Search Results (252,692)

ZG25MnCrNiMo steel samples were prepared by squeeze casting under pressure ranging from 0 to 150 MPa. The effects of pressure on the microstructure, low-temperature toughness, hardness, and impact wear performance of the prepared steels were experimentally investigated. The experimental results indicated that the samples fabricated under pressure exhibited finer grains and a significant ferrite content compared to those produced without pressure. Furthermore, the secondary dendrite arm spacing of the sample produced at 150 MPa decreased by 45.3%, and the ferrite content increased by 39.1% in comparison to the unpressurized sample. The low-temperature impact toughness of the steel at −40 °C initially increased and then decreased as the pressure varied from 0 MPa to 150 MPa. And the toughness achieved an optimal value at a pressure of 30 MPa, which was 65.4% greater than that of gravity casting (0 MPa), while the hardness decreased by only 6.17%. With a further increase in pressure, the impact work decreased linearly while the hardness increased slightly. Impact fracture analysis revealed that the fracture of the steel produced without pressure exhibited a quasi-cleavage morphology. The samples prepared by squeeze casting under 30 MPa still exhibited a large number of fine dimples even at −40 °C, indicative of ductile fracture. In addition, the impact wear performance of the steels displayed a trend of initially decreasing and subsequently increasing across the pressure range of 0–150 MPa. The wear resistance of samples prepared without pressure and at 30 MPa was superior to that at 60 MPa, and the wear resistance deteriorated when the pressure increased to 60 MPa, after which it exhibited an upward trend as the pressure continued to rise. The wear mechanisms of the samples predominantly consisted of impact wear, adhesive wear, and minimal abrasive wear, along with notable occurrences of plastic removal, furrows, and spalling. Full article

The aim of this study was to develop a material capable of rapidly absorbing bodily fluids and forming a resilient, adhesive, viscoelastic hydrogel in situ to prevent post-surgical adhesions. This material was formulated using O-carboxymethyl chitosan (O-CMCS), oxidized hyaluronic acid (OHA), and a crosslinking pigment derived from genipin and glutamic acid (G/GluP). Both crosslinked (O-CMCS/OHA-G/GluP) and non-crosslinked hydrogels (O-CMCS/OHA) were evaluated using a HAAKE™ MARS™ rheometer for their potential as post-surgical barriers. A rheological analysis, including dynamic oscillatory measurements, revealed that the crosslinked hydrogels exhibited significantly higher elastic moduli (G′), indicating superior gel formation and mechanical stability compared to non-crosslinked hydrogels. The G/GluP crosslinker enhanced gel stability by increasing the separation between G′ and G″ and achieving a lower loss tangent (tan δ < 1.0), indicating robustness under dynamic physiological conditions. The rapid hydration and gelation properties of the hydrogels underscore their effectiveness as physical barriers. Furthermore, the O-CMCS/OHA-G/GluP hydrogel demonstrated rapid self-healing and efficient application via spraying or spreading, with tissue adherence and viscoelasticity to facilitate movement between tissues and organs, effectively preventing adhesions. Additionally, the hydrogel proved to be both cost effective and scalable, highlighting its potential for clinical applications aimed at preventing post-surgical adhesions. Full article

The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire detection models have shortcomings, including low detection accuracy and efficiency. The YOLOv8 model exhibits robust capabilities in detecting forest fires and smoke. However, it struggles to balance accuracy, model complexity, and detection speed. This paper proposes LD-YOLO, a lightweight dynamic model based on the YOLOv8, to detect forest fires and smoke. Firstly, GhostConv is introduced to generate more smoke feature maps in forest fires through low-cost linear transformations, while maintaining high accuracy and reducing model parameters. Secondly, we propose C2f-Ghost-DynamicConv as an effective tool for increasing feature extraction and representing smoke from forest fires. This method aims to optimize the use of computing resources. Thirdly, we introduce DySample to address the loss of fine-grained detail in initial forest fire images. A point-based sampling method is utilized to enhance the resolution of small-target fire images without imposing an additional computational burden. Fourthly, the Spatial Context Awareness Module (SCAM) is introduced to address insufficient feature representation and background interference. Also, a lightweight self-attention detection head (SADH) is designed to capture global forest fire and smoke features. Lastly, Shape-IoU, which emphasizes the importance of boundaries’ shape and scale, is used to improve smoke detection in forest fires. The experimental results show that LD-YOLO realizes an mAP${}_{0.5}$ of 86.3% on a custom forest fire dataset, which is 4.2% better than the original model, with 36.79% fewer parameters, 48.24% lower FLOPs, and 15.99% higher FPS. Therefore, LD-YOLO indicates forest fires and smoke with high accuracy, fast detection speed, and a low model complexity. This is crucial to the timely detection of forest fires. Full article

Bus lanes are commonly implemented to ensure absolute priority for buses at signalized intersections. However, while prioritizing buses, existing bus lane management strategies often exacerbate traffic demand imbalances among lanes. To address this issue, this paper proposes a dynamic Multi-Function Lane (MFL) management strategy. The proposed strategy transforms traditional bus lanes into Multi-Function Lanes (MFLs) that permit access to Connected and Automated Vehicles (CAVs). By fully utilizing the idle right-of-way of the MFL, the proposed strategy can achieve traffic efficiency improvement. To evaluate the proposed strategy, some experiments are conducted under various demand levels and CAV penetration rates. The results reveal that the proposed strategy (i) improves the traffic intensity balance degree by up to 52.9 under high demand levels; (ii) reduces delay by up to 80.56% and stops by up to 89.35% with the increase in demand level and CAV penetration rate; (iii) guarantees absolute bus priority under various demand levels and CAV penetration rates. The proposed strategy performs well even when CAV penetration is low. This indicates that the proposed strategy has the potential for real-world application. Full article

The enantioselective mechanism of the esterase QeH against the two enantiomers of quizalofop–ethyl (QE) has been primitively studied using computational and experimental approaches. However, it is still unclear how the esterase QeH adjusts its conformation to adapt to substrate binding and promote enzyme–substrate interactions in the catalytic kinetics. The equilibrium processes of enzyme–substrate interactions and catalytic dynamics were reproduced by performing independent molecular dynamics (MD) runs on the QeH-(R)/(S)-QE complexes with a newly developed residue-specific force field (RSFF2C). Our results indicated that the benzene ring of the (R)-QE structure can simultaneously form anion–π and cation–π interactions with the side-chain group of Glu328 and Arg384 in the binding cavity of the QeH-(R)-QE complex, resulting in (R)-QE being closer to its catalytic triplet system (Ser78-Lys81-Tyr189) with the distances measured for the hydroxyl oxygen atom of the catalytic Ser78 of QeH and the carbonyl carbon atom of (R)-QE of 7.39 Å, compared to the 8.87 Å for (S)-QE, whereas the (S)-QE structure can only form an anion–π interaction with the side chain of Glu328 in the QeH-(S)-QE complex, being less close to its catalytic site. The computational alanine scanning mutation (CAS) calculations further demonstrated that the π–π stacking interaction between the indole ring of Trp351 and the benzene ring of (R)/(S)-QE contributed a lot to the binding stability of the enzyme–substrate (QeH-(R)/(S)-QE). These results facilitate the understanding of their catalytic processes and provide new theoretical guidance for the directional design of other key enzymes for the initial degradation of aryloxyphenoxypropionate (AOPP) herbicides with higher catalytic efficiencies. Full article

Urbanization is defining global change, and megacities are fast becoming a hallmark of the Anthropocene. Humanity’s pursuit toward sustainability is reliant on the successful management of these massive urban centers and their progression into sustainable and resilient settlements. Indicators and indices are applied assessment and surveillance tools used to measure, monitor, and gauge the sustainable development and urban resilience of megacities. Unknown is how indicator-based evaluations of sustainable development and urban resilience of the world’s largest 43 cities compare. In response, this review paper used the PRISMA reporting protocol, governed by 33 established and 10 emerging megacities, to compare and contrast evaluations of sustainable development and urban resilience. Results reveal that applied assessments of sustainable development of megacities appeared earlier in time and were more abundant than those of urban resilience. Geographically, China dominated other nations in affiliations to scientific research for both sustainable development and urban resilience of megacities. Among the 100 most recurrent terms, three distinct key term clusters formed for sustainable development; seven budding key term clusters formed for urban resilience suggesting breadth in contrast to sustainable development depth. The most cited assessments of sustainable development emphasize topics of energy, methodological approaches, and statistical modeling. The most cited assessments of urban resilience emphasize topics of flooding, transit networks, and disaster risk resilience. Megacities research is dominated by few countries, suggesting a need for inclusion and international partnerships. Lastly, as the world’s people become increasingly urbanized, sustainable development and urban resilience of megacities will serve as a key barometer for humanity’s progress toward sustainability. Full article

Background and Objectives: Non-specific neck and shoulder pain (NSNSP) is prevalent among working adults. The increased use of electronic devices and prevalence of remote working and study following the COVID-19 pandemic have raised concerns about the potential rise in such conditions. This study aims to investigate the associations between work-related, lifestyle factors and NSNSP in the adult Southeast Asian Singaporean population. Materials and Methods: An online survey was administered electronically to Singaporeans aged 21 and above. Demographic data, NSNSP prevalence, computer and smartphone usage durations, sleep patterns, and exercise frequency were captured after obtaining informed consent (SIT institutional review board approval #2023014). Results: A total of 302 validated responses were recorded, including 212 suffering from NSNSP versus 90 in the comparison group. The NSNSP group showed significantly longer smartphone usage (5.37 ± 3.50 h/day) compared to the comparison group (4.46 ± 3.36 h/day, p = 0.04). Furthermore, the NSNSP group had lower exercise frequency (2.10 ± 1.74 days/week vs. 2.93 ± 2.21 days/week, p < 0.01) and shorter weekly exercise duration (2.69 ± 3.05 h/week vs. 4.11 ± 4.15 h/week, p < 0.01). The average NSNSP severity in this group was 34.9 ± 19.96 out of 100, correlating significantly with age (r = 0.201, p < 0.01) and BMI (r = 0.27, p < 0.01). Conclusions: This preliminary cross-sectional study examines characteristics of adult Southeast Asians with NSNSP post-COVID-19 pandemic. The findings indicate significantly longer smartphone use and less exercise in NSNSP respondents, with both age and body mass index (BMI) demonstrating significant correlations with NSNSP severity. Full article

Traditional thermal insulation panels consume large amounts of energy during production and emits pollutants into the environment. To mitigate this impact, the development of bio-based materials is an attractive alternative. In this context, the characteristics of the Eucalyptus fiber bark (EGFB) make it a candidate for insulation applications. However, more knowledge about the manufacturing process and in-service performance is needed. The present study characterized the properties that determine the in-service behavior of the EGFB insulation panel. The assessment involved two different manufacturing processes. The results indicated that the hot plates and the saturated steam injection manufacturing system can produce panels with similar target and bulk density. The thermal conductivity fluctuated between 0.064 and 0.077 W/m·K, which indicated good insulation, and the values obtained for thermal diffusivity (0.10–0.37 m mm²/s) and water vapor permeability (0.032–0.055 m kg/GN s) are comparable with other commercially available panels. To guarantee a good in-service performance, the panels need to be treated with flame retardant and antifungal additive. The good performance of the panel is relevant because bio-based Eucalyptus bark panels generate less CO₂ eq and require less energy consumption compared to traditional alternatives, contributing to the sustainability of the forestry and the construction industry. Full article

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of “gross” errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas. Full article

The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture. Full article

In minimally invasive surgery, the tendency for human tissue to adhere to the electrosurgical scalpel can complicate procedures and elevate the risk of medical accidents. Consequently, the development of an electrosurgical scalpel with an anti-sticking coating is critically important. Drawing inspiration from nature, we identified that the leaves of Setaria Virids exhibit exceptional non-stick properties. Utilizing this natural surface texture as a model, we designed and fabricated a specialized anti-sticking surface for electrosurgical scalpels. Employing nanosecond laser direct writing ablation technology, we created a micro-nano textured surface on the high-frequency electrosurgical scalpel that mimics the structure found on Setaria Virids leaves. Subsequently, a TiO₂ coating was deposited onto the ablated scalpel surface via magnetron sputtering, followed by plasma-induced hydrophobic modification and treatment with octadecyltrichlorosilane (OTS) to enhance the surface’s affinity for silicone oil, thereby constructing a self-lubricating and anti-sticking surface. The spreading behavior of deionized water, absolute ethanol, and dimethyl silicone oil on this textured surface is investigated to confirm the effectiveness of the self-lubrication mechanism. Furthermore, the sticking force and quality are compared between the anti-sticking electrosurgical scalpel and a standard high-frequency electrosurgical scalpel to demonstrate the efficacy of the nanosecond laser-ablated micro-nano texture in preventing sticking. The findings indicate that the self-lubricating anti-sticking surface fabricated using this texture exhibits superior anti-sticking properties. Full article

This study investigated the effects of TiO₂ nanoparticles (TNPs) on the hydration and microstructure of cement. The primary experimental variable was the TNP content, which ranged from 0 to 10 wt% of the cement. Cement paste and mortar specimens incorporating TNPs were prepared to assess the hydration characteristics, microstructure, and mechanical properties of the cement composites. Hydration characteristics were evaluated using heat of hydration, setting time, and thermogravimetric (TG) analysis. The microstructure was assessed through mercury intrusion porosimetry (MIP). The results indicated that TNPs accelerated the hydration of cement and modified the matrix microstructure, decreasing porosity and improving early-age strength. However, their tendency to agglomerate makes proper dispersion crucial for their effective application in construction materials. Therefore, when developing new building materials incorporating TNPs, it is essential to consider the properties of the nanoparticles and their physical and chemical effects on cement. Full article

(1) Background: This study seeks to employ a machine learning (ML) algorithm to forecast the risk of distant metastasis (DM) in patients with T1 and T2 gallbladder cancer (GBC); (2) Methods: Data of patients diagnosed with T1 and T2 GBC was obtained from SEER, encompassing the period from 2004 to 2015, were utilized to apply seven ML algorithms. These algorithms were appraised by the area under the receiver operating characteristic curve (AUC) and other metrics; (3) Results: This study involved 4371 patients in total. Out of these patients, 764 (17.4%) cases progressed to develop DM. Utilizing a logistic regression (LR) model to identify independent risk factors for DM of gallbladder cancer (GBC). A nomogram has been developed to forecast DM in early T-stage gallbladder cancer patients. Through the evaluation of different models using relevant indicators, it was discovered that Random Forest (RF) exhibited the most outstanding predictive performance; (4) Conclusions: RF has demonstrated high accuracy in predicting DM in gallbladder cancer patients, assisting clinical physicians in enhancing the accuracy of diagnosis. This can be particularly valuable for improving patient outcomes and optimizing treatment strategies. We employ the RF algorithm to construct the corresponding web calculator. Full article

Correct design of the sheet metal forming process requires knowledge of the friction phenomenon occurring in various areas of the drawpiece. Additionally, the friction at the drawbead is decisive to ensure that the sheet flows in the desired direction. This article presents the results of experimental tests enabling the determination of the coefficient of friction at the drawbead and using a specially designed tribometer. The test material was a DC04 carbon steel sheet. The tests were carried out for different orientations of the samples in relation to the sheet rolling direction, different drawbead heights, different lubrication conditions and different average roughnesses of the countersamples. According to the aim of this work, the Features Importance analysis, conducted using the Gradient-Boosted Regression Trees algorithm, was used to find the influence of several parameter features on the coefficient of friction. The advantage of gradient-boosted decision trees is their ability to analyze complex relationships in the data and protect against overfitting. Another advantage is that there is no need for prior data processing. According to the best of the authors’ knowledge, the effectiveness of gradient-boosted decision trees in analyzing the friction occurring in the drawbead in sheet metal forming has not been previously studied. To improve the accuracy of the model, five MinLeafs were applied to the regression tree, together with 500 ensembles utilized for learning the previously learned nodes, noting that the MinLeaf indicates the minimum number of leaf node observations. The least-squares-boosting technique, often known as LSBoost, is used to train a group of regression trees. Features Importance analysis has shown that the friction conditions (dry friction of lubricated conditions) had the most significant influence on the coefficient of friction, at 56.98%, followed by the drawbead height, at 23.41%, and the sample width, at 11.95%. The average surface roughness of rollers and sample orientation have the smallest impact on the value of the coefficient of friction at 6.09% and 1.57%, respectively. The dispersion and deviation observed for the testing dataset from the experimental data indicate the model’s ability to predict the values of the coefficient of friction at a coefficient of determination of R² = 0.972 and a mean-squared error of MSE = 0.000048. It was qualitatively found that in order to ensure the optimal (the lowest) coefficient of friction, it is necessary to control the friction conditions (use of lubricant) and the drawbead height. Full article

Over the past year, there has been a significant rise in interest in the application of open-source artificial intelligence models (OSAIM) in the field of medicine. An increasing number of studies focus on evaluating the capabilities of these models in image analysis, including magnetic resonance imaging (MRI). This study aimed to investigate whether two of the most popular open-source AI models, namely ChatGPT 4o and Gemini Pro, can analyze MRI video sequences with single-phase contrast in sagittal and frontal projections, depicting a posterior fossa tumor corresponding to a medulloblastoma in a child. The study utilized video files from single-phase contrast-enhanced head MRI in two planes (frontal and sagittal) of a child diagnosed with a posterior fossa tumor, type medulloblastoma, confirmed by histopathological examination. Each model was separately provided with the video file, first in the sagittal plane, analyzing three different sets of commands from the most general to the most specific. The same procedure was applied to the video file in the frontal plane. The Gemini Pro model did not conduct a detailed analysis of the pathological change but correctly identified the content of the video file, indicating it was a brain MRI, and suggested that a specialist in the field should perform the evaluation. Conversely, ChatGPT 4o conducted image analysis but failed to recognize that the content was MRI. The attempts to detect the lesion were random and varied depending on the plane. These models could not accurately identify the video content or indicate the area of the neoplastic change, even after applying detailed queries. The results suggest that despite their widespread use in various fields, these models require further improvements and specialized training to effectively support medical diagnostics. Full article