Fluctuation Trend Research Articles

Seasonal persistence of the nitrogen oxides production in Mexico City

AbstractThis study investigates the seasonal influence on the nitrogen oxide NOx pollution records at four monitoring sites in Mexico City from 2010 to 2018. The analysis employs the second-order structure function to examine the trends in NOx concentration fluctuations. The findings reveal that the fluctuations follow a power law pattern characterized by Hurst exponents, predominantly in the statistical persistence regime, with a scaling range spanning three orders of magnitude. Specifically, the autumn period exhibits fluctuations with an exponent of $$\overline{H}=0.72$$ H ¯ = 0.72 , indicating relatively smaller fluctuations compared to other seasons, but with the potential for NOx concentrations to surpass those in other periods. In contrast, for spring, summer, and winter, the fluctuations are characterized by exponents of$$\overline{H}=0.59$$ H ¯ = 0.59 ,$$\overline{H}=0.61$$ H ¯ = 0.61 , and$$\overline{H}=0.62$$ H ¯ = 0.62 , respectively, demonstrating greater fluctuations with lower NOx concentrations compared to autumn. These results are consistent with various studies conducted worldwide. Additionally, a negative correlation between NOx and ozone (O3) has been established during the winter season, as NOx and O3 fluctuations display persistent and anti-persistent behavior, respectively.

Changes in the soil and vegetation cover of the Kochubey biosphere station under global warming in recent years

Objectives: The study and long-term observations of changes in the spatial structure of vegetation cover of pasture landscapes of the Kochubey Biosphere Station (KBS) due to global climate warming. Arid pasture landscapes, due to the peculiarities of their spatial structure (the presence of semi-desert vegetation groupings) are a good indicator of modern climatic changes. To achieve this goal, the spatial structure and the current state of the arid landscapes of the Northwestern Caspian Sea in the territory of the KBS have been studied. The analysis of the biological diversity of species was carried out. Also the characteristics of the common features of seasonal dynamics of landscapes were studied. Moreover, trends of climatic changes in the landscape structure of the territory and trends in anthropogenic transformation of arid landscapes of the region were identified. It has been revealed that the main reasons for the reduction of biodiversity are habitat fragmentation, the introduction of alien species, the settlement of native species outside the range, ecotonization and “islandization” of pasture ecosystems, and the displacement of range boundaries. Since the goal of preserving the components of vegetation biodiversity is to reduce the pressures caused by climate change, adaptation measures should be aimed at reducing the rate of fragmentation and degradation of pasture landscapes. Monitoring observations have shown that over the last 20-century period there have been rhythmically repeating changes in climate and soil and vegetation cover, which indicates high mobility of vegetation. It has been established that the contribution of anthropogenic land degradation to desertification is confirmed by a significant linear trend in interannual fluctuations in indicators of pasture digression in the region. It has been established that the contribution of anthropogenic land degradation to desertification is confirmed by a significant linear trend in interannual fluctuations in indicators of pasture digression in the region. Due to excessive pasture digression, “islands” of anthropogenic desertification are formed here, the lifespan of which is determined by human influence and precipitation fluctuations.

Industrial agglomeration, environmental regulation, and high‐quality development in rural China: From the perspective of spatial effect

AbstractExplore the interaction and spatial spillover effects between industrial agglomeration, environmental regulation, and high‐quality development of agriculture and rural areas, formulate appropriate industrial and environmental policies for different regions, guide the orderly agglomeration of agricultural sectors, and adjust the intensity of environmental regulation, which will help achieve high‐quality development. This manuscript firstly deconstructs the internal logic among industrial agglomeration, environmental regulation, and high‐quality development. Secondly, the Super‐SBM model is used to calculate the high‐quality development efficiency in rural China from 2000 to 2020. Finally, the Spatial Dubin Model (SDM) is used to analyze the spatial spillover effects and regional differences of industrial agglomeration, environmental regulation, and their integration impact on high‐quality development in rural China. The results show that: ① From 2000 to 2020, the high‐quality development efficiency shows a downward trend in fluctuation, and high‐quality development needs to be improved. ② There are significant regional differences in the effects of industrial agglomeration and environmental regulation on the high‐quality development efficiency. Industrial specialization agglomeration can effectively improve the high‐quality development efficiency, while industrial diversification agglomeration has uncertain effects. ③ The integration of environmental regulation and diversification agglomeration inhibits the high‐quality development efficiency, while the integration with specialization agglomeration has a stronger effect on improving high‐quality development efficiency. It is necessary to further enhance the effect of diversification agglomeration.

Internal Combustion Engine Fault Detection Based on Random Convolutional Neural Networks

The internal combustion engine plays a very important role in many fields, and when the internal combustion engine fails, if it is not found in time, it may cause continuous damage to the internal combustion engine, and further affect the life of the entire mechanical system. To solve the above problems, this paper proposes fault detection of internal combustion engines based on a random convolutional neural network. The cylinder burst noise signal of the internal combustion engine is decomposed by a stationary wavelet, and the inclusion matrix is composed of partial decomposition coefficients. Using singular value theory, the singular value containing matrix is extracted as the characteristic of cylinder burst noise signal. The singular value is used as the input of a random convolutional neural network to train and identify faults. The AdaBound optimizer was then applied to adapt the learning rate to changes, thereby accelerating the weight update of the model. At the same time, the neurons in the structure are randomly deactivated by dropout technology to prevent complex collaborative responses to the training data, and the diagnosis results of each network model are integrated by Dempster synthesis rules. The experimental results show that the minimum mean square error of 0.00165 is achieved when there are 15 neurons in the hidden layer, and it gradually increases as the number of neurons increases. Therefore, it is determined that there should be 15 neurons in the hidden layer, and the trainLM algorithm is used. The decision factors for training, validation, cross-validation, and overall data are 0.98947, 0.98597, 0.97738, and 0.9801, respectively, all reaching the control accuracy of 0.95, which indicates that the random convolutional neural network proposed has a high accuracy for internal combustion engine fault detection. For the main bearing Y-directional force, as the gap increases, its fluctuation trend strengthens; the main bearing force changes within a small range at first, then increases sharply when the gap reaches 0.2[Formula: see text]mm, and some main bearings even experience significant impact loads.

Systematic-ecological estimation of seasonal activity of dehydrogenase in drained swamp soils of interstream of Ob and Tom

Drained peat soils (Histosols) were studied. In the mode of weak and moderate drainage, the trend of seasonal fluctuations (quadratic parabola) shows a weekly average increase in dehydrogenase activity with a weekly average deceleration, in the intensive mode – a weekly average deceleration with a weekly acceleration for June–October. A reliable nonlinear relationship of enzyme activity was revealed: positive – with redox potential and pH, multidirectional – with soil bulk moisture and soil temperature. According to canonical analysis, the discussed set cumulatively determines the seasonal dehydrogenase activity by 50–81%. The hydrological regime is statistically proved as a dominant factor. A negative conjugate interaction of dehydrogenase and peroxidase activity has been established, confirming their participation in the biochemical transformation of organic matter as a whole.

Fluctuation trend of inflammatory indexes related to gestational diabetes mellitus from second trimester to third trimester of pregnancy

ObjectiveThis study aims to assess the prognostic and diagnostic value of inflammatory indexes related to gestational diabetes mellitus (GDM) from the second trimester to the third trimester of pregnancy.Materials and methodsIn this study, we randomly selected 65 pregnant women diagnosed with GDM at our hospital from December 2022 to June 2023 to form the GDM group (n = 65). Additionally, 65 pregnant women at the same gestational weeks without GDM were selected as the Normal group (n = 65). We collected gestational information and serum samples at 24 and 36 weeks of gestation from the participants. The levels of NLRP3, IL-1Ra, and TBP-2 were determined using enzyme-linked immunosorbent assay (ELISA) to explore their changes during pregnancy. Further, this study analyzed the changes in the levels of NLRP3, IL-1Ra, and TBP-2 at 24 and 36 weeks of gestation in GDM patients and their correlation with gestational diabetes mellitus.ResultsThe study showed that pre-pregnancy body mass index (BMI), neonatal weight, gestational hypertension, and macrosomia are significantly associated with the occurrence of GDM (P < 0.05). Statistical analysis comparing the normal and GDM groups found no significant changes in the levels of NLRP3, IL-1Ra, and TBP-2 with the progression of gestation in the normal group. In contrast, in the GDM group, the levels of IL-1Ra in serum samples at 24 and 36 weeks were significantly increased (P < 0.05) while the levels of NLRP3 and TBP-2 were significantly reduced (P < 0.05). At 36 weeks, there was a positive correlation between the levels of NLRP3, IL-1Ra, and TBP-2. Compared to the normal group, the overall levels of NLRP3, IL-1Ra, and TBP-2 in the GDM group were lower (P < 0.05) and the weight of the newborns was significantly correlated with these three indicators (P < 0.05), specifically newborn weight increased with the levels of NLRP3 and TBP-2 but decreased with the increase of IL-1Ra (P < 0.05). Multifactorial logistic regression analysis further revealed that NLRP3 is an independent factor influencing GDM (P < 0.05). ROC curve analysis of the NLRP3 level at 24 weeks of gestation found that NLRP3 has a good value in predicting GDM (AUC = 0.720, 95%CI 0.630–0.809, P < 0.001) and the combined prediction of NLRP3, IL-1Ra, and TBP-2 also showed a good predictive value for GDM.ConclusionThe changes in NLRP3, IL-1Ra, and TBP-2 persisted throughout the 24 to 36 weeks of gestation, playing an important role in predicting the occurrence of GDM and the weight of the newborn.

A new framework for ultra-short-term electricity load forecasting model using IVMD–SGMD two–layer decomposition and INGO–BiLSTM–TPA–TCN

Accurate forecasting of electricity loads is crucial for the development of electricity scheduling and supply services. With the increase in distributed electricity energy sources and the complexity of electricity systems, the strong volatility of load changes brings more challenges to the reliability of load forecasting. Therefore, we propose a novel ultra-short-term electricity load forecasting model using improved two-layer decomposition and an improved deep learning model with temporal pattern attention based on improved northern goshawk optimization (INGO). First, the load data were decomposed thoroughly using the two-layer decomposition model of improved variational mode decomposition (IVMD) with parameter optimization by INGO and symplectic geometry mode decomposition (SGMD) to improve the interpretability of the subsequences. Subsequently, INGO is used to optimize the parameters in bidirectional long short-term memory (BiLSTM). Temporal pattern attention (TPA) is added to BiLSTM, which extracts complex relationships from the hidden neurons of BiLSTM and selects relevant information from different time scales. After predicting and reconstructing the subsequences, the temporal convolutional network (TCN) prediction model is used to perform error correction to improve the final prediction accuracy. Because many government reports and policy information are summarized and published quarterly, to provide information support, we divide the electricity load datasets of two countries by quarters before forecasting. By performing multiple sets of experiments on the two datasets, it is demonstrated that the proposed model has high precision and robustness, and the obtained ultra-short-term electricity load forecasting results can accurately fit the load fluctuation trend.

Evaluation and control strategy analysis of influenza cases in Jiujiang City, Jiangxi Province, China from 2018 to 2022.

According to World Trade Organization (WTO) statistics, the incidence of seasonal influenza in China has been on the rise since 2018. The aim of this study was to identify and investigate the influence of factors related to the incidence of four common types of influenza viruses. Data of patients with common cold and associated virus infections are described, and a logistic regression model based on gender, age and season was established. The relationship between virus type and the above three factors was analyzed in depth and significant (p<0.05) associations noted. We noted a fluctuation trend, with the infection rate of influenza virus showing an upward trend from 2018 to 2019 and from 2021 to 2022 and a downward trend from 2019 to 2021. The total number of cases in adolescents aged 18-30 years was higher than that in the elderly. The impact of different types of influenza virus on the population ranked from large to small, with special roles played by Influenza B/Victoria, H3N2, Influenza A/H1N1 pdm and Influenza B/Yamagata.

Heat transfer-deformation characteristics and fracture damage analysis during LN2 freeze-thaw process in different rank coals

Exploring the heat transfer and deformation characteristics of coal bodies of different coal ranks during the freeze-thaw process is of significant importance for analyzing the fracture mechanism under the effect of liquid nitrogen (LN2). This experiment targets lignite, bituminite, and anthracite under both saturated and dry conditions. A real-time temperature-strain monitoring system was employed to observe the heat transfer and deformation characteristics of coal samples with different ranks throughout the freeze-thaw cycle. Additionally, a nuclear magnetic resonance system was utilized to examine the characteristics of pore damage before and after fracturing. The findings reveal: (1) During the freeze-thaw process, the absolute value of the temperature evolution rate for dry coal samples shows a negative correlation with coal rank, indicating a close link between temperature diffusion and intrinsic coal properties like oxygen content and porosity. (2) For saturated coal samples, the absolute value of the temperature change rate during freezing decreases as the coal rank increases, with the opposite trend observed during thawing. The phase change effect of water in fractures during freezing can enhance internal temperature diffusion in the coal body, while it acts as an inhibitor during thawing. (3) Based on the trend of strain fluctuations, the coal body deformation process during the freeze-thaw cycle can be segmented into seven stages, summarizing the general mechanisms of deformation failure. (4) Under saturated conditions, the amplitude of elastic deformation for each sample is negatively correlated with coal rank, with the sequence for dry coal samples being bituminite > anthracite > lignite. (5) The formation of a sealed space at the beginning of freezing is identified as a necessary condition for deformation during the freeze-thaw process, with the formation and strength of the sealed space depending on the temperature diffusion rate, moisture content, and inherent properties of the coal sample.

Климатические изменения годового стока рек Северного Приохотоморья

The aim of this study was to assess the climatic changes in the annual river runoff of the Northern Okhotsk Sea region within the Magadan region. In the present work the analyzed series end with data for 2019. Observation series of river runoff at eight hydrological stations were used in this work. By the method of hydrological analogy, these series were brought to a multiyear period by restoring year-on-year values. Thus, the series of annual runoff for 1958–2019 were analyzed. The research method was the analysis of runoff time series for the presence of homogeneity in the mean (Student’s criterion), as well as spectral analysis. Comparison of mean long-term values of runoff layers for two periods (1958–2000 and 2001–2019) showed that the “norm” increased by 5–37%. It should be noted that these changes are not random according to the Student’s criterion with significance level of 5%. The presence of a trend in long-term water availability fluctuations is also evidenced by the time course of the dynamic mean annual runoff. Analysis of the runoff series, mean annual air temperature and total annual precipitation made it possible to conclude that annual runoff increase is caused by climatic changes, mainly by an increase in precipitation. It is possible that runoff increase is also due to the thawing of ice in the permafrost strata. But you should pay attention to the fact that with climate warming, moisture losses for evapotranspiration increase, primarily due to the growth of trees and shrubs. Moreover, this effect is more noticeable in large catchments. A 6-year cyclicity was revealed in the long-term fluctuations of annual runoff. These fluctuations are most likely a non-stationary complex Markov process. The results of this work will be useful for hydrological calculations, long-term and ultra-long-term runoff forecasts, and studying the river – sea ecosystems. Further hydrological studies should be carried out with the obligatory study of evapotranspiration in various landscape conditions.

Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

ABSTRACTAccurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

Utilizing 3D Point Clouds and Deep Learning Technology in Multimedia Instruction for Sports Mechanics

In this paper, an optimization model based on 3D point cloud and deep learning theory is constructed, which improves the fluency and accuracy of multimedia sports operation. The model has performed well in sports mechanics teaching and proved its outstanding performance. It is found that the changes of the three kinds of point clouds have different laws, and the spherical neighborhood gradually decreases, while the adjacent neighborhood shows an upward fluctuation trend. The neighborhood of cylinder shows linear and nonlinear characteristics. The parameter curve of point cloud model fluctuates greatly, and the curve corresponding to function output value fluctuates widely, while the curve corresponding to feature extraction method fluctuates relatively little. The ability training index plays a significant role in improving the model output, the multimedia input index fluctuates well, and the teaching method index shows an approximate linear change law. The accuracy of the model is verified by data calculation method, which provides theoretical guidance for multimedia sports mechanics teaching.

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran’s I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

The mechanical coupling effect of α and β phases in Ti6Al4V alloy undergoing laser shock peening: A molecular dynamics study

In the dual- or multi-phase metallic materials, the influence of different microstructural constituents on the plastic strain/stress distribution is still an area of great controversy. Herein, the heterogeneous deformation behavior of α and β phases in Ti6Al4V duplex titanium alloy undergoing laser shock peening (LSP) has been investigated by molecular dynamics simulations, and the corresponding typical microstructural features are characterized at varied strains. Also, the relationship between the theoretical microstructural morphologies and the micro stress is revealed. It evidently indicates that, the grain refinement process can be summarized as: (i) dislocation motions1 interacting with deformation twins in α phases, (ii) a combination of dislocation propagations and the deformation-induced phase transition from β phase to α phase. In addition, unlike the α single-phase titanium alloy, the representative stress – strain curve of α + β titanium alloy presents a gentle fluctuation trend with smaller amplitude value, suggesting an occurrence of the coupling deformation behavior between α and β phases. The initial interfacial dislocation nucleation prefers to site in the α/α interface rather than the α/β interface, then emitting to the interior of α grains. Features of dislocation slip, twin growth, stacking fault, and phase transformation dominate the release of LSP-generated micro stress, while grain and interphase boundaries, as well as dislocation locks, contribute to the increment of the LSP-generated micro stress.

Evolution characteristics of an induced electric charge signal and identification method of charge for instability failure in loaded coal

A coal-loaded charge induction monitoring system is developed to effectively forecast the dynamic disasters caused by coal failure. Specifically, a digital finite impulse response (FIR) filter is designed to denoise and filter the signal, and the time-frequency domain evolution of induced charge signals is analyzed during coal failure experiments. The quantitative relationships between the induced electric charge and stress-strain energy, and ultimately, between induced electric charge and coal deformation/failure, are revealed. Ultimately, the electric charge sensor exhibits high signal collection frequency and high sensitivity, and the FIR low-pass filter constructed in MATLAB effectively denoises and filters induced charge signals. The main frequency range of the white noise is 50–500 Hz, and the main frequency of the charge signal induced by coal deformation and failure is concentrated in the range of 0–50 Hz. The optimal distances for monitoring cubic and cylindrical raw coal samples using this sensor are 9 mm and 11 mm, respectively. Notably, strain energy is released faster when it can dissipate more readily, and induced charge pulses become denser when more intense signals produce large fluctuations. A method is proposed to identify coal deformation and failure based on changes in the induced electric charge. This study provides a new means of monitoring the early warning signs of dynamic coal mine disasters. Based on our experimental results and conclusions, a new method is proposed to identify coal deformation and failure based on changes in the induced electric charge. The precursor to the moment of coal failure can be identified by monitoring the amplitude of the induced charge, the dynamic trend of fluctuation, and the cumulative number of induced electric charge pulses during the process of coal deformation.

Investigation of passenger thermal satisfaction across multi-space transitions in high-speed railway stations: A case study in cold regions of China

Passengers in high-speed railway stations experience multiple spatial transitions; however, existing research has typically examined thermal comfort in isolation, neglecting the entire sequence of thermal experiences. This study investigates passenger thermal comfort throughout the departure process, specifically analyzing two entry modes: Mode 1 (entry via the outdoor square) and Mode 2 (entry via the underground passage). Utilizing behavioral data from 17,354 passengers surveyed in a typical high-speed railway station, we replicated these conditions in climate chamber experiments. Participants were exposed to 30 combinations of outdoor environments and four indoor temperatures (24 °C, 26 °C, 28 °C, and 30 °C). Thirty subjects, dressed in 0.49 clo insulation garments, assessed thermal satisfaction (TSAT) in each space and overall thermal satisfaction (OTSAT) for the departure process through questionnaires. Results indicated that high outdoor temperatures led to lower satisfaction in Mode 1 compared to Mode 2. Strong correlations were observed between TSAT in the waiting room and check-in area with OTSAT, underscoring their significant impact on OTSAT. Optimal thermal sequences were identified as O-26-26-26-24 for Mode 1 and U-28-26-24-24 for Mode 2, achieving OTSAT values of 1.11 and 1.12, respectively. The study also proposed two effective thermal sequences: a downward trend and a fluctuation trend, highlighting the benefits of initial discomfort followed by improvement. The recency effect emphasized the importance of effective thermal management in the waiting room and check-in area to enhance OTSAT. These findings provide valuable insights for optimizing thermal environment control in high-speed railway stations, balancing passenger comfort with energy efficiency.

Impact of traffic signs on driving speed at mountain highway tunnel entrances − The role of low-volume intermittent information

Driver’s perception of speed is the basis of driving safety, and installing speed limit signs at tunnel entrances is an intuitive means of controlling a driver’s driving speed. Therefore, tunnel entrance signs should be set up effectively to ensure each signage can fulfil its intended effect. Quantifying the information volume conveyed by traffic signs, discovering the impact of different information volumes on driving speeds, and understanding the effects of typical signs are the prerequisites for the effective use of signage. This study collected the speed variations of 40 drivers driving at nine mountainous highway tunnels with different entrance traffic signs through a naturalistic driving real vehicle test. The effect of low-volume intermittent information at tunnel entrances is identified in terms of the black hole effect on drivers (BHD), white hole effect on drivers (WHD), velocity fluctuation trend (VFT), and effect of speed limit signs (ELS). The study’s results confirm that the effect of speed limit signage with traffic sign information volume (TSIV) in the range of 8.904 to 33.318 bit is significant. In comparison, the sign will lose its effectiveness in guiding drivers to control their speed when TSIV is greater than 58.641 bits. Using low-volume information speed limit signs or tunnel warning signs repeated twice before entering a tunnel can alert drivers and regulate their driving speeds. The speed limit sign (sign ①) for each type of vehicle is set individually, and the combined speed limit and no lane change sign (sign ②) used in two stacks have a strong speed control effect. Moreover, the sign ②, when used alone, has the best timeliness; the effective distance of speed control can reach 1522 m; using it before short tunnels or long tunnels less than 1500 m can achieve better effectiveness.

Cross-species real time detection of trends in pupil size fluctuation.

Pupillometry is a popular method because pupil size is easily measured, sensitive to central neural activity, and associated with behavior, cognition, emotion, and perception. Currently, there is no method for online monitoring phases of pupil size fluctuation. We introduce rtPupilPhase - an open source software that automatically detects trends in pupil size in real time, enabling novel implementations of real time pupillometry towards achieving numerous research and translational goals. We validated the performance of rtPupilPhase on human, rodent, and monkey pupil data and propose future applications of real time pupillometry.

Advancing Process-Oriented Geographical Regionalization Model

Existing regionalization methods have largely overlooked the temporal dimension, leading to outcomes that predominantly reflect spatial differentiation of regional variables only at a singular temporal instance, rather than across their entire evolutionary process. In response, this research proposes a novel model for geographic process regionalization based on spatial time-series (STS) data. This model is designed to amalgamate adjacent entities exhibiting analogous trends in regional variable fluctuations, while segregating those with disparate trajectories. The study starts by establishing methodologies for the assessment of three distinct types of STS data similarities: value-based, shape-oriented, and periodic patterns. Following this, a tailored regionalization methodology specific to STS data is delineated. This methodology facilitates a quantitative analysis of time-series variables within each geographical region, leading to the development of a process-oriented geographic boundary model (GBM). The efficacy of this model is validated through empirical testing with synthetic data sets and further elucidated via case studies focusing on the dynamics of population and pollution levels, underscoring its substantial applicability in both human and physical geography. The proposed methodology is adaptable for any time-series data that incorporate locational information for regionalization.

Revealing Greenhouse Gas Emission and Nitrogen Fertilizer Destination: A Case Study in Chengdu Plain Cultivation Industry

The cultivation industry occupies a large proportion of greenhouse gas emissions in agriculture. Assessing greenhouse gas emissions from the cultivation industry is pivotal for mitigating emissions and promoting sustainable cultivation. Utilizing greenhouse gas emission calculation methods recommended by the Intergovernmental Panel on Climate Change (IPCC) and other methods, this work evaluated annual emissions and the emission structure of major crops from 2005 to 2021 in the Chengdu Plain, a significant agricultural region in Southwest China. We identified nitrogen fertilizer as the primary contributing factor to high emissions from cultivation production. Subsequently, we analyzed the trend and utilization of nitrogen fertilizer, which proposes essential strategies for reducing greenhouse gas emissions. The results showed that greenhouse gas (GHG) emissions from the cultivation industry in the Chengdu Plain exhibited a growth, fluctuation, and eventual decline trend from 2005 to 2021. The emissions increased from 5,148,900 t in 2005 to 6,289,700 t in 2009, representing a 22.16% increase, and subsequently decreased to 5,109,900 t in 2021, marking a 23.31% decrease. Nitrogen fertilizer application emerges as the primary source of GHG emissions, constituting approximately half of the total, with nitrogen fertilizer manufacturing contributing significantly as well, collectively amounting to about 70%. We also found that the proportion of greenhouse gas emissions attributed to cash crop cultivation has gradually increased over the last decade. Among these crops, vegetables exhibit the highest emissions, comprising nearly half of the total emissions from 2019 onwards. However, the nitrogen fertilizer use efficiency of cash crops is less than 30%, with higher nitrogen surplus, ammonia volatilization, and nitrogen leaching per unit area, and the total amount is higher than that of grain crops. Among cash crops, vegetables exhibit the highest amount of nitrogen surplus, ammonia volatilization, and nitrogen leaching, constituting nearly half of the total amount in the study area since 2019. Our findings significantly affect sustainable and low-carbon cultivation industry development in the study area.