Intuitive Model Research Articles

Advanced Intuitive Model for Digital Forensics Collection Methods in the Context of Cloud Computing and IoT

Abstract: With the wide scale adoption of cloud computing paradigms and Internet of Things implementations, the arena of service delivery has changed significantly for the world of technology-oriented services. While these new modes of service delivery have provided innovations that were not fathomable earlier, they not only create new non-traditional pathways for evidence generation, nevertheless, they have also been seen to carry a potential of creating and exposing a whole new set of security vulnerabilities that malicious actors can exploit. Traditional forensic investigation process falls short in many ways including the way it aims to identify and link crucial pieces of evidence in these scenarios due to a myriad of internal and external factors. Challenges and inadequacies have been identified in the community in relation to these specific sets of advancing technologies that hinder the efficient identification, extraction and processing of evidence generated in crimes involving them. In this paper we wish to surmise the current forensic collection methods, how they pose challenges when put together with the latest cloud and IoT technologies and then attempt to propose an advanced, more intuitive, and theoretically validated digital forensic collection process guided by zero trust principle and enforced with artificial intelligence and machine learning methodologies with the aim to fill the gaps and provide a more reliable, robust and intuitive model to perform digital evidence collection in the given context.

Prognostic Value of Combined Biomarkers in Patients With Heart Failure: The Heartmarker Score.

Heart failure (HF) biomarkers have prognostic value. The aim of this study was to combine HF biomarkers into an objective classification system for risk stratification of patients with HF. HF biomarkers were analyzed in a population of HF outpatients and expressed relative to their cut-off values (N-terminal pro-B-type natriuretic peptide [NT-proBNP] >1,000 pg/mL, soluble suppression of tumorigenesis-2 [ST2] >35 ng/mL, growth differentiation factor-15 [GDF-15] >2,000 pg/mL, and fibroblast growth factor-23 [FGF-23] >95.4 pg/mL). Biomarkers that remained significant in multivariable analysis were combined to devise the Heartmarker score. The performance of the Heartmarker score was compared to the widely used New York Heart Association (NYHA) classification based on symptoms during ordinary activity. HF biomarkers of 245 patients were analyzed, 45 (18%) of whom experienced the composite endpoint of HF hospitalization, appropriate implantable cardioverter-defibrillator shock, or death. HF biomarkers were elevated more often in patients that reached the composite endpoint than in patients that did not reach the endpoint. NT-proBNP, ST2, and GDF-15 were independent predictors of the composite endpoint and were thus combined as the Heartmarker score. The event-free survival and distance covered in 6 minutes of walking decreased with an increasing Heartmarker score. Compared with the NYHA classification, the Heartmarker score was better at discriminating between different risk classes and had a comparable relationship to functional capacity. The Heartmarker score is a reproducible and intuitive model for risk stratification of outpatients with HF, using routine biomarker measurements.

Optimal Algorithms for Right-sizing Data Centers

Electricity cost is a dominant and rapidly growing expense in data centers. Unfortunately, much of the consumed energy is wasted, because servers are idle for extended periods of time. We study a capacity management problem that dynamically right-sizes a data center, matching the number of active servers with the varying demand for computing capacity. We resort to a data-center optimization problem introduced by Lin, Wierman, Andrew, and Thereska [ 25 , 27 ] that, over a time horizon, minimizes a combined objective function consisting of operating cost, modeled by a sequence of convex functions, and server switching cost. All prior work addresses a continuous setting in which the number of active servers, at any time, may take a fractional value. In this article, we investigate for the first time the discrete data-center optimization problem where the number of active servers, at any time, must be integer valued. Thereby, we seek truly feasible solutions. First, we show that the offline problem can be solved in polynomial time. Our algorithm relies on a new, yet intuitive graph theoretic model of the optimization problem and performs binary search in a layered graph. Second, we study the online problem and extend the algorithm Lazy Capacity Provisioning (LCP) by Lin et al. [ 25 , 27 ] to the discrete setting. We prove that LCP is 3-competitive. Moreover, we show that no deterministic online algorithm can achieve a competitive ratio smaller than 3. Hence, while LCP does not attain an optimal competitiveness in the continuous setting, it does so in the discrete problem examined here. We prove that the lower bound of 3 also holds in a problem variant with more restricted operating cost functions, introduced by Lin et al. [ 25 ]. In addition, we develop a randomized online algorithm that is 2-competitive against an oblivious adversary. It is based on the algorithm of Bansal et al. [ 7 ] (a deterministic, 2-competitive algorithm for the continuous setting) and uses randomized rounding to obtain an integral solution. Moreover, we prove that 2 is a lower bound for the competitive ratio of randomized online algorithms, so our algorithm is optimal. We prove that the lower bound still holds for the more restricted model. Finally, we address the continuous setting and give a lower bound of 2 on the best competitiveness of online algorithms. This matches an upper bound by Bansal et al. [ 7 ]. A lower bound of 2 was also shown by Antoniadis and Schewior [ 4 ]. We develop an independent proof that extends to the scenario with more restricted operating cost.

Parametric Modeling Method for 3D Symbols of Fold Structures

Most fabrication methods for three-dimensional (3D) geological symbols are limited to two types: directly increasing the dimensionality of a 2D geological symbol or performing appropriate modeling for an actual 3D geological situation. The former can express limited vertical information and only applies to the three-dimensional symbol-making of point mineral symbols, while the latter weakens the difference between 3D symbols and 3D geological models and has several disadvantages, such as high dependence on measured data, redundant 3D symbol information, and low efficiency when displayed in a 3D scene. Generating a 3D geological symbol is represented by the process of constructing a 3D geological model. This study proposes a parametric modeling method for 3D fold symbols according to the complexity and diversity of the fold structures. The method involves: (1) obtaining the location of each cross-section in the symbol model, based on the location parameters; (2) constructing the middle cross-section, based on morphological parameters and the Bezier curve; (3) performing affine transformation according to the morphology of the hinge zone and the middle section to generate the sections at both ends of the fold; (4) generating transition sections of the 3D symbol model, based on morphing interpolation; and (5) connecting the point sets of each transition section and stitching them to obtain a 3D fold-symbol model. Case studies for different typical fold structures show that this method can eliminate excessive dependence on geological survey data in the modeling process and realize efficient, intuitive, and abstract 3D symbol modeling of fold structures based on only a few parameters. This method also applies to the 3D geological symbol modeling of faults, joints, intrusions, and other geological structures and 3D geological modeling of typical geological structures with a relatively simple spatial morphology.

Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models.

Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parametrize and analyze large-scale kinetic models intuitively and efficiently. We present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression, and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes. The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. Supplementary data are available at Bioinformatics online.

Cartesian intuition

ABSTRACT This paper explicates Descartes’ theory of intuition (intuitus). Departing from certain commentators, I argue that intuition, for Descartes, is a form of clear and distinct intellectual perception. Because it is clear and distinct, it is indubitable, infallible, and provides a grade of certain knowledge he calls ‘cognitio’. I pay special attention to why he treats intuition as a form of perception, and what he means when he says it is ‘clear and distinct’. Finally, I situate his view in relation to those of his Scholastic predecessors on one hand and current theories on the other. His view anticipates the contemporary ‘perceptual model of intuition’, though it is much bolder with its promise of certainty.

The importance of the user voice in clinical decision making: a reflective account

Effective clinical decision making is crucial to patient safety. A tripartite case decision was analysed to find learning points to improve clinical decision making. The case decision was analysed using the dual process theory and the intuitive humanistic model. The place of individual thinkers was analysed and their use of ‘system 1’ and ‘system 2’ thinking was considered. The impact that the diversity of individuals and their skill levels had on the clinical decision was analysed. It was concluded that a team consisting of both system 1 and 2 thinkers, as well as individuals at different places on the novice to expert continuum, would be beneficial in creating a balanced choice. The importance of the patient voice in clinical decision making was highlighted.

MeshTaichi

Meshes are an indispensable representation in many graphics applications because they provide conformal spatial discretizations. However, mesh-based operations are often slow due to unstructured memory access patterns. We propose MeshTaichi, a novel mesh compiler that provides an intuitive programming model for efficient mesh-based operations. Our programming model hides the complex indexing system from users and allows users to write mesh-based operations using reference-style neighborhood queries. Our compiler achieves its high performance by exploiting data locality. We partition input meshes and prepare the wanted relations by inspecting users' code during compile time. During run time, we further utilize on-chip memory (shared memory on GPU and L1 cache on CPU) to access the wanted attributes of mesh elements efficiently. Our compiler decouples low-level optimization options with computations, so that users can explore different localized data attributes and different memory orderings without changing their computation code. As a result, users can write concise code using our programming model to generate efficient mesh-based computations on both CPU and GPU backends. We test MeshTaichi on a variety of physically-based simulation and geometry processing applications with both triangle and tetrahedron meshes. MeshTaichi achieves a consistent speedup ranging from 1.4× to 6×, compared to state-of-the-art mesh data structures and compilers.

Risk factor analysis and clinical decision tree model construction for diabetic retinopathy in Western China.

Diabetic retinopathy (DR) is the driving force of blindness in patients with type 2 diabetes mellitus (T2DM). DR has a high prevalence and lacks effective therapeutic strategies, underscoring the need for early prevention and treatment. Yunnan province, located in the southwest plateau of China, has a high pre-valence of DR and an underdeveloped economy. To build a clinical prediction model that will enable early prevention and treatment of DR. In this cross-sectional study, 1654 Han population with T2DM were divided into groups without (n = 826) and with DR (n = 828) based on fundus photography. The DR group was further subdivided into non-proliferative DR (n = 403) and proliferative DR (n = 425) groups. A univariate analysis and logistic regression analysis were conducted and a clinical decision tree model was constructed. Diabetes duration ≥ 10 years, female sex, standing- or supine systolic blood pressure (SBP) ≥ 140 mmHg, and cholesterol ≥ 6.22 mmol/L were risk factors for DR in logistic regression analysis (odds ratio = 2.118, 1.520, 1.417, 1.881, and 1.591, respectively). A greater severity of chronic kidney disease (CKD) or hemoglobin A 1c increased the risk of DR in patients with T2DM. In the decision tree model, diabetes duration was the primary risk factor affecting the occurrence of DR in patients with T2DM, followed by CKD stage, supine SBP, standing SBP, and body mass index (BMI). DR classification outcomes were obtained by evaluating standing SBP or BMI according to the CKD stage for diabetes duration < 10 years and by evaluating CKD stage according to the supine SBP for diabetes duration ≥ 10 years. Based on the simple and intuitive decision tree model constructed in this study, DR classification outcomes were easily obtained by evaluating diabetes duration, CKD stage, supine or standing SBP, and BMI.

Optical Fingerprint of Flat Substrate Surface and Marker-Free Lateral Displacement Detection with Angstrom-Level Precision.

We report that flat substrates such as glass coverslips with surface roughness well below 0.5nm feature notable speckle patterns when observed with high-sensitivity interference microscopy. We uncover that these speckle patterns unambiguously originate from the subnanometer surface undulations, and develop an intuitive model to illustrate how subnanometer nonresonant dielectric features could generate pronounced interference contrast in the far field. We introduce the concept of optical fingerprint for the deterministic speckle pattern associated with a particular substrate surface area and intentionally enhance the speckle amplitudes for potential applications. We demonstrate such optical fingerprints can be leveraged for reproducible position identification and marker-free lateral displacement detection with an experimental precision of 0.22nm. The reproducible position identification allows us to detect new nanoscopic features developed during laborious processes performed outside of the microscope. The demonstrated capability for ultrasensitive displacement detection may find applications in the semiconductor industry and superresolution optical microscopy.

Study of Dynamic Evolution of the Shear Band in Triaxial Soil Samples Using Photogrammetry Technology

In order to avoid the influence of end restraint on triaxial stress and strain measurements, this study combines photogrammetry and computer technology to apply to the unconsolidated undrained test of triaxial soil samples. The novel method can establish an intuitive shear band evolution model of soil samples and help attain a clear and intuitive shear band evolution law. On the basis of the conventional triaxial test, this new method needs to paste the RAD (Ringed Automatically Detected) code points on the rubber film, plexiglass cover and brackets and then take pictures of the RAD code points in the loading process in a surrounding manner, with which the 3D shape of the restored soil samples can be determined. After eliminating the influence of refraction, the coded point cloud coordinates measured in the experiment can be used to calculate the axial deformation and radial deformation of the soil sample in the deformation process and determine the local stress–strain curve and three-dimensional displacement field diagram of the soil sample. The test shows that the new method can clearly determine the peak value and inflection point of the stress–strain curve in the middle region of soil samples, enabling it to reflect the shear change process of the soil sample more accurately. In addition, the displacement field can be used to directly observe the formation, development and penetration process of the shear band in soil samples.

From hierarchical control to flexible interactive electricity services: A path to decarbonisation

This paper first reviews today’s hierarchical control and highlights modelling and control assumptions which may no longer hold in the changing electric energy systems. To overcome this fundamental problem, we conceptualise electric energy systems as complex dynamical systems using physically intuitive multi-layered energy modelling as the basis for systematic diverse technology integration and control. Such modelling exhibits unique structure which comes from the conservation of instantaneous power ( P ) and the rate of change of instantaneous reactive power ( Q ̇ ). We name P and Q ̇ as the rate of change of interaction variables (intVar) for characterising the interface of each system module with the rest of the system. The intVars are used as a means to model and control the interactive zoomed-out inter-modular (inter-area, inter-component) system dynamics. The control is distributed and modular as it utilises only a low-level detailed technology-specific model and intVar info exchange with the neighbours. As a result, the proposed approach can be used to support orderly and natural evolution from today’s hierarchical control to flexible interactive protocols for electricity services, key to implementing decarbonisation. • T&D system needs to be operated in a flexible data-enabled way in order to manage uncertainties in an efficient manner. • Efficient energy service requires temporal, spatial and functional alignment of energy resources and demand. • Models of interactions subject to conservation of instantaneous power and reactive power dynamics can be used to formulate optimization problems in the energy space as convex linear problems.

Using an Embedded Multi User Inverse Kinematic Solver for Intuitive Modeling and Simulation

Following the vision of Industry 4.0, manufacturers started to break down products into subsystems, each of which is developed and supplied by different suppliers to tackle the increasing complexity of product development (e.g., in vehicle, machine or plant construction). This process needs to be supported by intuitive Computer-Aided Design (CAD) tools and virtual modeling and simulation environments to effectively meet the challenges of increasingly complex product development. Therefore, in this paper, we present some outcoming of a research project called DisMoSim ( Dis tributed Mo deling and Sim ulation), which proposes an intuitive virtual environment for modeling and simulation that embeds a Multi User Inverse Kinematic Solver (MUKS). The MUKS allows (i) individual subsystems to be tested intuitively, i.e., Rapid Testing (RT), and (ii) people not yet familiar with a subsystem to understand it easily and quickly through simple kinematic actions, i.e., Rapid Understanding (RU), without unnaturally interrupting the modeling process. Finally, (iii) the MUKS enables subsystems to be easily assembled into complete systems, i.e., Rapid Assembling (RA), by means of a so-called snapping-point mechanism which supports the complex process of aligning and assembling different subsystems. To evaluate these capabilities, we present the results of a user study conducted with 20 participants. Special attention has been given to the embedded MUKS in contrast to existing approaches known from common CAD and simulation software. Thus, we demonstrate the practicability, usability, and versatility of our approach. • Finding modeling and simulation errors in CAD models is cumbersome with current tools. • CAD tools need intuitive means for testing during modeling. • Inverse kinematic solvers allow for rapid prototyping and testing in CAD systems. • New user interface and interaction paradigms ease interaction with CAD tools.

Development of a web-based modelling framework for harmful algal blooms transport simulation using open-source technologies

Desktop-based modelling packages presented typical limitations in interactive simulation. This study presents a web-based modelling framework that fully consolidated the simulation work-flow into a WebGIS application, providing a one-step solution for HABs transport simulation within an intuitive and interactive modelling environment. An improved Lagrangian particle-tracking scheme was proposed using fractional Brownian motion technique. The presented model was devoted to quickly forecast the transport pathways in both temporal and spatial dimensions, and evaluate the approximate trends and qualitative understanding of HABs development in data-poor situations. The web modelling platform was developed using multiple open-source JavaScript libraries. The developed WebGIS application provides user-friendly interfaces to prepare inputs, configure simulation settings, visualize, analyse, and validate simulation results within the same framework. The feasibility, capacity, and advantage of the proposed framework were tested and evaluated in a real-world application of red tide transport simulation in the Qinhuangdao coastal waters. The model results showed qualitative agreement with the red tide observed from remote sensing. Our experimental results demonstrated that the developed web-based modelling prototype would present a useful performance for study cases related to HABs transport simulation.

AN GROUND AND UNDER-GROUND URBAN ROADS SURVEYING APPROACH USING INTEGRATED 3D LIDAR AND 3D GPR TECHNOLOGY

Abstract. Digitalization of urban roads is an important part of smart city construction. In addition to having a basic understanding of the structure of the transportation network, we need to have a preliminary understanding of the information around the road, the current status of the road, and the impact that municipal projects may have on the road. At present, the three-dimensional information of the ground parts of roads can be obtained efficiently and accurately on a large scale by using three-dimensional scanning technology. However, there is a lack of comprehensive and intuitive understanding of the under-ground information and a lack of synergistic consideration of the ground and under-ground information. In this paper, a ground and under-ground urban road surveying system based on 3D LiDAR and 3D ground penetrating radar (GPR) is presented. The system covers multi-sensor coordinated control, time-space datum setup, and post-processing data. Experiments show that the system can realize the integrated ground and under-ground 3D surveying for urban roads, generate intuitive three-dimensional point cloud map model of ground and under-ground of urban roads, and provide effective technical support for smart city construction.

Eye Fitting Straight Lines in the Modern Era

How do statistical regression results compare to intuitive, visually fitted results? Fitting lines by eye through a set of points has been explored since the 20th century. Common methods of fitting trends by eye involve maneuvering a string, black thread, or ruler until the fit is suitable, then drawing the line through the set of points. In 2015, the New York Times introduced an interactive feature, called “You Draw It,” where readers were asked to input their own assumptions about various metrics and compare how these assumptions relate to reality. In this article, we validate “You Draw It” as a method for graphical testing, comparing results to the less technological method used in Mosteller et al. and extending that study with formal statistical analysis methods. Results were consistent with those found in the previous study; when shown points following a linear trend, participants tended to fit the slope of the principal axis over the slope of the least-squares regression line. This trend was most prominent when shown data simulated with larger variances. This study reinforces the differences between intuitive visual model fitting and statistical model fitting, providing information about human perception as it relates to the use of statistical graphics. Supplementary materials for this article are available online.

Electric field control of phonon angular momentum in perovskite BaTiO3

We find that in BaTiO$_3$ the phonon angular momentum is dominantly pointing in directions perpendicular to the electrical polarization. Therefore, external electric field in ferroelectric BaTiO$_3$ does not control only the direction of electrical polarization, but also the direction of phonon angular momentum. This finding opens up the possibility for electric-field control of physical phenomena that rely on phonon angular momentum. We construct an intuitive model, based on our first-principles calculations, that captures the origin of the relationship between phonon angular momentum and electric polarization.

Bayesian Model Averaging to Account for Model Uncertainty in Estimates of a Vaccine's Effectiveness.

PurposeVaccine effectiveness (VE) studies are often conducted after the introduction of new vaccines to ensure they provide protection in real-world settings. Control of confounding is often needed during the analyses, which is most efficiently done through multivariable modeling. When many confounders are being considered, it can be challenging to know which variables need to be included in the final model. We propose an intuitive Bayesian model averaging (BMA) framework for this task.Patients and MethodsData were used from a matched case–control study that aimed to assess the effectiveness of the Lyme vaccine post-licensure. Cases were residents of Connecticut, 15–70 years of age with confirmed Lyme disease. Up to 2 healthy controls were matched to each case subject by age. All participants were interviewed, and medical records were reviewed to ascertain immunization history and evaluate potential confounders. BMA was used to systematically search for potential models and calculate the weighted average VE estimate from the top subset of models. The performance of BMA was compared to three traditional single-best-model-selection methods: two-stage selection, stepwise elimination, and the leaps and bounds algorithm.ResultsThe analysis included 358 cases and 554 matched controls. VE ranged between 56% and 73% and 95% confidence intervals crossed zero in <5% of all candidate models. Averaging across the top 15 models, the BMA VE was 69% (95% CI: 18–88%). The two-stage, stepwise, and leaps and bounds algorithm yielded VE of 71% (95% CI: 21–90%), 73% (95% CI: 26–90%), and 74% (95% CI: 27–91%), respectively.ConclusionThis paper highlights how the BMA framework can be used to generate transparent and robust estimates of VE. The BMA-derived VE and confidence intervals were similar to those estimated using traditional methods. However, by incorporating model uncertainty into the parameter estimation, BMA can lend additional rigor and credibility to a well-designed study.

Multilayer graph spectral analysis for hyperspectral images

Hyperspectral imaging has broad applications and impacts in areas including environmental science, weather, and geo/space exploration. The intrinsic spectral–spatial structures and potential multi-level features in different frequency bands make multilayer graph an intuitive model for hyperspectral images (HSI). To study the underlying characteristics of HSI and to take the advantage of graph signal processing (GSP) tools, this work proposes a multilayer graph spectral analysis for hyperspectral images based on multilayer graph signal processing (M-GSP). More specifically, we present multilayer graph (MLG) models and tensor representations for HSI. By exploring multilayer graph spectral space, we develop MLG-based methods for HSI applications, including unsupervised segmentation and supervised classification. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral–spatial information extraction.

SOSIALISASI PENDIDIKAN SEKSUAL TERHADAP REMAJA OLEH KELUARGA DESA GELAP KECAMATAN LAREN KABUPATEN LAMONGAN

The increase in cases of premarital pregnancy and cases of submission is very difficult for parents in Desa Gelap to protect their children from deviant sexual behavior. This issue is a point of interest discussed by analysts. The purpose of this study was to find out what types of socialization of sex training for young adults in the elastic target family in the Gelap Village, Laren District, Lamongan Regency. To test this information, his research analyst used George Herbert Mead's role theory. The methodology in this research is qualitative which is a kind of contextual analysis. This research uses a process of observation and in-depth interviews with 6 informants. The validity of the data utilizes a triangulation strategy. The information obtained was investigated using Miles and Huberman's intuitive examination model with three stages, namely information decline, information display, withdrawal/final confirmation. Considering the impact of the examination in the field of socialization of young adult sex training carried out by parents in the Dark Village, the type of primary socialization regarding adolescent sex is separated into three aspects, in particular 1) biological socialization, presenting actual and new changes, things that can be done by children -children 2) socialization of sexual norms and boundaries, showing children there are standards that must be maintained as human beings 3) socialization of social roles and functions, helping children to experience their work as women and men. Barriers experienced by parents to foster free association are the association of children and the use of cell phones.