Low back pain is a leading cause of disability worldwide, putting a significant strain on individual sufferers, their families, and the economy as a whole. It has a significant economic impact on the global economy because of the costs associated with healthcare, lost productivity, activity limitation, and work absence. Self-management, education, and adopting healthy lifestyle behaviors, such as increasing physical activity, are all widely recommended treatments. Access to services provided by healthcare professionals who provide these treatments can be limited and costly. This evaluation study focuses on the application of the MyRelief serious game, with the goal of addressing such challenges by providing an accessible, interactive, and fun platform that incorporates self-management, behavior change strategies, and educational information consistent with recommendations for managing low-back pain, based on self-assessment models implemented through ontology-based mechanics. Functional disability measured using the Oswestry Disability Questionnaire showed the statistically significant (p < 0.001) improvement in subjects’ self-evaluation of their health status. System Usability Scale (SUS) test score of 77.6 also suggests that the MyRelief serious game can potentially influence patient enablement. Full article

Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job application. Current methods identify the general attitude of faking but fail to identify faking patterns and the exact responses affected. Moreover, these strategies often require extensive data collection of honest responses and faking patterns related to the specific questionnaire use case, e.g., the position that people are applying to. In this work, we propose a self-attention-based autoencoder (SABA) model that can spot faked responses in a questionnaire solely relying on a set of honest answers that are not necessarily related to its final use case. We collect data relative to a popular personality test (the 10-item Big Five test) in three different use cases, i.e., to obtain: (i) child custody in court, (ii) a position as a salesperson, and (iii) a role in a humanitarian organization. The proposed model outperforms by a sizeable margin in terms of F1 score three competitive baselines, i.e., an autoencoder based only on feedforward layers, a distribution model, and a k-nearest-neighbor-based model. Full article

Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo–apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins. Full article

The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)—in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment. Full article

This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade. Full article