Graphical Diagrams Research Articles

Design and application of a specialized information system for monitoring and decision support

The design is considered, involving the use of instrumental and analytical apparatus of GIS (geographic information systems) in order to provide monitoring and support for decision-making by government authorities. For these purposes, the basic models and methods for solving problems that arise before the administration of governing bodies have been analyzed. The design of a graphical-analytical model of a thematic layer of an electronic GIS map using a quadratomic tree is also described. A graph of conceptual-meaningful features and a graph diagram of the formation of graphic means for the design of signs are considered. The advantages and disadvantages of the quadratic tree model are described. In addition, the assessment of the temporal and capacitive complexity of information extraction, which is the main component of clarifying a clear algorithm for setting the problem during the functioning of the system being developed, is analyzed.

User Interface Design Research for Modeling Tools

Modeling tools constitute a class of software assisting modelers in creating, documenting, and maintaining conceptual models, for example, via graphical editors, diagram layouting, and syntax checking. Research on modeling tools has been a recurring theme in conceptual modeling research for more than 30 years. An evident focus in modeling tool research is on user interface design. In this literature study, we systematically identify and analyze 72 contributions to user interface design research for modeling tools published between 1980 and 2017 to develop a structuring overview of the current state of research. Building on this overview, we assess and discuss promising paths for future research, and compile recommendations informing user interface design of modeling tools.

Stability evaluation of cut slopes in highly weathered schists using limit equilibrium, kinematical and empirical methods

1. Extended abstractThe traditional limit equilibrium method and the kinematic analyses are the first choices in the geotechnical practice for the assessment of the slope stability of a particular rock slope. The reason is that very few empirical methods exist for this purpose.This paper presents results of the slope stability assessment with limit equilibrium, kinematical and empirical methods for over 50 deep cut slopes in highly weathered schists along the A2 motorway section Kicevo-Ohrid in RN Macedonia. The research area is mainly composed of phyllite, sericite schists, quartz-sericite schists and graphitic schists. These rocks are highly weathered, tectonically disturbed and often folded, which means they are prone to local deformations, especially in aspects of the long-term impact of atmospheric influences and the existence of unfavourable joint sets. Although most of the failures that occurred had local character, on several cut slopes global failure occurred. The slope stability assessment of the subject rock formations is a difficult challenge because the highly weathered phyllite and schists are difficult to be characterised, considering the uncertainties associated with the material anisotropy and the network of discontinuities.This study employs different failure mechanisms of cut slope stability in complex geological media. The rock masses were modelled according to the generalized Hoek-Brown criterion while the joints behaviour was modelled using the Barton-Bandis shear strength criterion. Rocscience software package was used for executing the limit equilibrium analyses and the stability analyses along discontinuities. The relatively new and intuitive empirical method Q-slope was applied for the stability assessment of the excavated rock slopes.According to the results and the findings, it can be emphasized that although the limit equilibrium analyses showed relatively stable slopes, significant failures on several cut slopes occurred on site. The stability conditions on site were confirmed with the kinematical analyses and the Q-slope method.The final decision of the slope geometry should be made after the inspection of the excavated surfaces because it is very difficult to register the discontinuity conditions before the construction phase starts. The best way to assess the stability condition is with a combination of different tools and methodologies, especially when we face highly weathered rock formations.The next step of this research will be to propose a methodology, where the long-term rock slope stability and the associated level of risk will be evaluated using graphical diagrams.

Comprehensive Systems: A formal foundation for Multi-Model Consistency Management

Model management is a central activity in Software Engineering. The most challenging aspect of model management is to keep inter-related models consistent with each other while they evolve. As a consequence, there is a lot of scientific activity in this area, which has produced an extensive body of knowledge, methods, results and tools. The majority of these approaches, however, are limited to binary inter-model relations; i.e. the synchronisation of exactly two models. Yet, not every multi-ary relation can be factored into a family of binary relations. In this paper, we propose and investigate a novel comprehensive system construction, which is able to represent multi-ary relations among multiple models in an integrated manner and thus serves as a formal foundation for artefacts used in consistency management activities involving multiple models. The construction is based on the definition of partial commonalities among a set of models using the same language, which is used to denote the (local) models. The main theoretical results of this paper are proofs of the facts that comprehensive systems are an admissible environment for (i) applying formal means of consistency verification (diagrammatic predicate framework), (ii) performing algebraic graph transformation (weak adhesive HLR category), and (iii) that they generalise the underlying setting of graph diagrams and triple graph grammars.

Machine Learning Prediction of Quartz Forming‐Environments

AbstractTrace elements of quartz document the physical‐chemical evolutions of quartz growth, which has been a great and most applied tool in the study of geological settings in quartz‐forming environments. A classic method is using graphic diagram plots visualizing the quartz trace element discriminations and trends, examples including the Al‐Ti diagram (Rusk, 2012, https://doi.org/10.1007/978-3-642-22161-3_14) and the Ti‐Al‐Ge diagram (Schrön et al., 1988, https://www.researchgate.net/publication/236149159_Geochemische_Untersuchungen_an_Pegmatitquarzen). However, those diagrams are limited to two dimensions and cannot show the information in a higher dimension. In the study, we thus used a machine learning‐based approach to evaluate quartz trace elements, and visualized them for the first time in the high‐dimensional diagrams. We revisited 1,626 quartz samples from nine geological environments from previous studies, and applied a support vector machine to characterize values of the contained trace elements, including Al, Ti, Li, Ge, and Sr. We demonstrate that support vector machines can identify the crystallization environment of quartz with a significantly higher accuracy than the traditional plotting methods. Our work can massively improve the confidence on distinguishing quartz origin from different geological environments with a high efficiency. The method may also be applicable for other minerals, and we anticipate our research is a starting point for investigating mineral trace elements with machine learning techniques. Our quartz classifier can be accessed via https://quartz-classifier.herokuapp.com.

Connecting Instrumental Variable methods for causal inference to the Estimand Framework.

Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in clinical trials to the E9 guideline of the International Council for Harmonisation. The E9 addendum emphasises the need to account for post-randomization or 'intercurrent' events that can potentially influence the interpretation of a treatment effect estimate at a trial's conclusion. Instrumental Variables (IV) methods have been used extensively in economics, epidemiology, and academic clinical studies for 'causal inference,' but less so in the pharmaceutical industry setting until now. In this tutorial article we review the basic tools for causal inference, including graphical diagrams and potential outcomes, as well as several conceptual frameworks that an IV analysis can sit within. We discuss in detail how to map these approaches to the Treatment Policy, Principal Stratum and Hypothetical 'estimand strategies' introduced in the E9 addendum, and provide details of their implementation using standard regression models. Specific attention is given to discussing the assumptions each estimation strategy relies on in order to be consistent, the extent to which they can be empirically tested and sensitivity analyses in which specific assumptions can be relaxed. We finish by applying the methods described to simulated data closely matching two recent pharmaceutical trials to further motivate and clarify the ideas.

Single pushout rewriting in comprehensive systems of graph-like structures

The elegance of the single-pushout (SPO) approach to graph transformations arises from substituting total morphisms by partial ones in the underlying category. SPO's applicability depends on the durability of pushouts after this transition. There is a wide range of work on the question when pushouts exist in categories with partial morphisms starting with the pioneering work of Löwe and Kennaway and ending with an essential characterisation in terms of an exactness property (for the interplay between pullbacks and pushouts) and an adjointness condition (w.r.t. inverse image functions) by Hayman and Heindel.Triple graphs and graph diagrams are frameworks to synchronise two or more updatable data sources by means of internal mappings, which identify common sub-structures. Comprehensive systems generalise these frameworks, treating the network of data sources and their structural inter-relations as a homogeneous comprehensive artefact, in which partial maps identify commonalities. Although this inherent partiality produces amplified complexity, we can show that Heindel's characterisation still yields existence of pushouts in the category of comprehensive systems and reflective partial morphisms and thus enables computing by typed SPO graph transformation.

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson's Disease Investigation.

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.

IoT-Driven Workflows for Risk Management and Control of Beehives

The internet of things (IoT) and Industry 4.0 technologies are becoming widely used in the field of apiculture to enhance honey production and reduce colony losses using connected scales combined with additional data, such as relative humidity and internal temperature. This paper exploits beehive weight measurements and builds appropriate business rules using two instruments. The first is an IoT fixed scale installed on one hive, taking rich continuous measurements, and used as a reference. The second is a portable nomad scale communicating with a smartphone and used for the remaining hives. A key contribution will be the run and triggering of a business process model based on apicultural business rules learned from experience and system observed events. Later, the evolution of the weight of each individual hive, obtained by either measurement or inference, will be associated with a graphical workflow diagram expressed with the business process model and notation (BPMN) language, and will trigger events that inform beekeepers to initiate relevant action. Finally, the BPMN processes will be transformed into executable models for model driven decision support. This contribution improves amateur and professional user-experience for honeybee keeping and opens the door for interoperability between the suggested model and other available simulations (weather, humidity, bee colony behavior, etc.).

Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings.

This paper proposes a new framework for epileptic seizure detection using non-invasive scalp electroencephalogram (sEEG) signals. The major innovation of the current study is using the Riemannian geometry for transforming the covariance matrices estimated from the EEG channels into a feature vector. The spatial covariance matrices are considered as features in order to extract the spatial information of the sEEG signals without applying any spatial filtering. Since these matrices are symmetric and positive definite (SPD), they belong to a special manifold called the Riemannian manifold. Furthermore, a kernel based on Riemannian geometry is proposed. This kernel maps the SPD matrices onto the Riemannian tangent space. The SPD matrices, obtained from all channels of the segmented sEEG signals, have high dimensions and extra information. For these reasons, the sequential forward feature selection method is applied to select the best features and reduce the computational burden in the classification step. The selected features are fed into a support vector machine (SVM) with an RBF kernel to classify the feature vectors into seizure and non-seizure classes. The performance of the proposed method is evaluated using two long-term scalp EEG (CHB-MIT benchmark and private) databases. Experimental results on all 23 subjects of the CHB-MIT database reveal an accuracy of 99.87%, a sensitivity of 99.91%, and a specificity of 99.82%. In addition, the introduced algorithm is tested on the private sEEG signals recorded from 20 patients, having 1380 seizures. The proposed approach achieves an accuracy, a sensitivity, and a specificity of 98.14%, 98.16%, and 98.12%, respectively. The experimental results on both sEEG databases demonstrate the effectiveness of the proposed method for automated epileptic seizure detection, especially for the private database which has noisier signals in comparison to the CHB-MIT database. Graphical Abstract Block diagram of the proposed epileptic seizure detection algorithm.

Soviet Power and System of Public Education in 1920s: Practice of Experiments in Conditions of Building New Statehood

The state policy of the Soviet government in relation to the system of public education in the initial period of the formation of the Soviet state is considered. The relevance of the study is due to the content and organizational side of the transformations of the Soviet power in the field of public education in the 1920s, which is today of substantive interest for modern education from the applied standpoint of pedagogical practices and innovations in terms of their effectiveness and professional suitability. The authors focus on the general nature of managerial experiments that took place in the school system in the 1920s in the context of the general transformation of the country. A detailed analysis of the organizational foundations of the reform of the education system was carried out, the structure of educational institutions of the period under review was studied, its graphical diagram was built, an overview of the content and overview of school education was made. The novelty of the research lies in the presented subject analysis of the content of curricula and school programs of the 1920s, the construction of a consistent line of reforms in education implemented by the Soviet government in the context of their further effectiveness and historical results. Conclusions are made about the content side of these transformations within the framework of a complex of pedagogical ideas and new practices in education.

Morpho-Physiological and Proteomic Response of Bt-Cotton and Non-Bt Cotton to Drought Stress.

Drought stress impacts cotton plant growth and productivity across countries. Plants can initiate morphological, cellular, and proteomic changes to adapt to unfavorable conditions. However, our knowledge of how cotton plants respond to drought stress at the proteome level is limited. Herein, we elucidated the molecular coordination underlining the drought tolerance of two inbred cotton varieties, Bacillus thuringiensis-cotton [Bt-cotton + Cry1 Ac gene and Cry 2 Ab gene; NCS BG II BT (BTCS/BTDS)] and Hybrid cotton variety [Non-Bt-cotton; (HCS/HDS)]. Our morphological observations and biochemical experiments showed a different tolerance level between two inbred lines to drought stress. Our proteomic analysis using 2D-DIGE revealed that the changes among them were not obviously in respect to their controls apart from under drought stress, illustrating the differential expression of 509 and 337 proteins in BTDS and HDS compared to their controls. Among these, we identified eight sets of differentially expressed proteins (DEPs) and characterized them using MALDI-TOF/TOF mass spectrometry. Furthermore, the quantitative real-time PCR analysis was carried out with the identified drought-related proteins and confirmed differential expressions. In silico analysis of DEPs using Cytoscape network finds ATPB, NAT9, ERD, LEA, and EMB2001 to be functionally correlative to various drought-responsive genes LEA, AP2/ERF, WRKY, and NAC. These proteins play a vital role in transcriptomic regulation under stress conditions. The higher drought response in Bt cotton (BTCS/BTDS) attributed to the overexpression of photosynthetic proteins enhanced lipid metabolism, increased cellular detoxification and activation chaperones, and reduced synthesis of unwanted proteins. Thus, the Bt variety had enhanced photosynthesis, elevated water retention potential, balanced leaf stomata ultrastructure, and substantially increased antioxidant activity than the Non-Bt cotton. Our results may aid breeders and provide further insights into developing new drought-tolerant and high-yielding cotton hybrid varieties.

LINC02308 promotes the progression of glioma through activating mTOR/AKT-signaling pathway by targeting miR-30e-3p/TM4SF1 axis.

Glioma is a common brain malignancy, and the purpose of this study is to investigate the function of LINC02308 in glioma. The differentially expressed lncRNAs were screened by microarray. The expression of LINC02308 in glioma tissues and cells was evaluated. The interaction among LINC02308, miR-30e-3p, and TM4SF1 was determined. Cell proliferation and apoptosis were evaluated. The expression of mTOR/AKT-signaling and apoptosis-related markers was detected by Western blot. A xenograft tumor mouse model was constructed to investigate the roles of LINC02308. LINC02308 was significantly overexpressed in glioma, and a high LINC02308 level was correlated with a poor prognosis. LINC02308 silencing markedly inhibited proliferation and reduced apoptosis of glioma cells and also suppressed tumor growth in the xenograft tumor mouse model. Finally, we demonstrated that LINC02308 played its oncogenic role through binding to miR-30e-3p so as to relieve miR-30e-3p-induced suppression of TM4SF1. LINC02308 promoted glioma tumorigenesis as a sponge of miR-30e-3p to upregulate TM4SF1 and activate AKT/mTOR pathway. Graphical Abstract Hypothesis diagram illustrates the function and mechanism of LINC02308 in glioma. A schematic representation of the functional mechanism of LINC02308 in glioma.

Electrochemical aptasensor based on Mo2C/Mo2N and gold nanoparticles for determination of chlorpyrifos.

Two-dimensional Mo2C/Mo2N composites were synthesized by high temperature ball milling and used as support materials for fabricating achlorpyrifos (CPF) aptasensor. Gold nanoparticles (Au NPs) were electrodeposited on the surface of aMo2C/Mo2N-modified electrode to connect with the ferrocene (Fc) probe via Au-S bonds. The Fc probe can hybridize with the aptamer probe to form a double-stranded structure. The addition of CPF made the double strands melt and the Fc probe approached the surface of the electrode, thereby resulting in amplification of the electrochemical response. The current response of the aptasensor for detecting CPF in solutions linearly varied from 0 to 400ngmL-1 (with a maximum at 0.98V vs. Ag/AgCl). The Au NPs/Mo2C/Mo2N composites exhibited satisfactory electrochemical behavior due to their excellent electrical conductivity and large surface area. This ultrasensitive aptasensor showed a low limit of detection of 0.036ngmL-1. It was applied to determine CPF in real samples with acceptable recoveries from 94.7 to 116.7%, and the relative standard deviation was from 2.57 to 7.08%.Graphical abstract Schematic diagram of the manufacturing process of the aptasensor. Electrochemical aptasensor based on Mo2C/Mo2N/Au NP composites show excellent performance in detecting CPF.

Technique for optimizing the technological process of manufacturing electrical radio products in the context of digital transformation of production

The article discusses the process of optimization of production processes for the manufacture of electronic products in the context of the implementation of the concept of digital transformation of industrial enterprises. One of the elements of digitalization used to improve the effectiveness of electronics manufacturing technology in industry is Design for Manufacturing (DFM) in the MatLab computer mathematics environment. The technological production cycle is illustrated in the form of a graph diagram using the apparatus of Markov chains. The application of the developed methodology, as well as the use of graph theory in the technological process of a modern industrial enterprise, allows achieving the required performance indicators, due to the optimal reflection and fixation in the database of non-stationary influences that cause deviations, which are leveled due to adaptive control actions.

A novel approach for estimation of discharge coefficient in broad-crested weirs based on Harris Hawks Optimization algorithm

Accurate determination of the discharge coefficient play a very important role in estimating the flow discharge over the weirs. As a result, it is significant to estimate the discharge coefficient correctly. The aim of this study is simulation and estimation the discharge coefficients (Cd) over the broad-crested weirs with cross section rectangular and suppressed. Hence, numerical simulation of hydraulic characteristics of these weirs were performed by ANSYS FLUENT software and results were obtained. Then two intelligent models of ANN, GPR and hybrid both of models namely ANN-HHO, GPR-HHO were used to determine the discharge coefficients using the efficient parameters and the results of these models were compared. Assessment of the results were performed using the statistical metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Kling-Gupta efficiency (KGE) and graphical diagrams including violin plot, percent relative error (RE%) plot and probability density function (PDF) plot of residuals. It was found that hybrid artificial neural network and gaussian process regression with Harris Hawks optimization (ANN-HHO and GPR-HHO) could improve ANN and GPR models performance in estimating the Cd in broad-crested weirs. Overall, results indicated that a combination of the HHO with the ANN (ANN-HHO) model performs better than GPR-HHO model for the estimation of the Cd.

Big Data Analysis of a Dedicated YouTube Channel as an Open Educational Resource in Hand Surgery

Background: Educationally designed videos have the advantage of explaining difficult concepts via graphic diagrams and dynamic illustrations. The benefits of videos will be enhanced if videos are well-designed, concise, explore scientifically correct content, and have a clear presentation.Aims: Video as an open educational resource (OER) is the cornerstone for online learning. YouTube has been widely used as a distribution platform for OER. The aim of this study was to evaluate the engagement of hand surgery videos on a dedicated education channel on YouTube.Methods: The senior author has been utilizing a YouTube channel dedicated to the education of clinicians in Hand Surgery, surgical education, and management since 2008. The degree of engagement was evaluated using YouTube Analytics, providing up-to-date metrics and reports.Results: Since 2008, there have been 6521 subscribers with 1,360,680 views, a total view time of 35,033 h and an average view time of 1.72 min. The channel views averaged 1000 views per day. There were 4,324,724 impressions with a 7.32% click-through rate, with the United States of America accounting for 23.5% of the audience. YouTube search accounted for 33.3% of the traffic source and suggested videos by others were 19.4%, and external links were 19%. Playback location was through mobile devices in 76.7%, while 16% was through the computer. The two popular playlists were “flaps in hand surgery” (50.2%) and “basic hand surgery workshop” (21.9%). Sharing by WhatsApp was most popular (27.9%) in embedded websites and apps. Overall there were 488 comments on the channel.Conclusion: This paper confirms the phenomenon of micro-learning by online learners. It is recommended that educational videos as OER should be confined to 2 min, made compatible for the mobile device, and be optimized for sharing on social media. These can be used as resources for blended learning, allowing better utilization of time for deliberate practice in surgical training.

Hydrochemical assessment of groundwater from taluka Dahili, Thar Desert, Pakistan, for irrigation purpose using water quality indices

ABSTRACT Groundwater is the most significant resource of fresh water for irrigation in semi-arid areas of the world. Geochemical characteristics of groundwater of taluka Dahili Thar desert was explored by analysing 23 dug well samples via irrigation indices. Physiochemical parameters and various graphical scatter diagrams were used to recognise major geochemical processes governing the groundwater quality. GIS interpolation tools were applied to demonstrate the spatial variation of groundwater quality parameters, irrigation parameters in the study area. Statistical summary of results revealed that analysed physicochemical parameters concentration in the study area were much higher than the allowable limit of WHO with limited exceptions. Among cations Na and Mg, concentration ranges from 71 to 2470 mg/l and 40 to 344 mg/l, and anions Cl and HCO3 values range from 103 to 4852 mg/l and 210 to 750 mg/l. Statistical data also reveals the dominant trend among the cations Na>Mg>Ca> K and in anions, Cl>HCO3> SO4. Gibbs and other graphical analysis reveal that dissolution of salts and minerals, ion exchange and evaporate dissolution, and weathering of rock remains the key factors affecting the study area’s water geochemistry. Piper diagram divulges that water of study area was dominantly Na-Cl. In addition to this according to Wilcox diagram and USSL diagram 82.61% locations of samples were not suitable for agriculture purposes. SAR results indicate that 71.92% for SAR, 91.30% for Na%, 95.65% for KI, and 86.96% for Magnesium hazard (MH) sample locations were unsafe for agriculture purposes. However, 86.96% for RSC and 69.57% for PI sample locations of the study area were safe for agricultural purposes.

Palettes of Dehn colorings for spatial graphs and the classification of vertex conditions

In this paper, we study Dehn colorings of spatial graph diagrams, and classify the vertex conditions, equivalently the palettes. We give some example of spatial graphs which can be distinguished by the number of Dehn colorings with selecting an appropriate palette. Furthermore, we also discuss the generalized version of palettes, which is defined for knot-theoretic ternary-quasigroups and region colorings of spatial graph diagrams.

MODELAREA ÎN MATLAB/SIMULINK A FUNCȚIONĂRII UNUI VEHICUL ELECTRIC ÎN DIFERITE CICLURI DE ACȚIONARE

Through this paper, we want to improve the methods of dynamic design of electric vehicles, by creating simulation models in Simulink. The models are represented by simulation schemes that consist of blocks of mathematical operators, interconnected based on mathematical calculation formulas. The initial input sizes, specified in Table 1, can be easily modified, giving the possibility to obtain several models for different design ideas. The results of the simulations are materialized in graphical diagrams and provide virtual time data on vehicle speed, acceleration, engine torque, wheel power, energy consumed, and distance traveled. In the first simulation scheme, the input variable shows a motor torque generator with values between 0-132 Nm. In the second simulation scheme, the input variable is speed, according to the ECE-15 urban cycle scenario.