Search Results (250)

The use of intelligent clinical decision support systems (CDSS) has the potential to improve the accuracy and speed of diagnoses significantly. These systems can analyze a patient’s medical data and generate comprehensive reports that help specialists better understand and evaluate the current clinical scenario. This capability is particularly important when dealing with medical images, as the heavy workload on healthcare professionals can hinder their ability to notice critical biomarkers, which may be difficult to detect with the naked eye due to stress and fatigue. Implementing a CDSS that uses computer vision (CV) techniques can alleviate this challenge. However, one of the main obstacles to the widespread use of CV and intelligent analysis methods in medical diagnostics is the lack of a clear understanding among diagnosticians of how these systems operate. A better understanding of their functioning and of the reliability of the identified biomarkers will enable medical professionals to more effectively address clinical problems. Additionally, it is essential to tailor the training process of machine learning models to medical data, which are often imbalanced due to varying probabilities of disease detection. Neglecting this factor can compromise the quality of the developed CDSS. This article presents the development of a CDSS module focused on diagnosing age-related macular degeneration. Unlike traditional methods that classify diseases or their stages based on optical coherence tomography (OCT) images, the proposed CDSS provides a more sophisticated and accurate analysis of biomarkers detected through a deep neural network. This approach combines interpretative reasoning with highly accurate models, although these models can be complex to describe. To address the issue of class imbalance, an algorithm was developed to optimally select biomarkers, taking into account both their statistical and clinical significance. As a result, the algorithm prioritizes the selection of classes that ensure high model accuracy while maintaining clinically relevant responses generated by the CDSS module. The results indicate that the overall accuracy of staging age-related macular degeneration increased by 63.3% compared with traditional methods of direct stage classification using a similar machine learning model. This improvement suggests that the CDSS module can significantly enhance disease diagnosis, particularly in situations with class imbalance in the original dataset. To improve interpretability, the process of determining the most likely disease stage was organized into two steps. At each step, the diagnostician could visually access information explaining the reasoning behind the intelligent diagnosis, thereby assisting experts in understanding the basis for clinical decision-making. Full article

Investors face the challenge of how to incorporate economic and financial forecasts into their investment strategy, especially in times of financial crisis. To model this situation, we consider a financial market consisting of a risk-free asset with a constant interest rate as well as a risky asset whose drift and volatility is influenced by a stochastic process indicating the probability of potential market downturns. We use a dynamic portfolio optimization approach in continuous time to maximize the expected utility of terminal wealth and solve the corresponding HJB equations for the general class of HARA utility functions. The resulting optimal strategy can be obtained in closed form. It corresponds to a CPPI strategy with a stochastic multiplier that depends on the information from the crisis indicator. In addition to the theoretical results, a performance analysis of the derived strategy is implemented. The specified model is fitted using historic market data and the performance is compared to the optimal portfolio strategy obtained in a Black–Scholes framework without crisis information. The new strategy clearly dominates the BS-based CPPI strategy with respect to the Sharpe Ratio and Adjusted Sharpe Ratio. Full article

The soil phytomonitoring and phytostabilization potential of Syringa vulgaris and Ficus carica was evaluated regarding 16 priority polycyclic aromatic hydrocarbons (PAHs) using a chemometric approach and the calculation of bioconcentration factors (BCFs) for each individual PAH in plants’ roots from each selected location in the Bor region. PAHs in roots and the corresponding soils were analyzed using the QuEChERS (Quick, Effective, Cheap, Easy, Rugged, Safe) method with some new modifications, gas chromatography/mass spectrometry, Pearson’s correlation study, hierarchical cluster analysis, and BCFs. Several central conclusions are as follows: Each plant species developed its own specific capability for PAH management, and root concentrations ranged from not detected (for several compounds) to 5592 μg/kg (for fluorene in S. vulgaris). In some cases, especially regarding benzo(a)pyrene and chrysene, both plants had a similar tactic—the total avoidance of assimilation (probably due to their high toxicity). Both plants retained significant quantities of different PAHs in their roots (many calculated BCFs were higher than 1 or were even extremely high), which recommends them for PAH phytostabilization (especially fluorene, benzo(b)fluoranthene, and benzo(k)fluoranthene). In soil monitoring, neither of the plants are helpful because their roots do not reflect the actual situation found in soil. Finally, the analysis of the corresponding soils provided useful monitoring information. Full article

Since Shannon’s pioneering work, the concept of entropy has been used in many major scientific fields. It is therefore a universal concept but also defined in different ways. Entropy is used in studies of system complexity and to investigate the information content of probability distributions. One of the areas of its applications is human lifespan, i.e., the link between entropy and the methods of survival analysis. These methods are also used in assessing the duration of any socio-economic phenomenon. The aim of this article is to assess the market situation on the basis of the entropy of duration in unemployment. This study determines the Shannon entropy, residual entropy, past entropy, and cumulative residual entropy under the assumption of an exponential distribution of duration. Ward’s hierarchical clustering and the Dynamic Time Warping measure were used to analyse entropy and its relationship with the unemployment rate. It was shown that not all of the analysed models determine the entropy of duration in unemployment well for an exponential distribution. It was substantiated that there is a similarity between the formation of the entropy of duration in unemployment and the registered unemployment rate. It is shown that high unemployment rates in the labour market are a destabilising element of the labour market, more so than crises. Full article

This paper investigates the implication of the COVID-19 pandemic on household food insecurity in Burkina Faso. We used data from the High-Frequency Phone Survey collected from the period June 2020 to June 2021 by the World Bank in collaboration with the National Institute of Statistics. To assess the persistence of food inadequacy, we estimated a dynamic linear probability model. Our results revealed that female and elderly household members were more likely to skip meals during the pandemic than their respective counterparts. For households that skipped a meal due to the pandemic, the likelihood of facing food insecurity in the subsequent month increased by 37 percent. Similarly, individuals who ran out of food in consecutive months were 0.28 times more likely to experience the same situation in the following month. While other shocks can cause food insecurity, the global health-related, economic, social, and information dimensions of COVID-19 created a distinctive and multifaceted form of food shortage that sets it apart from many other types of shock. These findings suggest the implementation of effective programs to respond to shocks and the mitigation effects experienced by most disadvantaged groups. Full article

In sonar collaborative search tasks, effectively evaluating the collaborative search efficiency is an important way to measure whether a task can be successful, which can also provide strong support for optimizing search schemes. In complex marine environments, sonar collaboration search faces challenges such as uncertain task scenes and real-time changing situations. Traditional evaluation methods cannot meet the evaluation requirements in these tasks since they do not analyze the involved dynamic modeling process. To bridge this gap, in this paper, we propose a novel evaluation method for sonar collaborative search efficiency based on adaptive information fusion and dynamic deduction. Specifically, we develop an information fusion method for multi-sensor detection based on adaptive weight calculation first, weights are assigned to each sensor based on the real-time changing detection probability to obtain more accurate detection probability fusion results. Then, we introduce the Monte Carlo sampling concept to establish an efficiency evaluation model based on the information fusion results. It discretizes the sonar search path and target motion trajectory in the time and space, and calculates the sonar detection efficiency point by point, which can overcome the challenge of uncertain situation conditions due to the uncertainty of target motion by dynamic spatial-temporal deduction. Compared with the average weighted fusion method, the variance of the proposed adaptive fusion method decreases from 0.01 to 0.0071, which proves its better stability. The results of the one-sample t-test indicate that at the level of $\alpha =0.05$, there is a significant difference between the average detection probability and the random probability of 0.5, indicating statistical significance. Moreover, we verify the effectiveness of the proposed method in fully-passive and multi-base working modes, and compare the impact of each sonar on the overall detection capability of the multi-sonar system, which also demonstrates the advantages and reliability of the new model. Full article

Food contamination by pathogenic and spoilage bacteria causes approximately 47 million cases of foodborne diseases in the United States and leads to tons of food spoilage, worsening the food loss situation worldwide. In addition, conventional preservation treatments implemented in the food industry decrease food’s nutritional and organoleptic quality. Therefore, there is a need for new alternatives to counteract food contamination without altering its characteristics. Endolysins are a promising strategy due to their unique properties, such as host specificity, synergism with other antibacterial agents, mode of action, and low probability of resistance development. These characteristics differentiate them from other antibacterial agents used in the food industry. Endolysins are enzymes produced by bacteriophages during the process of bacterial infection and lysis. This review describes the advances related to endolysin application systems in food, considering their potential for food safety and an overview of the application conditions according to the type of food and bacteria to be controlled. We also highlight the need for new studies on endolysin encapsulation and prolongation of the action time in cases of outbreaks that allow obtaining key information to improve the application of endolysins in different food matrices during food processing and storage Full article

Background: The literature on measuring the complexity of production systems employs the graph and information theory. This study analyzes these systems and their coordination under varying states of control, with a focus on the probability of unfavorable events and their temporal characteristics. Methods: Coordination systems are represented as temporal networks, using entropy and node influence metrics. Two case studies are presented: a factory operating under the principles of the Toyota Production System (TPS) with adjacent (local) coordination and andon (global) coordination and a university obstetrics clinic with only adjacent (local) coordination. Results: Adjacent coordination leads to zero entropy in 38.40% of all situations in the TPS example, contrasted to 76.62% in the same system with andon coordination. Degree centrality of nodes outside of zero-entropy situations exhibits higher average and maximum values in andon coordination networks, compared to those with adjacent coordination in TPS. Entropy values in the university obstetric clinic range from 0.92 to 2.23, average degrees vary between 3 and 4.08, and maximum degrees range from 7 to 9. Conclusions: Coordination systems modeled as temporal networks capture the evolving nature of centralizing and decentralizing coordination in production systems. Full article

In agriculture, manuring offers several benefits, which include improving soil fertility, structure, water retention, and aeration; all these factors favor plant health and productivity. However, improper handling and application of manure can pose risks, such as spread of pathogens and water pollution. Mitigation of such risks requires not only proper storage and composting practices, but also compliance with correct application periods and techniques. Spaceborne Earth observation can contribute to mapping manure applications and identifying possible critical situations, yet manure detection from satellite data is still a largely open question. The aim of this research is an automated, machine learning (ML)-based approach to detecting manure application on crop fields in time sequences of spaceborne, multi-source optical Earth Observation data. In the first stage of this research, multispectral data alone was considered; a pool of different spectral indexes were analyzed to identify the ones most impacted by manure application. Increments of the selected indexes from one satellite acquisition to the next were used as features to train and test various machine learning models. Two agricultural areas—one in Spain and one in Italy—were considered. Fair levels of accuracy were achieved when training and testing were carried out in the same geographical context, whereas ML models trained on one context and tested on the other reported significantly lower—albeit still acceptable—accuracy levels. In the stage that followed, thermal data was integrated and used alongside multispectral indexes. This addition led to significant improvements in accuracy levels, despite possible thermal-to-multispectral sampling mismatch in time series. Our results appear to indicate that ML-based approaches to manuring detection from space require training on the targeted geographical context, although transfer learning can probably be leveraged and only fine-tuning training will be needed. Spaceborne thermal data, where available, should be included in the input data pool to improve the quality of the final result. The proposed method is meant as a first step towards a suite of techniques that should enable large-scale, consistent monitoring of agricultural activities to check compliance with environmental regulations and provide enhanced traceability information for food products. Full article

The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based models and (d) expert meteorological and hydrological input by Bureau of Meteorology staff, to estimate the risk of reaching pre-specified river height thresholds at locations across the continent. A flood watch provides information about a developing weather situation including forecasting rainfall totals, catchments at risk of flooding, and indicative severity where required. Although there is uncertainty attached to a flood watch, its early dissemination can help individuals and communities to be better prepared should flooding eventuate. This paper investigates the utility of forecasts of daily gridded national runoff to inform the risk of riverine flooding up to 7 days in advance. The gridded national water balance model (AWRA-L) runoff outputs generated using post-processed 9-day Numerical Weather Prediction hindcasts were evaluated as to whether they could accurately predict exceedance probabilities of runoff at gauged locations. The approach was trialed over 75 forecast locations across North East Australia (Queensland). Forecast 3-, 5- and 7-day accumulations of runoff over the catchment corresponding to each location were produced, identifying whether accumulated runoff reached either 95% or 99% historical levels (analogous to minor, moderate and major threshold levels). The performance of AWRA-L runoff-based flood likelihood was benchmarked against that based on precipitation only (i.e., not rainfall–runoff transformation). Both products were evaluated against the observed runoff data measured at the site. Our analysis confirmed that this runoff-based flood likelihood guidance could be used to support the generation of flood watch products. Full article

Background: Laboratory monitoring of the effect of low-molecular-weight heparins (LMWHs) is generally not necessary. However, prompt evaluation of heparin inhibitory effects (i.e., anti-Xa activity) is important in cases of life-threatening bleeding, need for urgent surgery or acute thromboembolism under LMWH treatment. We aimed to establish a simple and reliable point-of-care method for the detection of enoxaparin. Methods: Eighty patients under enoxaparin therapy and ten healthy volunteers without any anticoagulant treatment were enrolled. Simultaneous measurements of anti-Xa activity using the chromogenic method and clotting times in the absence and presence of polybrene using viscoelastometric assays containing Russell’s viper venom (RVV-test) were performed on the ClotPro device. Results: Among the measured and derived RVV-test parameters, the ratio of the RVV clotting times (RVV CT) detected in the absence and presence of polybrene showed the best statistically significant correlation with anti-Xa activity (r = 0.774, p < 0.001). Based on ROC analysis, we designated RVV CT ratios of 1.02, 1.23 and 1.6 as the best cut-off values for separating anti-Xa ranges below and above 0.3 and 0.6 IU/mL, respectively. If the RVV CT ratio is below or above 1.23, the anti-Xa activity is suggested to be below 0.6 IU/mL or above 0.3 IU/mL with high certainty, respectively. Further differentiation is possible if the RVV CT ratio is measured below 1.02 or above 1.6. In these cases, the measured anti-Xa values are below 0.3 IU/mL or above 0.6 IU/mL, respectively, with high probability and good predictive values. Conclusions: Our method can provide semiquantitative information on the effect of enoxaparin and the expected anti-Xa activity within 10 min in real clinical situations. Full article

The performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses of dynamic systems. Based on process modeling, PPM also contributed to the classification of models describing linear and non-linear dynamic processes so that they approximate their dynamics using the smallest possible number of numerical parameters. From most bio-inspired procedures of artificial intelligence and optimization used for the design of automatic controllers, PPM is distinguished by the possibility of repeated application of once generated performance portraits (PPs). These represent information about the process obtained by evaluating the performance of setpoint and disturbance step responses for all relevant values of the determining loop parameters organized into a grid. It can be supported by the implementation of parallel calculations with optimized decomposition in the high-performance computing (HPC) cloud. The wide applicability of PPM ranges from verification of analytically calculated optimal settings achieved by various approaches to controller design, to the analysis as well as optimal and robust setting of controllers for processes where other known control design methods fail. One such situation is illustrated by an example of predictive integrating (PrI) controller design for processes with a dominant time-delayed sensor dynamics, representing a counterpart of proportional-integrating (PI) controllers, the most frequently used solutions in practice. PrI controllers can be considered as a generalization of the disturbance–response feedback—the oldest known method for the design of dead-time compensators by Reswick. In applications with dominant dead-time and loop time constants located in the feedback (sensors), as those, e.g., met in magnetoencephalography (MEG), it makes it possible to significantly improve the control performance. PPM shows that, despite the absence of effective analytical control design methods for such situations, it is possible to obtain high-quality optimal solutions for processes that require working with uncertain models specified by interval parameters, while achieving invariance to changes in uncertain parameters. Full article

Yellow crazy ants (YCAs) are an invasive ant with a pantropical distribution, largely due to the international movements of ships and produce. This invasive ant has the capacity to impact a broad range of environmental, domestic and agricultural situations and has the ability to develop into supercolonies and dominate landscapes if uncontrolled. YCAs have been detected in several locations in Australia. During 2018 in New South Wales, YCAs were detected in two locations in the Lismore region. Several awareness techniques were used to gain community support and engagement in the response program. The eradication program relied on the insecticide fipronil (several formulations), and the program subsequently used surveillance data to demonstrate that eradication had been achieved. We used the scenario tree analysis with stochastic models to estimate the likelihood of eradication. We combined the results of the passive and active surveillance systems to predict a 70.4% (62.7–80.7) probability of freedom of detecting one nest, 84.4% (73.9–94.4) probability of freedom for two nests and 98% (93.1–99.9) probability of freedom for five nests. The results from the scenario tree analysis were used to inform program managers regarding the termination of the eradication and surveillance activities. Full article

This study investigates how an increase in the quality of business ventures, measured as their success probability, affects trust and return on investment (ROI) in situations where the investor–entrepreneur interaction is affected by moral hazard and asymmetric information. We model a repeated trust problem between investors and entrepreneurs, featuring moral hazard and adverse selection. Hidden Markov techniques and computer simulations are used to derive the main results. We find that trust and ROI may decline as quality improves. Although lenders tend to reduce the requirements for granting initial credit, they nevertheless become less tolerant of current borrowers who fail to pay back. Additionally, we demonstrate a novel substitution effect, where lenders prefer new borrowers over existing borrowers that experienced early failures. The main conclusions of our study are that while impressing early on is effective in gaining first access to credit, it may nevertheless hurt the cause of getting credit in subsequent periods, following an early failure. In business environments plagued with ex post moral hazard, entrepreneurs might do better by gaining trust first and impressing later. Furthermore, our results imply that in a thriving economy, not only are bad loans made, but good loans are lost as well. Full article

The Arctic region, including vast shelf zones, has enormous resource and transport potential and is currently key to Russia’s strategic development. This region is promising and attractive for the intensification of global economic activity. When developing this region, it is very important to avoid emergency situations that could result in numerous negative environmental and socio-economic consequences. Therefore, when designing and constructing critical infrastructure facilities in the Arctic, it is necessary to conduct high-quality studies of potential geohazards. This paper reviews and summarizes the scattered information on the main geohazards in the Russian sector of the Arctic Ocean, such as earthquakes, underwater landslides, tsunamis, and focused fluid discharges (gas seeps), and discusses patterns of their spatial distribution and possible relationships with the geodynamic setting of the Arctic region. The study revealed that the main patterns of the mutual distribution of the main geohazards of the Russian sector of the Arctic seas are determined by both the modern geodynamic situation in the region and the history of the geodynamic evolution of the Arctic, namely the formation of the spreading axis and deep-sea basins of the Arctic Ocean. The high probability of the influence of seismotectonic activity on the state of subsea permafrost and massive methane release is emphasized. This review contributes toward better understanding and progress in the zoning of seismic and other geological hazards in the vast Arctic seas of Russia. Full article