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Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks
Emotion recognition in social media is a challenging task due to the complex and unstructured nature of user-generated content. In this paper, we propose Emotion-RGC Net, a novel deep learning model that integrates RoBERTa, Graph Neural Networks (GNN), and Conditional Random Fields (CRF) to enhance the accuracy and robustness of emotion classification. RoBERTa is employed for effective feature extraction from unstructured text, GNN captures the propagation and influence of emotions through user interactions, and CRF ensures global consistency in emotion label prediction. We evaluate the proposed model on two widely-used datasets, Sentiment140 and Emotion, demonstrating significant improvements over traditional machine learning models and other deep learning baselines in terms of accuracy, recall, F1-score, and AUC. Emotion-RGC Net achieves an accuracy of 89.70% on Sentiment140 and 88.50% on Emotion, highlighting its effectiveness in handling both coarse- and fine-grained emotion classification tasks. Despite its strong performance, we identify areas for future research, including reducing the model’s reliance on large labeled datasets, improving computational efficiency, and incorporating temporal dynamics to capture emotion evolution in social networks. Our results indicate that Emotion-RGC Net provides a robust solution for emotion recognition in diverse social media contexts.
Output power-density limit of a thermoradiative diode with an intermediate band
Abstract The application of the thermoradiative effect of photodiodes, in which photons are emitted to a cold reservoir in the far-field, is a promising approach for renewable electricity generation. Here we derive the radiative limit of the output power density of an ideal thermoradiative diode (TRD) with an intermediate band (IB) using detailed balance calculations. The output power density of an ideal IB-TRD with a given bandgap energy and an optimal IB position increases with the device temperature, and simultaneously the optimal position of the IB shifts away from the mid-gap position due to the current matching constraint. Since the intrinsic carrier density needs to be significantly lower than the doping concentration to form a p–n junction at the operating temperature, IB-TRDs can be advantageous compared to single-junction TRDs consisting of narrow-bandgap semiconductors.
Cerebellar cognitive affective syndrome in patients with spinocerebellar ataxia type 10
Background Spinocerebellar ataxia type 10 (SCA10) is an autosomal dominant cerebellar ataxia, characterized by epilepsy, ataxic symptoms, and cognitive impairments linked to Cerebellar Cognitive Affective Syndrome (CCAS). The Cerebellar Cognitive Affective Syndrome Scale (CCAS-S) has been developed to identify CCAS across various cerebellar pathologies. Objective To determine whether patients with SCA10 exhibit CCAS using the CCAS-S, and to compare its effectiveness with the Montreal Cognitive Assessment (MoCA). A secondary objective was to evaluate the effect of demographic and clinical data on CCAS-S performance. Method Fifteen patients with SCA10 and fifteen matched controls underwent assessments using the CCAS-S, the MoCA, the Scale for the Assessment and Rating of Ataxia (SARA), and the Center for Epidemiologic Studies Depression Scale (CES-D). Diagnostic accuracy was analyzed using ROC curve analysis, comparing total and subcategory scores between groups. Demographic and clinical data were examined for relations with CCAS-S scores. Results The CCAS-S effectively distinguished cognitive impairments in SCA10 patients, showing satisfactory sensitivity and specificity (AUC of 0.83). Although no significant differences were found in the AUCs between CCAS-S and MoCA (p = 0.45), the CCAS-S demonstrated a significantly larger effect size in the comparison between patients and control group (d = 2.33). Cognitive performance was poorer in patients than in controls (p = < 0.001), with depressive symptoms and age having a significant impact on CCAS-S outcomes. Conclusions Patients with the SCA10 mutation exhibit CCAS. Besides the significant cognitive impairment, also detected by MoCA, the CCAS-S score was significantly affected by indicators of depressive mood and age, highlighting the importance of considering these variables during outcome analyses.
Exploring the interplay of under-deposit corrosion and microbiologically influenced corrosion in the presence of deposits with varied electrical conductivities
Retraction: TOM40 Mediates Mitochondrial Dysfunction Induced by α-Synuclein Accumulation in Parkinson’s Disease
AKT activation triggers Rab14-mediated ADAM10 translocation to the cell surface in human aortic endothelial cells
RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention
The widespread cultivation of apples highlights the importance of efficient and accurate apple detection algorithms in robotic picking technology. The accuracy of current apple picking detection algorithms is still limited when the distribution is dense and occlusion exists, and there is a significant challenge in deploying current high accuracy detection models on edge devices with limited computational resources. To solve the above problems, this paper proposes an improved detection algorithm (RE-YOLO) based on YOLOv8n. First, this paper innovatively introduces Receptive-Field Attention Convolution (RFAConv) to improve the backbone and neck network of YOLOv8. It essentially solves the problem of convolution kernel parameter sharing and improves the consideration of the differential information from different locations, which significantly improves the accuracy of model recognition. Second, this paper innovatively proposes an EMA_C2f module. This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. The experimental results show that RE-YOLO improves the precision, recall, mAP@0.5, and mAP@0.5-0.95 by 2%, 2.1%, 2.7%, and 3.9%, respectively, compared with the original YOLOv8. Compared with YOLOv5, it improves 4%, 1.9%, 1.7% and 3%, respectively, which fully proves the advanced and practical nature of the proposed algorithm.
Mineral composition and pore structure on spontaneous imbibition in tight sandstone reservoirs
Use of a single case-finding questionnaire to simultaneously target multiple related diseases allows enhanced disease detection
Objective To develop a research methodology to apply a single case-finding tool to multiple related diseases and to evaluate the ability of a single tool to detect two or more related chronic diseases. Methods A case-finding study to detect two related respiratory diseases is used to demonstrate and explain the proposed methodology. Adults in the community with no prior history of physician-diagnosed lung disease who self-reported respiratory symptoms were contacted via random-digit dialing. Multiple risk scores, one for asthma and one for COPD, were developed using data from a single case-finding questionnaire administered to the study population. Each score was statistically optimized for targeted detection of cases having one disease in the class. External validation of tandem risk scores was prospectively conducted in an independent sample and predictive performance re-evaluated. Results Sensitivity for detection of asthma improved from 87% using single risk scores to 96% using tandem risk scores, and sensitivity for detection of COPD similarly improved from 87% to 99%. In the independent validation cohort, case-finding sensitivities increased from 64% and 59% using single risk scores to 95% and 96% using tandem risk scores for asthma and for COPD, respectively. Conclusions Use of a single questionnaire which incorporates risk scores for multiple diseases considered in tandem, rather than individually, enhances the yield of cases detected when compared with one-at-a-time application of risk scores for case discovery. Benefits include greater efficiency in case-finding and improved sensitivities for detection of each disease.
MUSE and PROPELLER DWI for ADC in parasagittal dura: insights from high-resolution and reduced-distortion DWI
Abstract The parasagittal dura (PSD) is a thin channel along the sagittal sinus vein at the brain’s upper convexities. Previous studies have shown that cerebrospinal fluid (CSF) flows directly into the PSD, with PSD dimensions and tracer clearance rates associated with aging and brain disorders. Since slow lymphatic drainage is sensitive to water diffusion, PSD circulation may be evaluated using diffusion-weighted imaging (DWI). However, traditional echo-planar DWI (EP-DWI) suffers from low resolution and image distortion, limiting its application to PSD assessment. This study employed high-resolution Multiplexed Sensitivity Encoding (MUSE) DWI and Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) DWI to investigate PSD water diffusion. These advanced techniques reduce image distortion while enhancing spatial resolution. Our results demonstrated that PSD structures are clearly visible on high-resolution DWI and apparent diffusion coefficient (ADC) maps, correlating with PSD locations identified on T2 FLAIR imaging. In addition, mean ADC values of PSD (1843.1–2062.2 × 10− 6 mm2/sec) were higher than those of gray and white matter but lower than CSF. These findings highlight the potential of MUSE and PROPELLER DWI for assessing PSD diffusion, offering a promising non-invasive tool for studying PSD circulation and its role in neurological disorders.
Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance
Copper (Cu) stamp mill mining in North America from the early 1900s produced a pulverized ore by-product now known as stamp sands (SS). In a mining operation near the city of Gay (Michigan, USA), SS were originally deposited near a Lake Superior beach, but erosion and wave action have moved many SS into beaches and reefs that are critical spawning and nursery areas for native fish (e.g., Lake Whitefish). Larval and juvenile native fish consume zooplankton and benthic invertebrates during their development, and many of these invertebrate taxa may be sensitive to metal contamination from the SS. Here, we sampled the invertebrate community from beaches with high SS, moderate SS and low SS, as well as a control beach 58 km from the source of the SS. The high SS site was characterized by fewer benthic taxa, and less density of several taxa than the low SS site, especially benthic copepods. All beaches had comparable zooplankton diversity, but the abundance was ~ 2 orders of magnitude lower at the high SS site. Cu and several other metals were elevated at beaches with more SS. We found support for associations between benthic density and diversity with depth (positive effect) and Cu concentration (negative effect). Cu concentration was a better predictor of declines in benthic invertebrate abundance and diversity than SS although sensitivity to Cu varied among taxa. We also observed that the relationship between Cu concentration and SS was non-linear, and highly variable. For example, 149 mg Cu/kg dry weight sediment is a consensus threshold used in the literature to identify Cu toxicity, but the prediction interval for estimating that concentration of Cu from measurements of SS is 26-851 mg Cu/kg dry weight. A better predictive model of this relationship would be beneficial to develop an understanding of what level of SS reduction would prevent Cu impacts on invertebrates.
Global, regional, and national burden of retinoblastoma in children aged under 10 years from 1990 to 2021 and projections for future disease burden
Evaluating the psychometric properties of the 24-item and 12-item real relationship inventory-client forms
The current study assessed the psychometric properties of the long (24 items) and brief (12 items) versions of the Real Relationship Inventory–Client (RRI-C) in a United States sample. The RRI-C is the most used quantitative measure of the real relationship construct, yet its psychometric properties have not been explored outside its development studies. A sample of 700 adults in individual psychotherapy was recruited in the study and filled out a comprehensive battery of measures. Analytical techniques included confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), multigroup CFA, multigroup factor analysis alignment, item response theory, internal reliability assessments, Bland-Altman regression analysis, and calculation of reliable change benchmark thresholds. Both RRI-C versions demonstrated a bifactor structure encompassing Genuineness and Realism dimensions. The bifactor ESEM model provided strong fit: χ2[210] = 482.464, CFI = 0.999, TLI = 0.998, RMSEA = 0.043, SRMR = 0.020 for the 24-item RRI-C; χ2[45] = 111.916, CFI = 0.999, TLI = 0.998, RMSEA = 0.046, SRMR = 0.028 for the 12-item RRI-C. McDonald’s omega total was 0.97 and 0.95 respectively. The correlation between the total scores of the two versions was r = 0.98; the average discrepancy was 1.85 points higher for the comprehensive version with a slope of -0.013 (p = 0.12). Both versions showed functionally identical reliability and factor structure when therapy is online vs. in-person. Significant correlations were found between the RRI-C and the Working Alliance Inventory (r = 0.68 and r = 0.67 for the 24-item and 12-item versions, respectively, both p < .001) and the Session Evaluation Scale (r = 0.62 and r = 0.58, respectively, both p < 0.001). This study substantiates the sound psychometric properties of the 24-item and 12-item RRI-C.
Improvement of the quality of heavy crude oil and reducing the concentration of asphaltene hydrocarbons using microwave radiation during acidizing
Spatial and temporal evolution of ecotourism development level and its driving factors under the perspective of sustainable development: the case of Ili river valley
Ecotourism, as an ideal model for sustainable tourism development, is a response to ecological problems and the way tourism is developed. The foundational elements of ecotourism serve as the basis for the development level evaluation index system. Ten counties and cities in the Ili River Valley are evaluated for their level of ecotourism development between 2010 and 2019 using the entropy weight TOPSIS approach. Using the standard deviation ellipse, classic Markov chain, and spatial Markov chain, the temporal and spatial evolution characteristics are examined. Geographic detectors are utilized to explore the driving factors of ecotourism development. The data indicates that: (1) With a notably diverse spatial structure, the growth rate of ecotourism varies among the counties and cities in the Ili River Valley. (2) The level of comprehensive ecotourism development is continuously improving, with significant gradient differences in spatial distribution, forming a dynamic spatial pattern of ‘high in the north and low in the south.’ (3) The standard deviation ellipses of each year show a “northwest – southeast” direction, and basically form a stable migration rule from northwest to southeast; (4) The level of tourism income and economic development have a significant impact on the development of ecotourism, and the influence of tourism reception capacity and industrial structure level is gradually enhanced, while the promotion effect of ecological environment level is not significant. The interaction of the two factors is greater than that of the single factor, indicating that the interaction connection is facilitated by the two elements. The findings of the study can offer some theoretical underpinnings and scientific references for raising the degree of ecotourism development and encouraging the Ili River Valley's tourism industry's sustainable growth.
Applications of different vortex identification methods in cavitation of a self-priming pump
Effects of weather scenarios and fertilizer on maize growth and yield: Insights from a greenhouse experiment
Maize is a major crop for food security, but its cultivation is threatened by climate change. Climate may affect the response of maize to fertilizer. This study examined the impact of weather parameters in combination with fertilizer types on maize growth and yield parameters in Benin. The experiment involved two sets of climatic scenarios. Scenario 1 (weather 1) had a moderate range of minimum and maximum temperatures and maximum humidity suitable for maize cultivation in Benin. Scenario 2 (Weather 2) featured a broader range of parameter values below and above those of Weather 1. Five types of fertilizers were tested: Organic (Cow dung), Chemical (NPK), Intermediate 1 (mixture of high NPK and low Cow dung), Intermediate 2 (mixture of middle NPK and middle cow dung), and Intermediate 3 (mixture of high cow dung and low NPK). These factors were combined in a split-plot design and data were collected on maize germination, growth, and yield variables. Models such as DNNsurv, Cox, linear mixed effect, and decision trees were used for data analysis. Results revealed that maize seeds had a higher probability of germination between 2 to 5 days after sowing, with over 80% of the seeds germinating the fifth day. Intermediate 1 and organic fertilizers were particularly effective in promoting maize growth, resulting in larger diameters and heights. Organic, chemical, and intermediate 1 fertilizers led to higher yields under weather scenario 2, while intermediate 3 and organic led to higher yields under weather 1, suggesting that organic fertilizers could be more sustainable and cost-effective than mineral fertilizers. Additionally, Weather 2 was associated with higher maize yields suggesting that, a relatively broader range of climate parameters would positively affect maize yield. These findings can assist farmers and policymakers in making well-informed decisions regarding the most suitable fertilizers to use under various weather conditions, maximizing their yield and profits.