Decision Support System For Management Research Articles

Developing a framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities

PurposeEarthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.Design/methodology/approachThis study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.FindingsThe study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.Originality/valueCurrent methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

Optimizing food pantry stocking through client preferences: a novel elicitation and classification process

Purpose This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences. Design/methodology/approach This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry. Findings This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry. Research limitations/implications This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains. Practical implications This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences. Social implications An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions. Originality/value This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

Design and Application of Decision Support System for Educational Management Based on Big Data

Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.

A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management

The phenomenon known as urban heat islands (UHIs) is becoming more common and widespread, especially in large cities and metropolises around the world. The main cause of these temperature variations between the city center and the suburbs is the replacement of large tracts of natural land with artificial (built-up) surfaces that absorb solar heat and radiate it back at night. UHIs have been the subject of numerous studies, most of which were about defining the main characteristics, factors, indexes, etc., of UHIs using remote sensing technologies or about determining mitigating activities. This paper provides a comprehensive overview of the literature, as well as a bibliometric analysis, to discover research trends related to the application of decision support systems and multi-criteria decision-making for UHI management, with a special emphasis on fuzzy theory. Data collection is conducted using the Scopus bibliographic database. Throughout the literature review, it was found that there were not many studies on multi-criteria analysis and decision support system applications regarding UHIs. The fuzzy theory application was also reviewed, resulting in only a few references. However, this topic is current, with an increase in published papers, and authors see this as an opportunity for improvement and further research.

Knowledge-based, computerized, patient clinical decision support system for perioperative pain, nausea and constipation management: a clinical feasibility study.

Opioid administration is particularly challenging in the perioperative period. Computerized-based Clinical Decision Support Systems (CDSS) are a promising innovation that might improve perioperative pain control. We report the development and feasibility validation of a knowledge-based CDSS aiming at optimizing the management of perioperative pain, postoperative nausea and vomiting (PONV), and laxative medications. This novel CDSS uses patient adaptive testing through a smartphone display, literature-based rules, and individual medical prescriptions to produce direct medical advice for the patient user. Our objective was to test the feasibility of the clinical use of our CDSS in the perioperative setting. This was a prospective single arm, single center, cohort study conducted in Strasbourg University Hospital. The primary outcome was the agreement between the recommendation provided by the experimental device and the recommendation provided by study personnel who interpreted the same care algorithm (control). Thirty-seven patients were included in the study of which 30 (81%) used the experimental device. Agreement between these two care recommendations (computer driven vs. clinician driven) was observed in 51 out 54 uses of the device (94.2% [95% CI 85.9-98.4%]). The agreement level had a probability of 86.6% to exceed the 90% clinically relevant agreement threshold. The knowledge-based, patient CDSS we developed was feasible at providing recommendations for the treatment of pain, PONV and constipation in a perioperative clinical setting.Trial registration number & date The study protocol was registered in ClinicalTrial.gov before enrollment began (NCT05707247 on January 26th, 2023).

Design of a Decision Support System for Power Plant Thermal Efficiency Management at PLTU X

Efficiency management is one of the important pillars of the asset management system implemented at PLTU X based on PJBS guidelines to manage the power plant reliably and efficiently, which can support realising the company's vision of becoming a trusted power plant manager in Southeast Asia. As with efficiency management functions in other lines, the aim of power plant efficiency management is also to reduce production costs and ensure that the plant can operate efficiently all the time by controlling thermal losses in the power plant cycle. Determining efficiency management recommendations is very necessary to minimise the level of thermal losses that occur in the power plant's thermal cycle after evaluation. There is a series of evaluation processes that must be carried out to determine recommendations for optimising generator efficiency management; however, the process is currently still manual and takes a long time. With the advantages of a decision support system, which is usually used in determining systematic strategic decisions, it is hoped that this design can help engineers determine steps based on recommendations produced by the system precisely and in real-time so that they can be executed immediately to increase the efficiency of power plants even more. optimal.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine

BackgroundThe nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks. PurposeThis study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment. MethodsFailure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine. FindingsResults prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data). ConclusionsDespite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

TECHNOLOGICAL AND TACTICAL SUBSYSTEMS OF THE INTELLIGENT MANAGEMENT INFORMATION SYSTEM (ON THE EXAMPLE OF AN OIL AND GAS CORPORATION)

Providing Ukraine with its own oil and natural gas is one of the main tasks of the national economy. The possibility of development of the oil and gas industry is primarily determined by the volume of explored and mined resources. Since the existing explored reserves will not ensure the growth of oil and gas production, it is necessary to increase the amount of investments in the exploration and development of new deposits and hydrocarbon deposits with the aim of increasing their reserves and further growth of production, which requires the adoption of management decisions, which are characterized by increased technological and economic risk, uncertainty.
 Taking into account the main reasons for the decline in oil and gas production in Ukraine, the specifics of the industry and the requirements for information systems in the oil and gas industry, it can be argued that the use of intelligent technologies not only at the strategic, but also at the tactical and technological levels of management - is one of the keys to success in creating/reengineering, implementation and effective use of management decision support systems.
 The scientific and practical results presented in the article regarding the innovative configurations of architecture and design, proposed intellectual methodology for tactical and technological subsystems of the corporation management information systems. The above-proposed solutions - should be taken into account when reengineering corporate information systems not only in the oil and gas industry in Ukraine [1-3]. 
 Moreover, the obtained results are relevant and applicable not only for local companies, but also for international corporations on emerging markets in the context of global and regional macroeconomic and military crisis phenomena.

Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management

A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Decision support systems for disaster management require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis, as well as organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate insights from the risk-informed analyses. For instance, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.

The process of clinical decision-making in chronic wound care: A scenario-based think-aloud study

AimsTo undertake a comprehensive investigation into both the process of information acquisition and the clinical decision-making process utilized by primary care nurses in the course of treating chronic wounds. DesignScenario-based think-aloud method, enriched by the integration of information processing theory. The study was conducted within the framework of home care nursing organizations situated in Flanders, the Flemish speaking part of Belgium. A cohort of primary care nurses (n = 10), each possessing a minimum of one year of nursing experience, was recruited through the collaboration of three home care nursing organizations. MethodsTwo real-life clinical practice scenarios were employed for the interviews, with the researcher adopting the roles of either the patient or another clinician to enhance the realism of the think-aloud process. Each think-aloud session was promptly succeeded by a subsequent follow-up interview. The Consolidated criteria for Reporting Qualitative research checklist was followed to guarantee a consistent and complete report of the study. ResultsAmidst noticeable variations, a discernible pattern surfaced, delineating three sequential concepts: 1. gathering overarching information, 2. collecting and documenting wound-specific data, and 3. interpreting information to formulate wound treatment strategies. These concepts encompassed collaborative discussions with stakeholders, while the refinement of wound treatment strategies was interwoven within both concepts 2 and 3. ConclusionsEvident variations were identified in chronic wound care clinical decision-making, regardless of educational background or experience. These insights hold the potential to inform the development of clinical decision support systems for chronic wound management and provide guidance to clinicians in their decision-making endeavours.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Temperature-bounded development of Dirofilaria immitis larvae restricts the geographical distribution and seasonality of its transmission: case study and decision support system for canine heartworm management in Australia

Dirofilaria immitis is the causative agent of canine heartworm disease. We used the established heartworm development unit (HDU) principle to map the extrinsic incubation period (EIP) of D. immitis in Australia using historical weather data from 2013–2022. We found weather conditions suitable for EIP completion showed substantial seasonality and geographical variability. Whilst a considerable percentage of the Australian territory showed suitable weather conditions to always support EIP completion (17%), only 2.7% of the 2021 Australian human population lived in this region. Therefore, 97% of the population lived in an area that changed its EIP suitability within the study period. EIP completion is required prior to D. immitis transmission, meaning that infection risk of D. immitis is seasonal and location-dependent, being disrupted each year for most of the human population’s dogs. We developed an online, open access tool allowing us to visualise EIP completion across Australia historically and in near real-time. We aim to support veterinarians to make risk-based recommendations for dirofilariosis prevention by using the tool, available at https://heartworm-mapping.adelaide.edu.au/shiny/.

Applying human-centered design to the construction of a cirrhosis management clinical decision support system.

Electronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx. We conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts. Workshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists). This is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.

Factors Affecting Mortality and Distribution of Co-morbidities in Chronic Obstructive Pulmonary Disease

Objectives: Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. This study aims to identify the rules associated with major comorbidities in patients with COPD and analyze how they affect death, thereby suggesting patient management measures that are efficient and can improve the quality of medical care.Methods: For this purpose, among the Korea Disease Control and Prevention Agency’s discharge injury in-depth survey data of more than 3 million cases from 2006 to 2021, adult patients aged 19 years or older with a principal diagnosis of chronic obstructive pulmonary disease were targeted. <i>χ</i><sup>2</sup>-test and independent sample t-test were performed to analyze the distribution of deaths by demographic characteristics, and ARM (Association Rule Mining) was analyzed for association rules for comorbidities. Logistic regression analysis was performed on factors influencing death.Results: As a result of the analysis, the mortality rate was high in patients over 75 years of age, patients admitted to the emergency room, and patients discharged from medical institutions with more than 1,000 beds. The prevalence of comorbidities was high for E11 (Type 2 diabetes mellitus) and I10 (essential hypertension). As a result of analyzing the impact on death based on major association rules, other sepsis (A41) and essential hypertension (I10), other sepsis (A41) and pneumonia, organism unspecified (J18), sequelae of tuberculosis (B90) and J96 (respiratory Failure, NEC), sequelae of tuberculosis (B90), and I27 (other pulmonary heart diseases) were found to have a high probability of death.Conclusions: Based on the major association rules affecting death, we propose to apply them to a clinical decision support system for efficient patient management and personalized treatment.

Modelling individual plants’ growth: competition of Viola arvensis and wheat

IntroductionCompetition by weeds is a severe threat to agricultural crops. While these days the broadcast of herbicides over the entire field is common praxis, new technologies promise to reduce chemical output by reducing the area sprayed. The maximum precision would be a single plant treatment. This precision will allow a single plant management, which requires single plant management decisions, which is far beyond the possibilities of current praxis. A plant specific management decision can only be made on the basis of a model simulation.Materials and methodsA simulation model was developed to evaluate the effect of spatially explicit weed management covering interaction between single plants. The governing equations consist of coupled nonlinear differential equations for growth and competition of crop and weed plants in a spatial setting i.e. a coordinate is assigned to each plant. The mutual interaction is determined by the parameters strength and range of competition. Furthermore, an experiment was carried out parallel to the development of the model involving wheat and Viola arvensis (Murr.), in which coordinates and growth curves for a large number of plants (~600) were recorded allowing for a reasonable parameterization of the model.Results and discussionThe model is able to evaluate spatially explicit management measures such as weed strip control based on the height growth of single plants. The model is capable of evaluating a variety of control measures such as the frequency and spatial allocation of treatments. In particular, the effect of the width of a treatment zone around the rows of the crop was simulated.ConclusionIn future, the developed model could be extended to a decision support system for single plant weed management. Making decisions plant-by-plant, allows to orchestrate the weed management in a way that takes into account competing goals in plant protection: yield and biodiversity.

Innovative approaches in soil carbon sequestration modelling for better prediction with limited data

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC models affect the SOC prediction in the presence of small and sparse time series data. We develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions.

Assessing capacitance soil moisture sensor probes’ ability to sense nitrogen, phosphorus, and potassium using volumetric ion content

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in the monitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes’ responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes’ readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management.

Application of a decision support system for monitoring and maintenance management of water valley tank facilities: A technological transformation toward sector sustainability

The conventional approach of monitoring and maintaining water valley tank facilities (WVTFs) by field visiting is allied to time and resource wastage and data inaccuracies due to human error. These shortcomings hinder the sustainable monitoring and maintenance management of WVTFs. Therefore, this study investigates the development of a user-friendly decision support system (DSS) for supporting the monitoring and maintenance management activities of WVTFs in Nakasongola, Uganda. An open-source cloud-based “Google AppSheet” was interlinked with “Google Sheets” to develop the “MWEMA” mobile application (App), built with four modules (i.e., PM plan and schedule, work order, functional status, and mapping). Additionally, the MWEMA-app was implemented in the case study area to monitor the operational status of WVTFs. A total of 41 WVTFs were monitored, and acquired data was plotted instantly on the map. Results showed that out of 41 WVTFs, 4 were functional, 27 were partially functional, 8 were non-functional, and 2 were pending construction. An economic estimation demonstrated that the implementation of the project initiative is profitable with benefit/cost ratio (B/C) and net-present worth (NPW) values of 2 and US$ 39,809, respectively. Implementing the proposed innovative DSS would support achieving society-, economy-, and environment-related targets of sustainable development goals (SDGs) for the United Nations 2030 Agenda. The technology would assist policymakers and technical managers in keeping track of the operational status of WVTFs for sustainable management and development of scarce water resources.

P1211 A faecal microbial signature, in combination with faecal calprotectin, to optimise endoscopic activity monitoring in Crohn’s Disease

Abstract Background Monitoring Crohn's Disease (CD) endoscopic activity (EA) is crucial to identify disease progression and tailor treatment. Faecal calprotectin (FC) provides a non-invasive and cost-effective alternative to colonoscopy; however, it exhibits only a moderate correlation with EA in CD, highlighting the need for a non-invasive approach that either replaces or complements FC. This study aimed to identify a faecal microbial signature that could offer more precise monitoring of EA in CD. Methods A cohort of 55 CD patients was recruited from four Spanish hospitals. All participants provided a faecal sample before undergoing colonoscopy for the analysis of FC levels and microbial markers abundances, including Eubacteria (EUB), Escherichia coli (ECO), Faecalibacterium prausnitzii (FPRA) and its phylogroups I and II (PHG-I, and PHG-II), Ruminococcus spp. (RUM), Akkermansia muciniphila (AKK), Methanobrevibacter smithii (MSM), Clostridium clusters I and XIV (CLO, and XIV), Enterococcus sp. (ENT), Roseburia sp. (ROS), and Gammaproteobacteria (GAM). The Simplified Endoscopic Score for Crohn’s Disease (SES-CD) was employed to assess EA, being remission and activity defined as SES-CD≤2 and SES-CD>2, respectively. Results FC levels showed a significant correlation with SES-CD (rs=0.778, p<0.001) and a cut-off of 250 μg/g discerned EA with 86.49% sensitivity (SS), 75.65% specificity (SP), 77.06% positive predictive value (PPV) and 84.53% negative predictive value (NPV). Significant differences were identified when comparing the relative abundances of ECO (p<0.005), GAM (p<0.005), and RUM (p=0.026) between activity and remission. The relative abundance of ECO (rs=-0.495, p<0.001), GAM (rs=-0.492, p<0.001), and RUM (rs=0.274, p=0.042) also showed a significant correlation with SES-CD. None of the microbial markers alone exceeded FC performance. The combination of FC levels with the abundances of the microbial markers ECO, RUM, GAM, PHG-II, and XIV in a unique signature outperformed FC alone in discerning EA, with 97.57% SS, 93.59% SP, 91.65% PPV, and 97.57% NPV. The false positive (FP) and negative (FN) rates were reduced by 60.00% and 75.00%, respectively, compared to FC alone. Notably, all the FN presented mild endoscopic activity, indicating accurate monitoring of patients with moderate or severe EA. Conclusion A new faecal microbial signature combined with FC has been defined to monitor EA in CD with higher performance than FC alone. This tool may improve clinical decision support systems in CD management, refining endoscopic allocation, and providing a more patient-friendly and cost-effective approach. Further studies are needed to validate these results.

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.