Decision Support System Software Research Articles

Decision Support System Software for Normal Pregnancy, in Maritime Medicine, Setting for Costal Situation, Recommended for Making A Decision Support System for Diving Fishermen

Background: The coastal situation in Indonesia with thousands of islands and its area, the use of Ultra Sono Graph (USG) as a tool for general practitioners will be useful to facilitate examinations, consult with experts, and make referrals easily and quickly. Objective: For ultrasound readings that require decisions, using the Decision Support System makes it easier and there are friends to discuss, indirectly with experts, even machines. Theoretical Basis: DSS is very important to clarify and speed up decision-making with the help of software and computers. Use Process: The ultrasound software will guide and direct the appropriate diagnosis so that decisions can be made or referred and handled quickly and precisely. DSS will be very useful in preventing accidents among diving fishermen, by using the P02 benchmark which is easy to use. Conclusion: The use of the Decision Support System for normal and abnormal pregnancy ultrasound is beneficial for coastal area support for early diagnosis and implementation of a referral system. By looking at examples of use like this, it is recommended to create a Decision Support System for Diving Fishermen.

Impact of a DSS-supported medication review on the safety of drug therapy and quality of life in patients with antithrombotic therapy

Polypharmacy is common among patients with antithrombotic medication, giving rise to concerns about Drug-Related Problems (DRPs). Therefore, these patients would benefit from a Medication Review (MR) along with pharmacist counselling to reduce the risks accompanying polymedication. This prospective study presents a concept for MRs that are applicable in German community pharmacies and can efficiently support pharmacist counselling and improve the safety of drug therapy. As this is a major challenge in everyday pharmacy practice, we used a Decision Support System (DSS) to evaluate its ability to support the process of pharmacist-led MRs. The primary endpoint was the impact of a community pharmacist on the reduction of DRPs. We investigated the impact of the interventions resulting from MRs on patients taking at least one antithrombotic drug as part of their polymedication regimen. Secondary endpoints were the reduction in the number of patients with bleeding risks and the improvement of patients’ Quality of Life (QoL) and therapy adherence. Furthermore, the DSS used in the study was controlled for correct data assessment and plausibility of data. We selected adult patients who were taking no less than three different medications for long-term treatment, at least one of which had to be an antithrombotic drug, and who were customers in one of eight selected pharmacies over a period of 6 months. Data from 87 patients were analyzed with DSS-support. A total of 234 DRPs were identified by the pharmacist (2.7 DRPs per patient). MR reduced DRPs by 43.2% which, resulting to a reduction of 1.2 DRPs per patient. The intervention also led to a significant improvement in the patients’ QoL (assessed via EQ-5D-5L questionnaire; p < 0.001) and enhanced therapy adherence (assessed via A14 questionnaire; p < 0.001). The control of correct data assessment (with 93.8% concordance) and plausibility of data (with 91.7% concordance) of the DSS software were conducted by an external auditor. No significant effect was found for overall bleeding risk. The results of this study indicate that DSS-supported and structured MR conducted by pharmacists can contribute to a reduction in DRPs and significantly improve patient’s QoL and adherence to treatment.

The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.

This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.

Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project.

Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study

ObjectiveThe aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section. Study DesignA total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions. ResultsThe F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively. ConclusionThe utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.

B2B Customer Engagement Customer Behaviour Forecast Application

The paper presents the "Casper Customer Behavior Forecast Application" a creative project focused on the creation of an advanced decision support software system. The purpose of this system is to closely observe, fully analyze, and precisely predict the behaviors of Casper Business-to-Business (B2B) clients. Our research study implements an innovative methodology by focusing on the enhancement of client interactions through the utilization of RESTful services. Our objective is to reimagine the future of customer relationship management by analyzing and forecasting client behavior in the business-to-business (B2B) setting. The project involves a comprehensive methodology that incorporates extensive study, complex software design, and careful data analysis. In more detail, it involves extensive monitoring of consecutive RESTful interactions carried out by business-to-business consumers across a period of time. By leveraging the capabilities of modern machine learning and deep learning algorithms, our objective is to develop prediction models that establish novel benchmarks within the industry. The methodology employed in this study includes the development of labeled behavioral datasets and the utilization of a supervised machine learning framework. The evaluation of model performance will be performed systematically using a range of metrics, such as F-Score and Accuracy, in order to establish the model's robustness and reliability in making accurate predictions. The expected outcomes of this project have the potential to bring about significant changes. Primarily, the integration of machine learning and deep learning algorithms will provide our company with an important amount of knowledge. Our organization will obtain an innovative software solution that possesses the capability to precisely forecast the future actions of B2B customers. This results in enhanced inventory management and a significant reduction in client waiting periods, resulting in increased levels of customer satisfaction. Additionally, this study has the potential to make significant additions to the global academic body of knowledge in the domains of machine learning and deep learning. In simple terms, the project titled "Casper Customer Behavior Forecast Application" embodies an innovative and academic effort aimed at enhancing customer engagement and predicting customer behavior within the context of business-to-business (B2B) interactions. This research project carries considerable importance, both in its potential to reinvent the field of customer relationship management and its ability to contribute to the global academic conversation on machine learning and deep learning.

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.

Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination.

Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the next phenotypic feature to assess, optimizing the diagnostic pathway to the smallest number of possible diagnoses. The objective of this study was to evaluate the performance of this real-time DSS using clinical data. This validation study was conducted on a database of 549 perinatal phenotypes collected from two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed ('ascertained') diagnosis, was performed by a panel of external experts. The proportion of ascertained diagnoses within the software's top-10 differential diagnoses output was evaluated, as well as the sensitivity and specificity of the software to select correctly as its best guess a syndromic or isolated condition. The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and 84%. The stepwise approach required an average of 13 queries to reach the final set of diagnoses. The DSS showed high performance when applied to real-world data. This validation study suggests that such software can improve perinatal care, efficiently providing complex and otherwise overlooked knowledge to care-providers involved in ultrasound-based prenatal diagnosis. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients

Determination of prospective non cash food assistance recipients, especially in Air Talas village, still uses a manual system so that in the process of determining the recipient there is a risk that the recipient will be inaccurate, so that the village government needs a system that can assist the process of determining prospective non cash food assistance recipients. This study aims to implement the K-Means and SAW methods in determining recipients of non cash food assistance in Air Talas village. The benefits of this research can help the Air Talas village government in determining and recommending prospective non cash food assistance recipients in accordance with established criteria, making it easier to filter, group, and rank appropriate population data according to criteria. In addition, this research is also useful for providing convenience to the community through data collection, clustering, and ranking in a transparent, real, and fast and accurate manner using decision support system software. The K-Means clustering method and the Simple Additive Weighting Ranking method were used in this study with data collection techniques through interviewing sources, in this case the village government, the social section of the community, and through collecting village archive data and relevant journals. The research location is Air Talas village with 316 data used. The results of the study are clustering data as much as 77 data obtained from feasible clusters. The cluster data was then tested using the accuracy value and obtained a value of 80%. Then the research is also in the form of ranking data using clustered data which obtains an accuracy value of 64%.

Determination of Priorities for the Development of Small and Medium Enterprises in the Province of South Sulawesi Using the Weighted Product Model

Small and Medium Enterprises (SMEs) are a sector that plays a vital role in running the national economy. The South Sulawesi Provincial Office of Industry as a regional government organization is tasked with administering affairs in the industrial sector of the South Sulawesi Province. In terms of developing SMEs, the South Sulawesi Provincial Industry Service has the right to determine priority industries that have the potential to be developed. The decision-making process at the South Sulawesi Industry Office is still experiencing several obstacles. This is due to the decision making does not use an objective method. In order to help solve this problem, we need a system that can be used to determine industrial development priorities. This research builds a web-based decision support system software regarding priority setting for SMEs development in South Sulawesi Province using the weighted product model. A method that will find a concluding solution by considering industry weights and criteria. This study uses the implementation of the data management subsystem using MySQL. Meanwhile, the implementation of the model management subsystem uses the weighted product model and the dialog management subsystem uses a website interface that can assist the South Sulawesi Provincial Office of Industry in determining priorities for the development of SMEs.

Sistem Pemilihan Paket Indihome Dengan Metode Topsis

t is necessary to create a system to make it easier for prospective customers to make decisions to choose a package that suits their needs, so there is a need for criteria to assist in the selection of the IndiHome package. The software development methodused is the prototype method. The test method used in writing this research is black box testing. The results showed that the main reason for designing a decision support system for selecting the IndiHome package was a problem that often occurred to consumers when choosing an internet package that did not suit their needs and lack of information. IndiHome package selection decision support system software using the resulting TOPSIS method can support decision-making for users who use it. Various parties can use this software in choosing the IndiHome package to provide convenience in supporting decisions before buying the package. The software has been designed with static and dynamic properties. In terms of interface, this application is only static becausethe user can only input data according to what has been provided. Suggestions from the conclusions that can be given are that this software can be developed using other programming languages such as Java or Python to make repairs and development more accessible and immediately acceptable to users.

The feasibility and impact of implementing a computer-guided consultation to target health inequality in Asthma

Greater Manchester has a greater prevalence and worse asthma outcomes than the national average. This study aims to evaluate a digital approach to primary care asthma management and in particular the initial impact of implementing Clinical Decision Support System software in the form of a computer-guided consultation (CGC) in the setting of primary care asthma reviews in deprived areas of Greater Manchester. The CGC (LungHealth Ltd) is an intelligent decision support system ensuring accurate guideline-based staging of asthma and assessment of asthma control with the software subsequently prompting guideline-standard management. Patients on asthma registers in Greater Manchester Primary Care Networks were identified and underwent remote review by nursing staff using the CGC linked directly to the GP clinical system. Three-hundred thirty-eight patients (mean age 59 (SD 17) years; 60% Female) were reviewed. The CGC reported the patient’s asthma control to be “Good” in 22%, “Partial” in 6% and “Poor” in 72%. ACT scores were significantly higher in those patients exhibiting “Good” and “Partial” control when compared to those with “Poor” control. The number of steroid courses and hospital admissions in the previous 12 months was significantly lower in those patients exhibiting “Good” and “Partial” control when compared to those with “Poor” control. Nineteen percent were found not to have a personalised asthma management plan during CGC review, which was alerted by the CGC and subsequently, all but 3 patients had this created on review completion (McNemar’s test; p < 0.001). 5% were found not to have been prescribed regular inhaled steroid therapy resulting in the operator being alerted by the CGC in all cases. Overall, 44% underwent alteration in asthma therapy following the CGC review with 82% of these representing treatment escalation. An end-to-end digital service solution is feasible for Asthma within primary care and the utilisation of a CGC when conducting primary care asthma reviews increases implementation of guideline-level management thus addressing healthcare inequality while enabling identification of “high risk” asthma patients and guiding appropriate therapy escalation and de-escalation.

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&amp;CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

Budidaya Tanaman Sayuran Menggunakan Sistem Pendukung Keputusan Dengan Metode Profile Matching

Cultivating vegetable plants is very important for farmers. In the cultivation of vegetable plants, there are problems that are often faced by farmers, one of which is the lack of information about natural conditions and limited knowledge of farmers. so it is necessary to have an auxiliary media in the form of a system that can provide solution information at any time. Selection of plants that are not suitable will get unsatisfactory results. Farmers must know that what needs to be considered in cultivating vegetable crops is compatibility with natural conditions. This research builds a web-based decision support system software regarding vegetable cultivation that meets the criteria using the profile matching method as a tool to facilitate the decision-making process with several comparison criteria, namely: temperature, sunlight, rainfall and humidity, altitude, harvest time, marketability, susceptibility to disease, and level of care. This application is made web-based using PHP as the programming language and MySQL as the data storage. The purpose of this application is to provide effective and efficient information to farmers in making decisions and alternatives that are in accordance with the natural conditions of the area. The result of making this system is that farmers make the right decisions when cultivating vegetables based on the criteria.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SEPEDA MENGGUNAKAN METODE PROMETHEE

Toko Sepeda Super Top is a shop that sells various kinds of bicycles in Sekadau Regency. Not a few consumers are confused in choosing a bicycle. Based on the survey, 59.3% of respondents stated that it is difficult to choose a new bicycle. To assist consumers in choosing a bicycle, we need a system that can support consumers in making decisions. This study utilizes a Decision Support System (DSS) to support Toko Sepeda Super Top consumers in choosing a bicycle that suits their needs. The DSS method used is the Promethee type I method with the criteria used in the form of brand, price, durability, design, weight, and accessories. The form of research used in this research is a case study with a software design method, namely the prototype method. The result of this research is a bicycle selection DSS software that can be accessed by two levels of users. Admin can process bicycle data by inputting, updating, and deleting data. Then ordinary users can use a decision support system with the Promethee method to assist in bicycle selection.

1342P Pharmacoeconomics in the antineoplastic therapy sector of a public hospital in the Brazilian Amazon aiming at cost minimization: Quantitative analysis

Pharmacoeconomics is characterized by the application of economy in the use of medicines, minimizing financial expenses without compromising treatment. Objective: To draw a guiding diagram for pharmacoeconomics in the oncology sector of a public hospital in the Brazilian Amazon aiming at the Cost Minimization Analysis (AMC), where only costs are subjected to comparisons, since the efficacies or effectiveness of comparable alternatives are the same. Exploratory study, with a quantitative approach, using pharmacoeconomic tools, focusing on sharing schedules on specific dates, optimizing the handling time of related chemotherapeutics, reducing costs and the length of stay of cancer patients in the chemotherapy room (QT) for greater bed turnover and treatment of a greater number of patients. The class A drug of choice was Bortezomib. To perform the tabulation and processing of data, the free software Epidata 3.1 and Microsoft Excel were used. The Integrated Decision Support System (SIAD) software and Action STAT were used for the application of Data Envelopment Analysis (EAD). From July 2017 to May 2018, 74 patients were treated with a total of 201 infusions of QT containing Bortezomib in the protocol, with an average of 4 infusions/cycle, at a cost of R$ 2020.00/bottle. The total expected expenditure was R$109.080. With the actions, 33 bottles were saved and a monetary value of R$66.600 in a period of 9 months, considering individual use per bottle saved. The evolution of the average length of stay (in its respective scale) was observed, and its fall always after the presence of pharmacoeconomics (peaks of low final cost). Affine manipulations are more effective. The p-value < 0.05 for the coefficient of the outcome variable of Final Cost. All tests - Pearson Correlation coefficients, Cluster Analysis, Median, Wilcoxon, Friedman and Kendall's non-parametric hypothesis tests and Generalized Linear Model (p-value <0.05) confirmed the hypothesis that the The presence of pharmacoeconomics has a significant effect on the final cost. With pharmacoeconomics, we were able to apply all resources to ensure the continuous supply of medication as a health instrument.

TEACHER CERTIFICATION DECISION SUPPORT SYSTEM

Technology in education in Indonesia has continued to improve throughout the 21st century. This results in a continuous evaluation of the education system, which in turn provides information on educational gaps. SMK Persada Bandar Lampung is an educational institution that has problems in assessing the feasibility of teacher certification, which does not yet have a system where errors often occur in the assessment process. Evaluating the feasibility of teacher certification, which is subjective will take time to process the results. In the process of assessing the feasibility of teachers, certification errors may occur. SMK Persada Bandar Lampung requires a decision support system that is expected to find a solution in the process of determining the best teacher. The decision system for assessing the feasibility of teacher certification can facilitate data processing of the results of the teacher certification feasibility assessment at SMK Persada Bandar Lampung.Therefore, the researcher proposes a decision support system software engineering by applying Simple Additive Weighting (SAW). This system can later be used to assess the feasibility of teacher certification at SMK Persada Bandar Lampung to assist schools in evaluating the shortcomings and strengths of teacher performance.

Sustainable development of smart cities and smart territories based on the model of minimizing externalities.

The development of conceptual models of a digital city poses numerous challenges for developers. The public sector concept model has become one of the most difficult models to use. When developing algorithms to find a solution, the multidirectional interests of businesses and public institutions are combined. This type of model reflects the most acute and urgent problems faced by megapolises with regard to combining numerous localized services provided to the community in a limited territory. The administrations of both cities and regions (the scale of the smart territories) must make decisions concerning overcoming the barriers existing between the profits of commercial structures, the negative externalities generated by their activities, and the social benefits to the population in the territory under their control. It is necessary to solve this problem to achieve the effective management of enterprises belonging to the segment of long-term participants in various business activities, interacting with the surrounding social and business environment in a complex. This study takes into account the complex structures of the economic processes characteristic of megacities. The periodicity of economic processes is also taken into account. When choosing an optimization criterion, functions reflecting the level of internalization of responsibility for external effects were considered. The authors propose a mathematical model that can be used as part of the management decision support systems software, aiming at taking into account the externalities of a wide range of national, institutional, business, and social activities.

Sustainable development of smart cities and smart territories based on the model of minimizing externalities

The development of conceptual models of a digital city poses numerous challenges for developers. The public sector concept model has become one of the most difficult models to use. When developing algorithms to find a solution, the multidirectional interests of businesses and public institutions are combined. This type of model reflects the most acute and urgent problems faced by megapolises with regard to combining numerous localized services provided to the community in a limited territory. The administrations of both cities and regions (the scale of the smart territories) must make decisions concerning overcoming the barriers existing between the profits of commercial structures, the negative externalities generated by their activities, and the social benefits to the population in the territory under their control. It is necessary to solve this problem to achieve the effective management of enterprises belonging to the segment of long-term participants in various business activities, interacting with the surrounding social and business environment in a complex. This study takes into account the complex structures of the economic processes characteristic of megacities. The periodicity of economic processes is also taken into account. When choosing an optimization criterion, functions reflecting the level of internalization of responsibility for external effects were considered. The authors propose a mathematical model that can be used as part of the management decision support systems software, aiming at taking into account the externalities of a wide range of national, institutional, business, and social activities.

Sustainable development of smart cities and smart territories based on the model of minimizing externalities

The development of conceptual models of a digital city poses numerous challenges for developers. The public sector concept model has become one of the most difficult models to use. When developing algorithms to find a solution, the multidirectional interests of businesses and public institutions are combined. This type of model reflects the most acute and urgent problems faced by megapolises with regard to combining numerous localized services provided to the community in a limited territory. The administrations of both cities and regions (the scale of the smart territories) must make decisions concerning overcoming the barriers existing between the profits of commercial structures, the negative externalities generated by their activities, and the social benefits to the population in the territory under their control. It is necessary to solve this problem to achieve the effective management of enterprises belonging to the segment of long-term participants in various business activities, interacting with the surrounding social and business environment in a complex. This study takes into account the complex structures of the economic processes characteristic of megacities. The periodicity of economic processes is also taken into account. When choosing an optimization criterion, functions reflecting the level of internalization of responsibility for external effects were considered. The authors propose a mathematical model that can be used as part of the management decision support systems software, aiming at taking into account the externalities of a wide range of national, institutional, business, and social activities.