Search Results (46,520)

Arbuscular mycorrhizal (AM) fungi establish mutualistic symbiosis with most land plants, facilitating mineral nutrient uptake in exchange for photosynthates. As one of the most commercially used rootstocks in citrus, Poncirus trifoliata heavily depends on AM fungi for nutrient absorption. The GRAS gene family plays essential roles in plant growth and development, signaling transduction, and responses to biotic and abiotic stresses. However, the identification and functional characterization of GRAS family genes in P. trifoliata remains largely unexplored. In this study, a comprehensive genome-wide analysis of PtGRAS family genes was conducted, including their identification, physicochemical properties, phylogenetic relationships, gene structures, conserved domains, chromosome localization, and collinear relationships. Additionally, the expression profiles and protein interaction of these genes under AM symbiosis were systematically investigated. As a result, 41 GRAS genes were identified in the P. trifoliata genome, and classified into nine distinct clades. Collinearity analysis revealed seven segmental duplications but no tandem duplications, suggesting that segmental duplication played a more important role in the expansion of the PtGRAS gene family compared to tandem duplication. Additionally, 18 PtGRAS genes were differentially expressed in response to AM symbiosis, including orthologs of RAD1, RAM1, and DELLA3 in P. trifoliata. Yeast two-hybrid (Y2H) screening further revealed that PtGRAS6 and PtGRAS20 interacted with both PtGRAS12 and PtGRAS18, respectively. The interactions were subsequently validated through bimolecular fluorescence complementation (BiFC) assays. These findings underscored the crucial role of GRAS genes in AM symbiosis in P. trifoliata, and provided valuable candidate genes for improving nutrient uptake and stress resistance in citrus rootstocks through molecular breeding approaches. Full article

Background: Body motion significantly contributes to understanding communicative and social interactions, especially when auditory information is impaired. The visual skills of people with hearing loss are often enhanced and compensate for some of the missing auditory information. In the present study, we investigated the recognition of social interactions by observing body motion in people with post-lingual sensorineural hearing loss (SNHL). Methods: In total, 38 participants with post-lingual SNHL and 38 matched normally hearing individuals (NHIs) were presented with point-light stimuli of two agents who were either engaged in a communicative interaction or acting independently. They were asked to classify the actions as communicative vs. independent and to select the correct action description. Results: No significant differences were found between the participants with SNHL and the NHIs when classifying the actions. However, the participants with SNHL showed significantly lower performance compared with the NHIs in the description task due to a higher tendency to misinterpret communicative stimuli. In addition, acquired SNHL was associated with a significantly higher number of errors, with a tendency to over-interpret independent stimuli as communicative and to misinterpret communicative actions. Conclusions: The findings of this study suggest a misinterpretation of visual understanding of social interactions in individuals with SNHL and over-interpretation of communicative intentions in SNHL acquired later in life. Full article

In the era of digital commerce, understanding consumer opinions has become crucial for businesses aiming to tailor their products and services effectively. This study investigates acoustic quality diagnostics of the latest generation of AirPods. From this perspective, the work examines consumer sentiment using text mining and sentiment analysis techniques applied to product reviews, focusing on Amazon’s AirPods reviews. Using the naïve Bayes classifier, a probabilistic machine learning approach grounded in Bayes’ theorem, this research analyzes textual data to classify consumer reviews as positive or negative. Data were collected via web scraping, following ethical guidelines, and preprocessed to ensure quality and relevance. Textual features were transformed using term frequency-inverse document frequency (TF-IDF) to create input vectors for the classifier. The results reveal that naïve Bayes provides satisfactory performance in categorizing sentiment, with metrics such as accuracy, sensitivity, specificity, and F1-score offering insight into the model’s effectiveness. Key findings highlight the divergence in consumer perception across ratings, identifying sentiment drivers such as noise cancellation quality and product integration. These insights underline the potential of sentiment analysis in enabling companies to address consumer concerns, improve offerings, and optimize business strategies. The study concludes that such methodologies are indispensable for leveraging consumer feedback in the rapidly evolving digital marketplace. Full article

Introduction: Breast cancer (BC) is a disease that affects about 2.2 million people worldwide. The prognosis and treatment of these patients depend on clinical and histopathologic staging, in which more aggressive cancers need a less conservative therapeutic approach. Previous studies showed that patients with BC have an increased frequency of systemic microvesicles (MVs) that are associated with invasion, progression, and metastasis, which can be used in liquid biopsy to predict the therapeutic response in individualized treatment. Objective: This study proposes the development of a minimally invasive BC diagnostic panel and follow-up biomarkers as a complementary method to screen patients. Methods: The quantification of circulating MVs in 48 healthy women and 100 BC patients who attended the Mário Penna Institute between 2019 and 2022 was performed by flow cytometry. In addition, the MVs of BC patients were analyzed before treatment and 6, 12, and 24 months post-treatment. Machine learning approaches were employed to determine the performance of MVs to identify BC and to propose BC classifier algorithms. Results: Patients with BC had more neutrophil- and endothelial cell-derived MVs than controls before treatment. After treatment, all MV populations were decreased compared to pre-treatment, but leukocyte- and erythrocyte-derived MVs were increased at 12 months after treatment, before decreasing again at 24 months. Conclusions: Performance analyses and machine learning approaches pointed out that MVs from neutrophils and endothelial cells are the best candidates for BC diagnostic biomarkers. Neutrophil- and endothelial cell-derived MVs are putative candidates for BC biomarkers to be employed as screening tests for BC diagnosis. Full article

Vehicle emissions pose significant environmental challenges, particularly in high-altitude regions, where atmospheric conditions amplify pollutant concentrations. This study evaluates CO₂ and hydrocarbon (HC) emissions from 79 gasoline-powered vehicles in Guaranda, Ecuador (2668 m.a.s.l.), by using STATIS, a multivariate statistical method. The vehicles were classified into six model year intervals and tested under idle and dynamic conditions, measuring idle CO₂ and HC (ICD and IHC) and dynamic CO₂ and HC (DCD and DHC). The results showed that vehicles manufactured before 2000 exhibited the highest emissions, with ICD of 3.18% vol. and IHC of 414 ppm, while vehicles produced after 2020 showed significantly lower values (ICD of 0.27% vol. and IHC of 101.44 ppm). Additionally, Chevrolet was the most represented brand, accounting for 41.78% of the analyzed sample, while 34.18% of the vehicles were from the 2010–2015 interval. The STATIS model revealed structural similarities among the 2000–2005, 2016–2019, and post-2020 models, whereas pre-2000 vehicles differed markedly from the 2010–2015 models. Outliers, including older vehicles with low emissions and newer models with unexpectedly high emissions, highlighted the role of maintenance and operational conditions. These findings demonstrate the effectiveness of STATIS in analyzing complex emission patterns and underscore the need for future studies that incorporate variables such as mileage and environmental factors to refine emission mitigation strategies. Full article

This paper investigates finite local non-chain rings associated by the well-known invariants $p,n,m,l,$ and k, where p is a prime number. In particular, we provide a comprehensive characterization of Frobenius local rings of length $l=5$ and index of nilpotency $t=3$, where t the index of nilpotency of the maximal ideal. The relevance of Frobenius rings is notable in coding theory, as it has been demonstrated that two classical results by MacWilliams—the Extension Theorem and the MacWilliams identities—are applicable not only to finite fields but also to finite Frobenius rings. We, therefore, classify and count Frobenius local rings of order $p^{5m}$ with $t=3,$ outlining their properties in connection with various values of $n.$ Full article

This article provides an overview of coworking spaces and flexible workspaces in Sofia Municipality, Bulgaria. The study aims at examining whether the type of urban plan zone, the particular municipal administrative district, and the purpose of the building in which they are located have an impact on such modern workspaces. Existing spaces in the municipality were counted and classified according to the following criteria: origin of the operators, accessibility, level of specialization, purpose of the hosting building, location in a particular district, and urban planning zone. The average rent for Hot desk, Dedicated desk, Private office, and Virtual office was calculated for the territory determined by the research. The findings demonstrate that the number of coworking and flexible workspaces is influenced by the type of urban planning zone, the municipal administrative district, and the purpose of the building. The study also revealed that the majority of such spaces are concentrated in the zone of the old town center or in a mixed multifunctional zones and are located in administrative, business buildings. Full article

Business Process Redesign (BPR) is a fundamental approach to enhancing efficiency, compliance, and digital transformation in public sector operations. Despite extensive theoretical advancements, its application in real-world settings remains limited. This study addresses this gap by applying the BPR Assessment Framework to business processes within the Greek Public Financial Management (PFM) domain, specifically analyzing workflows from the Greek Customs Service and the Financial and Economic Crime Unit (S.D.O.E.). This research employs a structured methodology that integrates internal process metrics with clustering techniques to systematically classify processes based on their redesign potential. The findings reveal that a significant proportion of public sector workflows demonstrate high redesign capacity, highlighting opportunities for efficiency gains and improved regulatory compliance. Furthermore, this study identifies key challenges, such as organizational resistance and technological constraints, that impact BPR implementation. By demonstrating the framework’s applicability in a complex, operational environment, this study provides actionable insights for policymakers and practitioners. Specifically, the results show how structured process evaluation enables targeted redesign initiatives that streamline administrative workflows, enhance compliance with financial regulations, and support digital transformation in public administration. These insights contribute to advancing BPR practices by bridging the gap between theoretical development and real-world application, offering a replicable methodology for improving public sector efficiency. Full article

Excavated rocks generated during tunnel construction may pose an environmental hazard due to the release of acidic leachate containing potentially toxic elements (PTEs). Addressing this concern requires strategic countermeasures against mitigating the release of PTEs. This study investigated the efficacy of a novel approach for managing altered excavated rocks that generate acidic leachates with elevated arsenic (As) by utilizing the finer altered rock as a base material for the sorption layer. The proposed method involves classifying the altered excavated rocks into coarse (9.5–37.5 mm) and finer (<9.5 mm) fractions, with the finer fractions incorporated with iron (Fe)-based adsorbent to form a bottom sorption layer for the disposal of coarser rock samples. Leaching behavior and As immobilization efficiency were assessed through shaking, stirring leaching tests, batch sorption tests, and column tests under varying particle size fractions of the rock samples. Results indicate that altered finer rock fractions exhibit increased As leaching under shaking conditions due to enhanced dissolution. The addition of >1% of Fe-based adsorbent to the finer rock in the sorption layer effectively suppressed As leaching concentration, meeting the management criterion of <0.3 mg/L for specially controlled contaminated soils in Japan. Batch sorption tests using the finer rock samples with the Fe-based adsorbent confirmed their efficacy as effective adsorbents. This efficacy was further elucidated in column experiments consisting of the coarse rock samples and fine altered rock samples mixed with the Fe based adsorbent at the bottom as a sorption layer. Results showed that the sorption layer effectively decreased the As leached from the rock layer, utilizing the altered excavated fine rock as a base material in the sorption layer. This approach highlights the potential for repurposing excavated rocks as sorption media, enabling sustainable management strategies for As-contaminated rocks. This study provides an innovative framework for integrating adsorption-based remediation, contributing to sustainable countermeasure strategies for excavated rocks. Full article

Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preserving system that accurately classifies pain levels (low, medium, and high) by leveraging the intricate relationship between pain perception and autonomic nervous system responses captured in ECG signals. At the heart of our methodology lies a signal processing approach that transforms one-dimensional ECG signals into rich, two-dimensional Continuous Wavelet Transform (CWT) images. These transformations capture both temporal and frequency characteristics of pain-induced cardiac variations, providing a comprehensive representation of autonomic nervous system responses to different pain intensities. Our framework processes these CWT images through a sophisticated quantum–classical hybrid architecture, where edge devices perform initial preprocessing and feature extraction while maintaining data privacy. The cornerstone of our system is a Quantum Convolutional Hybrid Neural Network (QCHNN) that harnesses quantum entanglement properties to enhance feature detection and classification robustness. This quantum-enhanced approach is seamlessly integrated into a federated learning framework, allowing distributed training across multiple healthcare facilities while preserving patient privacy through secure aggregation protocols. The QCHNN demonstrated remarkable performance, achieving a classification accuracy of 94.8% in pain level assessment, significantly outperforming traditional machine learning approaches. Full article

The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction of shale reservoir lithofacies is crucial for identifying sweet spots for oil and gas development. However, obtaining shale lithofacies through core sampling during drilling is challenging, and the accuracy of traditional logging curve intersection methods is insufficient. To efficiently and accurately predict shale lithofacies, this study proposes a hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting, Extreme Gradient Boosting, and Categorical Boosting. The model employs the Grid Search Method to automatically search for optimal hyperparameters, using the four classifiers as base learners. The predictions from these base learners are then used as new features, and a Logistic Regression model serves as the final meta-classifier for prediction. A total of 3323 data points were collected from six wells to train and test the model, with the final performance evaluated on two blind wells that were not involved in the training process. The results indicate that the stacking model accurately predicts shale lithofacies, achieving an Accuracy, Recall, Precision, and F1 Score of 0.9587, 0.959, 0.9587, and 0.9587, respectively, on the training set. This achievement provides technical support for reservoir evaluation and sweet spot prediction in oil and gas exploration. Full article

Keyhole imagery, acquired between the 1960s and 1980s, offers a unique opportunity to study land use changes prior to the era of modern remote sensing. This study evaluates the potential of free-download Keyhole imagery within China to detect land use changes over five 5-year periods (1960–1984). Using metadata and spatial analysis tools in Python 3.12, we classified images into three resolution categories (meter-level, five-meter-level, and ten-meter-level) and analyzed their spatial distribution and repeated coverage. Results show that 26.5%, 58.9%, and 34.0% of areas were capable of detecting at least one land-use change event for the respective resolution categories. The T3 period (1970–1974) exhibited the greatest diversity of imagery combinations among the five periods. However, uneven spatial and temporal coverage, particularly in western and rural regions, limits the ability of free Keyhole imagery to conduct continuous multi-temporal analysis, and collaboration with paid Keyhole imagery could fill gaps in coverage and improve the accuracy of land use change detection. The study highlights the potential of Keyhole imagery for historical land use research while underscoring the need for methodological refinements to address data limitations. The shared Python scripts and metadata processing techniques could also support other land-use change research using Keyhole imagery globally. Full article

As urbanization accelerates in developing countries and populations continue to grow, the demand for sustainable urban regeneration becomes increasingly urgent. This study explores how urban regeneration can be integrated with tourism development to promote sustainable and inclusive growth. Using the theory of organic regeneration, this study emphasizes the role of localized, small-scale interventions that enhance the urban environment while boosting tourism appeal. Through field research at the Tianjin Cruise Terminal, this study applies the KANO model to classify user needs and prioritize design interventions based on the needs of various user groups, including tourists, exercisers, anglers, and local residents. By analyzing user group activity frequencies, the study identifies Must-be, One-dimensional, and Attractive needs, and utilizes weight analysis to assess the impacts of different facilities on user satisfaction. This approach ensures that the design guidelines effectively address both essential and value-enhancing features. The findings provide more suitable design guidelines for improving tourism infrastructure, promoting sustainable development, and enhancing the overall urban experience. Full article

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions. Full article

This article proposes a fatigue detection algorithm for nuclear power plant control room operators based on random forest and BP neural networks, specifically targeting the control room scenario. This algorithm is capable of detecting fatigue-related operations in a timely manner, which is crucial for ensuring the safe operation of nuclear power plants. First, the random forest algorithm is used to classify the feature data according to different scenarios. Second, the data are distributed to different back propagation neural networks for prediction based on the scenario. Finally, experimental validation is conducted using a reactor simulation system. The results show that the algorithm achieves a recognition accuracy of 0.82, an accuracy of 0.69, a recall rate of 0.64, and an F1-Score of 0.66, indicating that the proposed algorithm has practical value for detecting operator fatigue in nuclear power plants. Compared to physiological data-based detection methods, it is simple, convenient, cost-effective, and does not interfere with operators. Full article