Search Results (7,431)

By assessing the short-term impact that various logging intensities have on stand state in middle-aged P. massoniana plantations, this investigation aimed to establish a theoretical foundation to support the judicious management of Pinus massoniana plantations. Five distinct logging intensity categories were delineated (0%, 10%, 20%, 30%, 40%). To construct a robust stand-state evaluation framework, nine representative indicators across the three dimensions of structure, vitality, and diversity were selected. We scrutinized the short-term impacts of logging intensity by employing the unit circle method. The findings revealed that (1) four indicators—stand density, tree health, species composition, and species diversity—exhibited pronounced sensitivity to logging intensity. These four exhibited significant improvements in the short-term post-logging (p < 0.05). Conversely, the indicators of species evenness, diameter distribution, height distribution, tree dominance, and stand growth exhibited a more subdued response to logging intensity. These five necessitated an extended period to begin to improve. (2) The comprehensive evaluation values measuring the stand state of middle-aged P. massoniana plantations initially ascended but then subsequently descended as logging intensity escalated. The stand-state zenith was pinpointed at an approximate 30% logging intensity. (3) A highly significant linear correlation emerged between the unit circle method results and the principal component analysis results in evaluating stand state (R² = 0.909, p < 0.001), and the unit circle method proved to be more intuitive and responsive. In summation, logging intensity exerted a substantial influence on stand state in middle-aged P. massoniana plantations, with moderate logging (circa 30% logging intensity) enhancing stand state the most. The unit circle method proficiently and effectively illuminated the short-term effects of logging intensity on the stand dynamics of middle-aged P. massoniana plantations, so it thereby may provide invaluable guidance for the formulation of specific forest management strategies. Full article

Background/Objectives: Vitamin K-dependent proteins (VKDPs) all commonly possess specially modified γ-carboxyglutamic acid residues created in a vitamin K-dependent manner. Several liver-derived coagulation factors are well characterised VKDPs. However, much less is known about extrahepatic VKDPs, which are more diverse in their molecular structures and functions, and some of which have been implicated in inflammatory disorders. Vitamin K metabolism was shown to be impaired in critically ill patients, in whom systemic inflammation and sepsis are common features. Therefore, the aim of this study was to investigate the effect of vitamin K administration to these patients on their circulating levels of selected VKDPs. A particular novelty of this study was the measurement of specifically carboxylated forms of these proteins in addition to their overall levels. Methods: Blood samples were taken from 47 patients in the intensive care unit before and approximately 24 h after intravenous vitamin K1 (10 mg) administration, and proteins were analysed by specific immunoassay. Results: Vitamin K1 induced increases in plasma levels of carboxylated osteocalcin and total Gas6 (p = 0.0002 and p = 0.0032, respectively). No changes were detected in levels of carboxylated Gas6 or PIVKA-II (undercarboxylated prothrombin), although the latter positively correlated with undercarboxylated osteocalcin (r = 0.38). Conclusion: Injected vitamin K1 increases the blood levels of two distinct VKDPs in critically ill patients, both of which have been implicated in inflammation regulation, including the increased carboxylation of one of them. Full article

In China, there are around 36.7 million hectares of saline–alkali lands that hold utilization potential. Precision fertilization stands as a vital measure for enhancing the quality of saline–alkali soil and promoting a significant increase in crop yields. The performance of the fertilization device is a decisive factor in determining the effectiveness of fertilization. To optimize the fertilizer utilization rate in coastal saline–alkali soils and substantially reduce fertilizer waste, it is imperative to transport fertilizers to the deep soil layers and execute layered variable-rate fertilization. In light of this, a chisel-type variable-rate layered electronically controlled deep-fertilization device specifically designed for saline–alkali soils has been developed. Extensive experimental research on its fertilization performance has also been carried out. Drawing on the principles of soil dynamics, this paper meticulously investigates the structures of key components and the operating parameters of the fertilization device. Key parameters such as the penetration angle of the fertilizer shovel, the penetration clearance angle, the curvature of the shovel handle, the angle between the fertilizer baffle and the fertilizer pipe wall, the angle between the fertilizer pipe and the horizontal plane, and the forward speed are precisely determined. Moreover, this study explores the quantitative relationship between the fertilizer discharge amount of the fertilizer applicator and the effective working width. Simultaneously, this research mainly focuses on analyzing the impact of the forward speed on the operational effect of layered and variable-rate fertilization. Through a series of field experiments, it was conclusively determined that the optimal fertilization effect was attained when the forward speed was set at 6 km/h. Under this condition, the average deviation in the fertilization amount was merely 2.76%, and the average coefficients of variation in the fertilizer amount uniformity in each soil layer were 7.62, 6.32, 6.06, and 5.65%, respectively. Evidently, the experimental results not only successfully met the pre-set objectives, but also fully satisfied the design requirements. Undoubtedly, this article can offer valuable methodological references for the research and development of fertilization devices tailored for diverse crops cultivated on saline–alkali lands. Full article

Air pollution concerns have led to the widespread deployment of air quality monitoring stations. While high-cost government stations provide accurate data, their deployment is limited, whereas low-cost sensors offer widespread coverage but with lower accuracy. To enhance the accuracy of measurement data from low-cost air monitoring sensors, this study proposes a Multi-Scale Convolutional Residual Time-Frequency Calibration Method (MCRTF-CM), focusing on the PM_2.5 sensor as an example. This method leverages multi-scale convolution in the feature extractor to capture diverse features at various scales using parallel convolutional kernels. Residual connections merge the original and multi-scale features, preserving the initial input for enhanced stability. The calibration module employs Gated Recurrent Units (GRUs) to capture long-term dependencies in time-series data through reset and update gates. Additionally, the Frequency Enhanced Channel Attention Mechanism (FECAM) uses Discrete Cosine Transform (DCT) to convert time-domain data to frequency-domain, assigning weights to different frequency components to enhance key features and suppress irrelevant ones. Experimental results demonstrate that MCRTF-CM outperforms optimal Long Short-Term Memory (LSTM) networks, reducing RMSE, MAE, MSE, and MAPE by 13.59%, 14.04%, 25.33%, and 8.22%, respectively, indicating its better performance. Full article

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW’s predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities. Full article

Increased application of organic fertilizer is an effective measure to improve greenhouse soil quality. However, prolonged and intensive application of organic manure has caused nutrient and certain heavy metal accumulation in greenhouse soil. Therefore, the optimal quantity of organic manure required to sustain soil fertility while mitigating the accumulation of heavy metals and other nutrients resulting from continuous application remains unclear. This study evaluated the impacts of sustained and reduced organic manure application on soil physicochemical properties, heavy metal contents, and microbial community through a 9-year greenhouse field experiment. Treatments included a control without any fertilizer (CK), conventional manure (M), and three reduced manure treatments (−25%M, −37.5%MNPK, and −50%MNPK). Compared to CK, either M treatment or manure reduction treatments either maintained or significantly elevated soil pH and soil organic matter, total nitrogen, total phosphorus, and available phosphorus. Notably, −37.5%MNPK exhibited further increases in the available nitrogen and potassium. The M treatment significantly increased in the total concentrations of cadmium, copper, lead, zinc, and the availability of chromium and zinc. However, reduced manure treatments showed no change or a significantly reduced in heavy metal availability. The −25%M and −37.5%MNPK treatments significantly improved bacterial diversity. Reducing organic manure altered microbial taxa abundance. The soil pH emerged as the primary driving factor for variation in the bacterial community structure, whereas available nitrogen, potassium, and lead were the key factors influencing fungal community structural changes. These results indicate that reducing excessive organic manure input is an effective strategy to control heavy metal accumulation, enhance soil fertility, and optimize microbial community structure. Full article

Potential applications of microwave energy, a developed form of clean energy, are diverse and extensive. To expand the applications of microwave heating in the metallurgical field, it is essential to obtain the permittivity of ores throughout the heating process. This paper presents the design of a 2.45 GHz ridge waveguide apparatus based on the transmission/reflection method to measure permittivity, which constitutes a system capable of measuring the complex relative permittivity of the material under test with a wide temperature range from room temperature up to 1100 °C. The experimental results indicate that the system is capable of performing rapid measurements during the heating process. Furthermore, the system is capable of accurately measuring dielectric properties when the real part of the permittivity and the loss tangent vary widely. This measurement system is suitable for high-temperature dielectric property measurements and has potential applications in microwave-assisted metallurgy. Full article

The relationship between the cognitive and affective dimensions of understanding has remained unexplored due to the lack of reliable methods for measuring emotions and feelings during learning. Focusing on five phases of understanding—nascent understanding, misunderstanding, confusion, emergent understanding, and deep understanding—this study introduces an AI-driven solution to measure subjective understanding by analyzing physiological activity manifested in facial expressions. To investigate these phases, 103 participants remotely worked on 15 riddles while their facial expressions were video recorded. Action units (AUs) for each phase instance were measured using AFFDEX software. AU patterns associated with each phase were then identified through the application of six supervised machine learning algorithms. Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. These findings suggest that physiological activity can be leveraged to reliably measure understanding. Further, they advance a novel approach for measuring and fostering understanding in educational settings, as well as developing adaptive learning technologies and personalized educational interventions. Future studies should explore how physiological signatures of understanding phases both reflect and influence their associated cognitive processes, as well as the generalizability of this study’s findings across diverse populations and learning contexts (A suite of AI tools was employed in the development of this paper: (1) ChatGPT4o (for writing clarity and reference checking), (2) Grammarly (for grammar and editorial corrections), and (3) ResearchRabbit (reference management)). Full article

Background and Objectives: The accumulation of fat in the body, especially visceral fat, is associated with various cardiometabolic conditions such as diabetes mellitus and fatty liver. The reasons for the accumulation of this fat are diverse. Some studies, also in the working population, have shown a clear association between sociodemographic variables and health habits with scales that assess overweight and obesity. This study aims to determine how certain sociodemographic variables, such as age, gender, and socioeconomic level, as well as certain healthy habits like physical activity and tobacco consumption, affect the levels of body and visceral fat. Materials and Methods: We conducted a descriptive and cross-sectional study involving 8590 Spanish workers. The percentage of body and visceral fat was measured using a bioimpedance analysis with a Tanita DC 430MA device. Results: Both the average values and the prevalence of elevated body and visceral fat increase with age and decrease with social class and lower levels of physical activity. These values are higher in smokers. A multivariate analysis shows that the variables most influential in increasing the risk of high levels of both body and visceral fat are age and low levels of physical activity. Conclusions: The profile of a person at high risk of having elevated body and visceral fat levels is an older male with a low socioeconomic status who smokes and leads a sedentary lifestyle. Full article

Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals. Leveraging these advantages, we introduce a novel concept, two-dimensional increment entropy (IncrEn2D), tailored for analyzing image textures. In our proposed method, increments are translated into two-letter words, encoding both the size (magnitude) and direction (sign) of the increments calculated from an image. We validate the effectiveness of this new entropy measure by applying it to MIX_2D(p) processes and synthetic textures. Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. To further validate our results, we employ a support vector machine model, utilizing multiscale entropy values as feature inputs. A comparative analysis with well-known bidimensional sample entropy (SampEn_2D) and bidimensional dispersion entropy (DispEn_2D) reveals that IncrEn_2D achieves an average classification accuracy surpassing that of other methods. In summary, IncrEn_2D emerges as an innovative and potent tool for image analysis and texture characterization, offering superior performance compared to existing bidimensional entropy measures. Full article

Lithium-ion batteries represent a significant component of the field of energy storage, with a diverse range of applications in consumer electronics, portable devices, and numerous other fields. In view of the growing concerns about the safety of batteries, it is of the utmost importance to develop a sensor that is capable of accurately monitoring the internal temperature of lithium-ion batteries. External sensors are subject to the necessity for additional space and ancillary equipment. Moreover, external sensors cannot accurately measure internal battery temperature due to packaging material interference, causing a temperature discrepancy between the interior and surface. Consequently, this study presents an integrated temperature sensor within the battery, based on PT1000 resistance temperature detector (RTD). The sensor is integrated with the anode via a flexible printed circuit (FPC), simplifying the assembly process. The PT1000 RTD microsensor’s temperature is linearly related to resistance (R = 3.71T + 1003.86). It measures about 15 °C temperature difference inside/outside the battery. On short-circuit, the battery’s internal temperature rises to 27 °C in 10 s and 32 °C in 20 s, measured by the sensor. A battery with the PT1000 sensor retains 89.8% capacity under 2 C, similar to the normal battery. Furthermore, a PT1000 temperature array sensor was designed and employed to enable precise monitoring and localization of internal temperature variations. Full article

Biological soil crusts are important components of dryland ecosystems, showing variations in appearance, morphology, and function across developmental stages. However, the methods for recording biocrust developmental stages have not been simplified and standardized. In this study, three developmental grades for both cyanobacterial crust and moss crust were defined based on visual indicators such as color, thickness, and moss height. A field survey was conducted across three precipitation regions in northern China, during which the developmental grades of cyanobacterial and moss crusts were visually recorded. Key biocrust developmental indicators, including shear strength, penetration resistance, coverage, chlorophyll a content, and bulk density were measured for each grade. The results showed that both cyanobacterial and moss crusts could be effectively classified into three developmental grades based on these indicators, with a 90% concordance between the measured indicators and the defined grading method. This finding validated that the method could accurately reflect biocrust developmental stages while simplifying field recordings. Developmental indicators in various grades of cyanobacterial and moss crusts showed a moderate (30% < CV < 100%) to strong (CV > 100%) variation, highlighting the importance of environmental heterogeneity at the regional scale. Moreover, the grading method proved effective across varying spatial scales, highlighting its broad applicability. However, its validation across the comprehensiveness of target objects and the geographical scope remains limited. Future research should focus on expanding the grading method to include lichen crust, refining it across diverse ecosystems, and exploring the integration of advanced technologies such as hyperspectral imaging and machine learning to automate and improve the classification process. This study provides a simple and effective grading method for visually recording the developmental stages of biological soil crusts, which is useful for ecological research and field applications. Full article

Risk management in construction projects is a critical process aimed at identifying, evaluating, and mitigating potential risks that could impact project performance. Preventive measures play a central role in this process, serving as proactive strategies to minimize the likelihood and impact of risks on project outcomes. This study involved 37 experts from multidisciplinary fields related to road infrastructure, ensuring a diverse and comprehensive perspective on risk evaluation and prevention. The DELPHI method was employed to systematically define key risks and their corresponding preventive measures, providing a structured foundation for further analysis. The experts evaluated 302 preventive measures across 56 risks using 4 predefined criteria: implementation costs, time required for implementation, implementation complexity, and probability of success. A multi-criteria decision making (MCDM) approach was then applied to analyze these evaluations, enabling the prioritization of preventive measures and the allocation of resources toward the most effective strategies. Additionally, fuzzy logic was employed to analyze and validate the results, providing a complementary approach to the MCDM methodology. The results of this research provide a robust framework for risk management, offering practical guidance for decision makers in the construction industry. By integrating expert judgment, systematic evaluation, and advanced analytical methods, this study delivers actionable insights and establishes a reliable methodology for enhancing the effectiveness of risk mitigation in road infrastructure projects. Full article

Conjugation of short-chain fatty acids (SDFAs) to amines containing ring structures allows for better measurement by liquid chromatography tandem mass spectroscopy (LC-MS/MS). However, collision-induced dissociation (CID) results in breaking the conjugate back to the original SCFA and amine. We therefore set out to find an amine that would remain on the SCFA after CID and create a unique daughter for selectivity of measurement. Of twenty-seven amines with ring structures, we found four that contain bicycle-type structures (two rings connected by a carbon) with nitrogen in the second ring. CID removes the second ring at the nitrogen, leaving the first ring on the daughter. Of the four amines, 4-(pyrrolidine-1-ylmethyl) benzylamine (4PyBA) showed the strongest conjugation. Conjugation of 4PyBA to SCFA (C3–C6), their isomers and their phenylated versions (and isomers) resulted in good chromatographic peaks and separation. CID resulted in unique daughters that allowed for selectivity of measurement. Using this method, standard curves were generated that show good linearity (r2 > 0.99) in the nM and μM range with lower limits of detection between 40 and 229 nM for a 10 μL sample. Finally, we used this method to measure SCFA in plasma, liver, platelets, and red blood cells, demonstrating its use in biological systems. Because SCFAs are an index of microbiome diversity in the gastrointestinal track, this method will allow us to study changes in SCFAs and the microbiome in pathologic conditions including trauma, hemorrhage, and sepsis. Full article

An eight-element MIMO antenna with a neutralization line was utilized for future 5G mm-wave applications. The MIMO configuration was designed for two ports, four ports and eight ports to validate the desired impedance and radiation characteristics. The measured results in terms of MIMO and scattering parameters correlate well with the simulated one. The printed eight-port antenna was a miniaturized size of 44 × 70 × 0.8 mm³. Roger RT/duroid 5880 substrate was used to print antennas. The presented antenna produced a vast bandwidth of 18 GHz, varying from 28 to 46 GHz, and achieved a reduced mutual coupling of 30 dB with 6.8–8.5 dBi gain. The eight-port antenna is compared with contemporary antennas considering size, isolation, impedance bandwidth, diversity characteristics and radiation properties, confirming that the presented antenna is a promising candidate for future 5G mm-wave applications. Full article