Different Levels Of Precision Research Articles

Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks

AbstractAssessing alternative solutions that have uncertain evaluations in conflicting multiple criteria is not straightforward. Probabilistic models such as Bayesian networks (BNs) can effectively model and represent the uncertainty in such problems, but they do not include built-in mechanisms to guide different decision makers (DMs) with varying preferences toward the final decision. We propose a systematic approach to combine outranking methods with BNs to provide decision support for solutions with multiple and conflicting criteria under uncertainty. The proposed approach is based on Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and it offers different levels of precision and flexibility to the DMs in assessing the solutions. Our approach includes graphical tools and summary metrics to enhance the presentation of its results to the DMs. We test our approach with a case study on supplier selection where the uncertainty in supplier performances is modeled with a BN. We demonstrate that the proposed approach can enable the joint use of multiple criteria techniques with probabilistic modeling techniques like BNs to provide decision support in complex environments including uncertainty.

Solar Jet Hunter: a citizen science initiative to identify and characterize coronal jets at 304 AA

Solar coronal jets seen in extreme ultraviolet (EUV) are ubiquitous on the Sun, and they have been found in and at the edges of active regions, at the boundaries of coronal holes, and in the quiet Sun. Jets have various shapes, sizes, brightness, velocities, and durations in time, which complicates their detection by automated algorithms. So far, solar jets reported in the Heliophysics Event Knowledgebase (HEK) have been mostly reported by humans looking for them in the data, with different levels of precision regarding their timing and positions. We created a catalog of solar jets observed in EUV at 304 \ containing precise and consistent information on the jet timing, position, and extent. We designed a citizen science project, Solar Jet Hunter, on the Zooniverse platform, to analyze EUV observations at 304 \ from the Solar Dynamic Observatory/Atmospheric Imaging Assembly (SDO/AIA). We created movie strips for regions of the Sun in which jets have been reported in HEK and ask the volunteers to 1) confirm the presence of at least one jet in the data and 2) report the timing, position, and extent of the jet. We report here the design of the project and the results obtained after the analysis of data from 2011 to 2016. We note that 365 "coronal jet" events from HEK served as input for the citizen science project, equivalent to more than 120,000 images distributed into 9,689 "movie strips." Classification by the citizen scientists resulted in 883 individual jets being identified. We demonstrate how citizen science can enhance the analysis of solar data with the example of Solar Jet Hunter. The catalog of jets thusly created is publicly available and will enable statistical studies of jets and related phenomena. This catalog will also be used as a training set for machines to learn to recognize jets in further datasets.

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

Acoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring. This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. In the latter half of the model, the BLSTM network was incorporated to extract time series-related features, local spatial uncorrelated features, and weak periodic pattern features from the acoustic emission signal data. To solve the problem that the recognition accuracy of minority samples decreases, this study replaces ReLU activation function with SELU. The results show that the multiscale 1DCNN-BLSTM model embedded in ECA module has a good antinoise performance, and the recognition accuracy can reach over 90%. The discovery of this work provides a new idea for exploring the mechanism of rock mass instability.

An automatic bioinformatic system for Identification and classification of Hymenoptera (The Apidae family)

In this paper we illustrate our bioinformatics system destined for an automatic insect classification in different taxonomic categories corresponding to different levels of precision according to their morphological characteristics such as: size; color; antennae; wings; mouthpart; etc. Our main goal is to automate the research; classification and to create an exhaustive database of insects in order to create a common directory of insects to save significant research time and facilitate classification. Our system is considered as a bioinformatics system, it uses artificial intelligence techniques to create an inference engine able to automatically recognize and classify a new insect. It also uses database creation techniques to create the knowledge and fact bases necessary for the proper functioning of the inference engine. Due to its interactive graphic interface, and by guiding its user, step by step, the system starts with the introduction of the morphological characteristics of the insect and the result is to automatically classify the insect in the database in the appropriate species. Our classification is focused on Hymenoptera precisely on the Apidae family, we have given a detailed classification of the Apidae family with its three subfamilies: Apinae, Xylocopinae and Nomadini. Our system is implemented completely in JAVA programming language using phpMyadmin as a database management system.

Design and Implementation of Static Analyses for Tezos Smart Contracts

Once deployed in blockchain, smart contracts become immutable: attackers can exploit bugs and vulnerabilities in their code, that cannot be replaced with a bug-free version. For this reason, the verification of smart contracts before they are deployed in blockchain is important. However, the development of verification tools is not easy, especially if one wants to obtain guarantees by using formal methods. This paper describes the development, from scratch, of a static analyzer based on abstract interpretation for the verification of real-world Tezos smart contracts. The analyzer is generic with respect to the property under analysis. This paper shows taint analysis as a concrete instantiation of the analyzer, at different levels of precision, to detect untrusted cross-contract invocations.

Case Study: Role of Supply Chain & Transportation in Food and Healthcare

This case study delves into the indispensable roles of supply chain and transportation networks in the pivotal sectors of food and healthcare. These domains are not only vital for our well-being but also profoundly impacted by dynamic market forces, evolving consumer preferences, and global events. In the food sector, the journey from field to fork is a complex web of growers, manufacturers, distributors, and retailers. It must strike a delicate balance between delivering a diverse range of products while ensuring safety, freshness, and sustainability. Conversely, the healthcare sector demands an entirely different level of precision. The timely availability of life-saving medicines, medical equipment, and personal protective gear is non-negotiable. The efficiency of healthcare supply chains can be a matter of life and death. Recent challenges, from pandemics to vaccine distribution, have illuminated the need for agile healthcare supply chains and robust transportation networks. This case study will explore the critical challenges faced by these sectors, innovative solutions that have been devised to address them, and the profound lessons learned in the pursuit of building more resilient, efficient, and responsive supply chain and transportation systems.

Copula-based risk aggregation with trapped ion quantum computers

Copulas are mathematical tools for modeling joint probability distributions. In the past 60 years they have become an essential analysis tool on classical computers in various fields. The recent finding that copulas can be expressed as maximally entangled quantum states has revealed a promising approach to practical quantum advantages: performing tasks faster, requiring less memory, or, as we show, yielding better predictions. Studying the scalability of this quantum approach as both the precision and the number of modeled variables increase is crucial for its adoption in real-world applications. In this paper, we successfully apply a Quantum Circuit Born Machine (QCBM) based approach to modeling 3- and 4-variable copulas on trapped ion quantum computers. We study the training of QCBMs with different levels of precision and circuit design on a simulator and a state-of-the-art trapped ion quantum computer. We observe decreased training efficacy due to the increased complexity in parameter optimization as the models scale up. To address this challenge, we introduce an annealing-inspired strategy that dramatically improves the training results. In our end-to-end tests, various configurations of the quantum models make a comparable or better prediction in risk aggregation tasks than the standard classical models.

A Wider Scope of Analysis on 38% Silver Diamine Fluoride.

This research was conducted to further support previous studies on the effectiveness of 38% silver diamine fluoride on caries arrest. In addition, the number of applications, time between subsequent applications, type of teeth (primary vs. permanent), final restoration following treatment, and the type of restoration, if any, were assessed. It is a retrospective longitudinal study that was conducted using a chart review technique for which a waiver of informed consent was obtained. The research participants' privacy was ensured and protected. All participants' identities remained anonymous and confidential. Furthermore, consentwas collected from participants' sitters during their appointments as a protocol prior to applying 38% silver diamine fluoride (SDF). The current research was reviewed and approved by the Research Ethics Committee at King Faisal Specialist Hospital and Research Center (KFSH&RC). All investigatorsabided by the rules and regulations of the Government of Saudi Arabia, KFSH&RC, and the Research Advisory Council. A population of pediatric patients with primary and/or mixed dentition at a tertiary care hospital who were treated at the dental office between the period from March 2020 to December 2022 were recruited. A consecutive non-random sampling technique was used. The inclusion criteria include cavitated, asymptomatic, and teeth with no signs or symptoms of pulpal involvement. The sample size estimation was done by considering different percentages of success in arresting dental carries by using 38% SDF, with different levels of precision of 95% confidence intervals and with 0.05 level of significance. A 95% success was assumed in arresting dental carries by using 38% SDF, with a precision of ±5%, and with α=0.05, the required teeth sample size was 101. Anticipating some incomplete information and documentation, the sample size may be dynamic. Nighty-seven teeth both primary and permanent were included in this study. Data were analyzed using IBM SPSS Statistical software version 26.0 (Armonk, NY: IBM Corp.) for Windows. Descriptive statistics (mean standard deviation, frequencies, and percentages) were used to describe the quantitative and categorical outcome variables. Pearson's chi-square test and Fisher's exact test were used to compare the proportion success of 38% SDF in relation to the characteristics of study subjects. The odds ratios were used to quantify the measure of association between the success and other categorical study variables. A binary multiple logistic regression was used to identify the independent variables associated with the success of 38% SDF in arresting dental caries. A p-value of <0.05 and 95% confidence intervals were used to report the statistical significance and precision of results. Our current research concluded that different variables associated with 38% SDF, such as thenumber of applications and the time-lapse between applications, had no significant effect on the successful results of the application. Moreover, receiving a final restoration did not affect the success of the treatment when compared to treating with 38% SDF alone. Additionally, the type of final restoration had no impact on the prognosis. Further research using standardized protocols for study designs, detection criteria, outcomes, and statistical designs is needed to supportfindings and establish treatment guidelines.

Deep reinforcement learning-based digital twin for droplet microfluidics control

This study applied deep reinforcement learning (DRL) with the Proximal Policy Optimization (PPO) algorithm within a two-dimensional computational fluid dynamics (CFD) model to achieve closed-loop control in microfluidics. The objective was to achieve the desired droplet size with minimal variability in a microfluidic capillary flow-focusing device. An artificial neural network was utilized to map sensing signals (flow pressure and droplet size) to control actions (continuous phase inlet pressure). To validate the numerical model, simulation results were compared with experimental data, which demonstrated a good agreement with errors below 11%. The PPO algorithm effectively controlled droplet size across various targets (50, 60, 70, and 80 μm) with different levels of precision. The optimized DRL + CFD framework successfully achieved droplet size control within a coefficient of variation (CV%) below 5% for all targets, outperforming the case without control. Furthermore, the adaptability of the PPO agent to external disturbances was extensively evaluated. By subjecting the system to sinusoidal mechanical vibrations with frequencies ranging from 10 Hz to 10 KHz and amplitudes between 50 and 500 Pa, the PPO algorithm demonstrated efficacy in handling disturbances within limits, highlighting its robustness. Overall, this study showcased the implementation of the DRL+CFD framework for designing and investigating novel control algorithms, advancing the field of droplet microfluidics control research.

Validation of the adaptive scan method in the quest for time-efficient methods of testing auditory processes

A major barrier to the clinical application of psychophysical testing of central auditory processes is the time required to obtain precise estimates of different listening abilities. In this study, we validate a novel adaptive scan (AS) method of threshold estimation that is designed to adapt on a range of values around threshold rather than on a single threshold value. This method has the advantage of providing the listener with greater familiarity with the stimulus characteristics near threshold while maintaining precise measurement and increasing time-efficiency. Additionally, we explore the time-efficiency of AS through comparison with two more conventional adaptive algorithms and the method of constant stimuli in two common psychophysical tasks: the detection of a gap in noise and the detection of a tone in noise. Seventy undergraduates without hearing complaints were tested using all four methods. The AS method provided similar threshold estimates with similar precision to those from the other adaptive methods and, thus, it is a valid adaptive method of psychophysical testing. We also provide an analysis of the AS method based on precision metrics to propose a shortened version of the algorithm that maximizes the time/precision tradeoff and can achieve similar thresholds to the adaptive methods tested in the validation. This work lays the foundation for using AS across a wide variety of psychophysical assessments and experimental situations where different levels of precision and/or time-efficiency may be required.

An information theoretic method to resolve millisecond-scale spike timing precision in a comprehensive motor program.

Sensory inputs in nervous systems are often encoded at the millisecond scale in a precise spike timing code. There is now growing evidence in behaviors ranging from slow breathing to rapid flight for the prevalence of precise timing encoding in motor systems. Despite this, we largely do not know at what scale timing matters in these circuits due to the difficulty of recording a complete set of spike-resolved motor signals and assessing spike timing precision for encoding continuous motor signals. We also do not know if the precision scale varies depending on the functional role of different motor units. We introduce a method to estimate spike timing precision in motor circuits using continuous MI estimation at increasing levels of added uniform noise. This method can assess spike timing precision at fine scales for encoding rich motor output variation. We demonstrate the advantages of this approach compared to a previously established discrete information theoretic method of assessing spike timing precision. We use this method to analyze the precision in a nearly complete, spike resolved recording of the 10 primary wing muscles control flight in an agile hawk moth, Manduca sexta. Tethered moths visually tracked a robotic flower producing a range of turning (yaw) torques. We know that all 10 muscles in this motor program encode the majority of information about yaw torque in spike timings, but we do not know whether individual muscles encode motor information at different levels of precision. We demonstrate that the scale of temporal precision in all motor units in this insect flight circuit is at the sub-millisecond or millisecond-scale, with variation in precision scale present between muscle types. This method can be applied broadly to estimate spike timing precision in sensory and motor circuits in both invertebrates and vertebrates.

Value certainty in drift-diffusion models of preferential choice.

The drift-diffusion model (DDM) is widely used and broadly accepted for its ability to account for binary choices (in both the perceptual and preferential domains) and response times (RT), as a function of the stimulus or the choice alternative (or option) values. The DDM is built on an evidence accumulation-to-bound concept, where, in the value domain, a decision maker repeatedly samples the mental representations of the values of the available options until satisfied that there is enough evidence (or support) in favor of one option over the other. As the signals that drive the evidence are derived from value estimates that are not known with certainty, repeated sequential samples are necessary to average out noise. The classic DDM does not allow for different options to have different levels of precision in their value representations. However, recent studies have shown that decision makers often report levels of certainty regarding value estimates that vary across choice options. There is therefore a need to extend the DDM to include an option-specific value certainty component. We present several such DDM extensions and validate them against empirical data from four previous studies. The data support best a DDM version in which the drift of the accumulation is based on a sort of signal-to-noise ratio of value for each option (rather than a mere accumulation of samples from the corresponding value distributions). This DDM variant accounts for the impact of value certainty on both choice consistency and RT present in the empirical data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Improving the decision-making process by considering supply uncertainty – a case study in the forest value chain

ABSTRACT Planning decisions are generally subject to some level of uncertainty. In forestry, data describing the resources available have a major impact on operations performance and productivity. This paper aims to present a method to improve decision-making in the forest supply chain by taking supply uncertainty into account using the results of data quality assessments. The case study describes the operations planning process of a Canadian forest products company dealing with an uncertain volume of wood supply. Three approaches to constructing probability distributions based on data quality are tested. Each approach offers a different level of precision: (1) a frequency distribution of accuracy, (2) a normal distribution based on average accuracy, and (3) a normal distribution based on data quality classification. Using stochastic programming to plan transport and production shows that lower costs can be achieved with a general characterisation of the data accuracy. Not considering uncertainty when planning operations leads to a significant replanning transportation cost. Using classes of data quality to include uncertainty in operations planning contributes to reducing the transportation cost from $15.90/m3 down to $15.32/m3 representing 3.6%.

GradFreeBits: Gradient-Free Bit Allocation for Mixed-Precision Neural Networks.

Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low-resource edge devices. Training QNNs using different levels of precision throughout the network (mixed-precision quantization) typically achieves superior trade-offs between performance and computational load. However, optimizing the different precision levels of QNNs can be complicated, as the values of the bit allocations are discrete and difficult to differentiate for. Moreover, adequately accounting for the dependencies between the bit allocation of different layers is not straightforward. To meet these challenges, in this work, we propose GradFreeBits: a novel joint optimization scheme for training mixed-precision QNNs, which alternates between gradient-based optimization for the weights and gradient-free optimization for the bit allocation. Our method achieves a better or on par performance with the current state-of-the-art low-precision classification networks on CIFAR10/100 and ImageNet, semantic segmentation networks on Cityscapes, and several graph neural networks benchmarks. Furthermore, our approach can be extended to a variety of other applications involving neural networks used in conjunction with parameters that are difficult to optimize for.

Further Development and Piloted Simulation Evaluation of the Break Turn ADS-33 Mission Task Element

Cofunded by the U. S. Army and industry, a Sikorsky-led team that features industry and academia developed and evaluated a set of mission task elements (MTE) to address rotorcraft high-speed handling qualities. The MTEs were designed to meet different levels of precision and aggressiveness. The Break Turn MTE was defined for nonprecision, aggressive applications. The MTE objectives, descriptions, and performance criteria were developed via piloted simulation sessions at each team's simulator, each featuring a unique high-speed platform including a coaxial compound helicopter, two tiltrotors, and a generic winged compound helicopter. Formal evaluations were conducted by U. S. Army test pilots. Baseline control laws were varied to achieve different handling qualities levels. Quantitative measures based on task performance and qualitative measures based on pilot ratings, comments, and questionnaires were used to assess MTE effectiveness. It was demonstrated that the Break Turn MTE provided an effective means to discern nonprecision, aggressive handling qualities in high-speed flight.

Validity of age estimation methods and reproducibility of bone/dental maturity indices for chronological age estimation: a systematic review and meta-analysis of validation studies

Several approaches have been developed to estimate age, an important aspect of forensics and orthodontics, using different measures and radiological examinations. Here, through meta-analysis, we determined the validity of age estimation methods and reproducibility of bone/dental maturity indices used for age estimation. The PubMed and Google Scholar databases were searched to December 31, 2021 for human cross-sectional studies meeting pre-defined PICOS criteria that simultaneously assessed the reproducibility and validity. Meta-estimates of validity (mean error: estimated age-chronological age) and intra- and inter-observer reproducibility (Cohen’s kappa, intraclass correlation coefficient) and their predictive intervals (PI) were calculated using mixed-effect models when heterogeneity was high (I2 > 50%). The literature search identified 433 studies, and 23 met the inclusion criteria. The mean error meta-estimate (mixed effects model) was 0.08 years (95% CI − 0.12; 0.29) in males and 0.09 (95% CI − 0.12; 0.30) in females. The PI of each method spanned zero; of nine reported estimation methods, Cameriere’s had the smallest (− 0.82; 0.47) and Haavikko’s the largest (− 7.24; 4.57) PI. The reproducibility meta-estimate (fixed effects model) was 0.98 (95% CI 0.97; 1.00) for intra- and 0.99 (95% CI 0.98; 1.00) for inter-observer agreement. All methods were valid but with different levels of precision. The intra- and inter-observer reproducibility was high and homogeneous across studies.

Graph embeddings in criminal investigation: towards combining precision, generalization and transparency

Criminal investigation adopts Artificial Intelligence to enhance the volume of the facts that can be investigated and documented in trials. However, the abstract reasoning implied in legal justification and argumentation requests to adopt solutions providing high precision, low generalization error, and retrospective transparency. Three requirements that hardly coexist in today’s Artificial Intelligence solutions. In a controlled experiment, we then investigated the use of graph embeddings procedures to retrieve potential criminal actions based on patterns defined in enquiry protocols. We observed that a significant level of accuracy can be achieved but different graph reformation procedures imply different levels of precision, generalization, and transparency.

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations.

In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has spawned a worldwide catastrophe and global emergency. Amidst several anti-viral targets of COVID-19, spike glycoprotein has been recognized as an essential target for the viral entry into the host cell. In the search of effective SARS CoV-2 inhibitors acting against spike glycoprotein, the virtual screening of 175,851 ligands from the 2020.1 Asinex BioDesign library has been performed using in silico tools like SiteMap analysis, pharmacophore-based screening, molecular docking using different levels of precision, such as high throughput virtual screening, standard precision and extra precision, followed by absorption, distribution, metabolism, excretion and toxicity analysis, and molecular dynamics (MD) simulation. Following a molecular docking study, seventeen molecules (with a docking score of less than − 6.0) were identified having the substantial interactions with the catalytic amino acid and nucleic acid residues of spike glycoprotein at the binding site. In investigations using MD simulations for 10 ns, the hit molecules (1 and 2) showed adequate compactness and uniqueness, as well as satisfactory stability. These computational research findings have offered a key starting point in the field of design and development of novel SARS CoV-2 entry inhibitors with appropriate drug likeliness.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11030-022-10394-9.

Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks

In this paper, we present hardware accelerators created with high-level synthesis techniques for sparse and dense matrix multiplication operations. The cores can operate with different precisions and are designed to be integrated in a heterogeneous CPU-FPGA system for Edge AI applications. The methodology involves quantization-sparsity aware training and it is applied to a case study consisting of human activity classification. We initially investigate the effects of quantization and sparsity on the accuracy of neural networks with convolution, dense and recurrent layers observing better tolerance to pruning when recurrent layers are present. Then, we propose the hardware accelerators that can switch precision at run-time and work with any matrix size up to a maximum configured at compile time. We compare the performance of these accelerators at different levels of precision and sparsity levels and create a performance model to enable workload balancing. The results show that the proposed sparse matrix multipliers can outperform dense multipliers when sparsity levels are higher than 70% and the improvements are more evident when higher precision arithmetic or structural pruning is used. Additionally, sparsity levels as high as 99% can maintain the level of accuracy required in the network especially when recurrent layers are deployed. Overall, the balance between sparse and dense performance depends on matrix shape, precision, structural pruning and sparsity levels and performance modelling can be used to balance concurrent execution in a heterogeneous configuration.

Application of Geostatistical Tools to the Geochemical Characterization of the Peloritani Mts (Sicily, Italy) Aquifers

Sources of groundwater contaminants in inhabited areas, located in complex geo-tectonic contexts, are often deeply interlocked, thus, making the discrimination between anthropic and natural origins difficult. In this study, we investigate the Peloritani Mountain aquifers (Sicily, Italy), using the combination of probability plots with concentration contour maps to retrieve an overall view of the groundwater geo-chemistry with a special focus on the flux of heavy metals. In particular, we present a methodology for integrating spatial data with very different levels of precision, acquired before and during the “geomatic era”. Our results depict a complex geochemical layout driven by a geo-puzzle of rocks with very different lithological natures, hydraulically connected by a dense tectonic network that is also responsible for the mixing of deep hydrothermal fluids with the meteoric recharge. Moreover, a double source, geogenic or anthropogenic, was individuated for many chemicals delivered to groundwater bodies. The concentration contour maps, based on the different data groups identified by the probability plots, fit the coherency and congruency criteria with the distribution of both rock matrices and anthropogenic sources for chemicals, indicating the success of our geostatistical approach.