Tracking Covid-19 Cases and Deaths in the United States Distribution of Events by Day of Pandemic

In this paper, we analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020, with a novel metric representing the partial-average day-of-event, where events are new cases and new deaths. The metric is calculated as a function of date and location to illustrate patterns of disease, showing growing or waning cases and deaths. The metrics enable the direct comparison of the time distribution of cases and deaths, revealing data coherence and revealing how patterns varied over a one-year period. We also compare different methods of estimating actual infections and deaths to get a better perspective on the timing and dynamics of the pandemic by state. We used three example states to graphically compare metrics as functions of date, and also compared statistics derived from all 50 states. Over the period studied, average case day and average death day vary by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana. The average day of death has preceded the average day of case in Centers for Disease Control (CDC) data for most states and most dates since June of 2020. For contrast, ″COVID-19 Projections″ more closely align deaths and cases, which are similarly distributed.

Coherence of Cadastral Data in Land Management—A Case Study of Rural Areas in Poland

The cadaster functions laid down in the law should guarantee the safety of one’s rights. The reliability of the data gathered in the cadaster affects decisions concerning specific real estate or taken within the sphere of economic management. The legislation often requires the use of cadastral data, which makes it necessary to keep it up-to-date and coherent with the situation in the field. The effects of a lack of coherence may impact public finances and land management. Maintaining high-quality cadastral data is time-consuming and expensive. This study analysed the data coherence between the state in the field and cadastral documents. The analysis was based mainly on the information about the area of a plot and land use. The coherence index showed that the differences between registers and the state in the field range from 30% to 80%. This can be changed by comprehensive data modernisation, which can be facilitated using modern technology. Given the diverse use of cadastral data and the global trends in cadaster development and implementation of the third dimension, the currency and reliability of cadastral data become particularly important.

Sentinel-1 InSAR data by LiCSAR system

<p>Looking Into the Continents from Space with Synthetic Aperture Radar (LiCSAR) is a system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR automatically produces geocoded wrapped and unwrapped interferograms combining every acquisition epoch with four preceding epochs, and complementary data (coherence, amplitude, line-of-sight unit vectors, digital elevation model, metadata, and atmospheric phase screen estimates by the Generic Atmospheric Correction Online Service, GACOS).</p><p>The LiCSAR products are generated in frame units where a standard frame covers ~220x250 km, at 0.001° resolution (WGS-84 coordinate system). Frames are continuously updated for tectonic and volcanic priority areas. In 2020, the LiCSAR system covered about 1,500 global frames in which we have processed over 89,000 Sentinel-1 acquisitions and generated over 300,000 interferograms. Among these, 470 frames cover 1,024 global volcanoes. We aim to cover the global seismic mask defined by the Committee on Earth Observation Satellites (CEOS), but focus initially on the Alpine-Himalayan belt and East African Rift.</p><p>We serve the products as open and freely accessible through our web portal: https://comet.nerc.ac.uk/comet-lics-portal and aim to provide them to shared infrastructures as the European Plate Observing System (EPOS). We also generate rapid response coseismic interferograms for earthquakes with moment magnitude (Mw)> 5.5  a few hours after the postseismic data become available, and we update frames covering active volcanoes twice per day.</p><p>Our products can be directly converted to displacement time series and velocities using  the LiCSBAS time series analysis software. We present solutions implemented in LiCSAR, and show several case studies that use LiCSAR and LiCSBAS products to measure tectonic and volcanic deformation.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.1c122b867cff59390830161/sdaolpUECMynit/12UGE&app=m&a=0&c=02895a62108de9393057db6a355e3b06&ct=x&pn=gnp.elif&d=1" alt=""></p>

Coherent diffraction imaging for enhanced fault and fracture network characterization

Faults and fractures represent unique features of the solid Earth and are especially pervasive in the shallow crust. Aside from directly relating to crustal dynamics and the systematic assessment of associated risk, fault and fracture networks enable the efficient migration of fluids and therefore have a direct impact on concrete topics relevant to society, including climate-change-mitigating measures like CO2 sequestration or geothermal exploration and production. Due to their small-scale complexity, fault zones and fracture networks are typically poorly resolved, and their presence can often only be inferred indirectly in seismic and ground-penetrating radar (GPR) subsurface reconstructions. We suggest a largely data-driven framework for the direct imaging of these features by making use of the faint and still often underexplored diffracted portion of the wave field. Finding inspiration in the fields of optics and visual perception, we introduce two different conceptual pathways for coherent diffraction imaging and discuss respective advantages and disadvantages in different contexts of application. At the heart of both of these strategies lies the assessment of data coherence, for which a range of quantitative measures is introduced. To illustrate the versatility and effectiveness of the approach for high-resolution geophysical imaging, several seismic and GPR field data examples are presented, in which the diffracted wave field sheds new light on crustal features like fluvial channels, erosional surfaces, and intricate fault and fracture networks on land and in the marine environment.

Coherent diffraction imaging for enhanced fault and fracture network characterization

Faults and fractures represent unique features of the solid Earth and are especially pervasive in the shallow crust. Aside from directly relating to crustal dynamics and the systematic assessment of associated risk, fault and fracture networks enable the efficient migration of fluids and, therefore, have a direct impact on concrete topics relevant to society, including climate-change mitigating measures like CO2 sequestration or geothermal exploration and production. Due to their small-scale complexity, fault zones and fracture networks are typically poorly resolved and their presence can often only be inferred indirectly in seismic and ground-penetrating radar (GPR) subsurface reconstructions. We suggest a largely data-driven framework for the direct imaging of these features by making use of the faint and still often under-explored diffracted portion of the wavefield. Finding inspiration in the fields of optics and visual perception, we introduce two different conceptual pathways for coherent diffraction imaging and discuss respective advantages and disadvantages in different contexts of application. At the heart of both of these strategies lies the assessment of data coherence, for which a range of quantitative measures is introduced. To illustrate the approaches versatility and effectiveness for high-resolution geophysical imaging, several seismic and GPR field data examples are presented, in which the diffracted wavefield sheds new light on crustal features like fluvial channels, erosional surfaces, and intricate fault and fracture networks on land and in the marine environment.

A Lock Free Approach To Parallelize The Cellular Potts Model: Application To Ductal Carcinoma In Situ

In the field of computational biology, in order to simulate multiscale biological systems, the Cellular Potts Model (CPM) has been used, which determines the actions that simulated cells can perform by determining a hamiltonian of energy that takes into account the influence that neighboring cells exert, under a wide range of parameters. There are some proposals in the literature that parallelize the CPM; in all cases, either lock-based techniques or other techniques that require large amounts of information to be disseminated among parallel tasks are used to preserve data coherence. In both cases, computational performance is limited. This work proposes an alternative approach for the parallelization of the model that uses transactional memory to maintain the coherence of the information. A Java implementation has been applied to the simulation of the ductal adenocarcinoma of breast in situ (DCIS). Times and speedups of the simulated execution of the model on the cluster of our university are analyzed. The results show a good speedup.

Revisiting Energy Demand Relationship: Theory and Empirical Application

In this paper, we revisit the theoretical framework for energy demand. We then use this theoretical framework to empirically model the Saudi Arabian industrial electricity demand. We show, in the case of Saudi Arabian data, that imposing parsimonious energy demand specification on data without testing relevant assumptions can lead to biased estimations and noticeably poor approximations, while imposing general energy demand specification without accounting for the data properties can lead to redundant estimations and lower approximation than what could be obtained otherwise. Combining the theory with the data can provide unbiased and irredundant estimations with high levels of approximations. Hence, this paper recommends, based on the empirical findings, that a better strategy would be the combination of theoretical coherence with data coherence in the General to Specific Modeling (GtSM) framework for the empirical analyses of energy demand.

Router-integrated Cache Hierarchy Design for Highly Parallel Computing in Efficient CMP Systems

In current Chip Multi-Processor (CMP) systems, data sharing existing in cache hierarchy acts as a critical issue which costs plenty of clock cycles for maintaining data coherence. Along with the integrated core number increasing, the only shared cache serves too many processing threads to maintain sharing data efficiently. In this work, an enhanced router network is integrated within the private cache level for fast interconnecting sharing data accesses existing in different threads. All sharing data in private cache level can be classified into seven access types by experimental pattern analysis. Then, both shared accesses and thread-crossed accesses can be rapidly detected and dealt with in the proposed router network. As a result, the access latency of private cache is decreased, and a conventional coherence traffic problem is alleviated. The process in the proposed path is composed of three steps. Firstly, the target accesses can be detected by exploring in the router network. Then, the proposed replacement logic can handle those accesses for maintaining data coherence. Finally, those accesses are delivered in the proposed data deliverer. Thus, the harmful data sharing accesses are solved within the first chip layer in 3D-IC structure. The proposed system is also implemented into a cycle-precise simulation platform, and experimental results illustrate that our model can improve the Instructions Per Cycle (IPC) of on-chip execution by maximum 31.85 percent, while energy consumption can be saved by about 17.61 percent compared to the base system.