Local Trends Research Articles

Forecasting economic growth by combining local linear and standard approaches

Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance.

Assessment of climate change knowledge of residents of informal sectors of Abuja Federal Capital City, Nigeria

ABSTRACT Urban informal settlement dwellers face uncertainties from climate change (CC) and need to understand its causes and impacts to adapt effectively. Few studies explore their levels of CC knowledge. This research surveyed 388 purposively selected households across five informal sectors in Abuja, Nigeria (Apo, Gaduwa, Garki, Jabi, and Mabushi), using structured questionnaires. Key variables assessed for their influence on CC knowledge included age, family size, education, income, livelihood type, tenure security, and marital status. Mann-Whitney and Chi-Square tests evaluated the significance of these factors. Findings showed that most households are aware of local CC trends, with perceptions closely aligning with scientific insights. However, understanding of CC causes and consequences remains limited. Among seven hypotheses tested, age, family size, income, and livelihood type significantly influenced CC knowledge, while education, tenure security, and marital status had little effect. The study highlights that even educated residents may lack comprehensive knowledge of CC causes. Enhancing scientific understanding in CC-prone communities is essential for effective adaptation and mitigation strategies.

A modeling approach to forecast local demographic trends in metapopulations.

Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two-scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time-varying proportionality parameters. This two-step approach ensures that the long-term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two-scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200-fold increase over 30 years for some subpopulations. The two-scale model predicted very sensible growths, never larger than a threefold increase over the 30-year time horizon, which is more in accordance with this species' life history. This two-scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full-dispersal models. Because the only data required are simple counts, this model could be useful to many large-scale wildlife monitoring programs.

Local polynomial trend regression for spatial data on Rd

Local polynomial trend regression for spatial data on Rd

The projected global burden of modifiable risk factors on cardiovascular disease from 2025 to 2100

Abstract Background Cardiovascular diseases (CVDs) are the primary cause of global mortality and morbidity. Controlling modifiable risk factors driving the CVD epidemic is a vital focus of primary prevention efforts worldwide. Understanding the epidemiological trends in these risk factors is therefore essential in designing effective countermeasures against CVDs for the years to come. Purpose This study aims to forecast the disability-adjusted life years (DALYs) associated with CVDs attributable to high systolic blood pressure (SBP), high body-mass index (BMI), high fasting plasma glucose (FPG), high low-density lipoprotein (LDL) and tobacco use from 2025 to 2100, stratified by GBD super-regions, sex, sociodemographic index (SDI), and age-groups. Methods Historical data on DALYs from 1990 to 2019 from the Global Burden of Disease (GBD) study was used to project DALYs associated with modifiable risk factors of CVDs from 2025 to 2100. Trends across GBD super-regions, sex, SDI, and age-groups were also examined. Results Between 2025 and 2100, age-standardised DALY rates (ASDR) per 100,000 population are forecasted to fall across all modifiable risk factors, with the greatest decline seen in tobacco use (-76.0%) and high LDL (-66.9%). Globally, high SBP and high BMI expect the greatest ASDR in 2100 (both 891 per 100,000 population), while tobacco use expects the least (213 per 100,000 population). High BMI is expected to rise 623.0% in South Asia and 221.5% in South-east Asia, East Asia and Oceania from 2025 to 2100, while high FPG is expected to rise 114.2% in North Africa and the Middle-east and 110.6% in South Asia. Females are expected to have lower overall and greater reductions in ASDR across all modifiable risk factors when compared to males from 2025 to 2100. Notably, males expect a 11.0% rise in high BMI worldwide across this period. The age-prevalence of modifiable risk factors is expected to be bimodal in 2100, highest in those aged 50-59 as well as 75-84 years old, possibly suggesting improvements in global screening efforts or the earlier development of metabolic dysfunction. All modifiable risk factors are predicted to fall in high and high-middle SDI regions from 2025 to 2100. In contrast, low, low-middle, and middle SDI regions expect a rise in high FPG and high BMI across this same time frame. The burden of modifiable CVD risk factors is expected to be highest in low-middle SDI regions in 2100, as compared to high-middle SDI regions in 2025. Conclusion While the global decline in ASDR of modifiable risk factors suggests overall improvements in the primary prevention of CVDs, regional spikes in high BMI and FPG calls for the implementation of timely, targeted interventions accounting for local trends in CVD. Moreover, the disproportionate metabolic burden expected in lower SDI regions in the coming decades requires concerted, international support to alleviate the global burden of CVDs.

Scientific production on robotic metabolic and bariatric surgery: a comprehensive bibliometric analysis on its current world status.

Robotic metabolic and bariatric surgery (RMBS) has emerged as an innovative approach in the treatment of severe obesity by combining the ergonomic precision of robotic technology and instrumentation with the established benefits of weight loss surgery. This study employs a bibliometric approach to identify local research trends and worldwide patterns in RMBS. The research methodology used "robotic" and "metabolic" or "bariatric surgery" to search Web of Science. Articles that were published prior to December 31st, 2023, were included. The analyses were developed using the Rayyan and Bibliometric, in R Studio. 265 articles from 51 different journals were included. Scientific production of RMBS experienced a significant annual growth rate of 21.96% from 2003 to 2023, resulting in an average of 12.6 papers published per year. A high correlation (R2 = 0.94) was found between the year and number of articles. The mean number of citations per document was 13.25. Approximately 90% of the journals were classified as zone 3, according to the Bradford categorization. International collaboration was identified in 10.57% of cases, with the University of California and the University of Illinois being the most common organizations. The countries with the highest number of corresponding authors, in descending order, were the United States of America, China, and Switzerland. Scientific production in RMBS has experienced sustained growth since the first original publications in 2003. While it has not yet reached the volume, impact, and international collaboration seen in studies related to non-robotic metabolic and bariatric surgery, RBMS holds potential that remains to be explored.

Differential responses to weather and land‐cover conditions explain spatial variation in winter abundance trends in a migratory bird of conservation concern

Abstract Effective conservation strategies for animal populations require knowledge of relationships between population dynamics and their environmental drivers. However, these processes often vary within animal populations, requiring site‐specific conservation planning. Given limited financial resources, identifying groups of sites with similar population dynamics can help practitioners efficiently implement conservation programs to larger areas. We evaluated spatial patterns and environmental drivers of wintering site trends in a migratory bird of conservation concern, the Greenland white‐fronted goose (Anser albifrons flavirostris). We used latent class analysis to identify trend patterns in 35 years of abundance data among 59 geographically discrete Greenland white‐fronted goose wintering sites. We developed a state‐space abundance model in a Bayesian framework to quantify the effects of weather and land‐cover conditions experienced throughout spring migration, summer breeding, autumn migration and wintering periods on variation in wintering site abundance. We identified two main patterns in Greenland white‐fronted goose abundance trends: northeastern wintering sites declined on average by 3% per year, while southwestern wintering sites declined on average by 14% per year. Differential responses to weather and habitat conditions likely explained variation among groups, as geese at southwestern wintering sites were more negatively affected by harsh weather conditions (e.g. low temperatures and high precipitation on breeding areas) and poor habitat conditions (i.e. low‐quality grasslands and croplands) on wintering areas. Future conservation efforts to improve the suitability and nutritional quality of agricultural areas, especially cereal croplands in autumn and early winter and grasslands in late winter and early spring, could potentially improve local habitat conditions, especially in the southwestern wintering sites where abundance declines were steepest. Synthesis and applications. We demonstrate the potential to delineate animal populations based on spatial patterns in population dynamics using long‐term abundance monitoring data, which are commonly the only available data for conservation practitioners. By grouping sites based on spatial patterns in local abundance trends, we can further test hypotheses about how these groups are differentially affected by changing environmental conditions. This information is important for informing efficient conservation planning over large areas when financial resources are limited.

Abstract A033: Pro-inflammatory tumor cells drive CD4+ T cell localization within heterogeneous tumors

Abstract Immunotherapies such as immune checkpoint inhibitors (ICIs) have greatly improved patient outcomes in multiple tumor types, but ICIs have failed to achieve responses in many patients. Meta-analyses of patient cohorts treated with ICIs have observed that worse clinical outcomes are strongly correlated with high levels of intratumoral genetic heterogeneity (ITH). ITH occurs when tumor cells mutate as they divide, typically due to oxidative stress or DNA damage from rapid proliferation and continued carcinogenic insults, generating tumor subclones. However, the mechanisms by which ITH impairs the anti-tumor immune response are not fully understood. We propose a novel mechanism for how ITH in highly T cell-infiltrated inflamed tumors can adversely affect T cell behavior. We hypothesize that strongly pro-inflammatory tumor subclones secreting robust pro-inflammatory chemokine profiles can dominate T cell trafficking within the tumor, resulting in a weakened immune response against other tumor subclones. Using a murine squamous cell skin carcinoma (SCC) model, we have demonstrated that in immune-inflamed tumors, the spatial distribution and behavior of tumor-infiltrating T cells is influenced by ITH. A strongly pro-inflammatory tumor line expressing an endogenous RFP fluorophore was mixed with a weakly pro-inflammatory tumor line expressing an endogenous YFP fluorophore at a 1:1 ratio. The resulting tumors contained distinct RFP+ and YFP+ regions. These were analyzed by immunofluorescence (IF) and by manually dissecting the tumors and analyzing each region by flow cytometry. Flow cytometry analysis determined that the strongly pro-inflammatory RFP+ regions contained significantly more total T cells and CD4+ T cells than YFP+ regions. Furthermore, FoxP3- “conventional” CD4+ T cells were significantly more likely to be “terminal effector-like” CD44+CD62L- in RFP+ regions, and significantly more likely to be “naïve-like” CD62L+CD44- in YFP+ regions. IF analysis confirmed the preferential distribution of CD4+ T cells in RFP+ regions, and observed that this spatial pattern was evident as early as 11 days after subcutaneous injection. Notably, this localization trend did not occur with CD8+ T cells, which were evenly dispersed throughout the tumors. ELISA analysis of lysates of both tumor cell lines determined that the pro-inflammatory RFP+ cell line secretes significantly higher levels of the chemokine CCL5, which has been observed by other studies to selectively mediate CD4+ T cell chemotaxis. These results demonstrate that intratumoral CD4+ trafficking is shaped by the spatial distribution of specific tumor subclones, and suggest tumor cell chemokine signaling may directly mediate T cell spatial localization and behavior within tumors. Citation Format: Robert Letchworth, Savannah Hughes, Abigail Keku, Melissa Reeves. Pro-inflammatory tumor cells drive CD4+ T cell localization within heterogeneous tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr A033.

Chilgoza Pine: A Comprehensive Review on Ecology, Cultivation, and Economic Potential of an Underutilized Nut Crop

Chilgoza pine (Pinus gerardiana L.) is an underutilized nut crop native to the high-altitude regions of South Asia, particularly the western Himalayas. This comprehensive review aims to provide an in-depth understanding of Chilgoza pine's ecology, cultivation practices, and economic potential. The review synthesizes current knowledge on the tree’s ecological requirements, including its adaptation to arid and mountainous environments. It examines cultivation techniques, challenges faced in agronomic practices, and recent advancements in improving yield and quality. Additionally, the economic potential of Chilgoza pine is explored, highlighting its role in local economies, market trends, and opportunities for commercialization. This review underscores the significance of Chilgoza pine in sustainable agriculture and its potential as a high-value nut crop.

Tribo-technological features of laser powder bed fusion process: scratch and wear resistance of AlSi10Mg aluminium alloy

Mechanical systems, regardless of their complexity, very often require that different parts must move relative to each other by sliding their surfaces, therefore appropriate tribological properties are needed. This request appears particularly evident for components fabricated through Metal Additive Manufacturing processes, due to their typical high surface roughness. In the current study, the Laser Powder Bed Fusion technique with optimized parameters is used to produce samples made of AlSi10Mg alloy. Their tribo-technological properties are investigated through progressive load scratch and dry ball-on-plate wear tests. Along with a global characterization, a local analysis has been performed to identify any variations induced by the building direction. The friction coefficient and the wear rate are generally higher than as-cast specimens. Finally, local trends suggest that the central parts of the samples average offer higher resistance to wear and scratch than the outer areas.

Liver Cancer in the Middle East and North Africa: Epidemiology, Trends, and Risk Factors

Objectives: Considering the growing concerns about the rising incidence of this cancer in the Middle East and North Africa (MENA), this literature review presents the local epidemiology and trends of liver cancer, along with a practical estimation of its epidemiology and contributing risk factors. Design: Review Article. Setting(s): Middle East and North Africa. Outcome Measures: MEDLINE (via PubMed), Scopus, and Web of Science databases, as well as Google Scholar, were searched up to September 2024 using representative keywords and MeSH terms. Studies reporting the epidemiology of liver and intrahepatic cholangiocarcinoma in the region or an analysis of the contributing infectious diseases, non-communicable disorders, genetic predispositions, behavioral aspects, and environmental factors were included. A qualitative synthesis was performed, and the results were presented using thematic narrative synthesis. Results: Liver cancer, particularly hepatocellular carcinoma (HCC), is rapidly growing in the MENA region, driven by a combination of viral hepatitis, underlying metabolic disorders, and environmental exposures. Liver cancer epidemiology varies across the region, with countries like Egypt historically burdened by hepatitis C, while Gulf states experience rising cases linked to metabolic disorders. Projections indicate a continued increase in the incidence of liver cancer due to the ongoing obesity and diabetes epidemics. Conclusions: Despite the success of viral hepatitis management campaigns in some countries, the region faces significant challenges in controlling the rising burden of liver cancer related to metabolic disorders. Prevention strategies should focus on lifestyle and dietary interventions, alongside the need for improved cancer registries, better data collection, and policy reforms. A multidisciplinary approach, combining public health initiatives and targeted healthcare improvements, is essential to reduce regional liver cancer incidence and improve outcomes.

Rocky Mountain Spotted Fever in Children along the US‒Mexico Border, 2017-2023.

Rocky mountain spotted fever (RMSF) causes significant illness and death in children. Although historically rare in California, USA, RMSF is endemic in areas of northern Mexico that border California. We describe 7 children with RMSF who were hospitalized at a tertiary pediatric referral center in California during 2017-2023. Five children had recent travel to Mexico with presumptive exposure, but 2 children did not report any travel outside of California. In all 7 patients, Rickettsia rickettsii DNA was detected by plasma microbial cell-free next-generation sequencing, which may be a useful diagnostic modality for RMSF, especially early in the course of illness, when standard diagnostic tests for RMSF are of limited sensitivity. A high index of suspicion and awareness of local epidemiologic trends remain most critical to recognizing the clinical syndrome of RMSF and initiating appropriate antimicrobial therapy in a timely fashion.

Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia

This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.

Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models.

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Seaweeds and derived bioactive compounds as food alternatives: Current status and future perspective in Africa

The urgency for food security and diversification has necessitated extensive exploration of all potential food options. Seaweeds, now considered potential functional foods are widely consumed across Asia and parts of Europe. In Africa, reports on consumption trends and food-related applications are scarce. About only 1 % of the annually harvested ~120,000 (fresh weight) tonnes of commercially useful eucheumatoids are utilized locally in the continent's top-producing country, Tanzania. Ultimately, the intensification of current efforts shall promote up-scaling of the seaweed industry. In this review, we have discussed the nutritional profile and nutraceutical potential of commercially viable species, paying attention to consumer safety measures. Also, prospective food-related application of seaweeds based on current international and local African consumption trends is reviewed. The review further addresses factors that hinder consumer acceptance in Africa and the up-scaling of the seaweed industry at large. This review aims to provide some theoretical reference for future developments and application of seaweed as food in Africa.

Bayesian State-space modelling of stock Markets in G7 Countries

This work examines the impact of Coronavirus disease (COVID-19) on the stock market of Group of Seven (G7) countries during the first wave of COVID-19 using daily data from March, 1st of 2020 to December, 31st of 2020. Focussing on such period, a Bayesian Structural Time Series Model (BSTSM) was used to capture the effects of first wave of COVID-19 on the stock market performance of these G7 countries by employing a Markov Chain Monte Carlo (MCMC) method. We considered an Autoregressive (AR) model with time-varying parameters and a local linear trend model to know if the stock price of these countries during the period of the first wave of COVID-19 is changing over time. There was a stochastic trend in stock prices of G7 countries during the period of the first wave of COVID-19 while the AR process itself was also changing over time. The stock market of the USA followed by Japan performed better than other G7 countries during the first phase of the COVID-19 pandemic while the stock market of France was affected during the COVID-19 pandemic.

On the Possible Relationship of a Set of Statistically Revealed Quasi-Linear Local Trends of Variations in the Magnetic Field Parameters Prior to Earthquakes in Seismically Active Zones of the Black Sea, Caucasus, and Western Asia

On the Possible Relationship of a Set of Statistically Revealed Quasi-Linear Local Trends of Variations in the Magnetic Field Parameters Prior to Earthquakes in Seismically Active Zones of the Black Sea, Caucasus, and Western Asia

로컬기반의 창업교육 교육과정 및 교과목 개발 사례 연구 : T대학 「로컬콘텐츠 중점대학」 사례를 중심으로

Recently, efforts to foster young entrepreneurs who can settle in the region are expanding through the active introduction and activation of various local entrepreneurship education and support programs for balanced development of the community. This study attempted to present examples and implications of startup-based local content-focused universities that local universities plan to operate, based on previous research on regional innovation systems, domestic and international advanced cases, and the results of reviewing local government startup support policies. . As a result of the study, looking at local industrial trends, it is necessary to operate courses that can offset the obstacles to starting a business and difficulties after starting a business, and there is a need to open related courses for nurturing local creators and courses on analyzing startup market trends.In addition, more attention is needed on the government and local governments' support policies related to start-ups, and practical training such as mentoring should be supplemented so that those preparing to start a business can receive support through the relevant policies. In addition, there is a need to clarify the identity of future roles of those receiving training, and the areas of commercializing resources such as community, culture, and infrastructure should be reflected in the curriculum. The results of this study may provide implications for expanding local-based entrepreneurship education leading models and curricular and non-curricular programs under the regional innovation system in the future.

Держава і державність: сучасні геополітичні та міжнародно-правові загрози

Modern doctrinal approaches to the definition of the concepts of “state” and “statehood” are debatable and sometimes controversial, which necessitates their clarification in view of the existing global and local trends. It is suggested that “statehood” is broader than the concept of “state”. The former is associated with independence, state sovereignty, the existence of state and municipal authorities, a system of legal norms and their international recognition. The main characteristics of statehood include the national selfidentity of the population of the State, the embodiment of the interests of the national community in the State’s independence, recognition of such a State on the world stage and its participation in international relations. The author clarifies that the concept of “statehood” is not identical to “state”. It is determined that the geopolitical and international legal threats that have a direct impact on the sovereignty and independence of the State include: external and internal armed conflicts, uncontrolled migration, global extremism and terrorism; non-linear methods of warfare, creation of new “zones of tension”, energy revolution, growing social and economic inequality, civil conflicts, strengthening of the role of nation-states, cross-border conflicts, etc. It is noted that these factors also have an indirect impact on statehood through the “blurring” of the self-identity of the state’s population, questioning of its sovereignty, and as a result, the loss of legal personality on the world stage. The author emphasizes that the negative consequences of these influences on statehood may lead to the disappearance of the state itself in the historical perspective. That is why timely countermeasures should be taken to counter such geopolitical and international legal threats.

Novel spatio-temporal attention causal convolutional neural network for multi-site PM2.5 prediction

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatio-temporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%–10%.