Timing Information Research Articles

ON THE ISSUE OF IMPROVING ACCOUNTING AND TAX ACCOUNTING OF FINANCIAL RESULTS OF TRADE ORGANIZATIONS

The article is devoted to the assessment of problems and methods of accounting and tax accounting of financial results. Accounting and tax accounting of financial results is an important part of economic activity. The relevance of the topic lies in the fact that due to constant changes in legislation and in the economy, it is necessary to revise the methods of accounting for financial results in the organization and identify current problems and inconsistencies, otherwise it may threaten the bankruptcy of the company or penalties from the tax. Information about financial results gives a general idea of the effectiveness of the organization. The more accurate this information is, and information in modern times is the most valuable resource, the better for the organization.
 The methods of accounting and tax accounting of financial results in an organization affect the accuracy and reliability of accounting statements, as well as form the tax burden, including income tax. Errors in accounting and tax accounting can significantly increase expenses and the amount of taxes paid. Also, errors can lead to an unplanned tax audit.
 Analytical work has been carried out in the organization under study to find factors affecting reliable and effective accounting of financial results, and their own ways of solving problems have been proposed.

Motion capture method for college basketball training based on AR/VR technology

Abstract To improve the effectiveness of basketball running training, this paper proposes an AR/VR technology-based motion capture method for college basketball sports training. This paper first describes the method steps of virtual reality motion capture technology, data fusion and skeletal data normalization of skeletal data, and calibration to obtain the rotation matrix and displacement vector of each Kinect sensor to integrate the skeleton data. Then the data features are extracted, 3D joint position, joint velocity, joint angle and angular velocity are extracted from the fused skeleton information of each frame, and then the LSTM algorithm is used to obtain the timing information in the action sequence and to classify the action for recognition. Finally, the method’s performance is evaluated in terms of accuracy, recall, and response time. Regarding accuracy, the recognition rates of “shooting” and “defense” were around 85%, while the recognition rates of other actions were 93% and above. In terms of recognition time, the recognition time of common equipment is about 350ms, while the recognition time of virtual reality equipment is about 210ms, which is 100ms less than that of traditional equipment, demonstrating the effectiveness and feasibility of this method.

Customer Complaints-Based Water Quality Analysis

Social media has become a useful instrument and forum for expressing worries about various difficulties and day-to-day concerns. The pertinent postings containing people’s complaints about water quality as an additional source of information can be automatically acquired/retrieved and analyzed using natural language processing and machine learning approaches. In this paper, we search social media for a water quality analysis and propose a scalable messaging system for quality-related issues to the subscribers. We classify the WaterQualityTweets dataset, our newly collected collection, in two phases. In the first phase, tweets are classified into two classes (water quality-related or not). In the second phase, water quality-related issues are classified into four classes (color, illness, odor/taste, and unusual state). The best performance results are BERT and CNN, respectively, for binary and multi-class classification. Also, these issues are sent to different subscribers via a topic-based system with their location and timing information. Depending on the topics that online users are interested in, some information spreads faster than others. In our dataset, we also predict the information diffusion to understand water quality issues’ spreading. The time and effort required for manual comments obtained through crowd-sourcing techniques will significantly decline as a result of this automatic analysis of water quality issues.

Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer’s disease and related dementia

BackgroundAlzheimer’s disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.MethodsWe developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.ResultsThe estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of Abeta _{42}.ConclusionBy leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.Trial registrationclinicaltrials.gov, NCT01926249. Registered on 16 August 2013.

Time and Energy Resolving Time-over-Threshold ASIC for MPPC module in TOF-PET system (ToT-ASIC2)

ToT-ASIC2 is a new custom front-end application specific integrated circuit (ASIC) to readout silicon photomultiplier (SiPM) and multi-pixel photon counter (MPPC) for time-of-flight positron emission tomography (ToF-PET). ToT-ASIC2 is a 64-channel ASIC fabricated using a commercial 0.25 μm CMOS process. Each channel has an input-current buffer that sends signals to the SLOW and FAST paths, which are used for incident radiation energy and timing information, respectively. Time-over-Threshold (ToT) signals from these paths are combined into one ToT signal to be output outside the ASIC. The power consumption is approximately 2.9 mW per channel.This study developed a detection system comprising an MPPC with coupled GAGG pixelated scintillators, a front-end board with ToT-ASIC2 and ToT-DAQ. Consequently, energy and timing measurement experiments were performed using this system to confirm the ToT-ASIC2 energy- and timing-resolving capability. Prototype positron emission tomography experiments with 22Na indicated an energy resolution of 6.7% for 511 keV with the coincidence timing resolution being 215 ps.

DownShift: Tuning Shift Reduction With Reliability for Racetrack Memories

Ultra-dense non-volatile <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">racetrack memories</i> (RTMs) have been investigated at various levels in the memory hierarchy for improved performance and reduced energy consumption. However, the innate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shift</i> operations in RTMs, required for data access, incur performance penalties and can induce position errors. These factors can hinder their applicability in replacing low-latency, reliable on-chip memories. Intelligent placement of memory objects in RTMs can significantly reduce the number of shifts per memory access with little to no hardware overhead. However, existing placement strategies may lead to sub-optimal performance when applied to different architectures. Additionally, the impact of these shift optimization techniques on RTM reliability has been insufficiently investigated. We propose DownShift, a generalized data placement mechanism that improves upon prior approaches by taking into account (1) the timing and liveliness information of memory objects and (2) the underlying memory architecture, including required shifting fault tolerance. Thus, we also propose a collaboratively designed new shift alignment reliability technique called GROGU. GROGU leverages the reduced shift window made possible through DownShift allowing improved reliability, area, and energy compared to the state-of-the-art reliability approaches. DownShift reduces the number of shifts, runtime, and energy consumption by 3.24×, 47.6%, and 70.8% compared to the state-of-the-art. GROGU consumes 2.2× less area and 1.3× less energy while providing 16.8× improvement in shift fault tolerance compared to the leading reliability approach for a latency degradation of only 3.2%.

FIELD SURVEY OF THE 30 OCTOBER 2020 SAMOS (AEGEAN SEA) TSUNAMI IN THE GREEK ISLANDS

On October 30th, 2020, a magnitude 7.0 earthquake struck offshore from the northern coast of Samos, Greece, generating a tsunami that impacted the nearshore Greek islands and the Aegean coastline of Turkey. The 2020 Samos (Aegean Sea) tsunami is arguably the most significant event in the Aegean since the 09 July 1956 Amorgos earthquake and tsunami that produced runup values as high as 20 m on the south coast of Amorgos (Okal et al., 2009). Maximum runup reached 3.8 m along the Turkish coast (Dogan et al., 2021), and ~3 m on the north coast of Samos Island (Kalligeris et al., 2021). We present detailed results from several post-event field surveys, and report first wave arrival timing and polarity information as well as tsunami height/runup measurements, from five islands.

High-precision digital time-amplitude analysis system for the neutron Time-Of-Flight spectrometer in HL-2M

Neutron time-of-flight (TOF) spectrometers are widely used for neutron diagnostics in large-scale fusion facilities. However, traditional amplitude and time analysis methods are limited in terms of resolution, flexibility, and other factors. To overcome these limitations, we propose the Digital Time-Amplitude Analysis (DTAA) system, which combines the advantages of both methods by using a high-performance FPGA for real-time time and amplitude analysis of particle pulses. The DTAA system provides accurate timing and pulse shape information, fast processing speed, and low implementation cost. We have also developed a multi-mode data joint time-of-flight correction algorithm and verified its performance through simulation. The DTAA system passed laboratory and tokamak tests, achieving a time measurement precision of 122 ps and accurately measuring neutron counts in tokamak experiments. This work represents a key step towards comprehensive high-performance integrated particle pulse analysis.

Bayes estimators with extension of Jeffery prior information for Time censored data and Failure censored data

In this research, the Bayes estimator was derived based on Time censored data of the first type, and the Failure censored data of the second type. Reliance has been made on extension of Jeffery prior information. Finally, the simulation was used based on the MATLAB program and with different inputs to find the best estimator among Maximum Likelihood estimator and Bayes estimators with extension that has the least mean percentage error

A dairy goat tracking method via lightweight fusion and Kullback Leibler divergence

As an important task in precision breeding, accurately tracking dairy goats is essential for their disease prediction and abnormal behavior recognition. For efficiently capturing local features in Transformer and reducing the noise in the template of dairy goat, we develop an innovative dairy goat tracking algorithm that introduces an asymmetric fusion architecture and Kullback Leibler loss to improve accuracy and generate a low-noise template. Based on TrDiMP, we construct a fusion mechanism that embeds high-dimensional timing information into its evaluation branch to enhance discrimination ability. Then, we exploit an asymmetric attention mechanism to incorporate convolutional features of different sizes and raise the percentage of local linear information in smaller features. Finally, we incorporate Kullback Leibler divergence to train the loss between current frames and the goat template, guiding the template to conform to the probability density distribution of dairy goats and reducing noise. The AUC (Area Under Curve) and Precision of the improved TrDiMP reach 77.56% and 66.10%, respectively, which are 2.91% and 4.55% higher than that of TrDiMP. The average tracking speed of the improved TrDiMP is 14.90 FPS (frames per second), 0.36 FPS faster than that of TrDiMP. The overall results demonstrate that our model can track dairy goats and provide an effective approach to capture their dynamic movement in a real pasture.

Retrieval of Monsoon Landslide Timings With Sentinel‐1 Reveals the Effects of Earthquakes and Extreme Rainfall

AbstractMonsoon rainfall triggers hundreds of landslides across Nepal every year, causing significant hazard and mass wasting. Annual inventories of these landslides have been mapped using multi‐spectral satellite images, but these images are obscured by cloud cover during the monsoon, making it impossible to use them to constrain landslide timing. We employ recently developed techniques to derive individual timings from Sentinel‐1 for 579 landslides in 2015, 2017, 2018, and 2019 in Nepal. We use this new timing information alongside satellite rainfall data to identify spatio‐temporal clusters of landslides and associate these with periods of particularly intense rainfall. We also observed that during the 2015 monsoon, many landslides failed earlier and in dryer conditions than in 2017–2019. We use physical models to demonstrate how this requires a temporary loss of hillslope strength following the Mw 7.8 Gorkha Earthquake sequence and suggest a modeled cohesion loss in the range 1–3 kPa.

Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons.

A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed network is designed for image recognition tasks, which are frequently used to evaluate the performance of conventional deep neural networks. To manifest the bio-realistic neural architecture, the learning is unsupervised, and the inhibition weight is dynamically changed; this, in turn, affects the synaptic wiring method based on Hebbian learning and the neuronal population. In the inference phase, Bayesian inference successfully classifies the input digits by counting the spikes from the responding neurons. The experimental results demonstrate that the proposed biological model ensures a performance improvement compared with other biologically plausible SNN models.

Infrared optical signature reveals the source–dependency and along–transport evolution of dust mineralogy as shown by laboratory study

Advancing knowledge of the mineralogical composition of dust is key for understanding and predicting its climate and environmental impacts. The variability of dust mineralogy from one source to another and its evolution during atmospheric transport is not measured at large scale. In this study we use laboratory measurements to demonstrate that the extinction signature of suspended dust aerosols in the 740 − 1250 cm−1 atmospheric window can be used to derive dust mineralogy in terms of the main infrared − active minerals, namely quartz, clays, feldspars and calcite. Various spectral signatures in dust extinction enable to distinguish between multiple global sources with changing composition, whereas modifications of the dust extinction spectra with time inform on size − dependent particles mineralogy changes during transport. The present study confirms that spectral and hyperspectral infrared remote sensing observations offer great potential for elucidating the size − segregated mineralogy of airborne dust at regional and global scales.

U-Pb ages of Gongga Shan granite, western Sichuan and its implication to the tectonic evolutions of eastern Tibet

The eastern margin of the Tibetan Plateau has given rise to much debate about mechanisms of plateau uplift and evolution and, in particular, the role of the lower crust in crustal thickening. Knowledge of the middle to lower crust conditions is critical for evaluating various models of crustal deformation, but data on crustal evolution through time are lacking. Here, we turn to the Gongga Shan granite, an intrusion along the Xianshuihe fault in easternmost Tibet that directly records local Cenozoic crustal conditions. We present 124 U-Pb samples from the Gongga Shan granite (GSG) that prove that the crust has been stepwise producing partial melt from 56 Ma to 4 Ma. According to the age distribution, the GSG can be separated into four major groups with ages of 4–10 Ma, 12–20 Ma, 25–40 Ma, and 43–56 Ma. Combining the timing information with geophysics and low-temperature thermochronology data, we suggest that events younger than 10 Ma may indicate the onset of recent crustal channel flow in the middle to lower crust. In contrast, the youngest 4 Ma ages indicate the ongoing partial melting of the middle crust. The 12–20 Ma events could be related to an earlier stage of crustal channel flow, consistent with the regional large-scale crustal channel flow in central Tibet.

Actuator fault and Bernoulli random delay-based H∞ reliable fuzzy control of singular hybrid vehicle suspension systems with impulsive perturbations

The problem of actuator fault and Bernoulli random delay-based H∞ reliable fuzzy state feedback control of singular hybrid vehicle suspension systems (SHVSSs) with impulsive perturbations is studied in this paper. By taking the acceleration of each part of the suspension as the component of the system state, the vehicle suspension system is successfully extended to a singular vehicle suspension system. The regularity and non-impulsiveness of the time-varying delays SHVSSs with impulsive perturbations are proved via singular value decomposition (SVD) method. By means of constructing a novel Lyapunov-Krasovskii (L-K) functional embodied with Markovian jump modes and impulse times information, neoteric conditions are smoothly concluded under linear matrix inequalities (LMIs) framework, which guarantee that the closed-loop SHVSS acquires H∞ stochastic admissibilization. By exploiting Takagi-Sugeno (T-S) fuzzy technique and parallel-distributed compensation (PDC) method, a reliable Bernoulli random delay-based fuzzy mode-dependent feedback (FMDF) controller is proposed to handle the change of sprung mass and actuator fault of the controlled time-varying delays SHVSSs. Simulation results verify the validity of the method and riding comfort level of the vehicle.

Evaluating Spatial, Cause-Specific and Seasonal Effects of Excess Mortality Associated with the COVID-19 Pandemic: The Case of Germany, 2020

BackgroundEvaluating mortality effects of the COVID-19 pandemic using all-cause mortality data for national populations is inevitably associated with the risk of masking important subnational differentials and hampering targeted health policies. This study aims at assessing simultaneously cause-specific, spatial and seasonal mortality effects attributable to the pandemic in Germany in 2020.MethodsOur analyses rely on official cause-of-death statistics consisting of 5.65 million individual death records reported for the German population during 2015–2020. We conduct differential mortality analyses by age, sex, cause, month and district (N = 400), using decomposition and standardisation methods, comparing each strata of the mortality level observed in 2020 with its expected value, as well as spatial regression to explore the association of excess mortality with pre-pandemic indicators.ResultsThe spatial analyses of excess mortality reveal a very heterogenous pattern, even within federal states. The coastal areas in the north were least affected, while the south of eastern Germany experienced the highest levels. Excess mortality in the most affected districts, with standardised mortality ratios reaching up to 20%, is driven widely by older ages and deaths reported in December, particularly from COVID-19 but also from cardiovascular and mental/nervous diseases.ConclusionsOur results suggest that increased psychosocial stress influenced the outcome of excess mortality in the most affected areas during the second lockdown, thus hinting at possible adverse effects of strict policy measures. It is essential to accelerate the collection of detailed mortality data to provide policymakers earlier with relevant information in times of crisis.

Novel radiation hard position sensitive detector using YAG:Ce fibers for ion beam instrumentation at FRIB

We are investigating the feasibility of a new detector concept for use in the beam line at the Facility for Rare Isotope Beams (FRIB). The detector is designed to withstand the high ion beam intensities and the high radiation environment while providing position, timing, and energy information. Based on the concept of a fiber detector, the device uses YAG:Ce crystals as sensing material, chosen for their extreme radiation tolerance to electromagnetic or hadronic exposure. The crystals are machined into long and thin rods which are laid out in orthogonal horizontal and vertical arrangements to obtain spatial sensitivity to impinging particles. The readout is performed at the extremities of the rods with strip Multi Pixel Photon Counters (MPPC) also referred to as Silicon Photomultipliers (SiPM) arrays model S13552 from Hamamatsu mounted on ribbon cables with connector ends.

Theoretic impetuses and lengths of Feinberg-Horodecki equation

The exact solution of the Feinberg-Horodecki equation for time-dependent harmonic vector potential has been investigated under a one-dimensional system. The quantized momentum and its corresponding un-normalized wave functions were explicitly obtained. The Fisher information (for time and momentum) and variance (for time and momentum) were calculated using expectation values of time and momentum via Hellman-Feynman Theory (HFT). The time and momentum Shannon entropy were obtained using an existing formula. Numerical results were for time and momentum Fisher information to confirm the Cramer-Rao inequality. Another numerical results were obtained for time and momentum Shannon entropy to confirm Bialynick-Birula, Mycielski (BBM) inequality. The effects of the potential parameters such as mass of the spring and the frequency on the theoretic quantities were fully examined. The new variance inequality was established using the inequalities of Fisher information. The established inequality was confirmed by numerical results which also satisfied the popular Cramer-Rao inequality. The theoretic impetuses for Fisher information, variance and Shannon entropy respectively, were calculated and their variation with some potentials were studied.

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

New energy vehicles are developing rapidly in the world, China and Europe are vigorously promoting new energy vehicles. The State of Charge (SOC) is circumscribed as the remaining charge of the lithium battery (Li-ion), that indicates the driving range of a pure electric vehicle. Additionally, it is the basis for SOH and fault state prediction. Nevertheless, the SOC is incapable of measuring directly. In this paper, an LSTM-Attention-R network framework is proposed. The LSTM algorithm is accustomed to present the timing information and past state information of the lithium battery data. The Attention algorithm is used to extract the global information of features and solve the problem of long-term dependency. To ensure the diversity of feature extraction, the Attention algorithm in this paper uses multi-headed self-attentiveness. The CACLE dataset from the University of Maryland is used in this paper. Through the training of the model and the comparison, it is concluded that the LSTM-Attention-R algorithm networks proposed in this article can predict the value of SOC well. Meanwhile, this paper compares the LSTM-Attention-R algorithm with the LSTM algorithm, and also compares the LSTM-Attention-R algorithm with the Attention algorithm. Finally, it is concluded that the accomplishment of the network framework contrived is superior to the performance of these two algorithms alone. Finally, the algorithm has good engineering practice implications. The algorithm proposed provides a better research direction for future parameter prediction in the field of lithium batteries. It has a better theoretical significance.

Public health impact of the U.S. Scenario Modeling Hub

Beginning in December 2020, the COVID-19 Scenario Modeling Hub has provided quantitative scenario-based projections for cases, hospitalizations, and deaths, aggregated across up to nine modeling groups. Projections spanned multiple months into the future and provided timely information on potential impacts of epidemiological uncertainties and interventions. Projections results were shared with the public, public health partners, and the Centers for Disease Control COVID-19 Response Team. The projections provided insights on situational awareness and informed decision-making to mitigate COVID-19 disease burden (e.g., vaccination strategies). By aggregating projections from multiple modeling teams, the Scenario Modeling Hub provided rapidly synthesized information in times of great uncertainty and conveyed possible trajectories in the presence of emerging variants. Here we detail several use cases of these projections in public health practice and communication, including assessments of whether modeling results directly or indirectly informed public health communication or guidance. These include multiple examples where comparisons of projected COVID-19 disease outcomes under different vaccination scenarios were used to inform Advisory Committee for Immunization Practices recommendations. We also describe challenges and lessons learned during this highly beneficial collaboration.