Developed Model Research Articles

Clinical prediction model for MODY type diabetes mellitus in children

BACKGROUND: MODY (maturity-onset diabetes of the young) is a rare monogenic form of diabetes mellitus, the gold standard of diagnosis is mutations detection in the genes responsible for the development of this form diabetes. Genetic test is expensive and takes a lot of time. The diagnostic criteria for MODY are well known. The development of clinical decision support system (CDSS) which allows physicians based on clinical data to determine who should have molecular genetic testing is relevant.AIM: Provided a retrospective analysis of clinical data of the patients with T1DM and MODY, from 0 to 18 years old, regardless of the duration of the disease to develop the model. Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. For the final configuration of NS the following predictors were selected: gender, age at passport age, age at the diagnosis with DM, HbA1c, BMI SDS, family history of DM, treatment. The performance (quality) assessment of the NN was carried out on a test sample (the area under the ROC (receiver operating characteristics) curve reached 0.97). The positive predictive value of PCPR was achieved at a cut-off value of 0.40 (predicted probability of MODY diabetes 40%). At which the sensitivity was 98%, specificity 93%, PCR with prevalence correction was 78%, and PCR with prevalence correction was 99%, the overall accuracy of the model was 94%.Based on the NN model, a CDSS was developed to determine whether a patient has MODY diabetes, implemented as an application.CONCLUSION: The clinical prediction model MODY developed in this work based on the NN, uses the clinical characteristic available for each patient to determine the probability of the patient having MODY. The use of the developed model in clinical practice will assist in the selection of patients for diagnostic genetic testing for MODY, which will allow for the efficient allocation of healthcare resources, the selection of personalized treatment and patient monitoring.

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network

ABSTRACT Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the “Symmetric Convolution-based Residual Attention Network (SC-RAN)” is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

Application of microphysiological systems for nonclinical evaluation of cell therapies.

Microphysiological systems (MPS) are gaining broader application in the pharmaceutical industry but have primarily been leveraged in early discovery toxicology and pharmacology studies with small molecules. The adoption of MPS offers a promising avenue to reduce animal use, improve in-vitro-to-in-vivo translation of pharmacokinetics/pharmacodynamics and toxicity correlation, and provide mechanistic understanding of model species suitability. While MPS have demonstrated utility in these areas with small molecules and biologics, cell therapeutic MPS models in drug development have not been fully explored, let alone validated. Distinguishing features of MPS, including long-term viability and physiologically relevant expression of functional enzymes, receptors, and pharmacological targets make them attractive tools for nonclinical characterization. However, there is currently limited published evidence of MPS being utilized to study the disposition, metabolism, pharmacology, and toxicity profiles of cell therapies. This review provides an industry perspective on the nonclinical application of MPS on cell therapies, first with a focus on oncology applications followed by examples in regenerative medicine.

Machine learning algorithm predicts urethral stricture following transurethral prostate resection.

To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters. A retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development. A total of 109 patients' data (55 patients without urethral stricture and 54 patients with urethral stricture) were included in the study after implementing strict inclusion and exclusion criteria. The preoperative Platelet Distribution Width, Mean Platelet Volume, Plateletcrit, Activated Partial Thromboplastin Time, and Prothrombin Time values were statistically meaningful between the two cohorts. After applying the data to the machine learning systems, the accuracy prediction scores for the diverse algorithms were as follows: decision trees (0.82), logistic regression (0.82), random forests (0.91), support vector machines (0.86), K-nearest neighbors (0.82), and naïve Bayes (0.77). Our machine learning models' accuracy in predicting the post-TURP urethral stricture probability has demonstrated significant success. Exploring prospective studies that integrate supplementary variables has the potential to enhance the precision and accuracy of machine learning models, consequently progressing their ability to predict post-TURP urethral stricture risk.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

Foundational Leadership and Organizational Wellness (FLOW) Model: Designing leadership learning for individuals and organizations.

This article shares the foundational leadership and organizational wellness (FLOW) model, which is a leadership development model that seeks to better understand the relationship between individual leadership development and organizational development and wellness. The model is presented as a whole, followed by deep exploration by each piece of the model undergirded in existing scholarship and practical discussion throughout. Implications for practice and future research are shared to conclude the article.

Decomposition in an extreme cold environment and associated microbiome—prediction model implications for the postmortem interval estimation

IntroductionThe accurate estimation of postmortem interval (PMI), the time between death and discovery of the body, is crucial in forensic science investigations as it impacts legal outcomes. PMI estimation in extremely cold environments becomes susceptible to errors and misinterpretations, especially with prolonged PMIs. This study addresses the lack of data on decomposition in extreme cold by providing the first overview of decomposition in such settings. Moreover, it proposes the first postmortem microbiome prediction model for PMI estimation in cold environments, applicable even when the visual decomposition is halted.MethodsThe experiment was conducted on animal models in the second-coldest region in the United States, Grand Forks, North Dakota, and covered 23 weeks, including the winter months with temperatures as low as −39°C. Random Forest analysis models were developed to estimate the PMI based either uniquely on 16s rRNA gene microbial data derived from nasal swabs or based on both microbial data and measurable environmental parameters such as snow depth and outdoor temperatures, on a total of 393 samples.ResultsAmong the six developed models, the best performing one was the complex model based on both internal and external swabs. It achieved a Mean Absolute Error (MAE) of 1.36 weeks and an R2 value of 0.91. On the other hand, the worst performing model was the minimal one that relied solely on external swabs. It had an MAE of 2.89 weeks and an R2 of 0.73. Furthermore, among the six developed models, the commonly identified predictors across at least five out of six models included the following genera: Psychrobacter (ASV1925 and ASV1929), Carnobacterium (ASV2872) and Pseudomonas (ASV1863).DiscussionThe outcome of this research provides the first microbial model able to predict PMI with an accuracy of 9.52 days over a six-month period of extreme winter conditions.

Development and validation of an early mortality risk model for pediatric hemophagocytic lymphohistiocytosis: a comparison with HScore, PELOD-2, P-MODS, and pSOFA.

There has been no severity evaluation model for pediatric patients with hemophagocytic lymphohistiocytosis (HLH) that uses readily available parameters. This study aimed to develop a novel model for predicting the early mortality risk in pediatric patients with HLH using easily obtained parameters whatever etiologic subtype. Patients from one center were divided into training and validation sets for model derivation. The developed model was validated using an independent validation cohort from the second center. The prediction model with nomogram was developed based on logistic regression. The model performance underwent internal and external evaluation and validation using the area under the receiver operating characteristic curve (AUC), calibration curve with 1000 bootstrap resampling, and decision curve analysis (DCA). Model performance was compared with the most prevalent severity evaluation scores, including the PELOD-2, P-MODS, and pSOFA scores. The prediction model included nine variables: glutamic-pyruvic transaminase, albumin, globulin, myohemoglobin, creatine kinase, serum potassium, procalcitonin, serum ferritin, and interval between onset and diagnosis. The AUC of the model for predicting the 28-day mortality was 0.933 and 0.932 in the training and validation sets, respectively. The AUC values of the HScore, PELOD-2, P-MODS and pSOFA were 0.815, 0.745, 0.659 and 0.788, respectively. The DCA of the 28-day mortality prediction exhibited a greater net benefit than the HScore, PELOD-2, P-MODS and pSOFA. Subgroup analyses demonstrated good model performance across HLH subtypes. The novel mortality prediction model in this study can contribute to the rapid assessment of early mortality risk after diagnosis with readily available parameters.

Improving instrumental neutron activation analysis detection limit using Monte Carlo simulation for lanthanides determination in Algerian phosphate

ABSTRACT Instrumental Neutron Activation Analysis (INAA) is one of the best methods to analyse Rare Earth Elements (REE) with a low detection limit in phosphate samples. Since most lanthanides emit gamma rays at the energy below 1000 keV, this energy range is affected by the Bremsstrahlung noise of beta emission produced by the 31P(n,γ)32P reaction, when the irradiation of the P2O5 composing the phosphate matrix (approximately 30%). This beta (β−) spectrum with a maximum energy of 1.71 MeV (average energy of 695 keV) affects the gamma spectrum and will reduce the detection limit significantly, other factors decrease the detection limit of lanthanides like neutron self-shielding and interference. In the present work, an analysis methodology based on optimising the measurement parameters of the INAA technique was implemented to improve the detection limits for the precise evaluation of lanthanide concentrations in Algerian phosphate deposits. The complete procedure was developed using a combination of a Monte Carlo simulation approach and experimental measurements. Two models were developed based on Monte Carlo simulation for the calibration of HPGe detector efficiency and the optimisation of type and filter thickness to reduce the beta spectrum (Bremsstrahlung) emitted by the 32P produced by neutron capture reaction 31P(n, γ) 32P. The developed model allowed a considerable improvement in the detection limit of the INAA technique and reduced the uncertainty in the lanthanide concentration. Finally, the methodology was applied to determine lanthanide elements in phosphate samples with good precision.

Cryptocurrency price fluctuation and time series analysis through candlestick pattern of bitcoin and ethereum using machine learning

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Modeling and simulation of grain growth for FGH96 superalloy using a developed cellular automaton model

Through heat treatment experiments and numerical simulations, the effects of the heating temperature (1313–1423 K) and holding time (10–240 min) on the grain growth behavior of the extruded FGH96 alloy were investigated. A two-dimensional cellular automata (CA) model that considered the dissolution of the γ′ phase over time and the distribution characteristics with different sizes was developed to explore the grain growth behavior above the γ′ phase over-solution temperature (1423 K) and below the γ′ sub-solution temperature (1383 K), respectively. The results showed that the rate of grain growth of FGH96 alloy was obviously enhanced when the heating temperature exceeded 1363 K, which was mainly related to the dissolution of the γ′ phase, and the grain growth of FGH96 alloy mainly occurred during the initial stage of insulation. The grain growth model of the extruded FHG96 alloy could accurately predict the grain growth behavior, and the simulation results were in good agreement with the experimental results at over-solution temperature or sub-solution temperature. The effects of volume fraction and radius of γ′ phase on the grain growth behavior of FGH96 alloy were studied by simulating the grain growth behavior of FGH96 alloy under different sizes and volume fractions of γ′ phase. The results follow the Zener relation, and the coefficient n in the Zener relation was determined by fitting the simulation results.

Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction

The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections for electric utilities and policymakers, leading to the adoption of machine learning-based modelling tools due to the limitations of traditional power usage projection approaches. However, successful modeling of power usage in neuro-fuzzy models depends on the optimal selection of hyper-parameters. Consequently, this research looked at the major impact clustering methods and hyper-parameter modifications on a particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS) model. The study examined two distinct clustering methods and other key hyperparameters such as the number of clusters and cluster radius, resulting in a total of 10 sub-models. The performance of the developed models was assessed using four widely recognized performance indicators: root mean square error, mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of variation of the root mean square error (CVRMSE). Additionally, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models based on three different PSO variants. The results revealed that the combination of the ANFIS approach and PSO, specifically with two clusters, yielded the most accurate forecasting scheme with the optimal values for MAPE (7.7778%), MAE (712.6094), CVRMSE (9.5464), and RMSE (909.4998).

Exploring the effectiveness of negative and positive inserts in machining Inconel 718 alloy: a comparative study

AbstractInconel 718 alloy is characterised by high strength and corrosion resistance and remains stable at high temperatures, so it is widely used in the energy and aerospace industries. However, machining this material is difficult due to its high strength, hardness, and high specific force coefficient exceeding 3000 MPa. Turning of the Inconel 718 alloy can be carried out with negative and positive inserts. Therefore, the impacts of the insert geometry on the turning process of Inconel 718, cutting force components, and surface roughness were studied. Three positive and three negative insert geometries were tested. It was shown that the key influence on the active components of the cutting force is the effective rake angle. The surface roughness, on the other hand, depends mainly on the cutting-edge radius. It has been shown that the negative insert geometry with γ = 6° and rn=22 μm provides a 30% lower cutting force than the positive inserts and the same surface roughness. The developed models of the cutting force components proved that when cutting with positive inserts, a higher specific cutting force occurs for the Inconel 718 alloy than for the negative insert. It was shown that technological parameters had a very similar effect on the cutting force components and surface roughness parameters regardless of the blade geometry. It was proven that the use of positive inserts makes sense only for depths of cut no greater than the size of the corner radius.

Investigation of Micropattern Forming Process on Zr‐4 Alloy Surface Considering Plowing Effects

Micropattern forming process is an efficient and low‐cost process for fabricating multifunctional surfaces in electronics, energy, and biomedical products, but this process of creating microdimples on the surface of zirconium alloys has not been investigated. Currently, there are few models describing the micropattern forming process, and these existing models have not considered the effect of roughness on forming. The influence of roughness on micropattern forming process is not to be neglected due to the size effect, especially the plowing effects caused by asperities in surface roughness during sliding process. In this article, a micropattern forming process model is developed by combining a rough plane indentation model and a plowing effects model, and the experimental study of the forming process is conducted, which can quantitatively evaluate the effects of different process parameters and different roughness of forming die on forming process. The applicability range of the developed model is obtained, and the model is capable of predicting the forming process with various process parameters and surface roughness of forming die by comparing the experiment results with the model results, which is of great significance to the design of the forming die and the improvement of the forming process.

Explore the Needs of Competency Development Model for Prospective Elementary School Teachers Based on Knowledge Management System In Indonesia

Explore the Needs of Competency Development Model for Prospective Elementary School Teachers Based on Knowledge Management System In Indonesia

ОЦІНЮВАННЯ ВПЛИВУ ЗАСОБІВ ПІДКЛЮЧЕННЯ РЕЗЕРВУ НА ПОКАЗНИКИ НАДІЙНОСТІ СИСТЕМИ ПЕРЕДАВАННЯ ДАНИХ

Structural redundancy is included in modern data transmission systems to ensure the specified reliability indicators. Structural redundancy gives the system fault tolerance and significantly increases its reliability. To manage the redundant resource, it is necessary to have additional tools that will connect the redundant equipment to the main configuration of the data transmission system. The redundant equipment is a separate technical means and, from the point of view of reliability, it is connected in series with other modules of the data transmission system. Thus, the inclusion of redundant connection facilities in the data transmission system reduces its reliability. In addition, even with perfect reliability, redundant connections may fail or malfunction and, under certain conditions, cause the transmission system to fail. To determine the impact of reserve connection facilities on the reliability of the transmission system, the article applies an original technology for building models of discrete-continuous stochastic systems. ASNA software was used as a software tool for modeling the reliable behavior of the data transmission system. This software provides automated construction of the model in the form of a graph of states and transitions based on a structural automaton model. Based on the graph of states and transitions, an analytical model is automatically generated in the form of a system of linear differential Kolmogorov-Chapman equations. Using the developed model, the article quantitatively analyzes the impact of redundant connection means (switches) on the reliability indicators of the data transmission system. In the study, the proposed models, unlike the existing ones, take into account both reliability indicators (probability of failure) and functionality indicators (probability of switching, speed) of the reserve connection means. On the basis of the developed model, the limit values of reliability and functionality of the redundant connection means (switches) were determined, at which they will minimize the reliability of the fault-tolerant data transmission system. Based on the analysis of the obtained results, practical recommendations for the selection of reliability indicators of the backup connection means, their speed and probability of switching are formed.

Advancing molecular modeling and reverse vaccinology in broad-spectrum yellow fever virus vaccine development

Yellow fever outbreaks are prevalent, particularly in endemic regions. Given the lack of an established treatment for this disease, significant attention has been directed toward managing this arbovirus. In response, we developed a multiepitope vaccine designed to elicit an immune response, utilizing advanced immunoinformatic and molecular modeling techniques. To achieve this, we predicted B- and T-cell epitopes using the sequences from all structural (E, prM, and C) and nonstructural proteins of 196 YFV strains. Through comprehensive analysis, we identified 10 cytotoxic T-lymphocyte (CTL) and 5T-helper (Th) epitopes that exhibited overlap with B-lymphocyte epitopes. These epitopes were further evaluated for their affinity to a wide range of human leukocyte antigen system alleles and were rigorously tested for antigenicity, immunogenicity, allergenicity, toxicity, and conservation. These epitopes were linked to an adjuvant (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-defensin) and to each other using ligands, resulting in a vaccine sequence with appropriate physicochemical properties. The 3D structure of this sequence was created, improved, and quality checked; then it was anchored to the Toll-like receptor. Molecular Dynamics and Quantum Mechanics/Molecular Mechanics simulations were employed to enhance the accuracy of docking calculations, with the QM portion of the simulations carried out utilizing the density functional theory formalism. Moreover, the inoculation model was able to provide an optimal codon sequence that was inserted into the pET-28a( +) vector for in silico cloning and could even stimulate highly relevant humoral and cellular immunological responses. Overall, these results suggest that the designed multi-epitope vaccine can serve as prophylaxis against the yellow fever virus.

СИГНАЛЬНА МАКРОМОДЕЛЬ ОПТОПАРИ НА СТРУКТУРАХ ОРГАНІЧНОЇ ЕЛЕКТРОНІКИ

The paper is devoted to the basic approaches to the realization of the macromodel of optocouplers on organic structures for sensor devices. It is shown that the use of such structures is largely limited due to the time drift of parameters and temperature instability. In the course of the research, the possibility of compensating for these parameters directly during the operation of organic optocouples by measuring and analyzing the parameter drift was established. For this purpose, specialized built-in in-situ diagnostic systems and controlled signal transducers are used. In order to verify the functioning and optimize the parameters of signal transducers of built-in in- situ diagnostic systems, the paper describes the main approaches and the result of the implementation of the optocouple macromodel. The macromodel provides the ability to specify the components of sensor electronics optocouplers in accordance with the approaches and syntax of SPICE modeling and reflects parametric modulation with changes in ambient temperature, time instability, and flicker noise. The macromodel consists of five modules that describe the processes and specify the characteristics of the optocoupler: the light emitter (LED), self-heating of the optocoupler structure by the supply current, the optical medium or active layer of the optosensor, time drift and flicker noise, and the light receiver (photodiode or photoresistor). The modules are represented by substitution schemes of electrical components in accordance with the principle of functional analogy. The main approaches to specifying the parameters of macromodel components and examples of parametric studies based on it are considered. The use of the developed model makes it possible to obtain the values of compensation parameters for operational adjustment, in accordance with the operating conditions and properties of organic structures.

PYTHON-МОДЕЛЬ ПРОТОКОЛУ УЗГОДЖЕННЯ СЕКРЕТНОГО КЛЮЧА У ГРУПІ З ДОВІЛЬНОЇ КІЛЬКОСТІ УЧАСНИКІВ

The article is devoted to the problem of common secret key agreement in a group of an arbitrary number of participants. Data is exchanged between participants through open data channels. The problem of sharing a secret key over open data channels arose due to the need for a secure exchange of information between two or more parties that may be remote from each other and do not have a common confidential communication channel. Reliable methods of secret key exchange, such as transferring the key in person or using a secure channel, are not practical in remote or scalable scenarios. In the process of developing and modelling cryptographic systems, in which there is a need of cryptographic keys agreement in a group of two or more participants, it is very convenient to have a model that implements these algorithms. The agreement protocol is based on the Diffie-Hellman protocol on elliptic curves (ECDH). The paper contains theoretical justifications, a flow chart of the algorithm, and a Python implementation of the algorithm that performs the secret key agreement in a group of an arbitrary number of participants. To implement cryptographic operations on elliptic curves, the Python library Cryptography is used, in particular, the X25519 algorithms that use the elliptic curve Curve25519. The results of the work are shown on an example for a group of four participants, which demonstrate the correct operation of the model and the same secret key obtained as a result of agreement process. The paper also contains the link to a GitHub repository with the full program. The multi_participant_ecdh.py file contains a secret key agreement program for N participants written in Python. The file two_participant_ecdh.py demonstrates a typical two-participant Diffie-Hellman protocol. Both programs use the X25519 algorithm implemented in the Cryptography Python library. Thus, this work makes it possible to better understand the principles of secret key exchange algorithms between two and an arbitrary number of participants, to compare the results with other implementations, to apply the developed model for educational and demonstration purposes, and may be useful for a number of other scientific and engineering tasks.

Augmenting Academic Efficiency: The Integration of Assessments Within the Addie Model for Pedagogical Development

This paper explores the integration of assessments within the ADDIE model for pedagogical development, emphasizing its crucial role in enhancing learning experiences. Through a meticulous literature review, theoretical frameworks, practical implementations, and outcomes of assessment integration are examined. Assessments, spanning formative, summative, and authentic types, are shown to be integral to each phase of the ADDIE model, promoting customized interventions, continuous improvement, and data-driven decision-making. While challenges such as resource constraints and expertise gaps exist, strategies including professional development and technology integration offer pathways to overcome them. Furthermore, actively involving learners in the assessment process, providing timely feedback, and aligning assessments with learner interests can enhance motivation and engagement, fostering intrinsic motivation and self-directed learning. This study underscores the importance of integrating assessments into the ADDIE Model, advocating for learner-centered approaches and instructional design practices.