iranian populations in the D‐loop gene fragment. Genetic results support the hypothesis that B. eichwaldi recently expanded into the Hyrcanian forests after the last glacial maximum (LGM). Species distribution models calibrated using maximum entropy (MaxEnt) under average current climatic conditions (1970–2000) were projected to the Mid‐Holocene (6 Kya) and LGM (21 Kya), as well as on 256 time slices of 10 ky BP to assess historic habitat stability. Our models showed good predictive accuracy, with the most influential environmental variable being precipitation of the wettest month (50.1%), followed by temperature annual range (39.7%). Suitable habitats for B. eichwaldi have become increasingly available in the Hyrcanian region after the LGM, in agreement with genetic evidence of a recent range expansion into these forests. This study highlights the value of integrating genetic and ecological data to inform conservation strategies for B. eichwaldi.

A novel approach for detecting malicious hosts based on RE-GCN in intranet

Internal network attacks pose a serious security threat to enterprises and organizations, potentially leading to critical information leaks and network system damage. Hosts, as the core data and service bearers, are often primary targets of cyber attacks. Therefore, accurately identifying hosts with malicious behavior in the network is crucial. However, detecting malicious hosts on this intranet presents several challenges. Firstly, the network state is unstructured data that dynamically changes in real-time. Secondly, the large amount of normal traffic in the network drowns out the traces generated by malicious behaviors, leading to the problem of category imbalance. Lastly, the traditional graph neural network model has limitations in processing edge information and is unable to directly learn the information in netflow. To overcome these challenges, this paper proposes a malicious host detection system. The system extracts the Host Communication Graph by time slicing and uses a random undersampling method to balance samples. For malicious host detection, this paper proposes the Relational-Edge Graph Convolutional Network (RE-GCN) model, which can directly aggregate and learn features on edges and use them to accurately classify nodes, compared to other GNN models. Comparative experiments were conducted on various netflow datasets, demonstrating the effectiveness of our approach. Our approach outperformed other common GNN models in detecting malicious hosts.

Local Time-Frequency Feature Fusion Using Cross-Attention for Acoustic Scene Classification

To address the interdependence of local time-frequency information in audio scene recognition, a segment-based time-frequency feature fusion method based on cross-attention is proposed. Since audio scene recognition is highly sensitive to individual sound events within a scene, the input features are segmented into multiple segments along the time dimension to obtain local features, allowing the subsequent attention mechanism to focus on the time slices of key sound events. Furthermore, to leverage the advantages of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are mainstream structures in audio scene recognition tasks, this paper employs a symmetry structure to separately obtain the time-frequency features output by CNNs and RNNs and then fuses the two sets of features using cross-attention. Experiments on the TUT2018, TAU2019, and TAU2020 datasets demonstrate that the performance of this algorithm improves the official baseline results by 17.78%, 15.95%, and 20.13%, respectively.

Long-Term Expansion of Regional Museums in Russia: A Quantitative Analysis of Historical Dynamics

Museums help to conserve regional heritage and sustain regional identity, which is especially important in large countries. Russian regional museums constitute a whole phenomenon. Indeed, this phenomenon is a large field for research, and it cannot be comprehended by any single study. This is why attention should be paid for its particular aspects such as a historical dynamics of the expansion of regional museums in Russia. The information from the official websites of 76 regional museums sensu stricto of the entire country and 10 smaller museums of the Vologda Region was analyzed to measure the dynamics of their foundation and the relative importance of the different founders. It was established that regional museums were created in the Imperial, Soviet, and Modern periods of the Russian history, and the majority of them appeared in 1861–1935. Governmental founders prevailed, although private and societal initiatives also mattered, especially in the particular time slices. The smaller museums of the Vologda Region were created chiefly in the Soviet period, and both governmental and private initiatives were important. The results of this analysis were interpreted regarding attention to the broad public education and activity of so-called intelligentsia in the late Imperial and Soviet periods. It is hypothesized that the regional museum foundation can be brought in correspondent to the idea of a wise state and society development. Importantly, major transformations of the Russian society (particularly, in the beginning of the 20th century) did not interrupt the expansion of regional museums. It is proposed that the existing national network of regional museums itself has a heritage value, which can be important to tourism development.

Present Variability and Future Change in Onset and Cessation of the Rainy Season Over Peru

ABSTRACTChanges in patterns of accumulated rainfall, as well as the rainy season onset, cessation and duration, can impact the availability of water resources and sectors such as agriculture, affecting the livelihoods of the population. The knowledge of these changes is crucial for regions driven by strong precipitation variability such as the Andean countries. Therefore, the aim of this work is to determine the present and future spatio‐temporal patterns of the onset, cessation and duration of the rainy season in Peru. For this purpose, we analysed in a first step the present variability and trends in 11 homogeneous regions using data from 377 ground stations for the period 1981–2019. The results showed significant trends (1981–2019) of earlier onset and increased duration only in the Southern Peruvian Amazon (Madre de Dios River basin). Furthermore, the accumulated rainfall has significant trends of increases in North East Andes, Northern and Southern Amazon. In a second step, we assessed future changes of the rainy season from an ensemble of statistically downscaled CMIP6 climate scenarios. A two‐tailed Student t‐test was used to evaluate the significance of changes. Two future time slices (2031–2060 and 2071–2100) relative to the reference period (1981–2010) were analysed. Future changes of the rainy season showed significant delays in the onset for the Central East Andes, South West Andes and Amazon regions in the period 2071–2100. Likewise, the rainy season duration presents future significant reductions in regions of the central and southern Andes under the SSP2‐4.5 scenario. Moreover, the accumulated precipitation is projected to increase significantly in the Pacific slope and Andes regions, mainly under the SSP5‐8.5 scenario. These findings are particularly important for sectors like agriculture, energy and water resources management.

Enhancing task execution: a dual-layer approach with multi-queue adaptive priority scheduling.

Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. This article introduces an innovative dual-layer scheduling algorithm, Multi-Queue Adaptive Priority Scheduling (MQAPS), for task execution. MQAPS features a dual-layer hierarchy with a ready queue (RQ) and a secondary queue (SQ). New tasks enter the RQ, where they are prioritized, while the SQ contains tasks that have already used computing resources at least once, with priorities below a predefined threshold. The algorithm dynamically calculates the time slice based on process priorities to ensure efficient CPU utilization. In the RQ, the task's priority level defines its prioritization, which ensures that important jobs are completed on time compared to other conventional methods where priority is fixed or no priority parameter is defined, resulting in starvation in low-priority jobs. The simulation results show that MQAPS better utilizes CPU resources and time than traditional round-robin (RR) and multi-level scheduling. The MQAPS showcases a promising scheduling technique ensuring a balanced framework for dynamic adjustment of time quantum and priority. The MQAPS algorithm demonstrated optimization, fairness, and efficiency in job scheduling.