Search Results (93)

The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems. Full article

Background: Atrial fibrillation (AF) has been identified as a risk factor for functional tricuspid regurgitation (FTR) in the absence of other known etiologies, although limited interventional options are available. K-Clip™, a novel transcatheter tricuspid annuloplasty device, is a clip-based annular plication approach for FTR. To date, no studies have investigated the short-term outcomes of K-Clip™ for patients with severe FTR associated with AF. Therefore, the aim of this study was to explore the feasibility and effectiveness of transcatheter annular repair with K-Clip™ for FTR in patients with persistent AF. Methods: Patients with FTR and persistent AF who underwent transcatheter annular repair with K-Clip™ at nine centers in China during the inclusion period were included (This study derived from Confirmatory Clinical Study of Treating Tricuspid Regurgitation With K-Clip™ Transcatheter Annuloplasty System [TriStar study}). Baseline data, imaging results, and follow-up data were collected. Results: All 52 patients (23 men, 74.02 ± 7.03 years) received successful intervention, and the mean operation time and radian exposure were 2.64 ± 1.09 h and 133.33 ± 743.06 mGy, respectively. No death cases and a low major adverse event occurrence rate were reported in 30 days. A significant decrease in FTR was documented, and TR remained severe in only two patients (3.8%). The regurgitation volume decreased significantly, accompanied by a notable reduction in the effective regurgitation orifice area and tricuspid annulus diameter, which subsequently led to the reversal of right heart remodeling. Furthermore, a decrease in pulmonary artery systolic pressure and an increase in cardiac output were documented. Conclusions: Transcatheter annular repair with K-Clip™ showed favorable short-term prognosis and significant improvement in FTR in patients with severe FTR associated with persistent AF. K-Clip™ could be a novel option for that group of patients. Full article

Aim: To compare caregiver satisfaction and children’s acceptance of silver fluoride/potassium iodide (AgF + KI) treatment (Riva Star Aqua^®, SDI Limited, Victoria, Australia) and glass-ionomer cement (GIC) application (Ionostar Plus + Easy Glaze, VOCO, Germany) in reducing hypersensitivity in permanent molars affected by molar incisor hypomineralisation (MIH) with the MIH treatment need index (MIH-TNI) 3 and 4 immediately after its application and after 12 weeks. Materials and Methods: This prospective, comparative, clinical study recruited schoolchildren with at least one hypersensitive MIH molar with a Schiff cold air sensitivity score (SCASS) of 2 and 3. Caregivers in both groups (AgF + KI and GIC + glaze) answered a questionnaire (5-Point Likert Scale) regarding the perception of the treatment immediately (15 min post application) and in the 12 weeks follow-up. Children’s behaviour during both applications was assessed using FBRS (Frankl Behaviour Rating Scale). Results: A total number of 47 children (n = 22 for AgF/KI and n = 25 for GIC) with a mean age of 8.6 ± 1.42 were recruited. A high proportion of the children in both arms (n = 40 out of 44; 90.1%) reported a reduction in hypersensitivity in the last 12 weeks. On average, children (n = 39; FBRS ≥ 3) in both groups showed positive behaviour, with a significantly more definitely positive behaviour in the GIC group (p < 0.05, independent student t-test). Caregiver satisfaction with both study procedures was high after immediate assessment (n = 19 out of 22, 86.4% for AgF/KI and n = 19 out of 25, 76.0% for GIC application) and in 12 weeks of follow-up (n = 17 out of 20, 85.0% for AgF/KI and n = 22 out of 24, 91.6% for GIC application). However, the taste AgF/KI is more frequently considered not acceptable for the child (n = 10; 45%) than smell (n = 2; 9%). Interestingly, there was a statistically significant difference in caregivers’ preference toward alternative desensitisation treatment (tooth restoration coverage, desensitisation paste, stainless steel crown and fluoride varnish) in both treatment groups (p < 0.05, Mann–Whitney U test). Conclusions: Both GIC and AgF/KI applications can be considered acceptable approaches to reduce hypersensitivity in permanent molars affected by MIH both immediately and in long-term follow-up for schoolchildren based on caregivers’ assessments. Full article

Sentiment analysis is a key technique in natural language processing that enables computers to understand human emotions expressed in text. It is widely used in applications such as customer feedback analysis, social media monitoring, and product reviews. However, sentiment analysis of customer reviews presents unique challenges, including the need for large datasets and the difficulty in accurately capturing subtle emotional nuances in text. In this paper, we present a comparative study of sentiment analysis on customer reviews using both deep learning and traditional machine learning techniques. The deep learning models include Convolutional Neural Network (CNN) and Recursive Neural Network (RNN), while the machine learning methods consist of Logistic Regression, Random Forest, and Naive Bayes. Our dataset is composed of Amazon product reviews, where we utilize the star rating as a proxy for the sentiment expressed in each review. Through comprehensive experiments, we assess the performance of each model in terms of accuracy and effectiveness in detecting sentiment. This study provides valuable insights into the strengths and limitations of both deep learning and traditional machine learning approaches for sentiment analysis. Full article

Introduction: Dysarthria, a motor speech disorder caused by neurological damage, significantly hampers speech intelligibility, creating communication barriers for affected individuals. Voice conversion (VC) systems have been developed to address this, yet accurately predicting phonemes in dysarthric speech remains a challenge due to its variability. This study proposes a novel approach that integrates Fuzzy Expectation Maximization (FEM) with diffusion models for enhanced phoneme prediction, aiming to improve the quality of dysarthric voice conversion. Methods: The proposed method combines FEM clustering with Diffusion Probabilistic Models (DPM). Diffusion models simulate noise addition and removal to enhance the robustness of speech signals, while FEM iteratively optimizes phoneme boundaries, reducing uncertainty. The system was trained using the Saarland University Voice Disorder dataset, consisting of dysarthric and normal speech samples, with the conversion process represented in the Mel-spectrogram domain. The framework employs both subjective (Mean Opinion Score, MOS) and objective (Word Error Rate, WER) metrics for evaluation, complemented by ablation studies. Results: Experimental results showed that the proposed method significantly improved phoneme prediction accuracy and overall voice conversion quality. It achieved higher MOSs for naturalness, intelligibility, and speaker similarity compared to existing models like StarGAN-VC and CycleGAN-VC. Additionally, the proposed method demonstrated a lower WER for both mild and severe dysarthria cases, indicating better performance in producing intelligible speech. Discussion: The integration of FEM with diffusion models offers substantial improvements in handling the irregularities of dysarthric speech. The method’s robustness, as evidenced by the ablation studies, shows that it can maintain speech naturalness and intelligibility even without a speaker-encoder. These findings suggest that the proposed approach can contribute to the development of more reliable assistive communication technologies for individuals with dysarthria, providing a promising foundation for future advancements in personalized speech therapy. Full article

Solvent extraction of metals from Ti ore was investigated with a view of enhancing extraction yields by changing the concentration of the ligands, the rate of mixing, the pH, and the temperature of the solution. Norwegian Ti ore was leached with 5M HNO₃ alongside 10% ascorbic acid to obtain a pregnant solution containing transition metals and some rare earth elements (REEs). Part Two of the study will address the recovery of the REEs in the ore. The elemental analysis of solid and aqueous samples was done by two models of total reflection X-ray fluorescence spectrometers (S2 PICOFOX, Bruker Corporation, Berlin, Germany; and T-STAR, Bruker Corporation, Berlin, Germany). The same analysis was repeated using an inductively coupled plasma-mass spectrometer (Perkin Elmer Sciex ELAN DRC II, Perkin Elmer, Waltham, MA, USA). The extraction process and parameters were examined by ICP-MS. The extraction efficiencies were studied under different conditions through the use of various concentrations of ligands at different pHs, temperatures, and mixing rates of the solution. At pH 1.0, 22.5 °C, and a mixing rate of 1400 rpm, the selectivity of 150 g/L trioctyl methyl ammonium chloride (Aliquat 336) was 99% Ti⁴⁺, 94% V⁴⁺, and 82% Hf⁴⁺, while 99% of Co²⁺ was recovered at pH 0.8. The extraction efficiency of triethyl phosphate (10% TEP) was 58% Cu²⁺, 68% Mn²⁺, and 63% V⁴⁺ at 55 °C, 1400 rpm, and without a pH change. Tributyl phosphate (10% TBP) was able to retrieve 87% Cu²⁺ and 78% Zn²⁺ at pH 1.3, 1400 rpm, and 22.5 °C, and 80% Ti⁴⁺ at pH 1.2. A 10% solution of 2,4,6-tris (allyloxy)-1,3,5-triazine (TAOT) demonstrated 61% Mn²⁺ and 56% Hf⁴⁺ extraction at pH 1.3, 22.5 °C, and 1400 rpm. Under the same conditions, 10% methyl salicylate (MS) was able to recover 56% Hf⁴⁺ at pH 1.3. Using 1400 rpm, di (2-ethylhexyl) phosphoric acid (10% D2EHPA) was found to selectively extract 87% Hf⁴⁺ at 22.5 °C without a pH change, and around 99% Co²⁺, Ti⁴⁺, and Fe²⁺ at pH 1.3. This study provides valuable insights into optimizing solvent extraction conditions for transition metals’ recovery and serves as a precursor to future research on the extraction of REEs from Ti ores. This process is relevant from the environmental and economic perspectives since it provides the best approach to recycling metals to reduce the rate of raw ore mining. Full article

This study explores the relationship between environmental, social, and governance (ESG) performance and enterprise value in Chinese tourism companies, with differing degrees of media attention and green innovation as the multiple-chained mediating factors. In this study, we adopted a quantitative approach to collect survey data from 804 samples selected from China’s A-level tourist attractions, star-rated hotels, and travel agencies. Through rigorous statistical analysis and hypothesis testing, our results reveal a significant positive relationship between ESG performance and enterprise value in tourism companies. Media attention and green innovation demonstrate crucial chained mediating effects in this relationship. The findings expand the understanding of ESG performance’s influence on enterprise value in the tourism sector, highlighting how media visibility and innovation initiatives amplify ESG’s positive effects. This study offers practical implications for tourism companies, emphasizing the importance of integrating ESG principles into core business strategies, engaging with media, and investing in green innovation to enhance firm value. In addition, it suggests that policymakers create incentive structures to promote sustainable practices and encourage media–tourism company collaborations to communicate ESG efforts effectively. Full article

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency. Full article

Travelers’ perceptions of hotels and their aspects have been the focus of much research and are often studied by analyzing consumers’ online reviews. Yet, little attention has been given to the effect of the trip mode, i.e., whether the person travels alone or with others, on travelers’ preferences as sentiment. Here, we study the influence of the trip mode and purpose using a mixed-methods approach. We conducted a user study to evaluate the perceptions of reviews across trip modes and found that star ratings do not consistently capture the sentiment in text reviews; on average, solo travelers’ text reviews are perceived as more negative than the star ratings they assigned, whether they travel for business or pleasure. We then analyzed over 137,000 reviews from TripAdvisor and Venere and found that a co-occurrence network approach naturally divides the text of reviews into hotel aspects. We used this result to measure the importance of hotel aspects across various traveler modes and purposes and identified significant differences in their preferences. These findings underscore the need for personalized marketing and services, highlighting the role of trip mode in shaping online review sentiment and traveler satisfaction. Full article

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision ([email protected]) of 98.5%. Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as a thick line and star crack, the model maintained high performance, with accuracies of 94% and 96%, respectively. The Recall–Confidence and Precision–Recall curves further demonstrate the model’s robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production. Although the model demonstrates high accuracy across most defect types, certain subtle defects, such as thick lines and star cracks, remain challenging, indicating potential areas for further optimization in future work. Full article

Understanding the spatial distribution and sustainable development of rural tourism is essential for promoting balanced regional growth and formulating optimized policy strategies. This study aims to provide insights into sustainable development and policy optimization. Utilizing geographic information system technology, dominance analysis, and Geodetector statistical methods, this research offers a comprehensive examination of the spatial patterns and determinants of these distributions. The findings reveal significant regional disparities and clustering, with a higher concentration of key villages in economically developed eastern and central regions and fewer in the less developed western regions. The dominance analysis highlights that provinces such as Zhejiang, Shandong, and Beijing demonstrate strong advantages across multiple dimensions, including ecological environment, economic development, tourism infrastructure, transportation accessibility, policy support, and social development. Conversely, regions such as Ningxia, Qinghai, and Tibet exhibit lower dominance scores, indicating challenges in rural tourism development due to limited resources and infrastructure. Key influencing factors include forest coverage, GDP per capita, the number of star-rated hotels, transportation network density, policy initiatives, and urbanization rates. The results underscore the importance of a multi-dimensional approach to enhance rural tourism competitiveness and suggest targeted strategies for underperforming regions. This study contributes to advancing the theoretical framework of sustainable rural tourism and provides actionable insights for policymakers to foster balanced regional development, ecological conservation, and community-centered tourism practices. Full article

The stellar initial mass function (IMF) represents a fundamental quantity in astrophysics and cosmology describing the mass distribution of stars from low mass all the way up to massive and very massive stars. It is intimately linked to a wide variety of topics, including stellar and binary evolution, galaxy evolution, chemical enrichment, and cosmological reionization. Nonetheless, the IMF still remains highly uncertain. In this work, we aim to determine the IMF with a novel approach based on the observed rates of transients of stellar origin. We parametrize the IMF with a simple but flexible Larson shape, and insert it into a parametric model for the cosmic UV luminosity density, local stellar mass density, type Ia supernova (SN Ia), core-collapse supernova (CCSN), and long gamma-ray burst (LGRB) rates as a function of redshift. We constrain our free parameters by matching the model predictions to a set of empirical determinations for the corresponding quantities via a Bayesian Markov Chain Monte Carlo method. Remarkably, we are able to provide an independent IMF determination with a characteristic mass $m_c=0.{10}_{-0.08}^{+0.24}{\mathrm{M}}_{\odot }$ and high-mass slope $\xi =-2.{53}_{-0.27}^{+0.24}$ that are in accordance with the widely used IMF parameterizations (e.g., Salpeter, Kroupa, Chabrier). Moreover, the adoption of an up-to-date recipe for the cosmic metallicity evolution allows us to constrain the maximum metallicity of LGRB progenitors to $Z_{max}=0.{12}_{-0.05}^{+0.29}{\mathrm{Z}}_{\odot }$. We also find which progenitor fraction actually leads to SN Ia or LGRB emission (e.g., due to binary interaction or jet-launching conditions), put constraints on the CCSN and LGRB progenitor mass ranges, and test the IMF universality. These results show the potential of this kind of approach for studying the IMF, its putative evolution with the galactic environment and cosmic history, and the properties of SN Ia, CCSN, and LGRB progenitors, especially considering the wealth of data incoming in the future. Full article

Since the Industrial Revolution, nearly 700 Gt of carbon (GtC) have been emitted into the atmosphere as CO₂ derived from human activities, of which 292 GtC remain uncontrolled. By the end of this century, the atmospheric CO₂ concentration is predicted to surpass 700 ppm. The effects of this sudden carbon release on the worldwide biogeochemical cycles and balances are not yet fully understood, but global warming and climate change are undeniable, with this gas playing a starring role. Governmental policies and international agreements on emission reduction are not producing results quickly enough, and the deadline to act is running out. Biological CO₂ capture is a fast-acting carbon cycle component capable of sequestering over 115 GtC annually through photosynthesis. This study analyses a hypothetical scenario in which this biological CO₂ capture is artificially enhanced through the large-scale cultivation of phytoplankton in partially natural photobioreactors (PBRs). To develop this approach, the current figures of the carbon cycle have been updated, and the key aspects of phytoplankton cultivation technology have been analysed. Our results show that a global increase of 6.5% in biological capture, along with the subsequent stabilization of the produced biomass, could counteract the current CO₂ emission rate and maintain atmospheric levels of this gas at their current levels. Based on a review of the available literature, an average production rate of 17 g/m²·day has been proposed for phytoplankton cultivation in horizontal PBRs. Using this value as a key reference, it is estimated that implementing a large-scale production system would require approximately 2.1 × 10⁶ km² of the Earth’s surface. From this, a production system model is proposed, and the key technological and political challenges associated with establishing these extensive cultivation areas are discussed. Full article

The study investigates the effectiveness of a novel Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) technology in enhancing the physical layer security of Multiple-Input Single-Output (MISO) systems. To address the complexity of the security challenge, we examine two distinct phase shift strategies, namely, the coupled phase scheme and the independent phase scheme. The coupled paradigm focuses on optimizing weighted sum secrecy rate (WSSR) through a customized Block Coordinate Descent (BCD) approach integrated with path tracking, aiming to achieve a balanced security enhancement. Furthermore, for the independent phase shift paradigm, an optimization algorithm based on the Concave–Convex Procedure (CCCP) is explored to provide a flexible security solution. The numerical results validate the superior performance of STAR-RIS, confirming its potential as a robust security enhancer for MISO systems. Full article

Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems. Full article