Average Map Research Articles

Deformation induced decoupling between U-Pb and trace elements in titanite revealed through petrochronology and study of localized deformation

In this contribution, we analyzed a pair of mafic samples collected from a recently identified shear zone and its proximal footwall from the Manicouagan Imbricate Zone (MIZ) of the central Grenville Province, Québec, Canada. Titanite petrochronology, metamorphic phase equilibria modelling, trace element thermometry, and electron backscattered diffraction data were used to define a Pressure-Temperature-time-Deformation path for the two samples. An interconnected dislocation network within titanite grains, as outlined with Kerneled Average Misorientation maps, are spatially correlated with variation in the U-Pb system but not with that observed for trace element These results suggest that the U-Pb system was decoupled from trace and rare earth elements and that deformation, rather than interface-coupled dissolution-precipitation reactions or re-crystallisation, was the main driver for this decoupling. In addition to highlighting a potential pitfall of titanite petrochronology, our P-T-t-D path reveals that ductile shear zones were active later than previously suggested within the MIZ.

A Thermal Imaging Flame-Detection Model for Firefighting Robot Based on YOLOv4-F Model

Fire robots are an effective way to save lives from fire, but their limited detection accuracy has greatly hampered their practical applications in complicated fire conditions. This study therefore proposes an advanced thermal imaging flame detection model of YOLOv4-F based on YOLOv4-tiny. We replaced the Leaky ReLU activation function with the Mish activation function in the YOLOV4-tiny feature extraction network. A Spatial Pyramid Pooling (SPP) was also added to increase the receiving range of the feature extraction network. To improve the feature fusion efficiency between multi-scale feature layers, a Path Aggregation Network (PANet) was adopted to replace the YOLOv4-tiny Feature Pyramid Network (FPN) with full use of feature information; a high-quality dataset containing 14,757 thermal imaging flame images was built according to the PASCAL VOC 2007 dataset standard. The results show that, when compared to the YOLOv4-tiny, YOLOv5-s, and YOLOv7-tiny models, the average detection accuracy of the proposed YOLOv4-F model is 5.75% higher, the average mAP of the five IOU cases rises by 7.02%, and the average detection confidence of three scaled flames shows a 18.09% gain. The proposed YOLOV4-F meets the requirements of fire robots on real-time responses and accurate flame detection, offering an important tool to improve the performance of the current fire robots.

A daily and complete PM2.5 dataset derived from space observations for Vietnam from 2012 to 2020

PM2.5 pollution is a serious problem in Vietnam and around the world, having bad impacts on human health, animals and environment. Regular monitoring at a large scale is important to assess the status of air pollution, develop solutions and evaluate the effectiveness of policy implementation. However, air quality monitoring stations in Vietnam are limited. In this article, we propose an approach to estimate daily PM2.5 concentration from 2012 to 2020 over the Vietnamese territory, which is strongly affected by cloudy conditions, using a modern statistical model named Mixed Effect Model (MEM) on a dataset consisting of ground PM2.5 measurements, integrated satellite Aerosol Optical Depth (AOD), meteorological and land use maps. The result of this approach is the first long-term, full coverage and high quality PM2.5 dataset of Vietnam. The daily mean PM2.5 maps have high validation results in comparison with ground PM2.5 measurement (Pearson r of 0.87, R2 of 0.75, RMSE of 11.76 μg/m3, and MRE of 36.57 % on a total of 13,886 data samples). The aggregated monthly and annual average maps from 2012 to 2020 in Vietnam have outstanding quality when compared with another global PM2.5 product. The PM2.5 concentration maps has shown spatial distribution and seasonal variations of PM2.5 concentration in Vietnam in a long period from 2012 to 2020 and has been used in other studies and applications in the environment and public health at the national scale, which has not been possible before because of the lack of monitoring stations and an appropriate PM2.5 modeling approach.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies.

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA's results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Effect of Heat Starvation Stress on Physiological Immunity and Metabolism of Mizuhopecten yessoensis

Mizuhopecten yessoensis is an economical maricultural bivalve mollusk in China and Japan. Due to the high mortality rate, academics have paid more attention to the effects of the environment recently. However, little is known about the physiological, immune, and metabolic effects of M. yessoensis under starvation stress at high temperatures. Herein, transcriptomic libraries of hemolymph and striated adductor muscle from feeding and starved M. yessoensis were constructed. The results showed that the immune indexes decreased in the concentration of M. yessoensis blood cells and liver lysozyme activity at 15 d, M. yessoensis fertility and liver ACP at 30 d of starvation stress, and T-AOC and BCA at 45 d of starvation stress. A total of 853.85 M clean reads were obtained from 20 libraries, with an average total mapping ratio of 83.38% to the reference genome. Based on an examination of the genes that differ in expression between the fasted and feeding groups, 27 up- and 41 down-regulated DEGs were identified in hemolymph, while the numbers in striated adductor muscle were 426 up- and 255 down-regulated. Determined by GO annotation and KEGG pathway mapping, annotations and categories of the DEGs presented diverse biological functions and processes. KEGG analysis showed that most downregulated DEGs in striated muscle were enriched in the pathways involved in metabolism. Genes encoding the enzymes, including eno, pgk, gapA, tpiA, fbp, pgi, and pgm in the gluconeogenesis pathway, were down-regulated, which was indicative of the negative effect of long-term starvation on gluconeogenesis. The down-regulation of PGD and tktA genes in the pentose phosphate pathway suggested that the carbohydrate decomposition and utilization were decreased in starved scallops. Together, the findings demonstrated the influences of food deprivation on carbohydrate metabolism and other processes in M. yessoensis. These results provide foundational information for further understanding of metabolism, especially carbohydrate metabolism of scallops under starvation, which may potentially benefit healthy aquaculture.

Deformation Induced Lattice Misorientation in Lath Martensite

Effect of plastic deformation on the lattice misorientation in lath martensite was investigated by Electron Backscatter Diffraction (EBSD). As-quenched rod-like specimens were plastically deformed until 9000 N, 14000 N, 19000 N and 21000 N of tensile loading. Grain average misorientation maps were collected by EBSD. It was shown that the average misorientation during plastic deformation increased from 0.887 to 1.156 degrees. Results showed that the density of geometrically necessary dislocations, which caused the lattice misorientation, slightly increased during plastic deformation of lath martensite.

Optical genome mapping identifies clinically relevant genomic rearrangements in prostate cancer biopsy sample

BackgroundProstate cancer (PCa) is characterized by complex genomic rearrangements such as the ETS oncogene family fusions, yet the clinical relevance is not well established. While paneled genetic tests of DNA repair genes are recommended in advanced PCa, conventional genomic or cytogenetic tools are not ideal for genome-wide screening of structural variations (SVs) such as balanced translocation due to cost and/or resolution issues.MethodsIn this study, we tested the feasibility of whole-genome optical genomic mapping (OGM), a newly developed platform for genome-wide SV analysis to detect complex genomic rearrangements in consecutive unselected PCa samples from MRI/US-fusion targeted biopsy.ResultsWe tested ten samples, and nine (90%) passed quality check. Average mapping rate and coverage depth were 58.1 ± 23.7% and 157.3 ± 97.7×, respectively (mean ± SD). OGM detected copy number alterations such as chr6q13 loss and chr8q12-24 gain. Two adjacent tumor samples were distinguished by inter/intra-chromosomal translocations, revealing that they’re from the same ancestor. Furthermore, OGM detected large deletion of chr13q13.1 accompanied by inter-chromosomal translocation t(13;20)(q13.1;p13) occurring within BRCA2 gene, suggesting complete loss of function.ConclusionIn conclusion, clinically relevant genomic SVs were successfully detected in PCa samples by OGM. We suggest that OGM can complement panel sequencing of DNA repair genes BRCA1/2 or ATM in high-risk PCa.

Video-Based Vehicle Counting and Analysis using YOLOv5 and DeepSORT with Deployment on Jetson Nano

In recent years, the advancements in deep learning and high-performance edge-computing systems have increased tremendously and have become the center of attention when it comes to the analysis of video-based systems on edge by making use of computer vision techniques. Intelligent Transportation Systems (ITS) is one area where deep learning can be used for several tasks including highway-based vehicle counting systems whereby making use of computer vision techniques, an edge computing device and cameras installed in specific locations on the road, we are able to obtain very accurate vehicle counting results and replace the use of traditional and laborious hardware devices with modern low-cost solutions. This paper proposes and implements a modern, compact, and reliable vehicle counting system based on the most recent and popular object detection algorithm as of writing this paper, known as the YOLOv5, combined with a state-of-the-art object tracking algorithm known as DeepSORT. The YOLOv5 will be used in the following system to detect and classify four different classes of vehicles, whereas DeepSORT will be used to track those vehicles across different frames in the video sequence. Finally, a unique and efficient vehicle counting method will be implemented and used to count tracked vehicles across the highway scenes. A new highway vehicle dataset consisting of four vehicle classes, namely: car, motorcycle, bus, and truck, was collected, cleaned, and annotated with a total of 11,982 images published in the following study and used for the training of our robust vehicle detection model. From the results obtained over real-world highway surveillance videos, the following system was able to obtain an average vehicle detection mAP score of 96.1% and a vehicle counting accuracy of 95.39%, all while being able to be deployed on a compact Nvidia Jetson Nano edge-computing device with an average speed of 15 FPS which outperforms other previously proposed tools in terms of both accuracy and speed.

Visualizing translation dynamics at atomic detail inside a bacterial cell

Translation is the fundamental process of protein synthesis and is catalysed by the ribosome in all living cells1. Here we use advances in cryo-electron tomography and sub-tomogram analysis2,3 to visualize the structural dynamics of translation inside the bacterium Mycoplasma pneumoniae. To interpret the functional states in detail, we first obtain a high-resolution in-cell average map of all translating ribosomes and build an atomic model for the M. pneumoniae ribosome that reveals distinct extensions of ribosomal proteins. Classification then resolves 13 ribosome states that differ in their conformation and composition. These recapitulate major states that were previously resolved in vitro, and reflect intermediates during active translation. On the basis of these states, we animate translation elongation inside native cells and show how antibiotics reshape the cellular translation landscapes. During translation elongation, ribosomes often assemble in defined three-dimensional arrangements to form polysomes4. By mapping the intracellular organization of translating ribosomes, we show that their association into polysomes involves a local coordination mechanism that is mediated by the ribosomal protein L9. We propose that an extended conformation of L9 within polysomes mitigates collisions to facilitate translation fidelity. Our work thus demonstrates the feasibility of visualizing molecular processes at atomic detail inside cells.

Similarity Retrieval Based on Image Background Analysis

Aiming at the problem of traditional portrait background similarity retrieval methods being low accuracy and time-consuming, a similarity retrieval method based on image background analysis is presented. The proposed method uses a combination of portrait segmentation and retrieval models. Firstly, the portrait segmentation model is used to remove the portraits in the images to eliminate the interference of portraits on background features; secondly, the image retrieval model is used to retrieve images with similar background features; LSH is added to improve the retrieval efficiency; finally, the retrieval results are used to further determine whether the background is similar. The experiment is implemented based on real data from a company. The results showed that the average precision, average map, and recall of this method reached 85%, 90%, and 50%, respectively. The average accuracy and recall are 10% better than the overall image retrieval model.

Hypotension Prediction Index Software to Prevent Intraoperative Hypotension during Major Non-Cardiac Surgery: Protocol for a European Multicenter Prospective Observational Registry (EU-HYPROTECT).

Background: Intraoperative hypotension is common in patients having non-cardiac surgery and associated with postoperative acute myocardial injury, acute kidney injury, and mortality. Avoiding intraoperative hypotension is a complex task for anesthesiologists. Using artificial intelligence to predict hypotension from clinical and hemodynamic data is an innovative and intriguing approach. The AcumenTM Hypotension Prediction Index (HPI) software (Edwards Lifesciences; Irvine, CA, USA) was developed using artificial intelligence—specifically machine learning—and predicts hypotension from blood pressure waveform features. We aimed to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery. Methods: We built up a European, multicenter, prospective, observational registry including at least 700 evaluable patients from five European countries. The registry includes consenting adults (≥18 years) who were scheduled for elective major non-cardiac surgery under general anesthesia that was expected to last at least 120 min and in whom arterial catheter placement and HPI monitoring was planned. The major objectives are to quantify and characterize intraoperative hypotension (defined as a mean arterial pressure [MAP] < 65 mmHg) when using HPI monitoring. This includes the time-weighted average (TWA) MAP < 65 mmHg, area under a MAP of 65 mmHg, the number of episodes of a MAP < 65 mmHg, the proportion of patients with at least one episode (1 min or more) of a MAP < 65 mmHg, and the absolute maximum decrease below a MAP of 65 mmHg. In addition, we will assess causes of intraoperative hypotension and investigate associations between intraoperative hypotension and postoperative outcomes. Discussion: There are only sparse data on the effect of using HPI monitoring on intraoperative hypotension in patients having elective major non-cardiac surgery. Therefore, we built up a European, multicenter, prospective, observational registry to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery.

Liquid-phase temperature in the SpraySyn flame measured by two-color laser-induced fluorescence thermometry and simulated by LES

In the SpraySyn burner (Schneider et al., 2019) a solution of metal salts in a combustible liquid is atomized in a two-fluid nozzle with molecular oxygen as dispersion gas to generate oxide nanoparticles via combustion synthesis. The flame is anchored by a methane (CH4)/oxygen (O2) premixed flat flame stabilized on a porous plate surrounding the spray nozzle. This burner features a standardized design with simple geometry, which provides well-defined boundary conditions for the simulation to support model development and the interaction between experiment and simulation. In this work, two-color laser-induced fluorescence (2cLIF) thermometry based on the fluorescence tracer coumarin 152 dissolved in ethanol is applied to provide temporally averaged but spatially resolved liquid-phase temperature maps. The operating conditions for the spray formation were modified by varying the injection rate and the dispersion gas flow. Liquid-phase temperatures were predicted by large-eddy simulation (LES), where the spray is modeled by a Eulerian-Lagrangian approach based on measured liquid droplet properties (initial liquid-phase temperature, droplet size, and velocity distribution). Droplet size and velocity were measured by phase-Doppler anemometry (PDA). Gas-phase turbulent combustion is modeled with the flamelet-generated manifold (FGM) approach. Droplet temperature distributions were provided by the simulation to assess the systematic measurement error caused by LIF-signal averaging. Experimental and simulated average liquid-phase temperature maps are compared. The simulation results were found to be in good agreement with the experiment by showing a suitable prediction of the droplet heat-up process within the experimentally probed spray region.

Automated optimization of TMS coil placement for personalized functional network engagement

Transcranial magnetic stimulation (TMS) is used to treat multiple psychiatric and neurological conditions by manipulating activity in particular brain networks and circuits, but individual responses are highly variable. In clinical settings, TMS coil placement is typically based on either group average functional maps or scalp heuristics. Here, we found that this approach can inadvertently target different functional networks in depressed patients due to variability in their functional brain organization. More precise TMS targeting should be feasible by accounting for each patient's unique functional neuroanatomy. To this end, we developed a targeting approach, termed targeted functional network stimulation (TANS). The TANS approach improved stimulation specificity in silico in 8 highly sampled patients with depression and 6 healthy individuals and invivo when targeting somatomotor functional networks representing the upper and lower limbs. Code for implementing TANS and an example dataset are provided as a resource.

The ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES). VI. The Core-scale CO Depletion

Studying the physical and chemical properties of cold and dense molecular clouds is crucial for the understanding of how stars form. Under the typical conditions of infrared dark clouds, CO is removed from the gas phase and trapped onto the surface of dust grains by the so-called depletion process. This suggests that the CO-depletion factor (f D ) can be a useful chemical indicator for identifying cold and dense regions (i.e., prestellar cores). We have used the 1.3 mm continuum and C18O (2–1) data observed at the resolution of ∼5000 au in the ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES) to construct averaged maps of f D in 12 clumps to characterize the earliest stages of the high-mass star formation process. The average f D determined for 277 of the 294 ASHES cores follows an unexpected increase from the prestellar to the protostellar stage. If we exclude the temperature effect due to the slight variations in the NH3 kinetic temperature among different cores, we explain this result as a dependence primarily on the average gas density, which increases in cores where protostellar conditions prevail. This shows that f D determined in high-mass star-forming regions at the core scale is insufficient to distinguish among prestellar and protostellar conditions for the individual cores and should be complemented by information provided by additional tracers. However, we confirm that the clump-averaged f D values correlate with the luminosity-to-mass ratio of each source, which is known to trace the evolution of the star formation process.

Application and Deconstruction of Visual Communication in Art Design Based on Linear Operator Theory

With the continuous development of society, information transmission can be seen everywhere, and various forms of visual communication with artistic design such as posters, periodicals, and electronic screens appear one after another. Visual communication is the effective communication of information through various visual elements. Visual communication of art design can promote the exchange of information and promote the development and progress of society. However, in the process of visual communication of artistic design, there are many problems of unclear images such as halo and inconsistent brightness, which brings bad visual experience to the information receiver. The linear operator theory has the ability to transform parameters and generalize to find the optimal solution, and the linear operator theory is applied to the visual communication of art design, which can effectively solve the problem of unclear images in traditional visual communication. This paper compares and analyzes the information entropy and mapping similarity between the visual communication of art design based on the linear operator theory and the traditional visual communication of art design by setting up an experimental group. The results showed that, among the four communication methods of art design visual communication, that is, figures, videos, animations, and movies, the mapping information entropy of art design visual communication based on linear operator theory in 6 different scenarios is 1.55 more than the traditional visual communication mode on average, and the average mapping similarity is 0.10 more. Therefore, the visual communication of artistic design based on the linear operator theory can convey more information and more accurate information and improve the effect of visual communication.

SignHRNet: Street-level traffic signs recognition with an attentive semi-anchoring guided high-resolution network

Traffic signs are indispensable fixtures in modern transportation activities, which are installed along the roadside or over the pathway to provide important prompt messages. The timely emergences, complete visibilities, and definite signals of traffic signs matter significantly to direct the driving behaviors and ensure the convenience and security of the transportation activities. Moreover, the efficient and accurate recognition of traffic signs can also provide decisive evidences to a variety of intelligent transportation systems. Nevertheless, the unignorable presence issues of traffic signs caused by vision factors, such as small sizes, dimness, deformations, and occlusions, impact essentially on the high-quality recognition of traffic signs. This paper develops a novel attentive semi-anchoring guided high-resolution network, named SignHRNet, for street-level traffic signs recognition purpose. First, stacked with a high-resolution network boosted by a dual-attention module as the feature extractor, the SignHRNet can exploit informative channel features and task-oriented spatial features to generate multiscale strong feature semantics for instance-level predictions. Second, designed with a semi-anchoring guided detection strategy assisted by instance-aware feature alignment, the SignHRNet can achieve highly-efficient sign type categorization and highly-accurate sign location determination. The proposed SignHRNet is intensively evaluated on three large-size datasets towards traffic signs recognition. Quantitative verifications demonstrate a competitive accuracy with an average mAP, mAP50, and mAP75 of 72.85%, 96.48%, and 85.31%, respectively, in processing traffic signs of varying self-conditions under diverse scenarios. Ablative and comparative analyses also confirmed the practical reliability and performance superiority of the SignHRNet in traffic signs recognition applications.

Spatial and Temporal Change of Reference Crop Evapotranspiration in Marmara Region

Bu çalışmada, Marmara Bölgesinde bulunan 18 meteoroloji istasyonu için 1990-2020 yılları arası aylık iklim verileri kullanılmış ve FAO Penman-Monteith yöntemi ile yıllık toplam ETo değerleri tahmin edilerek alansal ve zamansal değişimleri değerlendirilmiştir. Bu amaçla, her bir istasyon için parametrik olmayan Mann-Kendall testi ve Sen yöntemi kullanılarak yıllık toplam ETo eğilimleri belirlenmiş ve coğrafi bilgi sistemleri (CBS) Ters Mesafe Ağırlıklı (IDW) enterpolasyon yöntemi kullanılarak ta ETo haritaları hazırlanmıştır. Çalışma sonuçlarına göre, yıllık toplam ETo değerleri 742.3-1440.7 mm arasında değişim göstermiştir. Edirne, Kocaeli, Sakarya, Bozcaada, Çanakkale, Kırklareli, Uzunköprü, Tekirdağ, İpsala ve Dursunbey istasyonları için ETo değerlerinde istatistiksel olarak önemli artış eğilimleri belirlenirken, Kumköy-Kilyos ve Keles istasyonları için eğilimler önemli düzeyde azalan yönde olmuştur. Bununla birlikte, Bandırma, Bursa, Şile, Florya ve Gönen istasyonları için gerçekleşen ETo artış eğilimleri ile Bilecik için belirlenen azalış eğilimi istatistiksel olarak önemli olmamıştır. Yıllık ortalama ETo haritası değerlendirildiğinde, Marmara Bölgesi’nin batı kesimlerinde (Edirne’nin güneyi, Balıkesir’in batısı ve Çanakkale) ETo yüksek değerlere ulaşmıştır. Bölgenin kuzey kesimlerinde; Kırklareli ve Tekirdağ’ın doğusunda ve bölgenin doğu kesimlerinde; İstanbul’un doğusu, Kocaeli ve Sakarya’da ETo değerleri düşmüştür. Ayrıca Kırklareli, Tekirdağ’ın doğusu, Kocaeli ve Sakarya’da uzun yıllar ortalama ETo değerleri düşük bulunurken, bu illerde ki meteoroloji istasyonları verilerinden hesaplanan yıllık toplam ETo değerlerinde istatistiksel olarak önemli düzeyde artış eğilimleri belirlenmiştir.

Two Hours Ahead Prediction of the TEC over China Using a Deep Learning Method

In this paper, we forecast the regional total electron content (TEC) over China (0–60° N, 70–140° E) two hours in advance using a deep learning method called pix2pixhd that is based on Generative Adversarial Networks (GAN). We use the International GNSS Service (IGS) TEC maps over China during the 2003–2018 period for training and divide the data into three parts: a training set (2003–2013), a test set (2014–2017), and a validation set (2018). We evaluate the prediction effect of our model using Root Mean Square Error and correlation coefficient and compare our model with IRI-2016. The result demonstrates that our model shows a good performance for TEC prediction in China. Under different geomagnetic and solar activity conditions, the performance of our model is always better than IRI-2016. Analyzing the average difference map between the output of our model and the target IGS TEC map (+2 h), our model behaves well in China including the low-latitude region. In addition, our model behaves better during quiet time and high solar activity years. The successful application of pix2pixhd in forecasting the regional TEC maps over China demonstrates that deep learning methods can solve many geoscience problems, especially for ionospheric parameter forecasting.

Quantum state inference from coarse-grained descriptions: Analysis and an application to quantum thermodynamics

The characterization of physical systems relies on the observable properties which are measured, and how such measurements are performed. Here we analyze two ways of assigning a description to a quantum system assuming that we only have access to coarse-grained properties. More specifically, we compare the Maximum Entropy Principle method, with the Bayesian-inspired recently proposed Average Assignment Map method [P. S. Correia et al, Phys. Rev. A 103, 052210 (2021)]. Despite the fact that the assigned descriptions by both methods respect the measured constraints, and that they share the same conceptual foundations, the descriptions differ in scenarios that go beyond the traditional system-environment structure. The Average Assignment Map is thus shown to be a more sensible choice for the ever more prevalent scenario of complex quantum systems. We discuss the physics behind such a difference, and further exploit it in a quantum thermodynamics process.

Identification of Areas of Anomalous Tremor of the Earth’s Surface on the Japanese Islands According to GPS Data

Statistical properties of Earth surface tremors measured by means of GPS were investigated. This article considers measurements of the Earth’s surface displacements in three orthogonal directions relayed by a network of GPS sensors with about 1200 points distributed across Japan in 2009–2021. Next, the following characteristics of the tremors were considered: the entropy of the distribution of squared orthogonal wavelet coefficients, the entropy of the distribution of power spectrum values, and the spectral index. The anomalous regions of maxima of probability densities of the distribution of extreme values of the tremor statistics were determined: entropy minima and spectral index maxima. The average density maps of the distribution of extreme value tremor statistics were found to be highly correlated with one another. This made it possible to consider a weighted average density map and identify five anomalous regions in the center and south of Japan. A trajectory of visiting anomalous regions by a sequence of points realizing local maxima of the average probability density was obtained, for which seasonal periodicity was set. Estimates of changes in the average and maximum values of the correlation coefficients of tremor properties in an auxiliary network of 16 reference points in a semi-annual time window were obtained.