Search Results (330)

Poultry waste composting is a necessary technique for agricultural farm sustainability. Composting is a dynamic process influenced by multiple variables. Humidity and temperature play fundamental roles in analyzing its different phases according to the environment and composting technique. Current developments for monitoring these variables include automation via intelligent Internet of Things (IoT)-based sensor networks for variable tracking. These advancements serve as efficient tools for modeling that facilitate the simulation and prediction of composting process variables to improve system efficiency. Therefore, this paper presents the dynamic modeling of composting via forced aeration processes in high-mountain climates, with the intent of estimating biomass temperature dynamics in different phases using system identification techniques. To this end, four dynamic model estimation structures are employed: transfer function (TF), state space (SS), process (P), and Hammerstein–Wiener (HW). The and model quality, fitting results, and standard error metrics of the different models found in each phase are assessed through residual analysis from each structure by validation with real system data. Our results show that the second-order underdamped multiple-input–single-output (MISO) process model with added noise demonstrates the best fit and validation performance. Full article

Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications. Full article

Serious games (SGs) have been widely employed in the digital preservation and transmission of architectural heritage. However, the key determinants and underlying mechanisms driving users’ continuance intentions toward ancient-architecture cultural heritage serious games (CH-SGs) have not been thoroughly investigated. Accordingly, a conceptual model grounded in the stimulus–organism–response (S–O–R) framework was developed to elucidate the affective and behavioral effects experienced by CH-SG users. Partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANNs) were employed to capture both the linear and nonlinear relationships among model constructs. By integrating sufficiency logic (PLS-SEM) and necessity logic (necessary condition analysis, NCA), “must-have” and “should-have” factors were identified. Empirical results indicate that cultural authenticity, knowledge acquisition, perceived enjoyment, and design aesthetics each exert a positive influence—of varying magnitude—on perceived value, cultural identification, and perceived pleasure, thereby shaping users’ continuance intentions. Moreover, cultural authenticity and perceived enjoyment were found to be necessary and sufficient conditions, respectively, for enhancing perceived pleasure and perceived value, which in turn indirectly bolster CH-SG users’ sustained use intentions. By creating an immersive, narratively rich, and engaging cognitive experience, CH-SGs set against ancient architectural backdrops not only stimulate users’ willingness to visit and protect heritage sites but also provide designers and developers with critical insights for optimizing future CH-SG design, development, and dissemination. Full article

The 100-Day Mission, coordinated by the Coalition for Epidemic Preparedness Innovations (CEPI) and endorsed by significant international stakeholders, aims to shorten the timeframe for developing and implementing vaccines to 100 days after the report of a new pathogen. This ambitious goal is outlined as an essential first step in improving pandemic preparedness worldwide. This review highlights the mission’s implementation potential and challenges by examining it through the lens of low- and middle-income countries (LMICs), which often face barriers to equitable vaccine access. This article explores the scientific, economic, political, and social aspects that could influence the mission’s success, relying on lessons learned from previous pandemics, such as the Spanish flu, H1N1, and COVID-19. We also examined important cornerstones like prototype vaccine libraries, accelerated clinical trial preparedness, early biomarkers identification, scalable manufacturing capabilities, and rapid pathogen characterization. The review also explores the World Health Organization (WHO) Pandemic Agreement and the significance of Phase 4 surveillance in ensuring vaccine safety. We additionally evaluate societal issues that disproportionately impact LMICs, like vaccine reluctance, health literacy gaps, and digital access limitations. Without intentional attempts to incorporate under-resourced regions into global preparedness frameworks, we argue that the 100-Day Mission carries the risk of exacerbating already-existing disparities. Ultimately, our analysis emphasizes that success will not only rely on a scientific innovation but also on sustained international collaboration, transparent governance, and equitable funding that prioritizes inclusion from the beginning. Full article

The number of publications in the scientific literature dealing with geomorphological mapping has increased over the last two decades. Although geomorphological maps are utilised in various contexts, such as hazard assessment, archaeology, and tourism, there is a noticeable lack of interaction between geomorphological products and the social sciences. This study aims to provide a qualitative assessment of the literature on geomorphological maps published in the 2000s with the intent of identifying the purpose of mapping and its field of application. Additionally, a comparative analysis was conducted of the articles relating to both geomorphological maps and social issues to identify the tools that facilitate this interdisciplinary collaboration. The results facilitated the identification of the primary fields of interest for map production, showing that only a limited number of articles employed geomorphological maps for social purposes, for instance, enhancing risk awareness and educating the population about natural hazards. Moreover, the analysis reveals that only a limited number of geomorphological maps are intended to be accessible to people without a high degree of education in earth sciences. In particular, this study highlights a lack of attention to non-specialist users who may struggle to understand the information contained in geomorphological maps. This issue limits the dissemination of geomorphological maps, which are, however, vital for territorial planning and practical purposes. The analyses prompted the authors to consider novel applications of research tools to enhance the dissemination of geomorphological maps, even among non-specialist users. Full article

Background/Objectives: In 2022, 2.2 million adolescents were diagnosed with substance use disorders, including 265,000 with opioid use disorder. The National Survey on Drug Use and Health revealed that 130,000 adolescents misused prescription pain medications, often obtaining them from friends or relatives. This age group perceives weekly heroin use as less risky than those younger or older. Methods: A questionnaire was developed for 7th to 12th graders in a rural Texas school district as part of a fentanyl awareness curriculum. The questionnaire included Likert scale, multiple choice, and yes/no questions. The participants were categorized into younger (grades 7th and 8th) and older students (grades 9th through 12th), and associations were explored between demographic characteristics, responses, and grade groups using chi-square tests. To assess confidence, behavior, and the impact of education, we used chi-square and Fisher’s exact tests. Results: The participants (n = 94; 85.11%) identified as Hispanic or Latino, with a smaller percentage identifying as White or more than one race. An association was found between feeling more in control of actions related to substances and fentanyl (p-value = 0.04) after receiving education. No association was found between education and confidence in identifying fentanyl. Conclusions: This study aligns with a surge in fentanyl-related overdose deaths in a high-intensity drug trafficking region. Recent fentanyl overdoses among school-age children prompted legislative changes in 2023, making this study valuable for understanding the epidemic within the geographical context. These results suggest that school-based education may play a role in strengthening adolescents’ behavioral intentions to fentanyl exposure, though additional efforts are needed to improve risk identification. Full article

In the context of the Internet, the personalization and diversification of customer demands present a significant challenge for research on the identification, combination, and utilization of personalized demand feature elements. A key difficulty lies in achieving real-time perception, processing, and recognition of customer needs to dynamically identify and understand personalized customer intent. To address the limitations, we propose a Personalized customization-based Neural Network (PerNN), designed to enhance the performance and accuracy of recommendation systems in large-scale and complex information environments. The PerNN model introduces a Personalized Features Layer (PF), which effectively integrates multi-dimensional information—including historical interaction data, social network relationships, and users’ temporal behavior patterns—to generate fine-grained, personalized user feature representations. This approach significantly improves the model’s ability to predict user preferences. Extensive experiments conducted on public datasets demonstrate that the PerNN model consistently outperforms existing methods, particularly regarding the accuracy and response speed of personalized recommendations. The results validate the effectiveness and superiority of the proposed model in managing complex and recommendation tasks, offering a novel and efficient solution for personalized customization scenarios. Full article

Background: Forensic investigation of child homicides presents unique challenges due to the vulnerability of children and the complexity of distinguishing between natural, accidental, and intentional manner of death. A multidisciplinary approach integrating traditional forensic methods with emerging technologies is crucial to ensure accurate diagnosis and effective legal outcomes. Methods: This review examines current and emerging forensic techniques used in neonate, infant, and older child homicide investigations. It highlights advancements in postmortem imaging, histological examination, microbiological analysis, toxicology, and molecular autopsy. Results: Traditional forensic autopsy remains the cornerstone of child homicide investigations, providing critical insights into external and internal injuries. Histological examination enhances diagnostic accuracy by detecting microscopic evidence of trauma and infectious diseases. Postmortem imaging techniques are complementary for better identifying fractures, soft tissue injuries, and vascular abnormalities. Forensic toxicology plays a key role in detecting poisoning, while postmortem microbiology aids in identifying infectious causes of death. Furthermore, advancements in molecular autopsy and genetic testing have significantly enhanced the identification of hereditary conditions linked to sudden unexplained deaths in children, especially in cases involving multiple child fatalities within the same family, where forensic investigations are needed to accurately differentiate between natural causes and potential criminal involvement. Conclusions: A multidisciplinary approach incorporating traditional autopsy with postmortem imaging, histological examination, toxicology, postmortem microbiology, and molecular autopsy is essential for comprehensive forensic analysis, promoting both justice and prevention of fatal child abuse/homicide. Future research should focus on standardizing forensic protocols and exploring the potential of artificial intelligence (AI) in forensic investigations. Full article

This study investigates consumer reactions to mascots on e-commerce websites, focusing on how anthropomorphic visual cues influence website satisfaction, revisit intention, and purchase intention. Specifically, we examine how mascot movement affects consumers’ sense of social presence and engagement, as well as the role of team identification in these effects. A 3 (mascot type: none, static, animated) × 2 (team identification: high, low) between-subjects experiment was conducted with 203 participants recruited from Amazon Mechanical Turk. Our findings show that the presence of mascots significantly impacts consumer evaluations, with social presence and engagement acting as sequential mediators. Notably, high team identification moderates the effect of animated mascots on revisit and purchase intentions but does not affect website satisfaction. These results provide valuable theoretical and practical insights for marketing, highlighting the importance of mascot design and movement in enhancing e-commerce experiences. Full article

The trajectory data collected by an Automatic Identification System (AIS) are an essential resource for various ships, and effective filtering and querying approaches are fundamental for managing these data. Natural language has become the preferred way to express complex query requirements and intents, due to its intuitiveness and universal applicability. In light of this, we propose a natural language-based AIS trajectory query approach using large language models. Firstly, trajectory textualization was designed to convert the time sequences of trajectories into semantic descriptions by segmenting AIS trajectories, extracting semantics, and constructing trajectory documents. Then, the semantic trajectory querying was completed by rewriting queries, retrieving AIS trajectories, and generating answers. Finally, comparative experiments were conducted to highlight the improvements in accuracy and relevance achieved by our proposed method over traditional approaches. Furthermore, a human study demonstrated the user-friendly interaction experience enabled by our approach. Additionally, we conducted an ablation study to illustrate the significant contributions of each module within our framework. The results demonstrate that our approach effectively bridges the gap between AIS trajectories and natural language query intents, offering an intuitive, user-friendly, and accessible solution for domain experts and novices. Full article

Natural human–human communication consists of multiple modalities interacting together. When an intelligent robot or wheelchair is being developed, it is important to consider this aspect. One of the most common modality pairs in multimodal human–human communication is speech–hand gesture interaction. However, not all the hand gestures that can be identified in this type of interaction are useful. Some hand movements can be misinterpreted as useful hand gestures or intentional hand gestures. Failing to filter out these unintentional gestures could lead to severe faulty identifications of important hand gestures. When speech–hand gesture multimodal systems are designed for disabled/elderly users, the above-mentioned issue could result in grave consequences in terms of safety. Gesture identification systems developed for speech–hand gesture systems commonly use hand features and other gesture parameters. Hence, similar gesture features could result in the misidentification of an unintentional gesture as a known gesture. Therefore, in this paper, we have proposed an intelligent system to filter out these unnecessary gestures or unintentional gestures before the gesture identification process in multimodal navigational commands. Timeline parameters such as time lag, gesture range, gesture speed, etc., are used in this filtering system. They are calculated by comparing the vocal command timeline and gesture timeline. For the filtering algorithm, a combination of the Locally Weighted Naive Bayes (LWNB) and K-Nearest Neighbor Distance Weighting (KNNDW) classifiers is proposed. The filtering system performed with an overall accuracy of 94%, sensitivity of 97%, and specificity of 90%, and it had a Cohen’s Kappa value of 88%. Full article

People act in ways that align with the values and roles that constitute their identity. However, the consistency of identity’s influence across different behaviours, and whether identity influences behaviours directly or indirectly via intention, self-determined motivation, or habit, remains uncertain. Participants (N = 98; Mage = 30.4 years, SD = 11.7 years) completed up to 12 weekly surveys, self-reporting engagement in physical activity, student, and support-seeking behaviours, and behaviour-associated identity, intention strength, self-determined motivation, and habit. Stepwise multilevel models tested the between- and within-person associations of identity with behaviour, and whether the relationships remained after accounting for intention, self-determined motivation, and habit. Results suggested identity as stable, with the most variability at the between-person level. Identity was associated with behaviour at both within- and between-person levels, with the exception that support seeking and identity were only associated between-person. For student behaviour and physical activity, the identity–behaviour relationship at the within-person level waned and became non-significant after accounting for intention, but not self-determined motivation or habit. These findings highlight that identity may be difficult to change. However, as identity is associated with a range of behaviours, a person’s identification with a particular behaviour may be valuable for tailoring behaviour change interventions, specifically through or in the same way as behavioural intentions. Full article

Background/Objectives: Motivation is a key factor for improving motor function and cognitive control in patients. Motivation for rehabilitation is influenced by the relationship between the therapist and patient, wherein appropriate voice encouragement is necessary to increase motivation. Therefore, we examined the differences between mothers and other individuals, such as physical therapists (PTs), in their verbal interactions with children with cerebral palsy who have poor communication abilities, as well as the neurological and physiological effects of variations in the tone of their speech. Methods: The three participants were children with cerebral palsy (Participant A: boy, 3 years; Participant B: girl, 7 years; Participant C: girl, 9 years). Participants’ mothers and the assigned PTs were asked to speak under three conditions. During this, the brain activity of the participants was measured using a 19-channel electroencephalogram. The results were further analyzed using Independent Component Analysis frequency analysis with exact Low-Resolution Brain Electromagnetic Tomography, allowing for the identification and visualization of neural activity in three-dimensional brain functional networks. Results: The results of the ICA frequency analysis for each participant revealed distinct patterns of brain activity in response to verbal encouragement from the mother and PT, with differences observed across the theta, alpha, and beta frequency bands. Conclusions: Our study suggests that the children were attentive to their mothers’ inquiries and focused on their internal experiences. Furthermore, it was indicated that when addressed by the PT, the participants found it easier to grasp the meanings and intentions of the words. Full article

Amid escalating ecological concerns and regulatory pressures, firms are adopting environmental sustainability commitments to enhance competitiveness and fulfill social responsibilities. However, the internal mechanisms linking these commitments to environmental performance remain insufficiently explored. This study investigates how corporate environmental sustainability commitments improve environmental performance by integrating the Planned Behavior Theory and Organizational Change Theory. Using structural equation modeling with 324 firm-level responses, we identify a chain mediation pathway. Results indicate that environmental sustainability commitment positively influences corporate environmental performance through the chain-mediating effects of green readiness and green opportunity identification and exploitation. By extending the Technology–Organization–Environment (TOE) framework, we delineate three dimensions of green readiness, showing that organization readiness exhibits the strongest mediating role. This study advances theoretical understanding by mapping the pathway from sustainability intentions to performance through internal capabilities and actions. Practically, it helps firms systematically align environmental and economic goals while avoiding greenwashing risks. Full article

Great progress has been made in the treatment of pulmonary arterial hypertension (WHO group 1 PAH) over the past two decades, which has significantly improved the morbidity and mortality in this patient population. Likewise, the more recent availability of antifibrotic medications for interstitial lung disease (ILD) have also been effective in slowing down the progression of disease. There is no known cure for either of these disease states. When this combination coexists, treatment can be challenging. Interstitial lung disease is a heterogenous group of chronic inflammatory and/or fibrotic parenchymal lung disorders. A subset of patients with ILD, not related to connective tissue disease, can initially present with inflammatory-predominant disease which progresses to irreversible fibrosis. This population of patients is also at risk for developing pulmonary hypertension (PH) or World Health Organization (WHO) group 3 PH. This coexistence of ILD and PH is associated with early morbidity and mortality. The early identification, diagnosis, and treatment of this combination of ILD and PH is vital. Medications available for both ILD and PH require an individualized approach with the intention of slowing down disease progression. Referral to expert centers for clinical trials and transplant evaluation is recommended. The combination of PH-ILD can be challenging to diagnose and treat effectively. Patients require a thorough clinical evaluation to enable the most accurate diagnosis. A vital part of that evaluation is the early recognition of PH. Medications can help improve disease progression along with clinical trials that will further improve our gaps in knowledge. Full article