Search Results (110,215)

The main objective of this study was to investigate boar-to-boar variations in the quality characteristics of sperm from the sperm-rich fractions (SRFs) and whole ejaculates (WEs) following freezing–thawing. Several sperm attributes, such as motility patterns analyzed by the computer-assisted sperm analysis (CASA) system, mitochondrial function, membrane integrity, and DNA fragmentation were used to compare the cryo-survival of sperm from SRFs and WEs from boars with good and poor semen freezability (GSF and PSF, respectively). In this study, boars with post-thaw total motility (TMOT) more than 30% (>30%) were classified as having GSF, while those with post-thaw TMOT less than 30% (<30%) were classified as having PSF. Principal component analysis 1 (PCA1), which is the main component of the sample variation, explained approximately 75% of the variance between the GSF and PSF groups, reaffirming the reliability of post-thaw TMOT as a reliable criterion used to classify the animals. Most of the post-thaw sperm parameters of the SRFs and WEs were positively correlated. Furthermore, scatter plot analyses show stronger relationships between the analyzed post-thaw parameters of the frozen–thawed (FT) sperm of SRFs than those of WEs. Individual boar variations or the sperm source had marked effects on the quality characteristics of FT sperm. The higher TMOT, velocity straight line (VSL), and velocity average path (VAP) of FT sperm were more enhanced in the SRFs compared with the WEs of the PSF group. Furthermore, the mitochondrial function, membrane integrity, and DNA fragmentation of FT sperm were markedly higher in the SRFs than in the WEs, particularly for the poor freezability boars. We suggest that the freezability potential of sperm of the GSF group does not differ significantly between the SRFs and WEs, reaffirming that boar variability is an important factor that affects the cryo-survival of sperm. Full article

In the context of globalization, cross-cultural research is essential for understanding behaviors and values across different cultural backgrounds. The way audiences from diverse cultures interpret and accept film content significantly impacts the international dissemination and market performance of films. This study, grounded in Hofstede’s six cultural dimensions—power distance index (PDI), individualism vs. collectivism (IDV), uncertainty avoidance (UAI), masculinity vs. femininity (MAS), long-term vs. short-term orientation (LTO), and indulgence vs. restraint (IVR)—incorporates additional variables such as visual aesthetic appeal (VAA), narrative complexity (NCI), viewing motivation (VM), behavioral intentions (BIs), and brand loyalty (BL) to construct a multidimensional research framework. This framework aims to comprehensively examine the acceptance of American animated films among Chinese audiences and the cultural differences influencing such acceptance. Using structural equation modeling (SEM), this study analyzed the interrelationships between variables based on a sample of 507 participants with prior viewing experience. The findings reveal that different cultural dimensions significantly impact VM. PDI, UAI, and IDV exert significant negative influences on VM, with PDI being the most influential. Conversely, LTO and IVR do not demonstrate significant negative effects. In contrast, MAS, VAA, and NCI exhibit significant positive impacts on VM. Additionally, VM strongly influences audience acceptance, which, in turn, promotes the formation of BIs and repeated VM. This study extends the application of Hofstede’s cultural dimensions to the domain of cross-cultural media consumption, enriching the theoretical framework with additional dimensions and offering a novel perspective for cross-cultural research. Furthermore, the study uncovers the intricate interactions between cultural context and film content, proposing strategies to enhance the acceptance of cross-cultural films. These findings not only provide valuable insights for the production and marketing of animated films but also offer strategic guidance for filmmakers in diverse markets. Full article

Predicting crop performance is key to decision making for farmers and business owners. Tacna is the main olive-producing region in Perú, with an annual yield of 6.4 t/ha, mainly of the Sevillana variety. Recently, olive production levels have fluctuated due to severe weather conditions and disease outbreaks. These climatic phenomena are expected to continue in the coming years. The objective of the study was to evaluate the performance of the model in natural and specific environments of the olive grove and counting olive fruits using CNNs from images. Among the models evaluated, YOLOv8m proved to be the most effective (94.960), followed by YOLOv8s, Faster R-CNN and RetinaNet. For the mAP50-95 metric, YOLOv8m was also the most effective (0.775). YOLOv8m achieved the best performance with an RMSE of 402.458 and a coefficient of determination R² of (0.944), indicating a high correlation with the actual fruit count. As part of this study, a novel olive fruit dataset was developed to capture the variability under different fruit conditions. Concluded that the predicting crop from images requires consideration of field imaging conditions, color tones, and the similarity between olives and leaves. Full article

The cement industry is responsible for a significant portion of global CO₂ emissions, primarily due to the decarbonatization of limestone during clinker production. To mitigate this environmental impact, this study investigated the feasibility of using waste concrete fine powder, produced during the recycling of waste concrete, as a decarbonized raw material in cement clinker production. As a decarbonized material, waste concrete fine powder presents a valuable opportunity to reduce CO₂ emissions typically produced during the decarbonatization of limestone in clinker production. In addition, its use supports the recycling of construction waste, contributing to both emissions reduction and resource sustainability. In this study, samples were collected from 20 intermediate treatment plants in South Korea, where the chemical composition, particle size distribution, and carbonation rate of the fine powders were analyzed. The experimental results show that the properties of waste concrete fine powder vary significantly depending on the recycling process. Road construction aggregate production plants, which typically involve two to three crushing stages, produce fine powders with higher CaO content (28–31%) and consistent particle size distributions. In contrast, plants producing aggregates for concrete, which involve four to six crushing stages, produce powders with lower CaO content (around 20%) and greater variability in particle size. The average carbonation rate of 7.44% suggests that these fine powders can replace limestone in clinker production. It is estimated that substituting 5% of limestone with waste concrete fine powder could reduce CO₂ emissions from limestone decarbonatization by approximately 952,560 tons in 2023, representing a 3.34% decrease in total emissions from clinker production. However, it is important to note that the CO₂ emissions reduction calculation is not from a lifecycle perspective, without considering the energy-related emissions from recycling waste concrete fine powder. Nevertheless, this study highlights the potential for waste concrete fine powder to serve as a sustainable raw material for the cement industry, contributing to both CO₂ reduction and efficient recycling of construction waste. Full article

Background: This study investigates the demographic, genetic, and socioeconomic impact of the 1742–1743 plague epidemic on Córdoba, a key region within the Viceroyalty of Peru. The research focuses on the epidemic’s influence along the Royal Road (Camino Real), the main route connecting Buenos Aires and Lima, addressing a historically overlooked period with a multidimensional approach. Methods: Historical records of deaths, baptisms, and marriages from the Córdoba Archbishopric Archive were analyzed using techniques from historical demography and bioanthropology. Variables such as endogamy, exogamy, average marital distance (AMD), and consanguinity coefficients were evaluated. Results: The findings reveal a significant increase in endogamy (75–82%) and a peak exogamy index of 375 during the 1740–1744 cohort. The AMD rose to 705 km during the epidemic, indicating a shift toward marriages involving individuals from more distant regions. The total consanguinity coefficient (Ft) reached 0.00056, with the non-random component (Fn) driving this increase, suggesting heightened genetic isolation. Conclusions: The 1742–1743 plague epidemic intensified genetic isolation and consanguinity, potentially increasing homozygosity and the prevalence of recessive conditions. These changes highlight the epidemic’s long-term impact on Córdoba’s genetic diversity and demographic patterns within the colonial context. Full article

This study examined the responses of four legumes—chickpeas (Cicer arietinum L.), red kidney beans (Phaseolus vulgaris L., Taishokintoki), adzuki beans (Vigna angularis), and peanuts (Arachis hypogaea)—to storage and roasting under high-temperature and high-humidity conditions (HTC beans). Roasting enhanced antioxidant activity in HTC chickpeas and peanuts, with chickpeas also showing increased resistant starch. In contrast, kidney beans showed reduced resistant starch after storage, with minimal recovery upon roasting, while refrigeration better preserved resistant starch. For adzuki beans, roasting reduced resistant starch in control samples but not in HTC samples. Reducing sugars decreased in all beans after roasting. These findings highlight roasting as a promising method for repurposing HTC chickpeas and peanuts for functional food applications. Limitations include variability among legumes and the need for further mechanistic and sensory studies. Full article

Crowd management at turnstiles in metro stations is a critical task for ensuring safety, efficiency, and comfort for passengers. A methodology based on observation and microsimulation provides an advanced understanding and optimization of crowd flow through these turnstiles. The aim is to optimize crowd management and prevent overcrowding and delays at metro turnstiles through innovative solutions. The methodology is based on simulating passenger movements through turnstiles to observe and optimize crowd behavior. The results show that passenger decisions (e.g., choosing which turnstile to use, adjusting pace) are based on perceived crowd density, level of service, and usage of space. For instance, the number of turnstiles, their location, and the layout are important variables to be considered in the decision-making sequence. These decisions can be influenced by parameters like turnstile availability, walking paths, and real-time data (e.g., density of passengers). The methodology can help metro operators decide where to place additional turnstiles or adjust operational schedules. By simulating crowd behavior, operators can make informed decisions to reduce congestion and improve the efficiency of turnstile usage. This methodology could be implemented in various metro systems to optimize operations during different crowd conditions and peak times, ensuring smooth, safe, and efficient passenger flow. Full article

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm’s key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4–4.3% WMAPE improvement for billing cycle data and a 4.6–10.4% improvement for residential data over existing methods. Full article

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions. Full article

The current study examines the neural mechanisms underlying facial recognition, focusing on how emotional expression and mouth display modulate event-related potential (ERP) waveforms. 42 participants categorized faces by gender in one of two experimental setups: one featuring full-face images and another with cropped faces presented against neutral gray backgrounds. The stimuli included 288 images balanced across gender, race/ethnicity, emotional expression (“Fearful”, “Happy”, “Neutral”), and mouth display (“closed mouth” vs. “open mouth with exposed teeth”). Results revealed that N170 amplitude was significantly greater for open-mouth (exposed teeth) conditions (p < 0.01), independent of emotional expression, and no interaction between emotional expression and mouth display was found. However, the P100 amplitude exhibited a significant interaction between these variables (p < 0.05). Monte Carlo simulations analyzing N170 latency differences showed that fearful faces elicited a faster response than happy and neutral faces, with a 2 ms delay unlikely to occur by chance (p < 0.01). While these findings challenge prior research suggesting that N170 is directly influenced by emotional expression, they also highlight the potential role of emotional intensity as an alternative explanation. This underscores the importance of further studies to disentangle these effects. This study highlights the critical need to control for mouth display when investigating emotional face processing. The results not only refine our understanding of the neural dynamics of face perception but also confirm that the brain processes fearful expressions more rapidly than happy or neutral ones. These insights offer valuable methodological considerations for future neuroimaging research on emotion perception. Full article

With rapid economic development, the amount of the municipal solid waste (MSW) generated has increased dramatically. To improve the socio-economic benefits and environmental impacts of the low-carbon management of MSW, it is crucial to identify the drivers of Greenhouse Gas (GHG) emissions from MSW treatment and assess their systematic and comprehensive benefits. The factor decomposition method is one of the most commonly used methods focused on identifying GHG emission-influencing factors, while the system dynamics (SD) method is commonly used to analyze the causal relationships between linear and nonlinear variables in complex dynamic systems. Unlike existing studies that account for and evaluate MSW from a static perspective, this paper innovatively combines the LMDI-SD model to identify and quantify the GHG emission drivers of MSW and evaluate the benefits of decarbonizing the MSW management in China from a comprehensive and systematic perspective. The results show that the dominant factor driving MSW GHG emissions from 2010 to 2022 is the economic development factor, ∆E_ED, while the intensity of MSW generation ∆E_GI and the structure of MSW treatment ∆E_TS play a stronger inhibiting role. Based on this, the SD model is constructed to simulate different scenarios, and the analysis shows that increasing the waste separation rate (S3) is the most effective measure to improve the socio-economic benefits and environmental impacts of the system. Compared with the base scenario, the socio-economic benefits and environmental impacts in 2050, for example, are increased by 82.8% and 43.4%, respectively. Improving the utilization rate of landfill gas (S1), reducing the per capita amount of MSW generated (S4) and increasing the incineration rate of MSW (S2) also have significant advantages for the improvement of benefits. Finally, some policy recommendations for the improvement of the comprehensive benefits of low-carbon MSW management systems are proposed to help policymakers make appropriate decisions. Full article

Background and Objectives: We aimed to evaluate the current uterotonic administration practices among anesthesiologists and obstetricians and gynecologists (OBGYNs) during cesarean section (CS), focusing on variations in approaches for low- and high-risk postpartum hemorrhage (PPH) cases. The objective was to identify key differences and provide evidence that could contribute to the development of standardized national protocols for uterotonic usage. Materials and Methods: A snapshot online survey was employed between October 2021 and January 2022 and distributed to anesthesiologists and OBGYNs from university-affiliated, government, and private hospitals across Turkey, consisting of 23 questions addressing demographic data, institutional PPH rates, first-line uterotonic choices, administration methods, and dose adjustments for low- and high-risk PPH cases. Specific questions also targeted uterotonic usage in the presence of comorbidities such as pre-eclampsia and cardiac disease. Results: There were 204 responses (54% anesthesiologists and 46% OBGYNs) out of 220, yielding a response rate of 92.7%. Oxytocin was the most common first-line uterotonic for CS with low-risk PPH (99.1% of the anesthesiologists and 96.8% of the OBGYNs). In total, 60% of the anesthesiologists favored an intravenous (IV) bolus followed by infusion, while 56.4% of the OBGYNs preferred IV infusion alone (p < 0.001). For CS with high-risk PPH, approximately half of the participants reported increases in oxytocin dose, while 26.4% of the anesthesiologists and 20.2% of the OBGYNs opted for combined oxytocin and carbetocin use. During intrapartum CS, 69.1% of anesthesiologists and 77.7% of OBGYNs reported no change in dose. However, 11.8% of the anesthesiologists indicated combining oxytocin and carbetocin (p < 0.05). In managing pre-eclampsia and cardiac disease, the anesthesiologists were likely to reduce uterotonic doses (15.5%) and avoid methylergonovine (35.5%) compared to the OBGYNs, who reduced doses less frequently (4.3%), but 79.8% of the OBGYNs avoided methylergonovine (p < 0.001). Conclusions: There was considerable variability in uterotonic administration practices between the anesthesiologists and OBGYNs. Full article

Many information-theoretic quantities have corresponding representations in terms of sets. Many of these information quantities do not have a fixed sign—for example, the co-information can be both positive and negative. In previous work, we presented a signed measure space for entropy where the smallest sets (called atoms) all have fixed signs. In the present work, we demonstrate that these atoms have natural algebraic behaviour which can be expressed in terms of ideals (characterised here as upper sets), and we show that this behaviour allows us to make bounding arguments and describe many fixed-sign information quantity expressions. As an application, we give an algebraic proof that the only completely synergistic system of three finite variables X, Y and Z=f(X,Y) is the XOR gate. Full article

Alluvial aquifers are vital for agricultural communities in semiarid regions, where groundwater quality is often constrained by seasonal and spatial salinity variations. This study employed geostatistical methods to analyze the spatial and temporal variability of electrical conductivity (EC) and the sodium adsorption ratio (SAR) and elaborate an indicative quality map in the Mimoso Alluvial Aquifer, Pernambuco, Brazil. Groundwater samples were collected and analyzed for cations, total hardness (TH), and the percentage of sodium (PS). Moreover, the relation between EC and the SAR was used to determine the groundwater quality for irrigation. Cation concentrations followed the order Ca²⁺ > Mg²⁺ > Na⁺ > K⁺. EC and the SAR exhibited medium to high variability, with spatial dependence ranging from moderate to strong, and presented a strong cross-spatial dependence. Results showed that sequential Gaussian simulation (SGS) provided a more reliable groundwater classification for agricultural purposes compared to kriging methods, enabling a more rigorous evaluation. Based on the strong geostatistical cross correlation between EC and RAS, a novel water quality index was proposed, properly identifying regions with lower groundwater quality. The resulting spatial indicator maps classified groundwater as suitable (64.7%), restricted use (2.08%) and unsuitable (2.38%) for irrigation. The groundwater quality maps indicated that groundwater was mostly suitable for agriculture, except in silty areas, also corresponding to regions with low hydraulic conductivity at the saturated zone. Soil texture, rainfall, and water extraction significantly influenced spatial and temporal patterns of groundwater quality. Such correlations allow a better understanding of the groundwater quality in alluvial valleys, being highly relevant for water resources management in semiarid areas. Full article

Soil health is at the root of agricultural sustainability, and small-scale vegetable farmers are becoming an increasingly important part of the US food system. These farmers face unique challenges when it comes to managing soil on their farms. These challenges include reliance on intensive production practices, the use of primarily organic inputs with difficult to calculate nutrient concentrations, and lack of access to formal education tailored to their needs. We surveyed farmers at 100 small-scale vegetable farms in Minnesota to (1) develop a better baseline understanding of how small-scale vegetable farmers utilize key soil health practices including nutrient management, cover crops, and tillage; (2) explore how farm demographics influence the adoption of soil health practices; and (3) determine educational priorities to better support these growers. Here, we report a lack of understanding about the nutrient contributions of compost, which is often applied at very large volumes without guidance from soil test results, with implications for nutrient loading in the environment. Farmers in our study had high rates of cover crop adoption relative to other farmers in the region despite several barriers to using cover crops. More experienced farmers were more likely to utilize more tillage, with more use of deep tillage implements on larger farms. Overall, organic certification was correlated with higher adoption of soil health practices including utilization of soil tests and cover crop use, but it was not correlated with tillage. Other demographic variables including land access arrangement and race did not meaningfully influence soil health practices. Our findings suggest a need for more research, outreach, and education targeted to vegetable farmers about how to interpret laboratory soil test results, and how to responsibly utilize organic inputs including vegetative compost and composted manure at rates appropriate for crop production in a diversified farm setting. We also report a need to compensate farmers for their labor to incentive cover crop use on small farms, and a need for more research and support for farmers in the 3–50-acre range to utilize reduced tillage methods. Full article