Innovative Decision Support System Research Articles

Infrastructure Impact Assessment through Multi-Hazard Analysis at Different Scales: The 26 November 2022 Flood Event on the Island of Ischia and Debris Management

A multi-hazard analysis (seismic, landslide, flood) is conducted to verify the impact on the road network. The ENEA CIPCast platform is an innovative Decision Support System (DSS) that is used to implement the analyses using GIS. Using analytical and geoprocessing tools, the hazards were assessed and mapped. The overlapping of different geospatial layers allowed the implementation of a specific hazard map for the road network. Multi-hazard values were obtained using an appropriate matrix of single values, which were classified, and then summarized into four classes of values. The analyses were conducted at the regional (Campania region), provincial (Metropolitan City of Naples), and local scales (island of Ischia and municipality of Casamicciola Terme). In particular, the landslide event that struck Ischia island on 26 November 2022 and the municipality of Casamicciola Terme was considered as a case study to determine the impact on the road network, infrastructures, buildings, and jeopardizing inter-municipal connections. The results are mainly visualized through map processing and statistical summaries of the data. The management of the landslide debris, which can contain a multitude of fractions (waste, biomass and vegetation, sludge, soil, and rocks transported downstream by water), was also explored. This is a frontier issue for which international manuals and guidelines, as well as national and emergency acts, have been examined. A specific protocol for the sustainable management of the debris generated by floods and landslides is needed, and discussed in the present paper, to overcome emergencies after catastrophic events.

МЕТОДОЛОГИЯ СОЗДАНИЯ ИНДИВИДУАЛЬНОЙ МОДЕЛИ ЗДОРОВЬЯ ЧЕЛОВЕКА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Background. In the modern world, the role of artificial intelligence in healthcare is becoming increasingly significant, providing new opportunities to transform traditional methods of diagnosis, treatment and medical data management. This technological breakthrough not only improves the efficiency of medical procedures, but also opens up new per-spectives in the prevention and treatment of diseases. The aim. To emphasize the need to create a personalized health model using artificial intelligence to help an individual achieve and maintain optimal health and well-being. In the field of personalized treatment, artificial intelligence plays an important role, taking into account the unique characteristics of each patient. Algorithms analyze genetic information, medical history and responses to previous therapies to develop optimal treatment plans. This opens the way to individualized medicine, where the approach to each patient is based on his or her unique characteristics. Despite all the positive aspects, the introduction of artificial intelligence in healthcare also raises questions of data privacy, ethical issues and technology security. However, if these issues are resolved, artificial intelligence promises to significantly improve the quality and accessibility of medical treatment, opening new horizons in healthcare. Results. In this article, we describe breakthroughs in artificial intelligence technologies and biomedical applications, identify problems of using and further development in medical artificial intelligence systems and summarize the economic, legal and social consequences of using artificial intelligence in healthcare, and propose a scheme for constructing a model of individual human health using artificial intelligence. Conclusion. The results of the analysis of modern scientific literature allow us to draw a conclusion about the potential for creating more effective and personalized approaches to the problem of individual health using integrated artificial intelligence technologies. The proposed methodology can serve as the basis for the development of innovative deci-sion support systems in medicine and improving the quality of medical care.

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision-making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices.

Decision support system for ship energy efficiency management based on an optimization model

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool.

State of the Art and New Technologies to Recycle the Fertigation Effluents in Closed Soilless Cropping Systems Aiming to Maximise Water and Nutrient Use Efficiency in Greenhouse Crops

Inappropriate fertilisation results in the pollution of groundwater with nitrates and phosphates, eutrophication in surface water, emission of greenhouse gasses, and unwanted N deposition in natural environments, thereby harming the whole ecosystem. In greenhouses, the cultivation in closed-loop soilless culture systems (CLSs) allows for the collection and recycling of the drainage solution, thus minimising contamination of water resources by nutrient emissions originating from the fertigation effluents. Recycling of the DS represents an ecologically sound technology as it can reduce water consumption by 20–35% and fertiliser use by 40–50% in greenhouse crops, while minimising or even eliminating losses of nutrients, thereby preventing environmental pollution by NO3− and P. The nutrient supply in CLSs is largely based on the anticipated ratio between the mass of a nutrient absorbed by the crop and the volume of water, expressed as mmol L−1, commonly referenced to as “uptake concentration” (UC). However, although the UCs exhibit stability over time under optimal climatic conditions, some deviations at different locations and different cropping stages can occur, leading to the accumulation or depletion of nutrients in the root zone. Although these may be small in the short term, they can reach harmful levels when summed up over longer periods, resulting in serious nutrient imbalances and crop damage. To prevent large nutrient imbalances in the root zone, the composition of the supplied nutrient solution must be frequently readjusted, taking into consideration the current nutrient status in the root zone of the crop. The standard practice to estimate the current nutrient status in the root zone is to regularly collect samples of drainage solution and determine the nutrient concentrations through chemical analyses. However, as results from a chemical laboratory are available several days after sample selection, there is currently intensive research activity aiming to develop ion-selective electrodes (ISEs) for online measurement of the DS composition in real-time. Furthermore, innovative decision support systems (DSSs) fed with the analytical results transmitted either offline or online can substantially contribute to timely and appropriate readjustments of the nutrient supply using as feedback information the current nutrient status in the root zone. The purpose of the present paper is to review the currently applied technologies for nutrient and water recycling in CLSs, as well as the new trends based on ISEs and novel DSSs. Furthermore, a specialised DSS named NUTRISENSE, which can contribute to more efficient management of nutrient supply and salt accumulation in closed-loop soilless cultivations, is presented.

COMPLEX SUPPORT SYSTEMS FOR THE ACCEPTANCE OF INNOVATIVE DECISIONS IN ENVIRONMENTAL PROTECTION

Systematic approaches to the selection of innovative technologies for environmental protection are investigated. The features and directions of ensuring environmental safety are considered by means of a substantiated method of selecting innovative technologies. A method is used that substantiates the procedure for determining the relative importance of criteria and indicators of environmental protection technologies and methods using the program-analytical method. The basic and fundamental principles of project activities are well developed by specialists, but the practical orientation, application of aspects and algorithms for the implementation of these innovative decision support systems at the level of project activities at the regional level need further development. Decision support systems in the design of innovative environmental protection technologies will increase the complexity and effectiveness of solutions to improve environmental safety. According to the developed approach, criteria formulated as influencing groups of factors. Relevant experts, relying on various types of information on the specific purpose and innovative ecological solutions, give their own judgments regarding the priority of the advantages of the specified criteria. Expert judgments are processed according to the formal procedure of the MAI, which is implemented on a computer, and is the basis for decision-making when choosing the innovative ecological solutions in specific conditions. The criteria hierarchical structure of innovative ecological solutions is represented by a sequence of actions that include the following stages: development of elements of a methodical approach; verification of the consistency of the proposed hierarchical structure for determining priority technologies based on the input data of several objects of settlements; obtaining data on the priority of innovative ecological solutions for implementation on these objects. The advantages of the proposed methodical approach should include the possibility to link to a single algorithm for the justification of the required decision data that differ both in their content and in the form of presentation. Implementation of complex support systems for making innovative decisions in nature protection activities will increase the complexity and effectiveness of decisions to ensure environmental safety. Keywords: environmental safety, system analysis, innovative solutions, environmental protection tools and technologies, sustainable development.

A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation

The difficulty that hospital management has been experiencing over the past decade in balancing demand and capacity needs is unprecedented in the United Kingdom. Due to a shortage of capacity, hospitals cannot treat all patients. We developed a whole hospital-level decision support system to assess and respond to the needs of local populations. We integrated a comparative forecasting approach and discrete event simulation modelling using Hospital Episode Statistics and local datasets. It is clear from the literature that this level of whole hospital simulation model has never been developed before (an innovative decision support system). First, the demands of all hospital specialties were forecasted, and the forecasts were embedded into the simulation model as input. Secondly, a simulation model was developed to capture the patient pathway of all specialties. The model integrates every component of a hospital to aid with efficient and effective use of scarce resources (e.g., staff and beds). As a result, the hospital can meet the increasing demand with its current resources. According to the scenario analysis, the hospital bed occupancy rate will reach the national target (i.e., 85%), and the total hospital revenue will increase by approximately 13%, with a 10% increase in A&E and outpatient and a 20% increase in inpatient demand. In conclusion, the hospital-level simulation model can become a crucial instrument for decision-makers to provide an efficient service for hospitals in England and other parts of the world.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC= 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Methodology to Optimize Quality Costs in Manufacturing Based on Multi-Criteria Analysis and Lean Strategies

Manufacturing quality cost optimization is a priority in any manufacturing sector due to quality issues impacting companies’ reputations and has financial consequences. Quality costs are composed of tangible and intangible costs, however, only tangible costs used to be analyzed because there is no suitable methodology for measuring intangible costs. In this context, an innovative decision support system is developed with an empirical base, applying Analytical Hierarchy Process (AHP), Analytical Network Process (ANP), and Lean methodology to reduce all quality costs in an efficient way. In quality departments, perceptions, thoughts, and judgments (intangible costs) are not measured and controlled. This study develops an innovative methodology that allows to address this issue in an effective way. Another major innovation is the application of both multi-criteria methodologies to obtain the best combined result for decision making and the optimization of this result, developing an effort–impact matrix based on Lean manufacturing methodology. This system speeds up the decision-making process and assures its efficiency for quality department applications. Moreover, this decision support system may be applicable to any manufacturing sector.

A Decision Support System for Sustainable Agriculture: The Case Study of Coconut Oil Extraction Process

The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European Community, new approaches have been suggested and adopted in agriculture, giving rise to the so-called Agriculture 4.0. Improvements in automation, advanced information systems and Internet technologies allow for farmers to increase the productivity and to allocate the resources reasonably. For these reasons, agricultural decision support systems (DSS) for Agriculture 4.0 have become a very interesting research topic. DSS are interactive tools that enable users to make informed decisions about unstructured problems, and can be either fully computerized, human or a combination of both. In general, a DSS analyzes and synthesizes large amounts of data to assist in decision making. This paper presents an innovative decision support system solution to address the issues faced by coconut oil producers in making strategic decisions, particularly in the comparison of different methods of oil extraction. In more detail, the adopted methodology describes how to address the problems of coconut oil extraction in order to minimize the processing time and processing cost and to obtain energy savings. To this end, the coconut oil extraction process of the Leão São Tomé and Principe Company is presented as a case study: a DSS instance that analyzes the problem of the optimal selection between two different oil coconut extraction methods (fermentation-based and standard extraction processes) is developed as a meta-heuristics with a mixed integer linear programming problem. The obtained results show that there is clearly a trade-off between the increase in cost and reliability that the decision-maker may be willing to evaluate. In this respect, the proposed model provides a tool to support the decision-maker in choosing the best combination between the two different coconut oil extraction methods. The proposed DSS has been tested in a real application context through an experimental campaign.

DECISION TREE APPROACH FOR PLANTS STOPPAGE DECISION PROCESS DUE TO POWER LIMITATION CASE STUDY: STEEL COMPANY

By Mohamed Saied Essa Othman Hussein. Approach to the design and construction of an innovative decision support system to help management of multi-plant companies to choose a plant in case of power limitation is presented.

Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI.

Tinnitus is the perception of a phantom sound and the patient's reaction to it. Although much progress has been made, tinnitus remains a scientific and clinical enigma of high prevalence and high economic burden, with an estimated prevalence of 10%-20% among the adult population. The EU is funding a new collaborative project entitled "Unification of Treatments and Interventions for Tinnitus Patients" (UNITI, grant no. 848261) under its Horizon 2020 framework. The main goal of the UNITI project is to set the ground for a predictive computational model based on existing and longitudinal data attempting to address the question of which treatment or combination of treatments is optimal for a specific patient group based on certain parameters. Clinical, epidemiological, genetic and audiological data, including signals reflecting ear-brain communication, as well as patients' medical history, will be analyzed making use of existing databases. Predictive factors for different patient groups will be extracted and their prognostic relevance validated through a Randomized Clinical Trial (RCT) in which different patient groups will undergo a combination of tinnitus therapies targeting both auditory and central nervous systems. From a scientific point of view, the UNITI project can be summarized into the following research goals: (1) Analysis of existing data: Results of existing clinical studies will be analyzed to identify subgroups of patients with specific treatment responses and to identify systematic differences between the patient groups at the participating clinical centers. (2) Genetic and blood biomarker analysis: High throughput Whole Exome Sequencing (WES) will be performed in well-characterized chronic tinnitus cases, together with Proximity Extension Assays (PEA) for the identification of blood biomarkers for tinnitus. (3) RCT: A total of 500 patients will be recruited at five clinical centers across Europe comparing single treatments against combinational treatments. The four main treatments are Cognitive Behavioral Therapy (CBT), hearing aids, sound stimulation, and structured counseling. The consortium will also make use of e/m-health applications for the treatment and assessment of tinnitus. (4) Decision Support System: An innovative Decision Support System will be implemented, integrating all available parameters (epidemiological, clinical, audiometry, genetics, socioeconomic and medical history) to suggest specific examinations and the optimal intervention strategy based on the collected data. (5) Financial estimation analysis: A cost-effectiveness analysis for the respective interventions will be calculated to investigate the economic effects of the interventions based on quality-adjusted life years. In this paper, we will present the UNITI project, the scientific questions that it aims to address, the research consortium, and the organizational structure.

PREFAB Framework - PRoduct quality towards zEro deFects for melAmine surface Boards industry

Abstract Zero Defects is one of the ultimate targets for manufacturing quality control and assurance. Such systems are becoming common in advanced manufacturing industries but are at an initial stage in more traditional industrial sectors, such as wood panels, laminates production, pulp and paper processing and composite panels production. This paper proposes the PREFAB framework, applied to the wood-based panels industry, to minimize rejected products using AI, machine learning and IoT devices. The framework was built through action research with a Portuguese wood-based panel manufacturing. This framework delivered an innovative decision support system that provides relevant and timely recommendations for shopfloor decision making and to support process/product engineering.

A decision support system for territorial resilience assessment and planning: An application to the Douro Valley (Portugal)

This paper aims to assess the territorial resilience of a socio-ecological system through an innovative integrated evaluation framework to aid the decision-making process in the planning of transformation scenarios. This framework employs a set of resilience indicators through a Multicriteria Decision Analysis (MCDA) coupled with a Lotka-Volterra mathematical model of cooperative type. The set of indicators aims to calculate a composite index of Territorial Resilience (TRI), whereas the mathematical model is an extension of an existing model, aimed to predict possible long-time scenarios. The proposed operational framework for rural and vineyard landscapes aims to bridge the existing gap between territorial resilience theory and practice, with an innovative Decision Support System able to assist Decision Makers and territory planners in the planning and management of resilient territorial systems. This integrated evaluation framework is applied to a famous wine region in Portugal, the Douro Valley, where Port-wine grows. Such framework, especially in a context of adaptive governance, proves to be a suitable support in the field of landscape and urban planning to evaluate the dynamics of socio-ecological systems and to envision long-term policies and actions.

Validation of mobile Decision Support System for scheduling age-appropriate immunizations

Abstract Background The prevalence of missed opportunities for immunization among children is estimated to be 32.2%. The introduction of new vaccines in EPI schedule and lack of training of frontline health workers (FHWs) on the guidelines for number and spacing of doses and manual construction of vaccine schedules based on a child's age deteriorates efficiencies of FHWs, especially in forecasting catch-up schedules. We have designed and tested the validity of an innovative Decision Support System Application Programming Interface (DSS-API) which will automatically generate an age-appropriate immunization schedule for a child. Methods Using a multi-country setting approach, DSS was deployed in six immunization facilities of Pakistan and Bangladesh between July 2019 to April 2020. Details of all visiting children were recorded in database. Information on date of birth and past immunization history was used by the DSS-API to determine an individualized age-appropriate immunization schedule. These schedules were compared to the gold standard's outputs calculated by an immunization expert. Results A total of 6,241 vaccination events were recorded from all the sites. Using area under the curve, the calculated sensitivity of DSS to determine age-appropriate immunization schedule was found between 99-100% for the individual EPI vaccines, while the specificity of the DSS algorithm was found between 98-100%. Conclusions DSS is a feasible feature that can be incorporated in Digital Immunization Registries to accurately schedule routine and catch-up immunizations and can effectively reduce Missed Opportunities for Immunizations by assisting FHWs through automatic construction of immunization schedules. Key messages DSS-API is a unique application that can improve process efficiencies of frontline health workers by automatic generation of accurate age-appropriate catch-up vaccine schedules. DSS has the potential to reduce missed opportunities for immunization (MOI) and improve immunization coverage and timeliness among children aged under 2 years.

Knowledge-Based Decision Support in Healthcare via Near Field Communication.

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.

Implementing a decision support system in the transport process management of a small Slovak transport company

It is indisputable that the continuous development of digital technologies influences the business environment. Using information technologies means easier access to a huge amount of business information, which is hard to include in day-to-day decision-making. Traditional data processing methods in business management become inadequate. So, business process management approaches and business data analysis are the tools that could be utilized to optimize processes in a company and to harvest valuable information that can provide a variety of decision-making material for company management. This article deals with the analysis, modeling, and optimization of the transport process, as well as the design of a system for decision support in this process within a small transport company. The research is focused on the development of an innovative decision support system based on a company’s data analysis in order to improve the management of the transport service process.

An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.

Implementing a decision support system in the transport process management of a small Slovak transport company

It is indisputable that the continuous development of digital technologies influences the business environment. Using information technologies means easier access to a huge amount of business information, which is hard to include in day-to-day decision-making. Traditional data processing methods in business management become inadequate. So, business process management approaches and business data analysis are the tools that could be utilized to optimize processes in a company and to harvest valuable information that can provide a variety of decision-making material for company management. This article deals with the analysis, modeling, and optimization of the transport process, as well as the design of a system for decision support in this process within a small transport company. The research is focused on the development of an innovative decision support system based on a company’s data analysis in order to improve the management of the transport service process.

New approaches to enhance the water productivity in horticultural crops with special reference to Tamil Nadu

Water being a critical resource that is gradually diminishing, the need to increase Water Use Efficiency (WUE) becomes progressively more crucial. Limited success has been obtained so far through genetic approaches to modify complex traits such as transpiration efficiency. This is partly due to our limited understanding of the molecular basis and physiological mechanisms regulating WUE in stressed and non-stressed plants. There are various irrigation techniques to enhance the efficiency of the applied water to the crop. Improving irrigation techniques can directly affect WUE by increasing the yield per unit of water applied and reducing the amount of water loss. Major improvements could be obtained through management and innovative design of integrated water delivery and application schemes. Innovative decision support systems integrated by sensor networks to monitor soil and plant water status will help farmers to efficiently allocate limited water resources.