Search Results (114)

The stable operation of micromachine systems relies on reliable power management, where DC-DC converters provide energy with high efficiency to extend operational endurance. However, these converters also constitute significant electromagnetic interference (EMI) sources that may interfere with the normal functioning of micro-electromechanical systems. This paper proposes a boost converter utilizing Pulse Width Modulation (PWM) with peak current mode control to address the EMI issues inherent in the switching operation of DC-DC converters. The converter incorporates a Hybrid Spread Spectrum (HSS) technique to effectively mitigate EMI noise. The HSS combines a 1.2 MHz pseudo-random spread spectrum with a 9.4 kHz triangular periodic spread spectrum. At a standard switching frequency of 2 MHz, the spread spectrum range is set to ±7.8%. Simulations conducted using a 0.5 μm Bipolar Complementary Metal-Oxide-Semiconductor Double-diffused Metal-Oxide-Semiconductor (BCD) process demonstrate that the HSS technique reduces EMI around the switching frequency by 12.29 dBμV, while the converter’s efficiency decreases by less than 1%. Full article

With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization. Full article

Understanding the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its drivers is critical to sustainable land -carbon management, carbon-neutral development, and ecological restoration in fragile karst landscapes. This study proposes a Pearson Correlation—Deep Transformer (PCADT) model that integrates attention mechanisms and geospatial covariates to enhance NPP estimation accuracy in Guangxi, China—a global karst hotspot. Leveraging multisource remote sensing data (2015–2020), PCADT achieves 10.7% higher predictive accuracy (R² = 0.83 vs. conventional models) at 500 m resolution, thereby capturing the fine-scale heterogeneity essential for sustainability planning. The results reveal a significant annual NPP increase (4.14 gC·m⁻²·a⁻¹, p < 0.05), with eastern areas exhibiting higher baseline productivity (1129 gC·m⁻² in Wuzhou) but western regions showing steeper growth rates (5.2% vs. 2.1%). Vegetation carbon sequestration capacity, validated against MOD17A3HGF data (R² = 0.998), demonstrates spatial consistency (east > west), with forest-dominated Wuzhou contributing 6.5 TgC annually. Mechanistic analyses identify precipitation as the dominant climatic driver (partial r = 0.62, p < 0.01), surpassing temperature sensitivity, while bimodal NPP-altitude peaks (300 m and 900 m) and land -use transitions (e.g., forest-to-cropland conversions) explain 18.5% of NPP variability and reduce regional carbon stocks by 4593 tC. The PCADT framework offers a scalable solution for precision carbon management by emphasizing the role of anthropogenic interventions—such as afforestation—in alleviating climatic constraints. It advocates for spatially adaptive strategies to optimize water resource utilization, enhance forest conservation, and promote sustainable land -use transitions. By identifying areas where water -scarcity relief and targeted afforestation would yield the highest carbon returns, the PCADT framework directly supports SDG 13 (Climate Action) and SDG 15 (Life on Land), providing a strategic blueprint for sustainable development in water-limited karst regions globally. Full article

This study numerically investigates the solid–liquid mixing characteristics in solid–liquid stirred tanks with solid volume fraction as high as 35%, focusing on the effect of impeller and baffle configurations on solid and liquid flow behaviors. Three stirred tanks with different capacities and impellers were analyzed to evaluate liquid flow field, solid suspension, and free surface profiles. It has demonstrated superior shear rate uniformity in the multi-impeller systems compared to the single-impeller, attributed to the enhanced fluid circulation. Multi-impeller systems can achieve near-complete off-bottom suspension, while the single-impeller configuration exhibited band-shaped particle accumulation above the impeller. Free surface vortices, significantly deeper in the 6 m³ multi-impeller tank due to high blade tip velocities, were mitigated through the integration of four circumferentially arranged triangular baffles. The existence of baffles can suppress surface turbulence, promote axial flow patterns, and eliminate particle accumulation at the tank bottom, improving shear rate and solid concentration homogeneity. These findings provide a beneficial guideline for the optimization of solid–liquid mixing efficiency the similar flow system or processes. Full article

Understanding the effects of climate change on agricultural land green use efficiency (AGUE) is vital for shaping adaptive technologies and agricultural policies. Utilizing data from 30 Chinese provinces (2003–2022), this study applies the Geographically and Temporally Weighted Regression (GTWR) model to assess how climate change impacts AGUE and its spatial–temporal variations. Studies show that China’s climate demonstrates significant interannual variability and spatial heterogeneity. The regression coefficient of the annual precipitation is positive and gradually decreases from the periphery to the center. AGUE across provinces is declining and exhibits significant spatial clustering characteristics. The spatial–temporal analysis indicates that the annual average temperature has a significant negative impact on AUGE, and the regression coefficient decreases from southeast to northwest. The regression coefficient of the annual precipitation is positive and gradually decreases from the periphery to the center. Extreme weather conditions have negative impacts of varying degrees on inter-provincial climate change. The findings provide a reference for the green transformation and development of agriculture in China. Full article

The influence of heat input (HI) on the microstructure, microhardness, electrochemical corrosion performance of cold metal transfer additively manufactured (CMTAM) nickel–aluminum bronze alloys was investigated. The nickel–aluminum bronze exhibited an α-Cu austenite matrix with minor γ₂-Cu₉Al₄ and κ phases. As HI increased, the microstructure coarsened progressively. Electron backscatter diffraction (EBSD) analysis revealed that with increasing HI, the grain size gradually increased and the Schmid factor increased. Consequently, the microhardness declined from 198.3 HV to 171.7 HV. The decrease in microhardness with increasing heat input is primarily attributed to the grain coarsening and the coarsening and uneven distribution of the κ phase. As the heat input (HI) increased from 243.8 J/mm to 644.7 J/mm, the corrosion current density rose significantly from 2.56 ± 0.04 μA/cm² to 7.52 ± 0.07 μA/cm². This result indicates a marked deterioration in the material’s corrosion resistance. This phenomenon can be attributed to the grain coarsening and the distribution of Al solute within the microstructure. The CMTAM nickel–aluminum bronze alloys hold significant potential for enhancing the reliability and long-term protection of marine engineering equipment. Full article

Green technology innovation can effectively reduce the problem of pollution transfer in air pollution-intensive industries like thermal power and realize the green development of air pollution-intensive industries like thermal power. Based on green technology innovation, this paper analyzes the spatial–temporal characteristics of fog–haze in 31 provinces and municipalities. Taking the panel data of 31 provinces, municipalities, and autonomous regions from 2000 to 2017 as samples, this paper adopts the panel threshold regression method to examine the relationship between green technology innovation and fog–haze pollution in the transfer of air pollution-intensive industries like thermal power. The study found the following: China’s haze outbreak and the subsequent increasingly serious reasons for the implementation of weight detection haze policy seriously misled the haze prevention and control work, simple disorganized management aggravated the degree of haze pollution, and layer by layer, management methods caused the huge increase in secondary particulate matter; haze pollution aggregation occurs in the area of environmental self-purification capacity in the low air pollution-intensive industrial agglomeration to affect the atmospheric environment, a significant increase in the neighbouring industrial pollution agglomeration in resource-rich provinces; green technology innovation above the threshold has a significant inhibitory effect on the industrial transfer of haze pollution, and so on. There is a need for the scientific planning of pollution industry transfer to undertake the development of the place, the effective transfer of Beijing–Tianjin–Hebei haze pollution and other areas of air pollution-intensive industries, the development of targeted green technology innovation to strengthen policies, the scientific management of haze pollution, and the contribution of the scientific management of haze pollution in China. Full article

To improve the safety management and accident prevention capabilities of mine ventilation systems, the application of knowledge graph technology is proposed. By employing methodologies such as data analysis, entity relationship definition, and entity relationship extraction, and entity extraction using BERT + BiLSTM + CRF model, a safety knowledge graph for the mine ventilation system is constructed. This facilitates the structured processing of historical accident-related textual data and enables the visual analysis and application of accidents based on the knowledge graph. The research results demonstrate that knowledge graph technology can effectively integrate unstructured data and present it in visual graphs or tables. By utilizing Cypher query statements, multi-dimensional accident statistics and the frequency analysis of specific information can be generated, contributing to a comprehensive understanding of accident occurrence patterns. Leveraging the node-to-node characteristics of the knowledge graph, a correlation analysis between entities is conducted, deeply exploring relationships among different types of data, thereby providing new insights to prevent accidents in mine ventilation systems. Moreover, the analysis of mine ventilation accidents and system failure characteristics offers valuable guidance for the safety management of mine ventilation systems. Full article

To understand the characteristics of the pollution risk of potentially toxic elements (PTEs) at a rare and precious metal mining site in Guangxi and to provide scientific evidence for the comprehensive evaluation and soil remediation of PTE pollution at the site, the Cd, As, Co, Cu, Cr, Ni, Pb, and Zn contents of five areas were determined. Laboratory testing was conducted on five soil plots in the selected five suspected contaminated areas (electroplating workshop, sewage treatment area, and boiler room). Correlation analysis, infrared spectroscopy (FTIR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were used to evaluate and analyze PTE pollution. The average contents of Cd, Co, As, Pb, Zn, and Cu at the site were higher than the background values in the Guangxi soil. The Probability Mass Function (PMF) model was used to perform a source apportionment of the PTEs and determine the main pollution sources and their contribution rates. The results of the single factor pollution of the PTEs showed that Cd, Ar, and Cr were heavy pollutants, and Co was a light pollutant. The Nemerow comprehensive pollution index analysis showed that the study area was heavily polluted. The Earth accumulation index results show that Cd exhibited a very serious accumulation, Cu and Zn exhibited mild to moderate accumulations, and As and Co exhibited moderate accumulations. The FTIR results showed that C=O in the soil was chelated with PTEs in some samples, which weakened the characteristic peaks of C=O in proteins and polypeptides. The XRD results showed that cadmium hydroxide, lead oxide, and zinc hydroxide were present in the soil samples. The XPS results showed that the production of O²⁻ in the O 1s high-resolution spectra mainly came from the metal oxides produced by the polluting metals. Meanwhile, the microbial results showed that the pollution risk of PTEs affected the soil microbial community structure and diversity to some extent. Full article

Shade houses are critical for modern agriculture, providing optimal growing conditions for shade-sensitive crops. However, their rapid expansion poses ecological challenges, making the accurate extraction of their spatial distribution crucial for sustainable development. The unique dark appearance of shade houses leads to low accuracy and high misclassification rates in traditional spectral index-based extraction methods, while deep learning approaches face challenges such as insufficient datasets, limited receptive fields, and poor generalization capabilities. To address these challenges, we propose ShadeNet, a novel method for shade house detection using high-resolution remote sensing imagery and semantic segmentation. ShadeNet integrates the Swin Transformer and Mask2Former frameworks, enhanced by a Global-Channel and Local-Spatial Attention (GCLSA) module. This architecture significantly improves multi-scale feature extraction and global feature capture, thereby enhancing extraction accuracy. Tested on a self-labeled dataset, ShadeNet achieved a mean Intersection over Union (mIOU) improvement of 2.75% to 7.37% compared to existing methods, significantly reducing misclassification. The integration of the GCLSA module within the Swin Transformer framework enhances the model’s ability to capture both global and local features, addressing the limitations of traditional CNNs. This innovation provides a robust solution for shade houses detection, supporting sustainable agricultural development and environmental protection. Full article

The asb5 gene, a member of the Asb protein subfamily characterized by six ankyrin repeat domains, is highly conserved and comprises two subtypes, asb5a and asb5b, in zebrafish. Our previous research has demonstrated that a deficiency of the asb5 gene significantly impairs early cardiac contractile function, highlighting its close relationship with heart development. Zebrafish asb5 expression was disrupted by both morpholino (MO) antisense oligomer-mediated knockdown and a CRISPR-Cas9 system. A high-throughput RNA-Seq analysis was used to analyze the possible molecular regulatory mechanism of asb5 gene deletion leading to left–right (L-R) asymmetry defects in the heart. Whole-mount in situ hybridization (WISH) was conducted to evaluate gene expression patterns of Nodal signaling components and the positions of heart organs. Heart looping was defective in zebrafish asb5 morphants. Rescue experiments in the asb5-deficiency group (inactivating both asb5a and asb5b) demonstrated that the injection of either asb5a-mRNA or asb5b-mRNA alone was insufficient to rectify the abnormal L-R asymmetry of the heart. In contrast, the simultaneous injection of both asb5a-mRNA and asb5b-mRNA successfully rescued the morphological phenotype. A high-throughput RNA-Seq analysis of embryos at 48 h post fertilization (hpf) revealed that numerous genes associated with L-R asymmetry exhibited expression imbalances in the asb5-deficiency group. WISH further confirmed that the expression of genes such as fli1a, acta1b, hand2, has2, prrx1a, notch1b, and foxa3 were upregulated, while the expression of mei2a and tal1 was downregulated. These results indicated that loss of the asb5 gene in zebrafish led to the disordered development of L-R asymmetry in the heart, resulting in an imbalance in the expression of genes associated with the regulation of L-R asymmetry. Subsequently, we examined the expression patterns of classical Nodal signaling pathway-related genes using WISH. The results showed that the midline barrier factor gene lefty1 was downregulated at early stages in the asb5-deficiency group, and the expression of spaw and lefty2, which are specific to the left lateral plate mesoderm (LPM), was disrupted. This study reveals that the two subtypes of the asb5 gene in zebrafish, asb5a and asb5b, interact and jointly regulate the establishment of early cardiac L-R asymmetry through the Nodal-spaw-lefty signaling pathway. Full article

Why is fog-haze pollution very serious in Hebei province, where there are many pollution-intensive industries, and in Guangdong province, where it is not so serious? This paper uses the spatial Durbin model, the threshold effect model, and relevant local city data, etc., to explore the effect of the atmospheric environment’s self-purification capacity on haze pollution from the perspective of green technology innovation. We found that the great haze outbreak in China is due to the large amount of ultrafine-particle low-cost emissions caused by the haze detection by weight method implemented in 2011 and 2012. This study also found that haze pollution in China has a significant impact on the atmospheric environment’s self-purification capacity. The atmospheric environment’s self-purification capacity has an inhibitory effect on haze pollution. When green technology innovation reaches the first threshold, the atmospheric self-purification capacity can significantly reduce the impact of haze pollution. When green technology innovation reaches the second threshold, the atmospheric self-purification capacity to reduce haze pollution is significantly enhanced. China’s local haze pollution is serious due to the industrial layout being unreasonable, caused by high-pollution industries emitting particles beyond the limits of atmospheric environment self-purification capacity. Industries in Hebei Province and Guangdong Province are more pollution-intensive, and haze pollution in Hebei Province is serious due to the weak self-purification capacity of the atmospheric environment. Guangdong Province’s atmospheric environment self-purification capacity is strong, and its haze pollution is not serious. Given the scientific use of atmospheric environment self-purification capacity and regional differences in green technology innovation, the development of targeted green input and atmospheric self-purification capacity enhancement policies in areas with serious air pollution, along with green technology innovations based on a region with less pollution, would be beneficial. To increase the amount of green technology innovation investment in regions where the atmospheric environment is not seriously polluted and green technology innovation is based on a bad region, more green funds should be invested in the atmospheric environment’s self-purification capacity. In regions where the atmospheric environment is not seriously polluted and the foundation of green technology innovation needs improvement, more green funds should be invested into atmospheric environment self-purification capacity to fully harness its inhibition of haze pollution. This should be accompanied by scientific planning and adjustments to the high-pollution industrial layout, etc., to effectively enhance the self-purification capacity of the regional atmospheric environment. In addition, the gradient transfer of high-pollution industries should be implemented based on atmospheric environment self-purification capacity to effectively reduce the impact of haze pollution. Full article

An urban rain flood park refers to a park built with ecological function as the guide. The aim of this study is to examine the social benefits of urban stormwater landscapes. By establishing an evaluation model, conducting field research and analysis, comparing parks, and applying mathematical model analysis, the feedback from various user groups is assessed. The purpose is to explore whether ecologically oriented urban stormwater parks offer superior social benefits and to provide references for optimizing the benefits of urban stormwater park design. The paper selects Yanweizhou Park, Zhejiang Jinhua, a representative of innovative design practices in an urban rainwater park in China, as a case study for evaluation research and introduces the traditional park, Wuzhou Park, for comparison. The results show that Yanweizhou Park, which is designed based on ecology as the first principle, is still highly evaluated in terms of social performance. People think that ecological parks are more representative of the urban image. The eco-park is more popular with young people and more dispersed in activities. Both types of parks suffer from insufficient infrastructure construction. Full article

Bipartite graphs are widely used for modelling various real-world scenarios characterized with binary relations, such as, scholarly articles recommendation with author-paper relations, and product recommendation with user-product relations. Particularly, maximum butterfly as a special cohesive subgraph of bipartite graphs, is playing an critical role in many promising application such as recommendation systems and research groups detection. Enumerating maximal butterfly has been proved to be a NP-hard and suffers time and space complexity. To conquer this challenge, this paper pioneers a novel problem called maximal butterfly generators search (MBGS) for facilitating the detection of maximal butterflies. The MBGS problem is to find a subgraph $\mathbb{B}$ of G such that maximize the number of butterflies in $\mathbb{B}$ and it is mathematically proved to NP-Hard. To address this problem, an equivalence relation theorem between maximum butterfly generator and maximum butterfly concept is presented. Furthermore, an effective MBGS search algorithm is proposed. Extensive experiments on real-world networks with ground-truth communities and interesting case studies validated the effectiveness and efficiency of our MBGS model and algorithm. Full article

A high-quality optical path alignment is essential for achieving superior image quality in optical biological microscope (OBM) systems. The traditional automatic alignment methods for OBMs rely heavily on complex masker-detection techniques. This paper introduces an innovative, image-sensor-based optical path alignment approach designed for low-power objective (specifically 4×) automatic OBMs. The proposed method encompasses reference objective (RO) identification and alignment processes. For identification, a model depicting spot movement with objective rotation near the optical axis is developed, elucidating the influence of optical path parameters on spot characteristics. This insight leads to the proposal of an RO identification method utilizing an edge gradient and edge position probability. In the alignment phase, a symmetry-based weight distribution scheme for concentric arcs is introduced. A significant observation is that the received energy stabilizes with improved alignment precision, prompting the design of an advanced alignment evaluation method that surpasses conventional energy-based assessments. The experimental results confirm that the proposed RO identification method can effectively differentiate between 4× and 10× objectives across diverse light intensities and exposure levels, with a significant numerical difference of up to 100. The error–radius ratio of the weighted circular fitting method is maintained below 1.16%, and the fine alignment stage’s evaluation curve is notably sharper. Moreover, tests under various imaging conditions in artificially saturated environments indicate that the alignment estimation method, predicated on critical saturation positions, achieves an average error of 0.875 pixels. Full article