Search Results (509)

Aprotinin is a broad-spectrum inhibitor of human proteases that has been approved for the treatment of bleeding in single coronary artery bypass surgery because of its potent antifibrinolytic actions. Following the outbreak of the COVID-19 pandemic, there was an urgent need to find new antiviral drugs. Aprotinin is a good candidate for therapeutic repositioning as a broad-spectrum antiviral drug and for treating the symptomatic processes that characterise viral respiratory diseases, including COVID-19. This is due to its strong pharmacological ability to inhibit a plethora of host proteases used by respiratory viruses in their infective mechanisms. The proteases allow the cleavage and conformational change of proteins that make up their viral capsid, and thus enable them to anchor themselves by recognition of their target in the epithelial cell. In addition, the activation of these proteases initiates the inflammatory process that triggers the infection. The attraction of the drug is not only its pharmacodynamic characteristics but also the possibility of administration by the inhalation route, avoiding unwanted systemic effects. This, together with the low cost of treatment (≈2 Euro/dose), makes it a good candidate to reach countries with lower economic means. In this article, we will discuss the pharmacodynamic, pharmacokinetic, and toxicological characteristics of aprotinin administered by the inhalation route; analyse the main advances in our knowledge of this medication; and the future directions that should be taken in research in order to reposition this medication in therapeutics. Full article

Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method’s effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements. Full article

The remarkable human ability to predict others’ intent during physical interactions develops at a very early age and is crucial for development. Intent prediction, defined as the simultaneous recognition and generation of human–human interactions, has many applications such as in assistive robotics, human–robot interaction, video and robotic surveillance, and autonomous driving. However, models for solving the problem are scarce. This paper proposes two attention-based agent models to predict the intent of interacting 3D skeletons by sampling them via a sequence of glimpses. The novelty of these agent models is that they are inherently multimodal, consisting of perceptual and proprioceptive pathways. The action (attention) is driven by the agent’s generation error, and not by reinforcement. At each sampling instant, the agent completes the partially observed skeletal motion and infers the interaction class. It learns where and what to sample by minimizing the generation and classification errors. Extensive evaluation of our models is carried out on benchmark datasets and in comparison to a state-of-the-art model for intent prediction, which reveals that classification and generation accuracies of one of the proposed models are comparable to those of the state of the art even though our model contains fewer trainable parameters. The insights gained from our model designs can inform the development of efficient agents, the future of artificial intelligence (AI). Full article

This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human–robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system’s efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor’s intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system’s performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs. Full article

The recognition of head movements plays an important role in human–computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human–computer interfaces. Full article

WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency. Full article

Sensing and cognition by homeowners and technicians for home maintenance are prime examples of human–building interaction. Damage, decay, and pest infestation present signals that humans interpret and then act upon to remedy and mitigate. The maintenance cognition process has direct effects on sustainability and economic vitality, as well as the health and well-being of building occupants. While home maintenance practices date back to antiquity, they readily submit to augmentation and improvement with modern technologies. This paper describes the use of networked smart technologies embedded with machine learning (ML) and presented in electronic formats to better inform homeowners and occupants about safety and maintenance issues, as well as recommend courses of remedial action. The demonstrated technologies include robotic sensing in confined areas, LiDAR scans of structural shape and deformation, moisture and gas sensing, water leak detection, network embedded ML, and augmented reality interfaces with multi-user teaming capabilities. The sensor information passes through a private local dynamic network to processors with neural network pattern recognition capabilities to abstract the information, which then feeds to humans through augmented reality and conventional smart device interfaces. This networked sensor system serves as a testbed and demonstrator for home maintenance technologies, for what can be termed Home Maintenance 4.0. Full article

In the realms of operations management (OM) and supply chain management (SCM), the significance of the human factor (HF) is increasingly recognised as a pivotal determinant of corporate performance. This burgeoning interest aligns with the recognition that individual characteristics—spanning personality traits, attitudes, and soft skills—play a critical role in enhancing organisational outcomes. Despite growing scrutiny, the discourse is hampered by terminological ambiguity and the conflation of critical human-centric concepts within the OSCM context. Addressing this gap, our study embarks on a mission to dissect and delineate the nuanced distinctions among “soft skills”, “attitudes”, and “personality traits”. By proposing a clear and actionable taxonomy, this paper aims to facilitate the practical application and understanding of these terms within organisational settings. Leveraging the “Knowledge Development Process-Constructivist” (ProKnow-C), we conducted a systematic examination of the existing scientific literature to unearth and critically review pertinent bibliometric and content analyses. Our work not only illuminates the path for future research but also underscores the necessity of clarity and precision in the conceptualisation and application of human-factor considerations in OM and SCM. Full article

Previous research has been limited in examining the causal relationship between green transformational leadership (GTL) and green supply chain management (GSCM), with the intermediary influence of green human resource management (GHRM), within the pharmaceutical sector of the Kingdom of Saudi Arabia (KSA). This gap persisted despite the recognition in Saudi Vision 2030 of logistics, specifically, supply chain management (SCM), as fundamental to the national development agenda, given that contemporary competitiveness lies in the efficacy of supply chains (SCs) rather than individual companies. Moreover, the achievement of economic progress hinges significantly on how well these accomplishments align with sustainability demands and obstacles. This paper aims to investigate the extent to which GTL fosters GRHM practices to enhance the maturity of GSCM performance in the pharmaceutical industry in the KSA. In other words, the research goal is to explain the variance of GSCM due to GHRM and GTL. Drawing upon the Resource-Based View (RBV) and the Ability–Motivation–Opportunity theory (AMO), GTL can enhance many aspects of GHRM, such as employee performance measurement, training content design, recruitment criteria, and green-based rewards policies, which positively influence GSCM practices. The methodology employed is deductive and translated into a questionnaire derived from a comprehensive review of the existing literature. This questionnaire was subsequently tested through the collection of 111 responses from pharmaceutical companies operating in the KSA. The results show the critical effects of GTL and GHRM on GSCM in this sector. The research provides fresh theoretical perspectives and actionable recommendations based on the outcomes. As for originality, this research explores the contribution of transformational leadership and green human resource management in enhancing SC sustainability. The research provides directions for future research to investigate the mediating or moderating impacts of other significant factors, such as green thinking or eco-friendly behaviour, on SCM sustainability. As for practical implications, this research came up with an in-depth understanding of SC managers and professionals regarding their practices related to sustainability. Full article

Ethical dilemmas affect several essential elements of humanitarian aid, such as the adequate selection of crises to which to provide aid and a selection of beneficiaries based on needs and not political or geostrategic criteria. Other challenges encompass maintaining neutrality against aggressors, deciding whether to collaborate with governments that violate human rights, and managing the allocation and prioritization of limited resources. Additionally, issues arise concerning the safety and protection of aid recipients, the need for cultural and political sensitivity, and recognition of the importance of local knowledge, skills, and capacity. The appropriateness, sustainability, and long-term impact of actions; security risks for aid personnel; and the need for transparency and accountability are also crucial. Furthermore, humanitarian workers face the duty to report and engage in civil activism in response to human rights violations and the erosion of respect for international humanitarian law. Lastly, the rights of affected groups and local communities in the decision-making and implementation of humanitarian aid are vital. The traditional foundations and approaches of humanitarian aid appear insufficient in today’s landscape of disasters and crises, which are increasingly complex and divergent, marked by a diminished capacity and shifting roles of various actors in alleviating suffering. This article reviews the historical evolution of the conceptualization of humanitarian aid and addresses some of its ethical challenges and dilemmas. Full article

Hypergraphs have received widespread attention in modeling complex data correlations due to their superior performance. In recent years, some researchers have used hypergraph structures to characterize complex non-pairwise joints in the human skeleton and model higher-order correlations of the human skeleton. However, traditional methods of constructing hypergraphs based on physical connections ignore the dependencies among non-physically connected joints or bones, and it is difficult to model the correlation among joints or bones that are highly correlated in human action but are physically connected at long distances. To address these issues, we propose a skeleton-based action recognition method for hypergraph learning based on skeleton correlation, which explores the effects of physically and non-physically connected skeleton information on accurate action recognition. Specifically, in this paper, spatio-temporal correlation modeling is performed on the natural connections inherent in humans (physical connections) and the joints or bones that are more dependent but not directly connected (non-physical connection) during human actions. In order to better learn the hypergraph structure, we construct a spatio-temporal hypergraph neural network to extract the higher-order correlations of the human skeleton. In addition, we use an attentional mechanism to compute the attentional weights among different hypergraph features, and adaptively fuse the rich feature information in different hypergraphs. Extensive experiments are conducted on two datasets, NTU-RGB+D 60 and Kinetics-Skeleton, and the results show that compared with the state-of-the-art skeleton-based methods, our proposed method can achieve an optimal level of performance with significant advantages, providing a more accurate environmental perception and action analysis for the development of embodied intelligence. Full article

8-oxoguanine (oxoG) is formed in DNA by the action of reactive oxygen species. As a highly mutagenic and the most common oxidative DNA lesion, it is an important marker of oxidative stress. Human 8-oxoguanine-DNA glycosylase (OGG1) is responsible for its prompt removal in human cells. OGG1 is a bifunctional DNA glycosylase with N-glycosylase and AP lyase activities. Aspects of the detailed mechanism underlying the recognition of 8-oxoguanine among numerous intact bases and its subsequent interaction with the enzyme’s active site amino acid residues are still debated. The main objective of our work was to determine the effect (structural and thermodynamic) of introducing an oxoG-clamp in model DNA substrates on the process of 8-oxoG excision by OGG1. Towards that end, we used DNA duplexes modeling OGG1-specific lesions: 8-oxoguanine or an apurinic/apyrimidinic site with either cytidine or the oxoG-clamp in the complementary strand opposite to the lesion. It was revealed that there was neither hydrolysis of the N-glycosidic bond at oxoG nor cleavage of the sugar–phosphate backbone during the reaction between OGG1 and oxoG-clamp-containing duplexes. Possible structural reasons for the absence of OGG1 enzymatic activity were studied via the stopped-flow kinetic approach and molecular dynamics simulations. The base opposite the damage was found to have a critical effect on the formation of the enzyme–substrate complex and the initiation of DNA cleavage. The oxoG-clamp residue prevented the eversion of the oxoG base into the OGG1 active site pocket and impeded the correct convergence of the apurinic/apyrimidinic site of DNA and the attacking nucleophilic group of the enzyme. An obtained three-dimensional model of the OGG1 complex with DNA containing the oxoG-clamp, together with kinetic data, allowed us to clarify the role of the contact of amino acid residues with DNA in the formation of (and rearrangements in) the enzyme–substrate complex. Full article

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices. Full article

Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint-motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique’s contribution to enhancing feature representation, as well as the proposed method’s scalability and effectiveness in recognizing Taekwondo unit actions. Full article

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher’s gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher’s skeleton and then inputs the recognized sequence of the teacher’s skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed. Full article