A Web-Based Decision Support Systems Framework for Ill-Structured Decision Situations

Decisions and actions in an ill-structured situation often include high-time constraints, lack of information, and poor cognitive efforts. Obtaining the necessary information through an information systems tool is supposed to be the best solution in such situations. To expose the decision situation, this study has taken the fire and civil defense service as the field of study. In exploring the required information resources, elements of the system architecture, and suitability of the proposed system in the current field, this study has resorted to the qualitative approach. To assess the dependability and performance of the systems, this study has used the RAS metrics and a black-box test. The result showed that the reliability stood within 62.70–70.00%, and its availability stood at 99.00% with a downtime of 3.65 days/year from a three-month study. As per the black-box test with standard 4G network connection, the system takes an average loading time of 1.00s for alphanumeric contents, 3.50s for images and graphics, and 5.50s for loading maps and navigations. This research evidenced that, the local emergency response and rescue units in developing countries like Bangladesh might want to use a well-designed response support system for improved acquisition, dissemination, and utilization of response information.

What Determines Pig Farmers’ Epidemic Coping Behaviors: A Qualitative Analysis of Endemically Infected Areas in Relation to African Swine Fever

An animal epidemic is a big threat for economic development that may seriously disturb the breeding industry and people’s normal life. The most effective approach so far for epidemic control is biosecurity, zoning, culling animals exposed, and other relevant measures, which highly demands the cooperation of farmers in epidemic areas. However, an uncooperative phenomenon among individual farmers facing an epidemic has been recorded for a long time and includes unwilling to report the epidemic and selling infected pork. It is important to unravel the determinants of farmers’ coping behaviors during an animal epidemic outbreak and use corresponding strategies to reduce farmers’ inappropriate behaviors. Taking African Swine Fever (ASF) crisis as an example, this study aimed to reveal the determinants and underlying mechanism of pig farmers’ coping behaviors. We adopted qualitative interviews with 45 pig farmers across four endemically infected areas in Hunan provinces, and the data collected were subjected to a grounded theory analysis. Our results showed that emergency response, information sources, and information channels jointly affected pig farmers’ epidemic risk perception and their perception of coping behaviors. Meanwhile, both the characteristics of the government and pig farmers moderated this affect. Consequently, by processing information through either a heuristic or an analytical path, pig farmers’ behavioral intention was transformed into actual coping behaviors. Our study emphasizes the value of sufficient risk communication, proper compensation policies, and strong public trust in the government for improving the farmers’ participation in the epidemic response. Theoretical and practical implications to animal epidemic prevention and control are provided.

COVID-19 Rumor Detection on Social Networks Based on Content Information and User Response

The outbreak of COVID-19 has caused a huge shock for human society. As people experience the attack of the COVID-19 virus, they also are experiencing an information epidemic at the same time. Rumors about COVID-19 have caused severe panic and anxiety. Misinformation has even undermined epidemic prevention to some extent and exacerbated the epidemic. Social networks have allowed COVID-19 rumors to spread unchecked. Removing rumors could protect people’s health by reducing people’s anxiety and wrong behavior caused by the misinformation. Therefore, it is necessary to research COVID-19 rumor detection on social networks. Due to the development of deep learning, existing studies have proposed rumor detection methods from different perspectives. However, not all of these approaches could address COVID-19 rumor detection. COVID-19 rumors are more severe and profoundly influenced, and there are stricter time constraints on COVID-19 rumor detection. Therefore, this study proposed and verified the rumor detection method based on the content and user responses in limited time CR-LSTM-BE. The experimental results show that the performance of our approach is significantly improved compared with the existing baseline methods. User response information can effectively enhance COVID-19 rumor detection.

Data-Driven Approach for Identifying Mistuning in As-Manufactured Blisks

Sector-to-sector geometry or material property variations in as-manufactured bladed disks, or blisks, can result in significantly greater vibration responses during operation compared to nominally cyclic symmetric designs. The dynamics of blisks are sensitive to these unavoidable deviations, known as mistuning, making the identification of mistuning in as-manufactured blisks necessary for accurately predicting their vibration. Previous approaches to identify such mistuning parameters often require the identification of modal information or blade-isolation techniques such as blade detuning using masses or adding damping pads. However, modal information can be difficult to obtain accurately even in optimal bench conditions. Additionally, in practice it can be difficult to isolate individual blades by restricting blade motion or detuning individual blades through added masses due to geometric constraints. In this paper, we present a method for mistuning identification using a data-driven approach based on a neural network. Here, mistuning in all sectors of blisks with the same nominal geometry can be identified by using a small number of forced responses and the forcing phase information from traveling-wave excitation. In this approach, no system or sector-level modal response information, restrictive blade isolation, or mass detuning are required. Validation of this approach is presented using a finite element blisk model containing stiffness mistuning within the blades to create computationally generated surrogate data. It is shown that mistuning can be predicted accurately using forced responses containing a significant amount of absolute and relative measurement noise, mimicking responses collected from experimental measurements.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data’s variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70–0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.

Effective Coastal Climate Services—An End-User Perspective for Resilient Infrastructure

This paper focusses on identifying the responses to coastal climate change that are of interest for decision-making by end users and the delivery and the necessary communication process for this information. The focus is on representation of climate (response) information in a form that provides sufficient clarity in the midst of uncertainty for end-users who are seeking to develop or maintain resilient infrastructure. The paper recommends that the use of the term climate services in situations unrelated to supporting adaptation to and mitigation of climate change should be avoided. Better investment decisions could be made if Bayesian frameworks were used to assign probabilities to RCP scenarios. Associated predictions need to cover all types of climate change influences not just sea level rise and ideally provide concurrent time series to allow evaluation of dependencies. Guidance on climate information published by official bodies needs to adopt a consistent approach, with a clear narrative that describes the transition from science to guidance. The form in which climate services information is needed for the required end user decisions needs careful thought, including appropriate communication of the associated uncertainties using good practices and experiences from related sectors.

Data-Driven Approach for Identifying Mistuning in As-Manufactured Blisks

Sector-to-sector geometry or material property variations in as-manufactured bladed disks, or blisks, can result in significantly greater vibration responses during operation compared to nominally cyclic symmetric designs. The dynamics of blisks are sensitive to these unavoidable deviations, known as mistuning, making the identification of mistuning in as-manufactured blisks necessary for accurately predicting their vibration. As in previous mistuning modeling and identification approaches, the mistuning of interest is small and is parameterized by using deviations in cantilever blade-alone frequencies. Such mistuning parameterization is popular because it can be applied through blade-to-blade stiffness deviations in computational reduced-order models used to predict blisk dynamics. Previous approaches to identify such mistuning parameters often require the identification of modal information or blade-isolation techniques such blade detuning using masses or adding damping pads. However, modal information can be difficult to obtain accurately even in optimal bench conditions. Additionally, in practice it can be difficult to isolate individual blades by restricting blade motion around the blisk or detuning individual blades through added masses due to geometric constraints. In this paper, we present a method for mistuning identification using a data-driven approach based on a neural network. The network is first trained using surrogate computational data. Thus, the data-driven portion of the approach is executed using surrogate computational methods. With the trained network, mistuning in all sectors of blisks with the same nominal geometry can be identified by using a small number of forced responses and the forcing phase information from traveling-wave excitation. In this approach, no system or sector-level modal response information, restrictive blade isolation, or mass detuning are required. We additionally present a method for forcing frequency selection and response conditioning to improve identification accuracy. Validation of this approach is presented using a finite element blisk model containing stiffness mistuning within the blades to create computationally generated surrogate data. It is shown that mistuning can be predicted accurately using forced responses containing a significant amount of absolute and relative measurement noise, mimicking responses collected from experimental measurements. In addition, it is shown that mistuning can be predicted independently and accurately using different engine orders of excitation in regions of high modal density.