Search Results (674)

The safety of lithium-ion batteries during transportation is critically important. However, current standards exhibit limitations, as their environmental testing parameter thresholds fail to fully encompass actual transportation conditions. To enhance both safety and standard applicability, in this study, we focused on four representative environmental conditions: temperature, vibration, shock, and low atmospheric pressure. Field measurements were conducted across road, rail, and air transport modes using a self-developed data acquisition system based on the NearLink communication technology. The measured data were then compared with the threshold values defined in current international and national standards. The results reveal that certain measured values exceeded the upper limits prescribed by existing standards, indicating limitations in their applicability under extreme transport conditions. Based on these findings, we propose revised testing parameters that better reflect actual transport risks, including a temperature cycling range of 72 ± 2 °C (high) and −40 ± 2 °C (low), a shock acceleration limit of 50 gn, adjusted peak frequencies in the vibration PSD profile, and a minimum pressure threshold of 11.6 kPa. These results provide a scientific basis for optimising safety standards and improving the safety of lithium-ion battery transportation. Full article

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

The present paper deals with an inhabited, creeping mountainous landmass with profound surface deformation that affects the local community. The scope of the paper is to gather surficial and subsurface information in order to understand the parameters of this creeping mass, which is usually affected by several parameters, such as its geometry, subsurface water, and shear zone. Therefore, a combined aerial and surface investigation has been conducted. The aerial investigation involves UAV’s LiDAR acquisition for the terrain model and a comparison of historical aerial photographs for land use changes. The multi-technique surface investigation included resistivity (ERT) and seismic (SRT, MASW) measurements and density determination of geological formations. This combination of methods proved to be fruitful since several aspects of the landslide were clarified, such as water flow paths, the internal geological structure of the creeping mass, and its geometrical extent. The depth of the shear zone of the creeping mass is delineated at the first five to ten meters from the surface, especially from the difference in diachronic resistivity change. Full article

China’s internal control framework follows the Committee of Sponsoring Organizations (COSO) framework, emphasizing enterprise risk management and encompassing financial reporting, operations, compliance, and strategies. The authors review research that uses the COSO-based Internal Control Index to assess internal control quality among all publicly listed firms in China. Unlike the binary classification of internal control weaknesses under the Sarbanes-Oxley Act Section 404, this continuous index captures more nuanced variations in internal control effectiveness and provides two key advantages over traditional assessment of internal control over financial reporting (ICFR). First, while financial reporting can enhance a firm’s monitoring and decision-support systems, the underlying information is determined by operations. Thus, internal control over operations has a greater impact on a firm’s performance than ICFR. While U.S.-based research argues that the effects of ICFR extend to operations, the COSO-based index includes operational controls, allowing for a more direct study of internal control effects. Second, many U.S. corporations fail to report internal control weaknesses, particularly during misstatement years. In contrast, the COSO-based index, compiled by independent scholars, avoids managerial incentives to withhold negative internal control information. Covering institutional background and research evidence from China, the authors survey a wide range of internal control studies related to various aspects of enterprise risk management, such as earnings quality, crash risk, stock liquidity, resource extraction, cash holdings, mergers and acquisitions, corporate innovation, receivable management, operational efficiency, tax avoidance, and diversification strategy. Full article

The accurate acquisition of agricultural parcels from remote sensing images is crucial for agricultural management and crop production monitoring. Most of the existing agricultural parcel extraction methods comprise semantic segmentation through remote sensing images, pixel-level classification, and then vectorized raster data. However, this approach faces challenges such as internal cavities, unclosed boundaries, and fuzzy edges, which hinder the accurate extraction of complete agricultural parcels. Therefore, this paper proposes a vector contour segmentation network based on the hybrid backbone and multiscale edge feature extraction module (HEVNet). We use the extraction of vector polygons of agricultural parcels by predicting the location of contour points, which avoids the above problems that may occur when raster data is converted to vector data. Simultaneously, this paper proposes a hybrid backbone for feature extraction. A hybrid backbone combines the respective advantages of the Resnet and Transformer backbone networks to balance local features and global features in feature extraction. In addition, we propose a multiscale edge feature extraction module, which can extract and enhance the edge features of different scales to prevent the possible loss of edge details in down sampling. This paper uses the datasets of Denmark, the Netherlands, iFLYTEK, and Hengyang in China to evaluate our model. The obtained IOU indexes were 67.92%, 81.35%, 78.02%, and 66.35%, which are higher than previous IOU indexes based on the optimal model (DBBANet). The results demonstrate that the proposed model significantly enhances the integrity and edge accuracy of agricultural parcel extraction. Full article

As Environmental, Social, and Governance (ESG) gradually become the common language for sustainable development of international society and international cooperation in China, it is worth discussing whether ESG practices can help Chinese enterprises shape a responsible international image, overcome the liability of foreignness (LOF) and improve the long-term performance of cross-border mergers and acquisitions (M&As). On the basis of theoretical discussion, combined with the panel data of cross-border M&As of China A-share listed companies from 2010 to 2021, this paper empirically examines that the ESG performance of acquirers has a significant positive impact on the long-term performance of cross-border mergers and acquisitions (M&As) of China A-share listed companies. Furthermore, the ESG performance of environment and governance dimensions and heavily polluting enterprises has stronger incentive effects on the long-term performance of cross-border M&As. The ESG performance of the acquirer positively affects the long-term performance of cross-border M&As of China A-share listed companies by acquiring capital market resources, product market competitiveness, regulatory legitimacy, and enhancing internal synergy. Full article

(1) Background: This study aimed to evaluate the marginal and internal fit of ceramic crowns produced by various digital methods using microcomputed tomography (MCT) imaging. (2) Methods: The ceramic crown preparation was performed on typodont maxillary first premolar. The crown preparation was scanned with an intraoral scanner and a model scanner, and cone-beam computed tomography (CBCT) scans were performed with three different voxel sizes (0.075 mm, 0.1 mm, and 0.15 mm). The space between the crown and prepared teeth was measured at nine different points in both coronal and sagittal sections. Three different digital model acquisition techniques, namely, intraoral scanning, model scanning, and CBCT-based standard tessellation language (STL) reconstruction, were compared in terms of marginal and internal fit. (3) Results: Quantitative analyses revealed that model scanners exhibited the lowest marginal and internal gap values, indicating superior fit compared to intraoral scanners and CBCT-based models. The highest gap values were observed in the CBCT group with a voxel size of 0.15 mm. Overall, crowns obtained from model scanners demonstrated the highest success rates in both marginal and internal fit. (4) Conclusions: In conclusion, this study highlights the critical role of digital scanning accuracy in achieving clinically acceptable prosthetic fits and emphasizes the need for continued technological advancement. Full article

Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies. Full article

This study investigates the impact of microphone placement on honey bee audio monitoring for time-of-day classification, a key step toward automated activity monitoring and anomaly detection. Recognizing the time-dependent nature of bee behavior, we aimed to establish a baseline diurnal pattern recognition method. A custom apparatus enabled simultaneous audio acquisition from internal (brood frame, protected from propolization) and external hive locations. Sound signals were preprocessed using Power Spectral Density (PSD). Extra Trees and Convolutional Neural Network (CNN) classifiers were trained to identify diurnal activity patterns. Analysis focused on feature importance, particularly spectral characteristics. Interestingly, Extra Trees performance varied significantly. While achieving near-perfect accuracy (98–99%) with internal recordings, its accuracy was considerably lower (61–72%) with external recordings, even lower than CNNs trained on the same data (76–87%). Further investigation using Extra Trees and feature selection methods using Mean Decrease Impurity (MDI) and Recursive Feature Elimination with Cross-Validation (RFECV) revealed the importance of the 100–600 Hz band, with peaks around 100 Hz and 300 Hz. These findings inform future monitoring setups, suggesting potential for reduced sampling frequencies and underlining the need for monitoring of sound inside the beehive in order to validate methods being tested. Full article

Model updating is a challenging task with regard to maintaining the performance of non-destructive detection models while using hyperspectral imaging techniques for detecting the internal quality of fresh fruits like blueberries. Different sample batches and differences in hyperspectral image acquisition environments may lead to a significant decline in the performance of hyperspectral detection models. This study investigated the transferability of a hyperspectral model for the quantitating soluble solid content of blueberries across different batches for two harvest years. Hyperspectral images and SSC values of blueberries were collected from two batches, including 364 samples from 2024 and 175 samples from 2025. The differences between SSC measurements and spectral data across these two batches were analyzed. Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). This high-performance model could achieve a high determination coefficient (${\mathrm{R}}_{\mathrm{P}}^2$) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. The updated model performed better than those established using individual datasets from 2024 and 2025, and obtained an ${\mathrm{R}}_{\mathrm{P}}^2$ of 0.8347 and an RMSEP of 0.4930 °Brix. This indicates that the calibration transfer strategy is superior in improving hyperspectral model performance. This study demonstrated that the SS-PFCE algorithm, as a calibration transfer strategy, could effectively improve the transferability of the established model for detecting the SSC of blueberries across different sample batches. Full article

Platinum-induced ototoxicity constitutes a significant adverse effect in pediatric oncology, frequently resulting in permanent hearing impairment with profound implications for quality of life, language acquisition, and scholastic performance. This comprehensive review critically evaluates contemporary ototoxicity monitoring practices across various pediatric oncology settings, analyzes current guideline recommendations, and formulates strategies for implementing standardized surveillance protocols. Through examination of recent literature—encompassing retrospective cohort investigations, international consensus recommendations, and functional outcome assessments—we present an integrated analysis of challenges and opportunities in managing chemotherapy-associated hearing loss among childhood cancer survivors. Our findings demonstrate marked heterogeneity in monitoring methodologies, substantial implementation obstacles, and considerable impact on survivors’ functional status across multiple domains. Particularly concerning is the persistent absence of an evidence-based consensus regarding the appropriate duration of audiological surveillance for this vulnerable population. We propose a structured framework for comprehensive ototoxicity management emphasizing prompt detection, standardized assessment techniques, and integrated long-term follow-up care to minimize the developmental consequences of platinum-induced hearing impairment. This approach addresses critical gaps in current practice while acknowledging resource limitations across diverse healthcare environments. Full article

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer’s magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction. Full article

This study examines the effects of capital lease arrangements on the operating performance of shipping firms as proxied by Return on Assets (ROA). The maritime industry is highly capital-intensive, often requiring substantial investments in fleet acquisition and maintenance, making ROA particularly relevant as it captures the effectiveness of firms in utilizing their leased and owned assets to generate operating income. As such, many firms rely on lease arrangements to access necessary resources while preserving liquidity and financial flexibility. Using an international sample of 209 shipping firms, we estimate fixed effects regressions to assess the relationship between lease intensity and performance of the shipping firms. The findings reveal that capital lease intensity is positively associated with operating performance, indicating that leasing can be a value-enhancing financing strategy in this sector. However, the performance benefits of capital leases diminish under IFRS 16 reporting, particularly for firms with higher leverage. These findings offer important implications for investors, regulators, and managers evaluating capital structure decisions and financial reporting strategies in capital-intensive industries post-IFRS 16 implementation. Full article

This study explores how Speech and Elocution Training (SET) activates language potential and fosters career-oriented development among higher vocational students through self-efficacy mechanisms. Through qualitative interviews with four vocational graduates who participated in SET 5 to 10 years ago, the research identifies three key findings. First, SET comprises curriculum content (e.g., workplace communication modules such as hosting, storytelling, and sales pitching) and classroom training using multimodal TED resources and Toastmasters International-simulated practices, which spark language potential through skill-focused, realistic exercises. Second, these pedagogies facilitate a progression where initial language potential evolves from nascent career interests into concrete job-seeking intentions and long-term career plans: completing workplace-related speech tasks boosts confidence in career choices, planning, and job competencies, enabling adaptability to professional challenges. Third, SET aligns with Bandura’s four self-efficacy determinants; these are successful experiences (including personalized and virtual skill acquisition and certified affirmation), vicarious experiences (via observation platforms and constructive peer modeling), verbal persuasion (direct instructional feedback and indirect emotional support), and the arousal of optimistic emotions (the cognitive reframing of challenges and direct desensitization to anxieties). These mechanisms collectively create a positive cycle that enhances self-efficacy, amplifies language potential, and clarifies career intentions. While highlighting SET’s efficacy, this study notes a small sample size limitation, urging future mixed-methods studies with diverse samples to validate these mechanisms across broader vocational contexts and refine understanding of language training’s role in fostering linguistic competence and career readiness. Full article

In cold regions, the rock mass of open-pit mine slopes is continuously exposed to freeze–thaw (FT) environments, during which the fracture structures and their infilling materials undergo significant degradation, severely affecting slope stability and the assessment of service life. Conventional laboratory FT tests are typically based on uniform temperature settings, which fail to reflect the actual thermal variations at different burial depths, thereby limiting the accuracy of mechanical parameter acquisition. Taking the Wushan open-pit mine as the engineering background, this study establishes a temperature–depth relationship, defines multiple thermal intervals, and conducts direct shear tests on structural plane filling materials under various FT conditions to characterize the evolution of cohesion and internal friction angle. Results from rock mass testing and numerical simulation demonstrate that shear strength parameters exhibit an exponential decline with increasing FT cycles and decreasing burial depth, with the filling material playing a dominant role in the initial stage of degradation. Furthermore, a two-dimensional fracture network model of the rock mass was constructed, and the representative elementary volume (REV) was determined through the evolution of equivalent plastic strain. Based on this, spatial assignment of slope strength was performed, followed by stability analysis. Based on regression fitting using 0–25 FT cycles, regression model predictions indicate that when the number of FT cycles exceeds 42, the slope safety factor drops below 1.0, entering a critical instability state. This research successfully establishes a spatial field of mechanical parameters and evaluates slope stability, providing a theoretical foundation and parameter support for the long-term service evaluation and stability assessment of cold-region open-pit mine slopes. Full article