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Introduction Denmark is one of the safest places for childbirth, yet some women report dissatisfaction with their maternity care. However, some negative birth experiences may remain unreported due to thresholds for complaining. The study aimed to identify patterns of unreported negative birth experiences and to quantify the extent of these dark figures. Material and Methods A survey was distributed to 3081 women who gave birth at a Danish hospital in 2022, resulting in 1022 responses (response rate = 33.2%). The women reported their birth experiences in categories based on the Healthcare Complaints Analysis Tool (HCAT), specifying problems, harm caused, and whether they had filed a complaint or intended to. Dark figure ratios regarding problems and harm levels were calculated by comparing unreported negative experiences to formally filed complaints based on the survey responses, covering each problem type and harm level. Results Of the 1022 respondents, 336 (32.9%) women reported negative birth experiences, yet only 26 women had filed complaints. The remaining 310 unreported cases comprised 787 problems across HCAT categories. The most frequent problems were about communication and quality. The highest dark figure ratios were found within the management domain comprising institutional processes (13.0) and environment (9.9). The dark figure ratios showed an inverse relationship with harm severity, being highest for minimal (19.5) and minor (21.2) harm levels and decreasing for moderate (5.5), major (4.8) and catastrophic (0.3) harm levels. Conclusions This study demonstrates a substantial underestimation of negative birth experiences when relying solely on formal complaints, with dark figure ratios ranging from 4.8 to 13, depending on the issue. The inverse relationship between harm severity and dark figure ratios suggests a threshold for filing a complaint, as the likelihood of reporting increases with greater harm. These findings provide novel insights into unreported maternity care experiences, highlighting the need to integrate patient experiences into healthcare improvements. Patient complaints represent only a fraction of problems experienced during childbirth. Among 336 women with negative birth experiences, only 26 filed complaints, revealing a dark figure with up to 13 times more unreported issues. Integrating complaint data with dark figure estimates enables a more accurate assessment of the true scope and nature of patient‐perceived problems.

A hydrogen refueling station siting model that considers scheduling and routing decisions of individual vehicles is presented. By coupling a location strategy of the set covering problem (SCP) and a routing and scheduling strategy of the household activity pattern problem, this problem falls into the category of location routing problems. It introduces a tour-based approach to refueling station siting, with tour-construction capability within the model. There are multiple decision makers in this problem: the public sector as the service provider and the collection of individual households that make their own routing decisions to perform a given set of out-of-home activities together with a visit to a refueling location. A solution method that does not require the full information of the coverage matrix is developed to reduce the computational burden. Compared to the point-based SCP the results indicate that the minimum infrastructure requirement may be overestimated when vehicle (refueling demand)-infrastructure (refueling supply) interactions with daily out-of-home activities are excluded.

The pace of changes in automating cars has sped up in the last few decades. Autonomous Vehicles (AVs) will dramatically change the future of transportation, and household-level decisions will play a large role in the AV market. However, no data is readily available on household travel behavior using AVs. This study introduces a framework to assess households’ adaptation to AV operations. We developed a mixed integer program, Household Activity Pattern Problem with AV (HAPPAV), to model traveler behavior under realistic conditions while using AVs. The model generates feasible activity patterns for household members under spatial and temporal constraints. The model is able to consider complete driverless operations, such as AV pick-up and drop-off, parking availability, empty trips, and carpooling. A decomposition method is developed to solve the NP-hard problem HAPPAV. The method includes two major stages; the first stage is to generate all feasible travel patterns for household members and the second stage finds the best AV route along with detailed travel patterns. We also use novel pruning rules to enhance the performance of the decomposition method. The model is applied on the California Statewide Travel Survey. The results indicate that 62% of households can perform their daily activities with only one AV in place of two or three regular vehicles. However, AV empty trips increase total VMT by 15%. The new method improves the average runtime and solution quality by 86% and 23%, respectively.

Logical Analysis of Data is a procedure aimed at identifying relevant features in data sets with both positive and negative samples. The goal is to build Boolean formulas, represented by strings over 0,1,- called patterns, which can be used to classify new samples as positive or negative. Since a data set can be explained in alternative ways, many computational problems arise related to the choice of a particular set of patterns. In this paper we study the computational complexity of several of these pattern problems (showing that they are, in general, computationally hard) and we propose some integer programming models that appear to be effective. We describe an ILP model for finding the minimum-size set of patterns explaining a given set of samples and another one for the problem of determining whether two sets of patterns are equivalent, i.e., they explain exactly the same samples. We base our first model on a polynomial procedure that computes all patterns compatible with a given set of samples. Computational experiments substantiate the effectiveness of our models on fairly large instances. Finally, we conjecture that the existence of an effective ILP model for finding a minimum-size set of patterns equivalent to a given set of patterns is unlikely, due to the problem being NP-hard and co-NP-hard at the same time.

Heart problems are responsible for the majority of deaths worldwide. The use of intelligent techniques to assist in the identification of existing patterns in these diseases can facilitate treatments and decision making in the field of medicine. This work aims to extract knowledge from a dataset based on heart noise behaviors in order to determine whether heart murmur predilection exists or not in the analyzed patients. A heart murmur can be pathological due to defects in the heart, so the use of an evolving hybrid technique can assist in detecting this comorbidity team, and at the same time, extract knowledge through fuzzy linguistic rules, facilitating the understanding of the nature of the evaluated data. Heart disease detection tests were performed to compare the proposed hybrid model’s performance with state of the art for the subject. The results obtained (90.75% accuracy) prove that in addition to great assertiveness in detecting heart murmurs, the evolving hybrid model could be concomitant with the extraction of knowledge from data submitted to an intelligent approach.

Greek national discourse promotes linguistic and cultural homogenization within Greek borders often through racism against migrants. Racist homogenizing practices are not always explicit but are quite often “liquid,” namely, covert, ambiguous, and hard to trace. The effective promotion of national homogenization not only naturalizes linguistic and cultural assimilation but may also infiltrate antiracist discourse and eventually lead to migrants’ internalization of racism. Within the framework of critical discourse analysis, we investigate how and why migrants may align themselves with national discourse and internalize discrimination against themselves. To this end, this case study analyzes an article written by a young migrant in Greece and published in a newspaper of leftwing and antiracist orientation. By exploiting the problem-solution pattern and the concept of face, our analysis reveals that the migrant author appears to accept the expectations and impositions of national homogenizing discourse. Concurrently, racism emerges as liquid, since the text expressing the author’s internalized racism is published in an antiracist newspaper. The article reproduces racist standpoints typical of the dominant national discourse in a way (and in a context) that disguises such standpoints and deflects any antiracist criticism potentially raised against them. Thus, the hegemony of Greek national discourse is further reinforced.

This work deals with some properties of synthetic measures designed to differentiate objects in a multidimensional analysis. The aggregate synthetic measures are discussed here to rank the objects including those validating the concentration spread. The paper shows that currently used various measures (based either on a single or a multiple model object) do not satisfy the necessary conditions requested to be met by a \"good\" synthetic measure.

The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow the creation of intelligent systems in high-level languages with a robust level of identification of anomalies in Internet transactions, and the accuracy of the results of the test confirms that the fuzzy neural networks can act in anomaly detection in high-security attacks in computer networks.

Extreme learning machine (ELM) is a rapid classifier, evolved for batch learning mode which is not suitable for sequential input. As retrieving of data from new inventory which is leads to time extended process. Therefore, online sequential ELM (OSELM) algorithm is progressed to handle the sequential input in which data is read 1 by 1 or chunk by chunk mode. The overall system generalization performance may devalue because of the amalgamation of random initialization of OS-ELM and the presence of redundant and irrelevant features. To resolve the said problem, this paper proposes a correspondence improved genetic optimized feature selection paradigm for sequential input (IG-OSELM) for radial basis or function by using clinical datasets. For performance comparison, the proposed paradigm experimented and evaluated for ELM, improved genetic optimized for ELM classifier (IG-ELM), OS-ELM, IG-OSELM. Experimental results are calculated and analyzed accordingly. The comparative results analysis illustrates that IG-ELM provides 10.94% improved accuracy with 43.25% features as compared to ELM.

One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It would help in knowledge mining, idea generation, and information classification from scientific texts. It would also help to compare scientific papers and automatically generate review articles in a given field. However, computational research on problem–solution patterns has been scarce. The linguistic analysis, instructional-design research, theory, and empirical methods have not paid enough attention to the study of problem–solution patterns. This paper tries to solve this issue by applying the computational techniques of machine learning classifiers and neural networks to a set of features to intelligently classify a problem phrase from a non-problem phrase and a solution phrase from a non-solution phrase. Our analysis shows that deep learning networks outperform machine learning classifiers. Our best model was able to classify a problem phrase from a non-problem phrase with an accuracy of 90.0% and a solution phrase from a non-solution phrase with an accuracy of 86.0%.