Changes In Spatial Distribution Research Articles

Assessing the tradeoffs in emissions, air quality and health benefits from excess power generation due to climate-related policies for the transportation sector

As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM2.5 ∼ 0.06 μg m3, ΔNO2 ∼ 0.3 ppbv, ΔO3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM2.5 ∼ 1.2 μg m3, ΔNO2 ∼ 1.1 ppbv, ΔO3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO2, followed by PM2.5 and O3. However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.

Exposure of Peregrine Falcons to Halogenated Flame Retardants: A 30 Year Retrospective Biomonitoring Study across North America.

Compared to aquatic ecosystem, terrestrial systems have been subjected to fewer investigations on the exposure to halogenated flame retardants (HFRs). Our study utilized peregrine falcon eggs collected from multiple habitats across North America to retrospectively explore both spatial distribution and temporal changes in legacy (e.g., polybrominated diphenyl ethers) and alternative HFRs over a 30 year period (1984-2016). The results reveal intensive HFR exposure in terrestrial ecosystems and chemical-specific spatiotemporal distribution patterns. The correlations between egg levels of the selected HFRs and human population density clearly illustrated a significant urban influence on the exposure of this wildlife species to these HFRs and subsequent maternal transfer to their eggs. Temporal analyses suggest that, unlike aquatic systems, terrestrial ecosystems may undergo continual exposure to consistently high levels of legacy HFRs for a long period of time. Our findings collectively highlight the effectiveness of using peregrine eggs to monitor terrestrial exposure to HFRs and other bioaccumulative chemicals and the need for continuous monitoring of HFRs in terrestrial ecosystems.

Assessment Of Remote Sensing Approach For Urban Ecological Quality Evaluation In Pekanbaru City, Riau Province Indonesia

There are obstacles in estimating environmental dynamics behind its convenience, beginning with the development of effective policies for sustainable urban development. The objectives of this research were to comprehend the ability and performance of ecological indices integration and to identify the spatial distribution of changes from 2018 to 2021 in Pekanbaru City, Riau province, Indonesia. This study employed remote sensing data to create ecological parameters including the build-up index, vegetation index, soil index, and moisture index, as well as principal component analysis to generate ecological index integration. The findings indicate a correlation of over 90% among these parameters from 2018 to 2021. Overall, there has been a significant decrease in the ecological quality index’s high-quality categories, such as good and excellent, covering a total of 19.6% over 127 km². Conversely, the poor ecological quality category increased to 2.2%, encompassing an area of 15 km², up from the initial 21.2% covering 122 km². Additionally, the fair and moderate categories also experienced increases of 4% and 13.4%, respectively, reaching 28 km² and 84 km². The study area’s ecological quality is largely affected by increased anthropogenic activities, leading to a drastic decrease in the presence of ecological quality in the good and excellent categories. The importance of spatial planning is emphasized to incorporate aspects of ecological assessment rather than solely focusing on increasing economic activity. This outcome can be used to respond to the concept of sustainable development by caring for the ecological environment, particularly in urban areas, and mitigating ecological damage.

Dynamics of changes in broiler spatial distribution induced by a robot with autonomous navigation along the growing cycle

Welfare problems in broiler chickens are associated with accelerated growth in high density and barren environments. Encouraging broiler movement yields benefits by increasing locomotion, foraging, and environmental exploration. Robot sensors with autonomous navigation capabilities developed to collect husbandry information could collaterally induce movement of birds while traversing the chicken houses. This study examines the short-time dynamic of changes in broiler spatial distribution within the robot's zone of influence throughout the growing cycle. Two batches of mixed-sex Cobb-500 were raised in a commercial broiler farm until 42 d of age, in 2 houses divided into 4 equally sized sectors. In half of the sectors an AviSense robot sustained 2-h per day of autonomous navigation. The minute prior and the 4 min following the robot entering the zone of influence were video recorded weekly. Control sectors without a robot were analyzed equivalently. Number of individuals within the zone of influence of the robot were obtained at 1-s intervals and relative density (%) was estimated. Physical interactions between broilers and the robot, as well as interactions with the environment were also recorded. The entrance of the robot triggers within seconds a strong depopulation of the zone with birds walking to neighboring areas (P < 0.03, in all ages). The decreases in relative density induced by the robot appears more pronounced, and repopulation of the zone was slower, in younger than in older birds (P < 0.05). Broilers´ showed physical interactions towards the robot and were also touched and/or slightly pushed by the robots (19 and 84% of videos recorded, respectively). They were also found scratching and/or pecking the ground after the robot passed (64% of videos). Findings strongly suggest that robots, beyond their specific capabilities as environmental sensors, were effective in promoting increased movement in broilers along the growing cycle and could also favor additional exploratory behaviors. Thus, these robots could be considered as environmental enrichment elements that contribute to welfare improvements during intensive rearing.

Analysis of characteristics and changes in three-dimensional spatial and temporal distribution of aerosol types in Central Asia

Atmospheric aerosol types and characteristics have regional and seasonal characteristics mainly due spatial and temporal differences in emission sources and diffuse transport conditions. We explored regional three-dimensional spatial and temporal distribution characteristics of aerosol types in Central Asia from daytime to nighttime by using long-term (2007–2021) CALIPSO lidar measurements. The three results are as follows: (1) Average aerosol optical depth (AOD) values during the 14 years were 0.44 and 0.47 during daytime and nighttime, respectively, with an overall decreasing trend, among which the AOD in spring in the southern border region and in winter in the northern border region showed high values, 0.66 and 0.31 during daytime and 0.69 and 0.33 during nighttime, respectively, and nighttime AOD values were higher than those of daytime, possibly due to the lower signal-to-noise ratio of the CALIPSO during the daytime than during the nighttime. (2) The primary representative aerosol type in the Taklamakan Desert region being pure sand and dust, and more apparent winter-polluted sand and dust exist along the northern slope of the Tianshan Mountains in Xinjiang in winter than in other areas. High-altitude soot mainly existed below 4 km and was primarily concentrated in northern Central Asia, with the highest values (0.016 and 0.003) in summer and winter, respectively, which may be due to different diffusion and transport conditions. (3) Dust aerosols in spring were mainly concentrated in the region of 2–6 km in the Taklamakan Desert area; pure dust particles in summer and fall lifted height diffusion and gradually moved to the northern border region; polluted dust was mainly in northern Xinjiang in fall and winter and spread to northern Central Asia; and the average top height of aerosols in the transmission process reached the top of the troposphere, and transmission height was higher than source area.

INTEGRATION NEEDS AND CHALLENGES FOR GREEN AND SMART TRANSFORMATION OF PORT INDUSTRY BASED ON MULTI-SOURCE DATA

This research explores the integration needs and challenges for achieving green and smart transformation in the port industry, leveraging multi-source data and geographic information technology. The study utilizes comprehensive data, including information from the National Enterprise Credit Information Publicity System, the National Catalogue Service for Geographic Information, and road network maps of Jiangsu Province in China. Geographical analysis methods, such as weighted kernel density estimation and standard deviation ellipse analysis, are employed to identify spatial distribution patterns and changes in the port and shipping service industry. The results indicate a spatial distribution pattern with weak expansion from the center, along the "northwest-southeast" direction. Challenges such as decentralized management and competition for interests hinder port integration for sustainable development. By understanding the spatial dynamics, policymakers can make informed decisions for a greener, smarter, and more efficient port sector.

Accounting for Greenhouse Gas Emissions in the Agricultural System of China Based on the Life Cycle Assessment Method

Agricultural systems contribute nearly one-third of global anthropogenic GHG emissions and are an important source of GHGs globally. The clarification of the GHG emission pattern from agriculture is of paramount importance in the establishment of an agricultural emission reduction mechanism and the realization of China’s dual-carbon target. Based on the life cycle assessment method (LCA), this paper comprehensively quantifies the greenhouse gas emissions from the agricultural system in China, encompassing rice, wheat, and corn cultivation as well as animal husbandry including cows, horses, donkeys, mules, camels, pigs, and sheep. The analysis covers the period 2000–2020 and examines the spatial distribution, temporal trends, and structural changes in the greenhouse gas emissions within China’s agriculture sector. The main results are as follows: (1) from 2000 to 2020, China witnessed a consistent upward trajectory in its total agricultural GHG emissions, exhibiting an average annual growth rate of 0.73%. Notably, methane (CH4) emissions emerged as the largest contributor, displaying an overall fluctuating pattern. Carbon dioxide (CO2) emissions demonstrated intermittent increases with a noteworthy annual growth rate of 3%, signifying the most rapid expansion within this context. Conversely, nitrous oxide (N2O) emissions experienced decline over the specified period. (2) GHG emissions from cultivation demonstrate an upward trajectory, primarily driven by the CH4 emissions originating from rice cultivation and CO2 resulting from straw incineration. Conversely, GHG emissions stemming from animal husbandry declined, with the primary source being CH4 emissions arising from animal enteric fermentation. Agricultural N2O emissions predominantly arise due to manure management and nitrogen fertilizer application. (3) Agricultural greenhouse gas emissions exhibit significant variations in spatial distribution, gradually concentrating towards the North China Plain, the middle and lower reaches of the Yangtze River, and the northeast. Specifically, agricultural CH4 emissions are progressively concentrated in China’s pivotal rice-growing regions, encompassing the middle and lower reaches of the Yangtze River Plain, as well as livestock breeding areas like Inner Mongolia. Agricultural CO2 emissions primarily concentrate in dryland crop production zones such as North China and Northeast China. Meanwhile, Agricultural N2O emissions predominantly occur in Inner Mongolia and the North China Plain. China’s agricultural greenhouse gas emissions in 2020 show a significant spatial clustering effect, with hotspots primarily concentrated in Shandong, Anhui, Henan, and other regions and cold spots focused in the western and southern areas. The emission patterns of agricultural GHGs are closely intertwined with farming practices, regional development levels, and national policy; hence, tailored measures for emission reduction should be formulated based on specific crop types, livestock categories, agricultural production activities, and regional development characteristics.

Assessing Changes in Climatic Suitability for Sesame Cultivation in China (1978–2019) Based on Fuzzy Mathematics

Sesame is one of the important oil seed crops grown for the high-quality oil. Its growth, development, and yield are significantly affected by the changing climate conditions. Evaluating the sesame climatic suitability is crucial to optimize sesame cultivation patterns and planting distribution, and to aid strategic decision making for future agricultural adaptation. Based on agricultural climatic suitability theory and the fuzzy mathematics method, in this study, we established the temperature, precipitation, sunshine, and comprehensive suitability model. Then, we assessed the spatial distribution and chronological changes in climatic suitability under two periods, 1978–1998 (earlier 21 years) and 1999–2019 (latter 21 years). The results showed that compared with the meteorological data in the earlier 21 years, the mean temperature during the sesame-growing season in the latter 21 years increased from 24.48 °C to 25.05 °C, and the cumulative precipitation increased from 744.38 mm to 754.81 mm; however, the sunshine hours decreased from 6.05 h to 5.55 h. Temperature, precipitation, sunshine, and comprehensive suitability during the sesame-growing season in the main sesame-producing areas of China all had a downward trend. The distribution of temperature and comprehensive suitability in the north is higher than that in the south, while the precipitation and sunshine suitability had an uneven distribution. The area of high-temperature suitability and high-precipitation suitability increased from 43.45 × 106 ha to 46.34 × 106 ha and from 3.20 × 106 ha to 7.97 × 106 ha, respectively, whereas the area of high-sunshine suitability decreased from 4.04 × 106 ha to 2.09 × 106 ha. The climate change was more beneficial to sesame cultivation in northeast Anhui where the area of high climatic suitability clearly expanded, and in eastern Jiangxi where the area of the general climatic suitability increased. In contrast, it is worth noting that the area of high climatic suitability in northern Henan decreased and the area of low climatic suitability in Hubei increased. Our results have important implications for improving agricultural production to cope with ongoing climate change.

Spatiotemporal pattern evolution and influencing factors of population spatial distribution in Changsha-Zhuzhou-Xiangtan urban agglomeration, China

&lt;p class="MsoNormal" style="text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.&lt;/span&gt;&lt;/p&gt;

Platoon-aware cooperative lane-changing strategy for connected automated vehicles in mixed traffic flow

The platooning management technology for Connected Automated Vehicles (CAVs) can potentially increase the efficiency of the traffic system. However, the randomness in the spatial distribution of CAVs poses a new challenge for CAVs’ platooning strategy in mixed traffic flow. To effectively utilize the advantages of platooning technology, this paper proposes a platoon-aware cooperative lane-changing (PCLC) strategy inspired by platoon formation. This strategy focuses on the formation of CAV platoons in mixed traffic flow and restructures the spatial distribution of CAVs on the road segment. First, the cooperative lane-changing behavior of CAVs is modeled based on vehicle-to-vehicle communication, and a gap suitable for lane-changing is created by controlling the acceleration of cooperative CAVs to complete the lane-changing process smoothly. Meanwhile, considering the maximum platoon size, a decision-making framework of cooperative lane-changing for CAVs is designed. Then, considering the change in spatial distribution of CAVs (i.e., platoon intensity) caused by PCLC, the impact of PCLC on traffic capacity is analyzed from theoretical and numerical verification perspectives. Finally, simulation experiments are conducted to investigate the impact of PCLC on lane-changing indicators, traffic flow stability, and traffic efficiency. The simulation experiments show that compared with the discretionary lane-changing (DLC) operation, the PCLC strategy may reduce the lane-changing rate and raise the average platoon size on road segments, increasing road capacity. In addition, the vehicles on the road segment have modest swings in average speed and acceleration, proving the safety and stability of the proposed strategy. Particularly, scenarios with moderate to heavy traffic flow and fairly balanced CAVs penetration rates favor the effectiveness of this strategy. With 50% CAVs and moderate traffic density, the lane capacity increases by 450 veh/h and improves by 10.135%. These findings support the ability of PCLC strategy to reduce traffic congestion and effectively explore traffic management strategies for mixed traffic flow.

Interannual Variation of Half-Hourly Extreme Precipitation in Africa: The Assessment of the Warmest Decade (2011–2020) Since the Beginning of Observations

AbstractAfrica is experiencing widespread extreme precipitation. However, the continent remains understudied regarding extreme precipitation variation, particularly at high temporal resolution. This study assessed the changes in spatial distribution, frequency, and intensity of half-hourly violent precipitation (≥ 50 mm/h) in Africa during 2011–2020. The data were sourced from the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) program. The results reflect that the 50–60 mm/h precipitation category was the most densely spatially distributed with less-discernable interannual changes. There was a strong interannual variation in the spatial distribution of the 60–70 mm/h and ≥ 70 mm/h precipitation categories. There was a general decrease in the frequency of occurrence of violent precipitation in all regions, except in North Africa (NA). Among the study regions, NA had the broadest precipitation intensity distribution extended to high values and consequently, on average, the most intense violent precipitation, followed by Southern Africa (SA) and Southwest Indian Ocean (SWIO). The regions in the tropics, i.e., West Africa (WA), Central Africa (CA), and East Africa (EA), had a relatively narrow intense violent precipitation distribution. The results further illustrate that the strength of climate variability modes has a strong influence in the distribution, frequency, and intensity of violent precipitation over the continent particularly the ≥ 70 mm/h precipitation category. This study underscores a need for a strengthened capacity in forecasting climate variability modes impacting on Africa, augmentation of continental precipitation observation network, and improved extreme precipitation adaptation infrastructure.

Ensemble species distribution modeling and multilocus phylogeography provide insight into the spatial genetic patterns and distribution dynamics of a keystone forest species, Quercus glauca

BackgroundForests are essential for maintaining species diversity, stabilizing local and global climate, and providing ecosystem services. Exploring the impact of paleogeographic events and climate change on the genetic structure and distribution dynamics of forest keystone species could help predict responses to future climate change. In this study, we combined an ensemble species distribution model (eSDM) and multilocus phylogeography to investigate the spatial genetic patterns and distribution change of Quercus glauca Thunb, a keystone of East Asian subtropical evergreen broad-leaved forest.ResultsA total of 781 samples were collected from 77 populations, largely covering the natural distribution of Q. glauca. The eSDM showed that the suitable habitat experienced a significant expansion after the last glacial maximum (LGM) but will recede in the future under a general climate warming scenario. The distribution centroid will migrate toward the northeast as the climate warms. Using nuclear SSR data, two distinct lineages split between east and west were detected. Within-group genetic differentiation was higher in the West than in the East. Based on the identified 58 haplotypes, no clear phylogeographic structure was found. Populations in the Nanling Mountains, Wuyi Mountains, and the southwest region were found to have high genetic diversity.ConclusionsA significant negative correlation between habitat stability and heterozygosity might be explained by the mixing of different lineages in the expansion region after LGM and/or hybridization between Q. glauca and closely related species. The Nanling Mountains may be important for organisms as a dispersal corridor in the west-east direction and as a refugium during the glacial period. This study provided new insights into spatial genetic patterns and distribution dynamics of Q. glauca.

Alkali induced changes in spatial distribution of functional groups in carboxymethylated cellulose

The aim of the work was to investigate treatment parameters that exert most influence on performance of cellulose fibers carboxymethylated from aqueous solutions. Viscose fibers were carboxymethylated in alkaline solutions of sodium monochloroacetate at two temperatures (30 °C, 50 °C) and with different levels of alkali (0.5 mol/L and 4 mol/L NaOH). The degree of carboxymethylation was assessed with both back titration and conductometric titration methods, and the performance of carboxymethylated fibers was assessed from their propensity for sorption of the cationic dye methylene blue, a putative wastewater contaminant. Higher degrees of carboxymethylation were generally observed for fibers carboxymethylated in 4 mol/L NaOH, but in dye sorption propensities, the fibers carboxymethylated in 0.5 mol/L NaOH performed better. A combination of observations from dye sorption, color measurement and conductometric titration suggested that dye permeation was greater through fibers carboxymethylated in 0.5 mol/L NaOH as compared to 4 mol/L NaOH. As permeability differences were evinced in cases also where the degrees of carboxymethylation were very similar, it appears that the reaction conditions (i.e., alkali content during carboxymethylation), in addition to degree of carboxymethylation, affected fiber performance. The effect of alkali may stem from their influence on the topographical distribution of the substituted carboxymethyl functional groups.

[Identification and visual analysis of ginsenosides in multi-steamed roots of Panax quinquefolium based on UPLC-Q-TOF-MS/MS and MALDI-MSI].

This study aims to investigate the component variations and spatial distribution of ginsenosides in Panax quinquefolium roots during repeated steaming and drying. Ultra performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS) was employed to identify the ginsenosides in the root extract. Matrix-assisted laser desorption/ionization mass spectrometry imaging(MALDI-MSI) was employed to visualize the spatial distribution and spatiotemporal changes of prototype ginsenosides and metabolites in P. quinquefolium roots. The UPLC results showed that 90 ginsenosides were identified during the steaming process of the roots, and polar ginsenosides were converted into low polar or non-polar ginsenosides. The content of prototype ginsenosides decreased, while that of rare ginsenosides increased, which included 20(S/R)-ginsenoside Rg_3, 20(S/R)-ginsenoside Rh_2, and ginsenosides Rk_1, Rg_5, Rs_5, and Rs_4. MALDI-MSI results showed that ginsenosides were mainly distributed in the epidermis and phloem. As the steaming times increased, ginsenosides were transported to the xylem and medulla. This study provides fundamental information for revealing the changes of biological activity and pharmacological effect of P. quinquefolium roots that are caused by repeated steaming and drying and gives a reference for expanding the application scope of this herbal medicine.

Improved prediction of groundwater depth in urban areas using the EWT-S–G-GRU and LSSVM model: a case study of Xinxiang City

Abstract The improvement in predicting groundwater depth accuracy has significant implications for the management, ecological environment protection, and economic and social development of regional water resources. Employing empirical wavelet transform (EWT) for nonlinear processing, Savitzky–Golay (S–G) filtering to reduce high-frequency noise, gate recurrent unit (GRU) neural network for linear feature signal processing, and least squares support vector machine (LSSVM) for nonlinear signal handling, we established a comprehensive model combining EWT-S–G-GRU and LSSVM. The results demonstrate that the proposed model exhibits superior accuracy in groundwater depth prediction, with an average relative error of only 2.14% and Nash–Sutcliffe efficiency (NSE) of 0.93. This outperforms the other four models, with average relative errors of 12.88%, 11.90%, 7.07%, and 11.10%, and NSE values of 0.58, 0.56, 0.71, and 0.73, respectively. The superiority of the model established in this study is attributed to its effective handling of both nonlinear and linear features of groundwater, thereby enhancing predictive accuracy. The EWT-S–G-GRU and LSSVM model proves to be more reliable in revealing the spatial distribution and dynamic changes of future groundwater, providing a robust reference for the rational development and utilization of groundwater in the urban area of Xinxiang City.

Glycerophospholipid dysregulation after traumatic brain injury

Brain tissue is highly enriched in lipids, the majority of which are glycerophospholipids. Glycerophospholipids are the major constituents of cellular membranes and play an important role in maintaining integrity and function of cellular and subcellular structures. Any changes in glycerophospholipid homeostasis can adversely affect brain functions. Traumatic brain injury (TBI), an acquired injury caused by the impact of external forces to the brain, triggers activation of secondary biochemical events that include perturbation of lipid homeostasis. Several studies have demonstrated glycerophospholipid dysregulation in the brain and circulation after TBI. This includes spatial and temporal changes in abundance and distribution of glycerophospholipids in the injured brain. This is at least in part mediated by TBI-induced oxidative stress and by activation of lipid metabolism pathways involved in tissue repairing. In this review, we discuss current advances in understanding of the mechanisms and implications of glycerophospholipid dysregulation following TBI.

Intelligent High-Awareness and Channel-Efficient Adaptive Beaconing Based on Density and Distribution for Vehicular Networks

In vehicle ad hoc networks (VANETs), a beacon is a periodic message sent to nearby vehicles containing essential details like the sender’s vehicle ID, location, speed, and direction. Maintaining the freshness of this information without causing network congestion requires adaptive beaconing to adjust to changes in mobility and network density. Our research, based on extensive simulation experiments, identifies specific parameter sets optimal for adapting beaconing rates to different scenarios. From this analysis, we introduce a novel scheme called high-awareness and channel-efficient adaptive beaconing (HACEAB), employing fuzzy logic to adapt to various environments and conditions. Initially, the protocol gauges network density using an adaptive threshold function, followed by estimating the node spatial distribution through the quadrat statistic method to discern uniform distribution or clustering. Utilizing these data, the protocol adjusts beaconing rates via appropriate input parameters for the fuzzy logic system. Remarkably, HACEAB represents the first beaconing scheme capable of simultaneously adjusting to changes in network density and spatial distribution. Furthermore, the protocol enhances performance by adapting transmission power to fluctuations in node density and distribution. NS-3 simulations validate the efficacy of these improvements.

Prediction of Historical, Current, and Future Configuration of Tibetan Medicinal Herb Gymnadenia orchidis Based on the Optimized MaxEnt in the Qinghai-Tibet Plateau.

Climate change plays a pivotal role in shaping the shifting patterns of plant distribution, and gaining insights into how medicinal plants in the plateau region adapt to climate change will be instrumental in safeguarding the rich biodiversity of the highlands. Gymnosia orchidis Lindl. (G. orchidis) is a valuable Tibetan medicinal resource with significant medicinal, ecological, and economic value. However, the growth of G. orchidis is severely constrained by stringent natural conditions, leading to a drastic decline in its resources. Therefore, it is crucial to study the suitable habitat areas of G. orchidis to facilitate future artificial cultivation and maintain ecological balance. In this study, we investigated the suitable zones of G. orchidis based on 79 occurrence points in the Qinghai-Tibet Plateau (QTP) and 23 major environmental variables, including climate, topography, and soil type. We employed the Maximum Entropy model (MaxEnt) to simulate and predict the spatial distribution and configuration changes in G. orchidis during different time periods, including the last interglacial (LIG), the Last Glacial Maximum (LGM), the Mid-Holocene (MH), the present, and future scenarios (2041-2060 and 2061-2080) under three different climate scenarios (SSP126, SSP370, and SSP585). Our results indicated that annual precipitation (Bio12, 613-2466 mm) and mean temperature of the coldest quarter (Bio11, -5.8-8.5 °C) were the primary factors influencing the suitable habitat of G. orchidis, with a cumulative contribution of 78.5%. The precipitation and temperature during the driest season had the most significant overall impact. Under current climate conditions, the suitable areas of G. orchidis covered approximately 63.72 × 104/km2, encompassing Yunnan, Gansu, Sichuan, and parts of Xizang provinces, with the highest suitability observed in the Hengduan, Yunlin, and Himalayan mountain regions. In the past, the suitable area of G. orchidis experienced significant changes during the Mid-Holocene, including variations in the total area and centroid migration direction. In future scenarios, the suitable habitat of G. orchidis is projected to expand significantly under SSP370 (30.33-46.19%), followed by SSP585 (1.41-22.3%), while contraction is expected under SSP126. Moreover, the centroids of suitable areas exhibited multidirectional movement, with the most extensive displacement observed under SSP585 (100.38 km2). This study provides a theoretical foundation for the conservation of biodiversity and endangered medicinal plants in the QTP.

Building a reference indicator model using co-kriging interpolation to determine the geographical origin of the flighted spongy moth complex in China.

Using stable isotopes to detect and analyze the geographical origin of insects represents an important traceability technology, which requires a rich isotope database. In this study, we representatively sampled the Chinese provinces where flighted spongy moth complex (FSMC) has been reported and, for the first time, used co-kriging interpolation to predict the distribution patterns of FSMC δ13 C values in the main distribution areas. From 2020 to 2022, we set up 60 traps in 12 provinces and cities in China and collected 795 FSMCs. Then, 6 main climatic factors were obtained by multi-collinearity screening from 21 types of meteorological data collected at the sample plots, and a correlation analysis was carried out by combining longitude, latitude, and altitude data with the δ13 C values of FSMC. Next, we performed a co-kriging interpolation using the 2 climatic factors with the highest correlation (isothermality and altitude) and the δ13 C values of FSMC. A cross-validation was performed to systematically test 11 candidate models and select the best semi-variogram model ("Exponential"), which was then used to build a co-kriging interpolation model. The geographical distribution patterns of the FSMC δ13 C values obtained from the 2 interpolation models (i.e., interpolated with isothermality and altitude, respectively) were almost the same. Moreover, the δ13 C values varied significantly at the regional scale, showing regular changes in spatial distribution. Overall, the reference indicator map of the δ13 C values generated from stable isotopes can be used to greatly improve the efficiency of discrimination analyses on the geographical origin of FSMC.

Spatial and temporal changes in population distribution and population projection at county level in China

Counties in China play a pivotal role in economic and social development, acting as essential leadership hubs for large and medium-sized cities, contributing to rural revitalization, and facilitating urban-rural integration. Using ArcGIS spatial analysis, this study examines the population distribution spatial and dispersion patterns in Chinese counties based on 40 years of data from the first to the seventh national population census. Results reveal noticeable growth trends and regional disparities in county populations, with an increase in large-population counties and a decrease in small-population ones. Recent population growth concentrates in urban agglomerations, metropolitan areas, and southeastern coastal regions, while reductions occur in the northeastern and Inner Mongolia border areas. Furthermore, the study identifies “high-high” agglomerations around provincial capitals and “low-low” agglomerations in economically underdeveloped western and northeastern border regions. China’s population distribution spatial agglomeration has been increasing, with acceleration toward specific areas. The first through seventh census show rapid growth at low density, followed by growth at medium density, stable growth, and eventually negative growth. This suggests a likely slowdown and potential reversal in China’s future population growth. Additionally, an Auto Regression Integrated Moving Average (ARIMA) model is employed to forecast China’s total population, projecting a decline to 1343.68 million by 2035. The emergence of “population loss counties” in contemporary China underscores the need for a rational understanding of their development status and trends to optimize population development strategies and promote economic and social progress.