Precise Ones Research Articles

Fully relativistic energies, transition properties, and lifetimes of lithium-like germanium

Abstract Employing two fully relativistic methods, the multi-reference configuration Dirac–Hartree–Fock (MCDHF) method and the relativistic many-body perturbation theory (RMBPT) method, we report energies and lifetime values for the lowest 35 energy levels of the (1s2)nl configurations (where the principal quantum number n = 2–6 and the angular quantum number l = 0, …, n–1) of lithium-like germanium (Ge XXX), as well as complete data on the transition wavelengths, radiative rates, absorption oscillator strengths, and line strengths between the levels. Both the allowed (E1) and forbidden (magnetic dipole M1, magnetic quadrupole M2, and electric quadrupole E2) ones are reported. The results from the two methods are consistent with each other and align well with previous accurate experimental and theoretical findings. We assess the overall accuracies of present RMBPT results to be likely the most precise ones to date. The present fully relativistic results should be helpful for soft x-ray laser research, spectral line identification, plasma modeling and diagnosing. The datasets presented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00113.00135.

Semi-autonomous sensor fusion-based strategy for unmanned aerial atmospheric surveillance system in open field environments

<p>This paper explores the convergence of semi-autonomous systems and sensor fusion for monitoring hazardous atmospheric substances in open and semi-confined environments such as open-pit mines. As the heart of this system, a universal and flexible device leveraging wireless technologies to collect and analyze large volumes of data, designed by the authors, is proposed. The article outlines the key components of the platform, emphasizing its potential for increasing personnel safety levels as stipulated by the international public exposure guidelines. In modern mines, it becomes crucial to monitor elevated concentrations of nitrogen and carbon oxides, as well as other pollutants after blasting and the toxic gas emissions produced by the heavy transportation equipment such as mining trucks and excavators. In this paper, industrial-grade electrochemical sensors are compared with price-affordable but less precise microelectrochemical ones. The performed experiments for lowering computational requirements show promising results. The integration of an ultrasonic anemometer enhances the system’s capabilities, contributing to a comprehensive understanding of atmospheric conditions. In parallel, the research discusses the role of computer vision in autonomous control systems, with a focus on the architecture and processing pipeline of the state-of-the-art system-on-module Kria by AMD. Advocating the potential of adaptive computing for enhanced efficiency in dynamic environments, the paper underlines the importance of the integrated approach in developing a semi-autonomous atmospheric surveillance system and highlights the impact of adaptive computing in dynamic scenarios. The main part of the measuring equipment and methods has been experimented with in real conditions in open-pit mines in Bulgaria. This is an initial phase of ongoing research aimed at serving as a foundation for future improvements and the elaboration of a fully autonomous prototype for hazardous substances evaluation in the atmosphere of open-pit mines.<strong></strong></p>

Skeletal Editing: A Novel Method for "Psychiatric Drug Flipping" to Produce New, Precise Ones with Fewer Side Effects.

Skeletal Editing: A Novel Method for "Psychiatric Drug Flipping" to Produce New, Precise Ones with Fewer Side Effects.

Improvement of a mine’s ventilation test bench, using industrial communication and open-source microcontrollers.

This paper shows the results of optimizing a test bed used to simulate parameters related to Underground mines ventilation. The data acquisition card was changed for an Arduino microcontroller; likewise, some sensors were changed for more precise ones, and a sensor was adapted to measure relative humidity, dry temperature, and heat index. The measurements will continue to be observed using the LabVIEW software and complemented by an industrial communication system, the Modbus protocol. This protocol increases the data transmission speed of Ethernet. With the improvement of the system, the precision in the parameters (Pressure, temperatures, relative humidity, concentration of methane gas, Voltage, current) increases, and ultra-fast data transmission is guaranteed.

Analyzing the Relationship between Compressive Strength and Modulus of Elasticity in Concrete with Ladle Furnace Slag

The addition of Ladle Furnace Slag (LFS) to concrete modifies its compressive strength and modulus of elasticity and consequently impacts their relationship. This research evaluated both properties at 28, 90, and 180 days in concrete mixes produced with 5%, 10%, and 20% of two LFS types, both stabilized and non-stabilized. The relationship between them was then analyzed through these experimental results by adopting a statistical approach. A three-way analysis of variance revealed that both properties were affected by LFS differently. Thus, the effect of each LFS content on both features varied depending on its composition and pre-treatment. Furthermore, the effect of the LFS content on the compressive strength was also influenced by the age of the concrete. These facets implied that when analyzing the relationship between both mechanical properties, the monotonic correlations were stronger than the linear ones, reaching values between 0.90 and 1.00. Therefore, the double reciprocal regression models were the most precise ones for expressing the modulus of elasticity as a function of compressive strength. The model accuracy was further enhanced when discriminating based on the LFS type and introducing concrete age as a predictive variable. With all these considerations, the average deviations between the estimated and experimental values of 1–3% and the maximum deviations of 4–7% were reached, as well as R2 coefficients of up to 97%. These aspects are central to the further development of LFS concrete models.

Distributed Tracing for Troubleshooting of Native Cloud Applications via Rule-Induction Systems

Diagnosing IT issues is a challenging problem for large-scale distributed cloud environments due to complex and non-deterministic interrelations between the system components. Modern monitoring tools rely on AI-empowered data analytics for detection, root cause analysis, and rapid resolution of performance degradation. However, the successful adoption of AI solutions is anchored on trust. System administrators will not unthinkingly follow the recommendations without sufficient interpretability of solutions. Explainable AI is gaining popularity by enabling improved confidence and trust in intelligent solutions. For many industrial applications, explainable models with moderate accuracy are preferable to highly precise black-box ones. This paper shows the benefits of rule-induction classification methods, particularly RIPPER, for the root cause analysis of performance degradations. RIPPER reveals the causes of problems in a set of rules system administrators can use in remediation processes. Native cloud applications are based on the microservices architecture to consume the benefits of distributed computing. Monitoring such applications can be accomplished via distributed tracing, which inspects the passage of requests through different microservices. We discuss the application of rule-learning approaches to trace traffic passing through a malfunctioning microservice for the explanations of the problem. Experiments performed on datasets from cloud environments proved the applicability of such approaches and unveiled the benefits.

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging.

An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care. In collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics. The observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs. This study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results.

Fully relativistic many-body perturbation energies, transition properties, and lifetimes of lithium-like iron Fe XXIV

Employing the advanced relativistic configuration interaction (RCI) combined with the many-body perturbation theory (RMBPT) method, we report energies and lifetime values for the lowest 35 energy levels from the (1s2)nl configurations (where the principal quantum number n = 2–6 and the angular quantum number l = 0,…,n–1) of lithium-like iron Fe XXIV, as well as complete data on the transition wavelengths, radiative rates, absorption oscillator strengths, and line strengths between the levels. Both the allowed (E1) and forbidden (magnetic dipole M1, magnetic quadrupole M2, and electric quadrupole E2) ones are reported. Through detailed comparisons with previous results, we assess the overall accuracies of present RMBPT results to be likely the most precise ones to date. Configuration interaction effects are found to be very important for the energies and radiative properties for the ion. The present RMBPT results are valuable for spectral line identification, plasma modeling, and diagnosing.

Asymmetric information, signaling, and round listing prices: evidence from China’s housing market

ABSTRACT This paper argues that in housing transaction with asymmetric information, sellers may signal their private information with special design of listing prices. We build a cheap-talk signalling model in which sellers strategically choose round number listing prices to signal their weakness, i.e. they are willing to accept lower prices in order to sell their houses more quickly. Using housing market transaction data in Beijing, we find that round number listings sell 0.13% lower and 0.086 months sooner than precise ones. We also show that these results are unlikely to be driven by housing unobserved attributes or round number self-attractiveness. In addition, we find that as the roundness of listing price increases, the signalling effects become more pronounced. Our results present empirical evidence that signalling helps improve negotiation efficiency under asymmetric information.

Stochastic Dynamic Mass Spectrometric Quantitative and Structural Analyses of Pharmaceutics and Biocides in Biota and Sewage Sludge.

Mass spectrometric innovations in analytical instrumentation tend to be accompanied by the development of a data-processing methodology, expecting to gain molecular-level insights into real-life objects. Qualitative and semi-quantitative methods have been replaced routinely by precise, accurate, selective, and sensitive quantitative ones. Currently, mass spectrometric 3D molecular structural methods are attractive. As an attempt to establish a reliable link between quantitative and 3D structural analyses, there has been developed an innovative formula [DSD″,tot=∑inDSD″,i=∑in2.6388.10-17×Ii2¯-Ii¯2] capable of the exact determination of the analyte amount and its 3D structure. It processed, herein, ultra-high resolution mass spectrometric variables of paracetamol, atenolol, propranolol, and benzalkonium chlorides in biota, using mussel tissue and sewage sludge. Quantum chemistry and chemometrics were also used. Results: Data on mixtures of antibiotics and surfactants in biota and the linear dynamic range of concentrations 2-80 ng.(mL)-1 and collision energy CE = 5-60 V are provided. Quantitative analysis of surfactants in biota via calibration equation ln[D″SD] = f(conc.) yields the exact parameter |r| = 0.99991, examining the peaks of BAC-C12 at m/z 212.209 ± 0.1 and 211.75 ± 0.15 for tautomers of fragmentation ions. Exact parameter |r| = 1 has been obtained, correlating the theory and experiments in determining the 3D molecular structures of ions of paracetamol at m/z 152, 158, 174, 301, and 325 in biota.

Influence of Solar Activity on Precise Orbit Prediction of LEO Satellites

The perturbations of low earth orbit (LEO) satellites operating in the orbit of 300 ∼ 2000 km are complicated. In particular, the atmospheric drag force and solar radiation pressure force change rapidly over a short period of time due to solar activities. Using spaceborne global positioning system (GPS) data of the CHAMP, GRACE and SWARM satellites from 2002 to 2020, this paper studies in depth the influence of solar activity on LEO satellites’ precise orbit prediction by performing a series of orbit prediction experiments. The quality of GPS data is more susceptible to being influenced by solar activity during years when this activity is high and the changes in dynamic parameters are consistent with those of solar activity. The effects of solar activity on LEO orbit prediction accuracy are analyzed by comparing the predicted orbits with the precise ones. During years of high solar activity, the average root-mean-squares prediction errors at 10, 20, and 30 minutes are 0.15, 0.20, and 0.26 m, respectively, which are larger than the corresponding values in low-solar-activity years by 59%, 63%, and 68%, respectively. These results demonstrate that solar activity has a great influence on the orbit prediction accuracy, especially during high-solar-activity years. We should strengthen the real-time monitoring of solar activity and geomagnetic activity, and formulate corresponding orbit prediction strategies for the active solar period.

PHILNet: A novel efficient approach for time series forecasting using deep learning

Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

A Multi-Centric Evaluation of AI-Driven Synthetic CT Generation Form Low Field Magnetic Resonance Imaging

<h3>Purpose/Objective(s)</h3> Magnetic Resonance is an essential modality in the context of radiotherapy primarily for providing additional information with respect to tumor functional information leading to better target delineation (brain, prostate, etc.) and secondary in the context of emergence of magnetic resonance guided radiotherapy. However, in both cases computed tomography acquisition remains necessary for dosimetric purposes hampering the workflow, introducing additional cost and dose simulation/optimization discrepancies (due to the registration issues between MR & CT) while being associated with additional patient toxicity. This study aims to investigate in a retrospective manner the relevance deep learning synthetic CT generation as an alternative for simulation and planning for pelvic tumors treated with low field (0.35T) MRgRT. <h3>Materials/Methods</h3> In the context of this study, a cycle generative adversarial neural network (GAN) deep learning architecture was trained to determine a bijective transformation between a low field (0.35T) MR and the associated computed tomography scan acquisition. The training set involved 350 pairs of weakly aligned data of pelvis cases. A retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) involving different CT vendors was considered for testing. <h3>Results</h3> Reconstruction performance was assessed using the OARs used for treatment. The observed average scaled (between maximum and minimum HU values of the CT) difference between the reconstructed and the CT used for planning was 35.08 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 & 4.77) while rectum and sigmoid were the less precise ones (53.13 & 51.48). In terms of qualitative evaluation, the presence of fiducial markers heavily penalizes the reconstruction due to the implicit propagation of errors associated with the "convolution" nature of deep learning. The presence of air bubbles visible on the CT (sigmoid and rectum) is also an issue since these elements are marginally perceptible in the MR and therefore can hardly be predicted from generator. Detailed reconstruction results per organ are appended. <h3>Conclusion</h3> This retrospective multi-centric study is a first step toward assessing the potential of a fully low field MR-based treatment planning workflow that eliminates the need of CT acquisition. Integration of the synthetic CT in the simulation/optimization and assessment of the induced dosimetric errors are necessary steps to determine the clinical relevance of our preliminary findings.

The Causal Relationship Between Radiofrequency-Electromagnetic Radiation From Wireless Phones and Brain Tumor, Part 2: A Meta-Analysis of Current Literature Using Crude Proxies for RF-EMR Exposure Assessment

Introduction: Considering the imprecise characteristic of the currently used exposure classification categories discussed in the first part of this series article, in this second part, the authors will conduct a series of meta-analyses and subgroup analyses using various exposure classification categories from crude ones to more precise ones. Methods: The medical librarian searched MEDLINE (PubMed), EMBASE, and the Cochrane Library until 16 December 2020. Results and Discussion: As for the separate analysis stratified by each tumor type, the odds ratio for meningioma for regular users was statistically significantly decreased, 0.86 (95% CI 0.77-0.95). For ipsilateral users, the pooled odds ratio for meningioma (1.20 (95% CI 1.04-1.39)), glioma (1.45 (95% CI 1.16-1.82)), and malignant tumors (1.93 (95% CI 1.55-2.39)) showed a statistically significant increased estimate. For years of use, the pooled odds ratio for glioma for &amp;#x3e; 10 years of use group showed a statistically significant increased estimate (1.32 (95% CI 1.01-1.71)). To sum up, these results stratified by each tumor type, glioma, and malignant tumors were strong candidate tumor types of the possible tumorigenic effect of RF-EMR from wireless phones. In the meta-analysis for the total cumulative hours of use &amp;#x3e; 867 hours, the pooled odds ratio was 1.56 (95% CI 1.27-1.91). This statistically significant increased pooled odds ratio indicates that a rather moderate amount of cumulative exposure to RF-EMR from wireless phones could induce brain tumors. Conclusion: The study hypothesis, ‘the causal relationship between RF-EMR exposure and brain tumor incidence can be clearly validated only when accurate exposure assessment methods are applied,’ was validated by the increasing pattern of estimated odds ratios from the use of rather crude exposure categories to more precise exposure categories.

Expressing Times in Indonesian and English: A Contrastive Study

This paper deals with the differences and similarities of how Indonesian and English speakers asking about or informing time. By using data collected intuitively and empirically tested with other native speakers, and compared with their English equivalents, it is found that in spite of their few similarities, both languages that belong to different language family group, show a lot structural and pragmatic differences with regard to asking about and informing times, of either precise and non-prise ones.

Review of bioinformatic tools used in Computer Aided Drug Design (CADD)

Drug discovery is а time consuming рrосess of finding out a new drug molecule. The process takes many years to complete and needs human resource. These is difficulties have been overcome by introducing computer programmes in drug discovery (CADD) which includes target identification, hit identification, and molecular modification of а lead compound to optimize desired effects and minimize side effects, based on the knowledge of their biological targets. Molecular modelling is the process of designing a molecule with a computer-based collection of programmes (in-silico design) for deriving, representing, and manipulating the structures and reactions of molecules. Numerous Software tools, online data bases and computer programmes are used in the field of CADD in which some relevant, user friendly and precise ones are reviewed in this article. Software is available for personal use and for commercial purposes. All these tools are highly useful in the field of drug design and discovery. The article will be helpful for selecting a tool for computer aided drug design.

Mining weighted sequential patterns in incremental uncertain databases

Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact. In many real cases, weights of items and patterns are introduced to find interesting sequences as a measure of importance. Hence, a constraint of weight needs to be handled while mining sequential patterns. Besides, due to the dynamic nature of databases, mining important information has become more challenging. Instead of mining patterns from scratch after each increment, incremental mining algorithms utilize previously mined information to update the result immediately. Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases. However, these algorithms are confined to mine the precise ones. Therefore, in this work, we develop an algorithm to mine frequent sequences in an uncertain database in this work. Furthermore, we propose two new techniques for mining when the database is incremental. Extensive experiments have been conducted for performance evaluation. The analysis showed the efficiency of our proposed framework.

What Is the Value of Vagueness?

AbstractClassically, vagueness has been considered something bad. It leads to the Sorites paradox, borderline cases, and the (apparent) violation of the logical principle of bivalence. Nevertheless, there have always been scholars claiming that vagueness is also valuable. Many have pointed out that we could not communicate as successfully or efficiently as we do if we would not use vague language. Indeed, we often use vague terms when we could have used more precise ones instead. Many scholars (implicitly or explicitly) assume that we do so because their vagueness has a positive function. But how and in what sense can vagueness be said to have a function or value? This paper is an attempt to give an answer to this question. After clarifying the concepts of vagueness and value, it examines nine arguments for the value of vagueness, which have been discussed in the literature. The (negative) result of this examination is, however, that there is not much reason to believe that vagueness has a value or positive function at all because none of the arguments is conclusive. A tenth argument that has not been discussed so far seems most promising but rests on a solely strategic notion of function.

The Money Should Be Base Point in the Economy

In modern time the world really have forgotten what the money is. Why the humanity invented it, what was (and should be) its function in the society? Maybe it is better to compare this tool to other inventions done during centuries, used and developed? Why the society was developing the used units to make them as exact as possible? Here is a trial to show that the society step by step developed the tools of social cooperation. Mainstream goal always was to find better possibility to make honest contracts, to do easy exchanges, to trade easily. To look at in history it is possible to see how it was changing all the measurement units during time: many different measure unit of weight mainly came into one precise kg, many different measurement units of length mainly came into one precise Meter, many different measurement units of time came to second, so, nowadays kg, meter, second and some more different units are most precise ones and used by humanity as they are very usable in the contracts. The money too, in the 19^th century came to gold (and silver) and became most trustable tool of measurement of value. And something happened in 20^th century, something what the money did not deserve. Modern world has stepped back in case of money.