High value information for managers in organisations

Due to information overload, information management for managers in organisations is a big task. To avoid information overload and to retain the right information for reuse, an effective mechanism for evaluating information is required. Various tools and strategies are presented in an attempt to obtain information's "value." This study examines the topic of information overload, the definition of information value, and associated research on the value of information in many fields to resolve this issue. The Bayesian Theorem and information characteristics are used to offer a framework for evaluating information.

Time-lapse difference inversion based on the modified reflectivity method with differentiable Hyper-Laplacian blocky constraint

Time-lapse (TL) seismic has great potential in monitoring and interpreting time-varying variations in reservoir fluid properties during hydrocarbon exploitation. Obtaining the disparities of reservoir elastic parameters by inversion is essential for TL reservoir monitoring. Conventional TL inversion is carried out by stages without coupling processing and leads to inaccuracy of the results. We directly use the differences in seismic data responses from different vintages, namely difference inversion, to improve the results credibility. It may reduce the deviations of the subtraction of base and monitor inversions in traditional methods. Moreover, most existing studies involving pre-stack inversion methods use the Zoeppritz equation or its approximants, which failed to consider the wave propagation effects. Here, we propose a new TL difference inversion based on the modified reflectivity method (MRM), the internal multiples and transmission losses are taken into consideration to fine-tune the characterization of the seismic wave propagating underground. The new method is modified on the basis of reflectivity method (RM) making it feasible in TL difference inversion, and derived from the Bayesian theorem. For further delineating the boundaries between layers, the differentiable Hyper-Laplacian blocky constraint (DHLBC) is introduced into the prior information of Bayesian framework, which heightens the sparseness in the vertical gradients of inversion results and leads to sharp interlayer boundaries of difference parameters. The synthetic and field data examples demonstrate that the proposed TL difference inversion method has clear advantages in accuracy and resolution compared to Zoeppritz method and MRM without DHLBC.

Effects of Day/Night Factor on the Detection Performance of FY4A Lightning Mapping Imager in Hainan, China

In this study, the effect of day/night factor on the detection performance of the FY4A lightning mapping imager (LMI) is evaluated using the Bayesian theorem, and by comparing it to the measurements made by a ground-based low-frequency magnetic field lightning location system. Both the datasets were collected in the summers of 2019–2020 in Hainan, China. The results show that for the observed summer thunderstorms in Hainan, the daytime detection efficiencies of LMI (DELMI) were 20.41~35.53% lower than the nighttime DELMI. Compared to other space-based lightning sensors (lightning imaging sensors/optical transient detectors (LIS/OTD) and geostationary lightning mapper (GLM)), the detection performance of LMI is more significantly influenced by the day/night factor. The DELMI rapidly dropped within about four hours after sunrise while it increased before sunset. For the storms that formed at night and lasted for an entire day, the DELMI remained relatively low during the daytime, even as the thunderstorms intensified. The poor detection performance of LMI during daytime is probably because of the sunlight reflection by clouds and atmosphere, which results in larger background radiative energy density (RED) than that at night. During night, LMI captured the lightning signals well with low RED (8.38~10.63 μJ sr−1 m−2 nm−1). However, during daytime, signals with RED less than 77.12 μJ sr−1 m−2 nm−1 were filtered, thus lightning groups could rarely be identified by LMI, except those with extremely high RED. Due to the limitations of the Bayesian theorem, the obtained DE in this study was “relative” DE rather than “absolute” DE. To obtain the absolute DE of LMI, the total lightning density is necessary but can hardly be measured. Nonetheless, the results shown here clearly indicate the strong impact of day/night factor on the detection performance of LMI, and can be used to improve the design and post-processing method of LMI.

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

Background Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions. Methods We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input. Results We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases. Conclusion The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

Diagnosis Prediction of Tumours of Unknown Origin Using ImmunoGenius, A Machine Learning-based Expert System for Immunohistochemistry Profile Interpretation

BackgroundImmunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions. MethodsWe developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input. ResultsWe trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5%, 78.0% and 89.0% in training, validation and test dataset respectively. which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.Conclusion The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

Decision Making in Complex Environments

Bayesian probability theory, signal detection theory, and operational decision theory are combined to understand how one can operate effectively in complex environments, which requires uncommon skill sets for performance optimization. The analytics of uncertainty in the form of Bayesian theorem applied to a moving object is presented, followed by how operational decision making is applicable to all complex environments. Large-scale dynamic systems have erratic behavior, so there is a need to effectively manage risk. Risk management needs to be addressed from the standpoint of convergent technology applications and performance modeling. The example of an airplane during takeoff shows how a risk continuum needs to be developed. An unambiguous demarcation line for low, moderate, and high risk is made and the decision analytical structure for all operational decisions is developed. Three mission-critical decisions are discussed to optimize performance: to continue or abandon the mission, the approach go-around maneuver, and the takeoff go/no-go decision.

Application of the Parsimony Principle of Exponential Functions to the Production of COVID-19 Immunity, According to Bayesian Theorem and Cybernetics, to Tame Anti-scientific Doxastic Logic and the Pandemic

The purpose of this work is to provide evidence to the scientific community that there is solid scientific knowledge available to tame the pandemic, which is mainly a behavioral problem that requires cybernetics through behavioral engineering. Scientifically it is clear that the problem of the pandemic originates in human behavior and misinformation. Behavioral problems are addressed by cybernetics through behavioral engineering. Aristotelian causes of the pandemic are aberrant behavior. This is the field of battle and the obsession of the subject is the rise of the neurotransmitter dopamine. The question is not what is the probability that a patient with COVID-19 has a certain symptom or sign? Rather it is to calculate the probability that a patient with a certain sign or symptom has COVID-19. Without grasping the differential equations modeled by Kermack and McKendrick, it is impossible to have an idea of what is happening in the pandemic. Our straightforward theoretical approach is to use the wild unmodified SARS-CoV-2 to produce immunity by the simple expedient of diminishing the amount of the inoculum to the minimum minimorum. The problem with allowing people, deliberately attempting herd immunity, is that it has the dire effect that a high percentage will necessarily die. It is a matter of competence between two exponential functions. On one hand the exponential reproduction of the virus, and on the other hand, the exponential production of antibodies and activation of T cells. The aim is to diminish the amount of the inoculum to the minimum minimorum capable of infecting the minimum susceptible cell subpopulation. In this manner, herd immunity could be reached, which would allow a parsimonical response in the viral exponential growth that would not overwhelm the exponential immune response. It is expected that susceptible subjects could be infected in a variolation modality through the universal use of masks, maximizing the distance, rather than in a noregulated exposure of a putative low-risk segment of the population. In the logic of the decision, we must distinguish a desideratum from what is physically, economically, legally, and politically implementable. It is a matter of policy-making supported by science and law instead of doxastic logic based on misinformation and bigotry. It is a matter of policy enforcement by cybernetics, by behavior engineering, not of a recommendation. The guidelines, if they are to be implemented, depend on the application of cybernetics, and behavioral engineering. The apodictic inference from fallacies, in a doxastic and desiderative logic, is the origin of disinformation. Keywords: COVID-19 Inoculum; Bayes Theorem; Cybernetics; Variolation; Herd immunity

Direct inversion for reservoir parameters from prestack seismic data

Reliably estimating reservoir parameters is the final target in reservoir characterisation. Conventionally, estimating reservoir characters from seismic inversion is implemented by indirect approaches. The indirect estimation of reservoir parameters from inverted elastic parameters, however, will produce large bias due to the propagation of errors in the procedure of inversion. Therefore, directly obtaining reservoir parameters from prestack seismic data through a rock-physical model and prestack amplitude variation with offset (AVO) inversion is proposed. A generalised AVO equation in terms of oil-porosity (OP), sand indicator (SI) and density is derived by combining a physical rock model and the Aki–Richards equation in a whole system. This makes it possible to perform direct inversion for reservoir parameters. Next, under Bayesian theorem, we develop a robust prestack inversion approach based on the new AVO equation. Tests on synthetic seismic gathers show that it can dramatically reduce the prediction error of reservoir parameters. Furthermore, field data application illustrates that reliable reservoir parameters can be directly obtained from prestack inversion.