NET Framework Research Articles

Insertion attack identification in discrete event systems using petri nets with an observer.

This study addresses the problem of attack identification in discrete event systems modeled with Petri nets, focusing specifically on sensor attacks that mislead observers to making incorrect decisions. Insertion attacks are one of the sensor attacks that are considered in this work. First, we formulate a novel observation structure to systematically model insertion attacks within the Petri net framework. Second, by generating an extended reachability graph that incorporates the observation structure, we can find a special class of markings whose components can have negative markings. Third, an observation place is computed by formulating an integer linear programming problem, enabling precise detection of attack occurrences. The occurrence of an attack can be identified by the number of tokens in the designed observation place. Finally, examples are provided to verify the proposed approach. Comparative analysis with existing techniques demonstrates that the reported approach offers enhanced detection accuracy and robustness, making it a significant advancement in the field of secure discrete event systems.

Investigating the critical drivers of Fintech adoption to promote business sustainability of SMEs

Purpose Under the integrated perspective of the Net Valence Framework (NVF) and Theory of Reasoned action (TRA), this research intends to examine the impact of drivers (perceived risks (PRs) and benefits) on FinTech adoption (FA) as well as on business sustainability (BS) of small and medium-sized enterprises (SMEs) in Oman. The purpose of this study is to examine the impact of drivers (perceived risks and benefits) on FinTech adoption (FA) and business sustainability (BS) of SMEs in Oman , under the integrated perspective of the Net Valence Framework (NVF) and Theory of Reasoned Action (TRA). Design/methodology/approach Primary data was collected from 402 owners and managers of SMEs in Muscat, Oman using a purposive sampling method. Structural Equation Modeling with Partial Least Squares was mainly applied to assess the PRs and perceived benefits (PBs) determining both FA and BS of SMEs. Findings The results indicate that PBs (economic, convenience, transaction efficiency) positively impact FA, while PRs (financial, legal, security, operational) act as deterrents toward FA. Both the benefits and risks significantly influence BS. FA itself fosters sustainability, and PBs directly enhance it through ease of use, cost reduction and knowledge acquisition. Conversely, PRs hinder sustainability due to concerns about skills gaps, security, social inclusion and environmental considerations. Practical implications The research forms a model capturing the pros and cons of FA of SMEs to provide stakeholders with ideas that would promote sustainability and drive fintech-based growth and innovation. For instance, by improving financial management efficiency, increasing market access and enhancing competitiveness, SMEs can adopt fintech solutions to support sustainable growth and achieve Sustainable Development Goals across the region’s economy. Originality/value The current research, through the lens of the NVF as well as TRA, explores the contribution of FA determined by PRs and benefits toward BS of SMEs in the context of Oman.

Towards the Implementation of Heuristics Miner in .Net Framework

Process mining is an interdisciplinary field that bridges the gap between data mining and business process analysis. It revolves around the core concept of leveraging recorded event logs to automate the generation, validation, and refinement of process model. A majority of currently accessible process mining solutions are distributed in form of black box systems - software with no openly accessible source code - or as libraries for scripting languages like Python or R. In this sense, it is necessary to design and build an understandable foundation of a process mining library with the intention of presenting the possibility of incorporating process mining into many programs written in the language or aiding other programmers interested in the potential of such technologies. Therefore, this paper describes and demonstrates the usability of an advanced algorithm for process mining in the .NET platform, namely, the Heuristic miner for process modelling.

From pilot to national roll-out of the improved Community Health Fund (iCHF) in Tanzania: lessons learnt and way forward

IntroductionScaling up public health interventions in the health systems of resource poor settings come with technical and operational challenges. Little is documented on scaling up complex health financing interventions and their related outcomes, especially the voluntary health insurance schemes. This study aimed to analyse the scale-up steps, successes and challenges of the improved community health fund (iCHF), a voluntary health insurance scheme in Tanzania,MethodsIn this paper, guided by the Expand Net framework (a scale-up framework for health system interventions), we present a systematic analysis of countrywide scale-up of the iCHF that started in 2019 and implemented in partnership between the government and development partners. We systematically collected information on the scale-up steps and the success and challenges. The collected data was analysed using descriptive statistics.ResultsThe scale-up involved multiple steps and actions at different levels of the health system. The initial step involved gathering stakeholders’ views on scale-up options and strategies. The subsequent steps focused on mobilizing resources for scale-up, advocacy and promotion of the scheme through media, community leaders and role models, capacity building to implementing organs, institutionalizing the scale-up processes, intensifying the scale-upscale-up activities for expansion and spontaneous scale-up and technical backstopping to lower levels of the health system on the scale-up process. We found success and challenges as the scale-upscale-up progressed to mature stages. The success included acceptability and institutionalization of the scale-up activities and growing enrolments and funds in the scheme. The challenges included: the costs to sustaining advocacy and enrolments, equity in scale-upscale-up activities across regions, relying on top-down scale-upscale-up approaches, influence of contextual factors and lack of implementation research alongside the scale-upscale-up process.ConclusionThis paper underscores the scale up steps and success and challenges of scaling-up a voluntary health insurance scheme in a resource-constrained health system. Sustaining the scale-upscale-up gains will require utilizing program data and experiences to sustainably improve the scheme performance while also harnessing support from stakeholders. Further research is needed to assess equity and quality of outcomes of the scale up.

Durability of carbon dioxide removal is critical for Paris climate goals

Carbon Dioxide Removal is essential for achieving net zero emissions, as it is required to neutralize any residual CO2 emissions. The scientifically recognized definition of Carbon Dioxide Removal requires removed atmospheric CO2 to be stored “durably”; however, it remains unclear what is meant by durably, and interpretations have varied from decades to millennia. Using a reduced-complexity climate model, here we examined the effect of Carbon Dioxide Removal with varying CO2 storage durations. We found that storage duration substantially affects whether net zero emissions achieve the desired temperature outcomes. With a typical 100-year storage duration, net zero CO2 emissions with 6 GtCO2 per year residual emissions result in an additional warming of 0.8 °C by 2500 compared to permanent storage, thus putting the internationally agreed temperature limits at risk. Our findings suggest that a CO2 storage period of less than 1000 years is insufficient for neutralizing remaining fossil CO2 emissions under net zero emissions. These results reinforce the principle that credible neutralization claims using Carbon Dioxide Removal in a net zero framework require balancing emissions with removals of similar atmospheric residence time and storage reservoir, e.g., geological or biogenic.

Smart Monitoring and Control System for Crude Oil Pipeline Vandalism

Crude oil pipeline vandalism, theft, and unauthorized interference present a significant challenge to the oil industry's security, profitability, and sustainability. Despite initial measures such as security patrols, traditional methods have proven inadequate, particularly in difficult terrains and aquatic regions. These methods often fail to promptly detect and prevent vandalism and theft, which results in significant economic, environmental, and safety risks. This study developed and implemented a smart monitoring and control system to address these challenges by improving the detection of oil pipeline vandalism, including incidents in aquatic environments. The Prototyping methodology was adopted, allowing for continuous testing and improvements throughout the development process. The system was built using C#.NET under the .NET Framework, with Microsoft SQL Server as the database, incorporating an encryption layer for database security. To enhance its effectiveness, an oil theft detection algorithm was integrated with a web-based interface to facilitate real-time monitoring, visualization, and reporting. Testing scenarios demonstrated that the system successfully detected threats and ensured seamless data flow from sensors to the central system. This solution marks a significant advancement in the security and sustainability of oil infrastructure, contributing to environmental protection and the safety of stakeholders

A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety.

QR-based Canteen Management System

In recent years, the digitization of administrative and operational processes has transformed various sectors in the world, including food service management. This research presents the development and implementation of a QR-based canteen management system, designed to enhance the efficiency of canteen operations by leveraging contemporary technological advancements. The system employs a desktop application built with C# and the .NET framework, enabling users to order and pay for food items using a QR code-based login and cashless payment system. This solution addresses the inefficiencies of traditional pen-and-paper-based canteen management by automating order processing, payment, and sales record maintenance. The primary objective is to streamline canteen operations, reduce service time, and improve customer satisfaction through a user-friendly and reliable system.

АПАРАТНО-ПРОГРАМНИЙ КОМПЛЕКС ОПТИЧНОЇ ІДЕНТИФІКАЦІЇ ПАСИВНИХ ІНФОРМАЦІЙНИХ КАНАЛІВ БЕЗКОНТАКТНИХ СЕНСОРІВ ТИСКУ-ТЕМПЕРАТУРИ

The work proposes the use of a unique method of creating passive, multifunctional, non-contact pressure-temperature sensors. The basis of this method is a combination of inorganic semiconductors and high-molecular organic cholesteric crystals. According to their morphology, such crystals represent a spiral structure that is sensitive to changes in external physical factors, such as temperatures, due to changes in the periodicity of the structure, which leads to Bragg diffraction scattering of light on it. The consequence of such influence is the coloring of the cholesteric, which can be identified by external spectrosensitive devices on a non-contact basis. On the other hand, the use of inorganic semiconductors involves the production of a micro-profiled base with a thin silicon membrane that is sensitive to external pressure. The thickness of the membrane determines the operating conditions of the sensor depending on the range of applied pressure from 0.3 bar and above. A hardware and software complex was developed for continuous monitoring of changes in the color of passive pressure-temperature sensors, tracking the spectral distribution of the light intensity of the color of the liquid crystal depending on the operating conditions on a non-contact basis with an external spectrometer. The basis of such a system is a software module created on the basis of the MVVM (Model–View–View Model) architecture template. A feature of the software module is the use of the .NET and WPF frameworks, which natively support this architectural pattern for .NET Windows platforms and are supported by all popular versions of operating systems. The SQlite database, which is a relational database management system, is used to store data in the software application. The OmniDriver library was used in the system to operate and configure the spectrometer. The software module has two modes of operation with spectrometers. The first mode is characterized by the reading of a single spectrum, while the second mode is characterized by periodic reading and processing of the intensity spectral distribution in real time with a given period. When using the second mode, the software module allows you to dynamically change the periods and parameters of changing the color parameters of the light over time. The main algorithm of the software module is the transformation of the spectral intensity distribution normalized in the CIE XYZ color model, which is the basis for all further calculations, into the RGB model.

Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning

In the realm of urban planning and environmental evaluation, the delineation and categorization of land types are pivotal. This study introduces a convolutional neural network-based image semantic segmentation approach to delineate parcel data in remote sensing imagery. The initial phase involved a comparative analysis of various CNN architectures. ResNet and VGG serve as the foundational networks for training, followed by a comparative assessment of the experimental outcomes. Subsequently, the VGG+U-Net model, which demonstrated superior efficacy, was chosen as the primary network. Enhancements to this model were made by integrating attention mechanisms. Specifically, three distinct attention mechanisms—spatial, SE, and channel—were incorporated into the VGG+U-Net framework, and various loss functions were evaluated and selected. The impact of these attention mechanisms, in conjunction with different loss functions, was scrutinized. This study proposes a novel network model, designated VGG+U-Net+Channel, that leverages the VGG architecture as the backbone network in conjunction with the U-Net structure and augments it with the channel attention mechanism to refine the model’s performance. This refinement resulted in a 1.14% enhancement in the network’s overall precision and marked improvements in MPA and MioU. A comparative analysis of the detection capabilities between the enhanced and original models was conducted, including a pixel count for each category to ascertain the extent of various semantic information. The experimental validation confirms the viability and efficacy of the proposed methodology.

High-quality implementation for a continuous-in-time financial API in C#

In recent years, there has been a rising interest in potentially complex software and financial industries with applications in many engineering fields. With this rise comes a host of developing a usable and consistent Application Programming Interface (API). Prioritize designing and building the software ensures to enrich the platform and emphasize inventorying APIs. In this paper, we proposed a high-quality API to implement the continuous-in-time financial model. The existing discrete framework cannot be evaluated at any time period, involving drawbacks in operating the data structures. Then, the continuous framework is implemented based on the measure theory paradigm. Our proposal uses mathematical modeling, which consists of some objects as measures and fields. It is suitable to develop this API in C# to provide the requirement quality in programming language professionally. This also integrates demands, codes, and verification in the system development life cycle. The advantages are aimed at increasing the structuring and readability. The presented work provides an overview of the design, implementation, testing, and delivery aspects of the API, highlighting the importance of architecture, testing, and numerical choices. The article gives an overview of the API by describing the implementation concerning the data structures and algorithms. These algorithms are based on using the Task Parallel Library (TPL) that makes the API easier and more fruitful for data parallel to benefit from the advantages provided by the .NET Framework.

ESenTRy: an on-site earthquake early warning system based on the instrumental modified Mercalli intensity

Earthquake early warning systems (EEWSs) are real-time seismology-based applications that purpose to reduce earthquake damage by taking some precautions for environments that are likely to be damaged. These systems are divided into two categories as on-site and regional. In this study, an on-site EEWS called ESenTRy, consisting of an accelerometer, GPS and communication components, has been designed. In this scope, a Windows operating system-based software has been developed using the .NET Framework and C# language. The developed software has the ability to read the Güralp Compressed Format strong motion data in real-time, compute the strong motion indices such as destructive intensity, real-time intensity and instrumental modified Mercalli intensity, issue an alarm in the target area and alert the defined Modbus clients. To test the reliability of ESenTRy, two simulations have been performed using the strong motion data belonging to the magnitude 7.7 Kahramanmaraş-Pazarcık earthquake that occurred in Türkiye on February 6, 2023, at 01:17:32 UTC and magnitude 5.9 Düzce-Gölyaka earthquake that occurred in Türkiye on December 23, 2022, at 01:08:15 UTC. In the test applications, depending on the distance between the stations and earthquake epicenters, the time difference between the Level-2 alarm trigger times and S-wave arrival times varied between 1.5 and 16 s, while the time difference between the Level-2 alarm trigger times and P wave arrival times varied between 0.5 and 29.5 s. The flowchart diagrams of the developed algorithm and results obtained from the simulations are presented in detail.

Three-dimensional morphological characterization of blood droplets during the dynamic coagulation process.

In this study, we employed a method integrating optical coherence tomography (OCT) with the U-Net and Visual Geometry Group (VGG)-Net frameworks within a convolutional neural network for quantitative characterization of the three dimensional whole blood during the dynamic coagulation process. VGG-Net architecture for the identification of blood droplets across three distinct coagulation stages including drop, gelation, and coagulation achieves an accuracy of up to 99%. In addition, the U-Net architecture demonstrated proficiency in effectively segmenting uncoagulated and coagulated portions of whole blood, as well as the background. Notably, parameters such as volume of uncoagulated and coagulated segments of the whole blood were successfully employed for the precise quantification of the coagulation process, which indicates well for the potential of future clinical diagnostics and analyses.

Innovative Solutions for Democratic Processes: A Case Study on Secure JNEC Voting System

In the digital era, there is a need to convert the traditional voting approach. Thus, this paper introduces the JNEC Voting System (JVS), an offline desktop application designed to modernize the voting process. Traditional paper voting systems practiced in Jigme Namgyel Engineering College (JNEC) present challenges such as time-consuming procedures, the potential for invalid votes, and vulnerability to fraud. To address these issues, JVS employs a .NET framework, with C# for frontend and backend development, and MySQL as the database. The development process follows an iterative method, allowing for continuous improvement, flexibility, and adaptability. JVS enables voters to cast their votes by scanning ID cards, while administrators can create events, manage candidates, and manage voter registration. By eliminating the limitations of traditional approaches, JVS aims to enhance the efficiency and simplicity of voting. The primary goal was to replace paper voting systems in college and reduce the time for voting, counting, and result declaration. To tackle these challenges, JVS seeks to boost voter confidence and involvement. Further, it contributes to more transparent, secure, and accountable elections.

Utilizing a water-stable terbium (III) metal-organic framework for luminescent sensing of nitroimidazole antibiotics

A new 3D terbium (III)-based metal–organic framework, [Tb(TDC)(µ2-CH3COO)(H2O)] (Tb-MOF, H2TDC = 2,5-thiophenedicar-boxylic acid), was obtained with Tb3+ ions as metal source and TDC ligands as the auxiliary ligand through self-assemble mode. The Tb-MOF features a 3D network constructed by bridging a 2D wave-like network connected by TDC ligands with monodentate coordination mode, which results in the formation of a 3D net framework. The luminescent properties and sensing antibiotics experiments were extensively investigated for the Tb-MOF. Remarkably, the Tb-MOF exhibits a substantial quantum yield (QY) at 55.0 % and an average photoluminescence lifetime of 534 µs. Furthermore, the Tb-MOF shows selective luminescence quenching for nitroimidazole antibiotics, including metronidazole (MDZ), dimetridazole (DTZ), and ornidazole (ODZ), with a particularly strong response for MDZ (Ksv = 5.49 × 104 M−1, LOD value is 7.9 × 10−5 M). Finally, the Tb-MOF enables a simple and convenient test paper that allows luminescent color changes to be observed with the naked eye for detecting MDZ.

China’s ecological footprint via biomass import and consumption is increasing

As China’s economic and population demands grow, the need for biomass imports rises, presenting challenges for sustainable development and global ecosystem protection. Here we examine China’s increasing influence on global ecosystems through its biomass product consumption and trade using the Human Appropriation of Net Primary Productivity framework, analyzing data from 2004 to 2017 with projections until 2050. In 2017, China was the world’s largest biomass consumer, consuming approximately 1.75 petagrams of carbon per year, projected to rise by 317.6% by 2050, highlighting China’s major role in global ecosystem stress. Our findings also show an increase in biomass imports from lower-middle-income and low-income countries between 2004 and 2017, and these imports are expected to continue increasing by 402.9% in 2050. The analysis reveals that domestic product demand and changes in foreign production efficiency are key drivers of this trend, suggesting the need for China to shift towards more efficient trade practices and support cleaner production methods internationally.

Detection of Diabetic Foot Ulcers Using Dense Net Framework

Diabetic Foot Ulcers (DFUs) pose a significant health care challenge due to their prevalence and potential complications. Early and accurate detection of DFUs is crucial for timely intervention and prevention of severe complications. This study proposes an innovative approach for automated DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful class of deep learning algorithms widely recognized for their proficiency in image analysis tasks. The proposed CNN model is trained on a comprehensive dataset of foot images, encompassing a diverse range of DFU types, stages, and conditions. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection. The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management.

DESIGNING A CUSTOMER LOYALTY PROGRAM WEB SERVICE

Currently, bonus programs from various companies are very popular, using which companies increase the loyalty of the company's customers, thereby ultimately increasing profits. The article describes the process of developing a web service of the company's loyalty program. The subject area has been studied, different types of loyalty programs have been considered, and its own loyalty program has been developed, which formed the basis for the model of the web service being developed. Next, an overview and analysis of development tools is performed, the most suitable technology stack for implementation is proposed using the example of the .Net Framework platform for implementing the server side and using open libraries to implement the user interface. Based on the analysis of the subject area and the development tools intended for use, a database model of the system was developed, and a database management system represented by MySQL was selected. The architecture of the system was designed using the MVC architectural pattern, Onion design patterns and Repositories, and a scheme for the implementation of the methods of operation of the web service with client accounting systems was proposed. A cashback service project has been developed, which is a web application that interacts with client programs. A module for integration into accounting systems has been developed, using the example of such software products as Topaz-gas station, Frontol and 1C.

Formulation of billing policy for residential scale solar PV systems and its impact in the Kingdom of Saudi Arabia

Governments worldwide have introduced diverse billing methods and regulations to promote the adoption of photovoltaic (PV) systems, aiming to reduce greenhouse gas emissions. This paper aims to develop a billing policy for installing rooftop PV systems in the Kingdom of Saudi Arabia by studying policies implemented to support installing PV systems in leading countries. Initially, the system is evaluated using the tariff predetermined by Saudi Electricity Cogeneration Regulation Authority under three different policies: a Feed-in Tariff (FIT), net metering, and net billing for different sizes of solar systems 10 kW, 15 kW, and 20 kW. The findings suggest a considerable discounted payback period ranging from 14 to 20 years under the net metering framework with the current tariff structure. In contrast, investments under FIT and net billing schemes do not appear to be viable. Thus, a tariff schedule, tailored according to the consumer-preferred payback period, is proposed. For a payback timeframe of 6–10 years, the tariff should range between $0.09–0.06 $/kWh within the FIT scheme. However, in the case of a net billing policy, the tariff should vary between $0.123–0.066 $/kWh. These findings suggest the supremacy of the FIT scheme, particularly if the government determined to use the low tariff policy.

Quantifying opacity of discrete event systems modeled with probabilistic Petri nets

The verification and enforcement problem of opacity that falls into the category of security properties of information flows in a cyber-physical system has been extensively studied from the view of discrete event systems. Recent years have witnessed growing interest in the quantitative analysis of opacity. However, documented results on quantifying opacity in the literature are not formulated within the framework of Petri nets. By introducing a new class of Petri net as a modeling vehicle for opacity quantification, we suggest a novel notion of opacity referred to as worthy opacity, which describes the worth associated with the information that a system might leak during its evolution. Depending on how much system information is captured by an intruder, we propose two types of worthy opacity: stepwise and observational worthy opacity. We provide a necessary and sufficient condition for stepwise worthy opacity and prove its decidability. Observational worthy opacity is established by considering both system and observation aspects, with its verification shown to be undecidable. Compared with the existing works on quantifying opacity, we argue that the worthy opacity is of practical concern, as being aware of the information worth that a system may leak during its evolution is a more realistic guide than the probability that it may reveal a system secret.