Search Results (680)

In this study, a laboratory-scale slag–steel reaction experiment was conducted to systematically evaluate the influence of the initial MgO content (3–7 wt.%) in LF refining slag on the cleanliness of GCr15 bearing steel. The assessment was performed from multiple perspectives by comparing the total oxygen content (T[O]) in molten steel, the inclusion area fraction, and the inclusion number density after 30 min of slag–steel interaction. To further elucidate the thermodynamic driving forces and kinetic mechanisms governing inclusion capture by slag, a predictive slag adsorption model was developed using an in-house computational code coupled with FactSage 8.1. Under conditions of slag basicity R (CaO/SiO₂) ranging from 4.0 to 8.0, MgO content varying from 0 to 7 wt.%, and a constant Al₂O₃ content of 32 wt.%, the chemical driving force ΔC (the mass-fraction difference between slag components and inclusions), the slag viscosity η, and the combined parameter ΔC/η were calculated at 1600 °C for three representative inclusion types: Al₂O₃, MgO·Al₂O₃, and MgO. In addition, the model was employed to quantitatively characterize the adsorption capacity of slag toward Mg–Al binary inclusions under varying MgO levels. Both experimental observations and model calculations demonstrate that the slag–steel reaction markedly enhances inclusion removal, as evidenced by pronounced decreases in T[O], inclusion number density, and inclusion area fraction after reaction. With increasing MgO content in slag, T[O] and inclusion-related indices exhibit a consistent trend of first decreasing and then increasing, reaching minimum values at an MgO level of 5 wt.%. Further analysis reveals a positive correlation between the apparent inclusion-removal rate constant ko and ΔC/η corresponding to MgO·Al₂O₃ inclusions. Moreover, the slag’s adsorption capacity toward Mg–Al binary inclusions decreases overall as the MgO fraction in inclusions increases. Notably, when the MgO content in inclusions exceeds 29 wt.%, the adsorption capacity undergoes an abrupt drop, indicating a pronounced cliff-like attenuation behavior. Full article

Background/Objectives: Detecting copy number variations (CNVs) from next-generation sequencing (NGS) is challenging, particularly in targeted sequencing panels, especially for cell-free DNA (cfDNA), where the signal is weak and noise is high. Methods: We present BayesCNV, a Bayesian hierarchical model for gene-level copy ratio estimation from targeted amplicon read depths compared to a CNV-neutral reference sample. The model provides posterior uncertainty for each gene and supports interpretable calling based on effect size and posterior confidence. The model also provides a principled quality-control strategy based on the marginal log likelihood of each sample, with low values indicating low confidence in the calls. BayesCNV uses thermodynamic integration, a technique to reliably estimate this quantity. We benchmark our method against two publicly available CNV callers using Seracare^® reference samples with known CNVs on the OncoReveal^® Core Lbx panel. Results: Our method achieves a sensitivity of 0.87 and specificity of 0.996, dramatically outperforming two competitor methods, IonCopy and DeviCNV. In a separate FFPE dataset using the OncoReveal^® Essential Lbx panel, we show that the marginal log likelihood cleanly separates, degraded from high-quality samples, even when conventional sequencing QC metrics do not. Conclusions: BayesCNV provides accurate and interpretable gene-level CNV estimates and uncertainty quantification, along with an evidence-based quality control metric that improves robustness in targeted cfDNA workflows. Full article

Feminization is an important biotechnological approach in aquaculture for species in which females exhibit superior growth and higher market value. The long-whiskered catfish (Mystus gulio), a euryhaline species cultivated in both monoculture and co-culture systems, contributes to sustainable aquaculture by grazing on uneaten feed and maintaining pond cleanliness. This study evaluated the effects of dietary 17β-estradiol (E2) at 0, 10, 30, and 60 mg/kg, incorporated into conventional and microencapsulated feeds, on the feminization and early growth of M. gulio larvae. Treatments were administered during the weaning stage for 14 and 21 days under controlled rearing conditions. Results showed that larvae fed microencapsulated feed containing 60 mg/kg E2 achieved the highest specific growth rate (26.91 ± 1.92%/day), feed efficiency (164.76 ± 33.23%), and feminization success (99.73 ± 0.04%). Hormonal assays confirmed elevated estradiol and reduced testosterone levels, consistent with ovarian development observed in histological sections. Gene expression analysis further supported these findings through the significant upregulation of cyp19a, erb1, and erb2 mRNA levels. Overall, this study demonstrates that microencapsulated hormone feeding is an effective and environmentally responsible strategy for achieving monosex female populations in M. gulio, enhancing productivity, reproductive performance, and sustainability in aquaculture systems. Full article

Radio-frequency interference (RFI) severely degrades the imaging quality of synthetic aperture radar (SAR), especially when the interference energy is strongly coupled with ground backscatter in both the time and frequency domains. Existing algorithms typically rely on energy contrast or component decomposition in transform domains, which limits their ability to cleanly separate complex RFI from high-power echoes. Exploiting the fact that kurtosis is insensitive to ground clutter and background noise, this paper proposes an interference suppression network based on the temporal kurtosis guidance mechanism. Specifically, a statistical prior vector capturing the non-Gaussian characteristics of RFI is constructed using kurtosis in the time–frequency domain and is integrated into a multi-scale attention mechanism, allowing the network to more effectively concentrate on interfered regions. Meanwhile, a systematic framework is established for the quantitative assessment of phase fidelity in the reconstruction of complex-valued SAR echoes. On this basis, by exploiting the strong generalization capability and high processing efficiency of data-driven models, the proposed network achieves improved RFI separation and enhanced reconstruction accuracy of underlying scene features. Ablation experiments validated that the design of a kurtosis-guided module can reduce the mean square error (MSE) loss by 14.87% compared to the basic model. Furthermore, regarding the phase fidelity, the correlation coefficient between the suppressed signal and the original true signal reached 0.99. Finally, GF-3 satellite data are used to further demonstrate the effectiveness and practicality of the proposed method. Full article

Integrating antibacterial fabrics into wearable technology represents a transformative advancement in healthcare, fashion, and personal hygiene. Antibacterial fabrics, designed to inhibit microbial growth, are gaining prominence due to their potential to reduce infections, enhance durability, and maintain cleanliness in wearable devices. These fabrics offer effective antimicrobial properties while retaining comfort and functionality by incorporating nanotechnology and advanced materials, such as silver nanoparticles, zinc oxide, titanium dioxide, and graphene. The production techniques for antibacterial textiles range from chemical and physical surface modifications to biological treatments, each tailored to achieve long-lasting antibacterial performance while preserving fabric comfort and breathability. Advanced methods such as nanoparticle embedding, sol–gel coating, electrospinning, and green synthesis approaches have shown significant promise in enhancing antibacterial efficacy and material compatibility. Wearable technology, including fitness trackers, smart clothing, and medical monitoring devices, relies on prolonged skin contact, making the prevention of bacterial colonization essential for user safety and product longevity. Antibacterial fabrics address these concerns by reducing odor, preventing skin irritation, and minimizing the risk of infection, especially in medical applications such as wound dressings and patient monitoring systems. Despite their potential, integrating antibacterial fabrics into wearable technology presents several challenges. This review provides a comprehensive overview of the key antibacterial agents, the production strategies used to fabricate antibacterial textiles, and their emerging applications in wearable technologies. It also highlights the need for interdisciplinary research to overcome current limitations and promote the development of sustainable, safe, and functional antibacterial fabrics for next-generation wearable. Full article

Under high-density urban development, Residential Pilotis have been widely constructed in Chinese cities as a critical measure to mitigate public space shortages. However, a mismatch between spatial supply and residents’ needs remains prevalent. This study develops a resident satisfaction evaluation framework comprising 23 indicators across four dimensions: Spatial Usability and Sociality, Landscape and Visual Experience, Physical Environment Comfort, and Governance and Operational Maintenance. Using the Integrated KANO-IPA Model, 553 questionnaires from Hefei were analyzed to classify the quality attributes and improvement priorities of the indicators. Results suggest a structural supply–demand mismatch, with the Governance and Operational Maintenance dimension emerging as a particularly prominent area of concern. Satisfaction with Must-be and One-dimensional attributes, especially cleanliness and facility maintenance, age-friendly design, and resting facilities, all of which are highly valued by residents, is generally low. Conversely, landscape-related attributes receive higher satisfaction and have a lower priority for improvement. Based on these findings, a phased optimization strategy is proposed, encompassing short-term priority improvements, medium-term gradual enhancements, and long-term maintenance or flexible adjustments. This research provides an operable methodological framework for supply–demand diagnosis and optimization in similar spatial contexts. Full article

The quantity, size, and distribution of non-metallic inclusions in High-Strength Low-Alloy (HSLA) steel critically influence its service performance. Conventional detection methods often fail to adequately characterize extreme inclusion distributions in large-section components. This study developed an integrated full-process inclusion analysis system combining high-precision motion control, parallel optical imaging, and laser spectral analysis technologies to achieve rapid and automated identification and compositional analysis of inclusions in meter-scale samples. Through systematic investigation across the industrial process chain—from a dia. 740 mm consumable electrode to a dia. 810 mm electroslag remelting (ESR) ingot and finally to a dia. 400 mm forged billet—key process-specific insights were obtained. The results revealed the effective removal of Type D (globular oxides) inclusions during ESR, with their counts reducing from over 8000 in the electrode to approximately 4000–7000 in the ingot. Concurrently, the mechanism underlying the pronounced enrichment of Type C (silicates) in the ingot tail was elucidated, showing a nearly fourfold increase to 1767 compared to the ingot head, attributed to terminal solidification segregation and flotation dynamics. Subsequent forging further demonstrated exceptional refinement and dispersion of all inclusion types. The billet tail achieved exceptionally high purity, with counts of all inclusion types dropping to extremely low levels (e.g., Types A, B, and C were nearly eliminated), representing a reduction of approximately one order of magnitude. Based on these findings, enhanced process strategies were proposed, including shallow molten pool control, slag system optimization, and multi-dimensional quality monitoring. An intelligent analysis framework integrating a YOLOv11 detection model with spectral feedback was also established. This work provides crucial process knowledge and technological support for achieving the quality control objective of “known and controllable defects” in HSLA steel. Full article

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages. Full article

In urban design, public open spaces (POS) are essential for enhancing health and well-being across the lifetime. High-quality public open spaces facilitate the maintenance of optimal physical and mental health in older individuals by encouraging activities like physical exercise and social engagement. Preserving the physical and mental well-being of elderly individuals is a fundamental concern for aging policy. Nevertheless, urbanization presents considerable problems with the provision of public open spaces for activities aimed at the elderly. South Korea has more significant issues than other nations globally. This study, based on data from 477 valid questionnaires collected in and around Tapgol Park in Jung-gu, Seoul, employed a decision tree approach to identify key factors and paths that influence overall satisfaction. The goal was to identify decision paths that improve satisfaction while ensuring interpretability, thereby providing a scientific basis for urban space design and renovation. The results show that: (1) The decision tree of this study presents a hierarchical logic of quietness first, then accessibility and cleanliness, and finally price and vitality, which is consistent with the high frequency of use of Tapgol Park by the elderly and the diverse facilities in the surrounding area. (2) The key to improving the management and satisfaction of Tapgol Park in Seoul is the quietness of the site. (3) When the park is not quiet, users are most sensitive to bottom-line factors, such as commercial supply, evacuation safety, transportation accessibility, price perception, barrier-free, and anti-slips. (4) When the park is quiet, basic comfort factors such as smooth walking, all-day opening, sunlight, and no odor constitute the minimum condition set for entering the comfort zone. (5) Water experience, waterfront accessibility, proximity to cultural resources, and moderate business and community-oriented leisure facilities are key plus points. Methodologically, this study is among the first to apply a decision tree approach to low-income elderly using a small public open space in a historic city center, clarifying the nonlinear and hierarchical relationships among environmental factors within these low-income elderly groups. This provides empirical support and reference for the aging-friendly urban space in world heritage cities and other historical and cultural cities. Full article

The cleanliness and functionalization of metal surfaces are critical factors to determining their performance in high-performance microelectronic packaging, reliable biomedical implants, advanced composite bonding, and other fields. Compared to traditional wet cleaning methods, plasma cleaning technology has emerged as a research hotspot in surface engineering due to its unique advantages, such as high efficiency and environmental friendliness. It operates under versatile conditions (e.g., power: tens of watts to several kilowatts; pressure: atmospheric to low vacuum; treatment time: seconds to minutes), enabling not only efficient contaminant removal but also targeted surface functionalization, including dramatically enhanced hydrophilicity (e.g., contact angles from >80° to <10°), significantly improved adhesion (e.g., up to 40% increase in bond strength), and modifications in surface roughness, corrosion resistance, and biocompatibility. This review systematically elaborates on the physical, chemical, and synergistic mechanisms of plasma cleaning technology as it acts on metal surfaces. It focuses on plasma cleaning applied to copper, aluminum, titanium and their respective alloys, as well as alloy steels, providing a detailed analysis of contaminant types, plasma cleaning methodologies, common challenges, surface functionalization responses, and subsequent functional applications. Furthermore, this review discusses the current challenges faced by plasma cleaning technology and offers perspectives on its future development directions. It aims to systematize the research progress in plasma cleaning of metal surfaces, thereby facilitating the transition of this technology towards large-scale industrial applications for metal surface functionalization. Full article

Modern deep neural network (DNN) accelerators must sustain high throughput while avoiding performance degradation from supply voltage (VDD) droop, which occurs when large arrays of multiply–accumulate (MAC) units switch concurrently and induce high peak current ($IC{C}_{max}$) transients on the power delivery network (PDN). In this work, we focus on ASIC-class DNN accelerators with tightly synchronized MAC arrays rather than FPGA-based implementations, where such cycle-aligned switching is most pronounced. Conventional guardbanding and reactive countermeasures (e.g., throttling, clock stretching, or emergency DVFS) either waste energy or incur non-trivial throughput penalties. We propose SparseDroop, a unified hardware-conscious framework that proactively shapes instantaneous current demand to mitigate droop without reducing sustained computing rate. SparseDroop comprises two complementary techniques. (1) SparseStagger, a lightweight hardware-friendly droop scheduler that exploits the inherent unstructured sparsity already present in the weights and activations—it does not introduce any additional sparsification. SparseStagger dynamically inspects the zero patterns mapped to each processing element (PE) column and staggers MAC start times within a column so that high-activity bursts are temporally interleaved. This fine-grain reordering smooths $ICC$ trajectories, lowers the probability and depth of transient VDD dips, and preserves cycle-level alignment at tile/row boundaries—thereby maintaining no throughput loss and negligible control overhead. (2) SparseBlock, an architecture-aware, block-wise-structured sparsity induction method that intentionally introduces additional sparsity aligned with the accelerator’s dataflow. By co-designing block layout with the dataflow, SparseBlock reduces the likelihood that all PEs in a column become simultaneously active, directly constraining $IC{C}_{max}$ and peak dynamic power on the PDN. Together, SparseStagger’s opportunistic staggering (from existing unstructured weight zeros) and SparseBlock’s structured, layout-aware sparsity induction (added to prevent peak-power excursions) deliver a scalable, low-overhead solution that improves voltage stability, energy efficiency, and robustness, integrates cleanly with the accelerator dataflow, and preserves model accuracy with modest retraining or fine-tuning. Full article

Air pollution is an important environmental issue that affects social development and human life. Atmospheric fine particulate matter (PM_2.5) is the primary pollutant affecting the air quality of most cities in the authors’ country. It can cause severe haze, reduce air visibility and cleanliness, and affect people’s daily lives and health. Therefore, it has become a primary research object. Ground monitoring and satellite remote sensing are currently the main ways to obtain PM_2.5 data. Satellite remote sensing technology has the advantages of macro-scale, dynamic, and real-time functioning, which can make up for the limitations of the uneven distribution and high cost of ground monitoring stations. Therefore, it provides an effective means to establish a mathematical model—based on atmospheric aerosol optical thickness data obtained through satellite remote sensing and PM_2.5 concentration data measured by ground monitoring stations—in order to estimate the PM_2.5 concentration and temporal and spatial distribution. This study takes the Yangtze River Delta region as the research area. Based on the measured PM_2.5 concentration data obtained from 184 ground monitoring stations in 2023, the newly released sixth version of the MODIS aerosol optical depth product obtained via the US Terra and Aqua satellites is used as the main prediction factor. Dark-pixel AOD data with a 3 km resolution and dark-blue AOD data with a 10 km resolution are combined with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis meteorological, land use, road network, and population density data and other auxiliary prediction factors, and XGBoost and LSTM models are used to achieve high-precision estimation of the spatiotemporal changes in PM_2.5 concentration in the Yangtze River Delta region. Full article

Background and Aims: Colon capsule endoscopy (CCE) provides a non-invasive alternative to traditional colonoscopy. This study evaluated the feasibility, safety, diagnostic yield (DY), and patient satisfaction of the OMOM CC100 CCE system, with special focus on fully remote (n = 30) and partially remote (n = 89) administration across four centres to advance decentralised models. Methods: This prospective, investigator-initiated, international multicentre feasibility study enrolled 119 patients aged 18–75 years at centres in Denmark, Sweden, Portugal, and Poland from July 2024 to May 2025. Indications included rectal bleeding, iron-deficiency anaemia, a positive faecal immunochemical test, changes in bowel habit, suspected inflammatory bowel disease (IBD), post-polypectomy or colorectal cancer (CRC) surgery surveillance, and a family history of CRC. The OMOM CC100 capsule was employed with a standardised bowel preparation regimen. Administration was fully remote in Denmark using the IntelliGI™ platform and partially remote (clinic ingestion, home completion) at the other sites. Primary outcomes encompassed procedure feasibility, completion rate (capsule excretion or anal verge visualisation), bowel cleanliness (Leighton-Rex scale ≥ 3), diagnostic yield, and patient satisfaction. Secondary measures included transit times, adverse events, and technical failures. Results: Median age was 55.7 years (65 males, 54 females). Overall completion rate was 79%, varying by centre: Sweden (90%), Portugal (81%), Denmark (80%), and Poland (63%). Adequate bowel cleanliness was achieved in 71% of cases. Diagnostic findings included polyps (25 patients), angioectasia (20), diverticulosis (17), and mucosal inflammation (17); 42% were normal. Fully remote administration yielded 80% completion and 89.7% satisfaction. No serious adverse events occurred; overall satisfaction was 81%, with 87% preferring home-based procedures. Conclusions: The OMOM CC100 CCE system is feasible, safe, with DY comparable to established systems. IntelliGI™-enabled remote administration promotes decentralised care, enhancing accessibility. Full article

Hydrogen (H₂) is a key clean energy carrier for achieving carbon neutrality, featuring both cleanliness and high efficiency. Biomass-to-hydrogen technologies, with the advantages of strong renewability and low emissions, provide a highly promising alternative to fossil fuel-based hydrogen production. This review summarizes the main pathways and latest research progress in catalytic hydrogen production from biomass, focusing on the role of catalysts and optimization directions in the two major processes of thermochemical and biochemical methods. Despite the rapid development in this field, the large-scale application of biomass-to-hydrogen technologies is still limited by issues such as catalyst deactivation, feedstock composition fluctuations, and low energy efficiency. Traditional biomass-to-hydrogen technologies cannot achieve breakthrough progress in large-scale production in the short term; however, through coupled emerging technologies like biomass electrooxidation for hydrogen production and on-site hydrogen production via aqueous ethanol reforming, biomass-based hydrogen production is expected to solve problems such as low energy efficiency and high transportation difficulties, thereby making an important contribution to the construction of a green and low-carbon hydrogen economy system. Future research should focus on the rational design of stable nanocatalysts, artificial intelligence-driven research and development as well as advanced characterization technologies and the application of integrated systems and process innovation, along with diverse feedstocks and high-value-added product systems. Full article

With the dramatic development of 5G technology, Internet of Things (IoT), and other technologies, the traditional offline market has been gradually altered with applying technologies to improve their efficient or cost-performance. The unmanned stores have been one of the significant and popular forms. To maintain the sustainable development of this retail form, it is essential to know what factors to foster and the mechanism of the formation of customers’ loyalty. Thus, the present study was performed to explore what the selection attributes of unmanned stores are and examine how these attributes impact on the formation of customers’ loyalty through their perceived value and satisfaction. Structural equation modeling was applied with a valid sample of 350 respondents to testify the casual relationship among research variables. As results, it was found that practicality (β = 0.229, t = 3.164, p < 0.01) and convenience (β = 0.152, t = 2.044, p < 0.05) of unmanned stores have positive influence on their perceived value. Moreover, practicality (β = 0.164, t = 2.392, p < 0.05), cleanliness (β = 0.198, t = 3.595, p < 0.001), and pleasantness (β = 0.337, t = 4.722, p < 0.001) could positively impact on their satisfaction. Both perceived value (β = 0.151, t = 2.366, p < 0.05) and satisfaction (β = 0.123, t = 2.023, p < 0.05) could contribute to the formation of their loyalty to unmanned stores. Finally, the moderating effect of social risk has been examined. Consequently, the casual relationships confirmed among research variables could provide insights for the service improvement of unmanned stores from the perspectives of the selection attributes of unmanned stores and customers perceived value. Full article