Geographical Topology Research Articles

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features

BackgroundAccurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting influenza outbreaks, aiming to achieve real-time and accurate influenza prediction.MethodsWe proposed a GNN-based approach with dual topology processing, capturing both geographical and socio-economic associations among counties/cities. The model inputs consist of weekly matrices of influenza-like illness (ILI) rates at city level, along with geographical topology and functional topology. The model construction involves temporal feature extraction through 1-dimensional gated causal convolution, spatial feature embedding through graph convolution, and additional adjustments to enhance spatiotemporal interaction exploration. Evaluation metrics include four commonly used measures: root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and Pearson correlation (Corr).ResultsOur approach for predicting influenza outbreaks achieves competitive performance on real-world datasets (Corr = 0.8202; RMSE = 0.0017; MAE = 0.0013; MAPE = 0.0966), surpassing established baselines. Notably, our approach exhibits excellent capability in accurately and timely capturing short-term influenza outbreaks during the flu season, outperforming competitors across all evaluation metrics.ConclusionThe incorporation of dual topology processing and the subsequent fusion mechanism allows the model to explore in-depth spatiotemporal feature interactions. Demonstrating superior performance, our approach shows great potential in early detection of flu trends for facilitating public health decisions and resource optimization.

Spatial Economic Correlations via Geographically Weighted Neural Network Regression with A New Dataset

The GDP and unemployment rates of two geographic units are influenced by their population density and economic characteristics, as well as their distance and spatial relationship. This work aim to use Geographically Weighted Regression (GWR), a classic and widely used method for modeling spatial heterogeneity, to analyze the correlation of these multiple factors. However, GWR does not precisely express its weighting kernel, making it insufficient to estimate complex geographic processes. Therefore, the work employed the Geographically Weighted Neural Network Regression (GNNWR) model, which combines Ordinary Least Squares (OLS) and neural networks, to estimate spatial heterogeneity based on GWR. This work collected various indicators, such as population density and GDP of first-level administrative units of the top 20 countries by GDP at the end of 2022, along with the Euclidean distance and geographic topology between these units. Using GNNWR, the work analyzed the correlation of their GDP growth. The results show that GNNWR outperforms OLS and GWR in fitting accuracy and provides more accurate predictions of the economic correlation between two geographic units.

Elevational Variation in and Environmental Determinants of Fungal Diversity in Forest Ecosystems of Korean Peninsula.

Exploring species diversity along elevational gradients is important for understanding the underlying mechanisms. Our study focused on analyzing the species diversity of fungal communities and their subcommunities at different trophic and taxonomic levels across three high mountains of the Korean Peninsula, each situated in a different climatic zone. Using high-throughput sequencing, we aimed to assess fungal diversity patterns and investigate the primary environmental factors influencing fungal diversity. Our results indicate that soil fungal diversity exhibits different elevational distribution patterns on different mountains, highlighting the combined effects of climate, soil properties, and geographic topology. Notably, the total and available phosphorus contents in the soil emerged as key determinants in explaining the differences in diversity attributed to soil properties. Despite the varied responses of fungal diversity to elevational gradients among different trophic guilds and taxonomic levels, their primary environmental determinants remained remarkably consistent. In particular, total and available phosphorus contents showed significant correlations with the diversity of the majority of the trophic guilds and taxonomic levels. Our study reveals the absence of a uniform diversity pattern along elevational gradients, underscoring the general sensitivity of fungi to soil conditions. By enriching our understanding of fungal diversity dynamics, this research enhances our comprehension of the formation and maintenance of elevational fungal diversity and the response of microbial communities in mountain ecosystems to climate change. This study provides valuable insights for future ecological studies of similar biotic communities.

Comparative phylogeography reveals dissimilar genetic differentiation patterns in two sympatric amphibian species.

Global climate change is expected to have a profound effect on species distribution. Due to the temperature constraints, some narrow niche species could shift their narrow range to higher altitudes or latitudes. In this study, we explored the correlation between species traits, genetic structure, and geographical range size. More specifically, we analyzed how these variables are affected by differences in fundamental niche breadth or dispersal ability in the members of two sympatrically distributed stream-dwelling amphibian species (frog, Quasipaa yei; salamander, Pachyhynobius shangchengensis), in Dabie Mountains, East China. Both species showed relatively high genetic diversity in most geographical populations and similar genetic diversity patterns (JTX, low; BYM, high) correlation with habitat changes and population demography. Multiple clustering analyses were used to disclose differentiation among the geographical populations of these two amphibian species. Q. yei disclosed the relatively shallow genetic differentiation, while P. shangchengensis showed an opposite pattern. Under different historical climatic conditions, all ecological niche modeling disclosed a larger suitable habitat area for Q. yei than for P. shangchengensis; these results indicated a wider environment tolerance or wider niche width of Q. yei than P. shangchengensis. Our findings suggest that the synergistic effects of environmental niche variation and dispersal ability may help shape genetic structure across geographical topology, particularly for species with extremely narrow distribution.

ST-GRF: Spatiotemporal graph neural networks for rainfall forecasting

Accurate and real-time rainfall forecasting plays an important role in the weather forecast system and is of great significance for travel planning, engineering planning, and crop management. However, rainfall forecasting has always been considered an open scientific issue, owing to the constraints of geographic topology structure, namely, spatial dependence and temporal dependence. Besides, there are long-term dependencies between climate eigenvalues and the correlation between the atmosphere-related signals at the measuring stations. Traditional approaches usually only focus on temporal dependencies. To capture the spatial and temporal dependence simultaneously, and take into account long-range dependencies and correlations between meteorological signals, we propose a novel neural network-based rainfall forecasting method, the spatiotemporal graph neural networks (ST-GRF) model. In the first step, we innovatively model the generation pattern of rainfall. In the second step, the GRF Module of ST-GRF is used to capture complex topological structures and the dependencies between climate factors. In the third step, the bidirectional temporal dependence capture module of ST-GRF is employed for rainfall forecasting based on spatiotemporal information. Experiments demonstrate that our ST-GRF model can obtain the spatiotemporal correlation from rainfall data and the predictions outperform state-of-art baselines on real-world rainfall datasets. We also discuss and demonstrate the effectiveness of each module in the ablation experiment section.

Utilizing Unmanned Land and Air Vehicles Based AI to Mitigate Climate and Cascading Hazards from Critical Minerals Mining on Communities of Color

Understanding and planning for equitable community infrastructures resilience that mitigate natural and man-made hazards can support a net circular world in a meaningful way. Emerging technologies such as AI algorithms from information captured from unmanned land and air vehicles through sensors, RFID, and barcodes about geographical topology and land will be crucial for a green mining supply chain for rare elements such as lithium. Tech like blockchains which will capture this information will be utilized to confirm green transactions for companies that are dedicated to ESG investments. The Resilient America Program of the National Academies convened two committees to address the human and financial toll of disasters caused by natural hazards and other large-scale emergencies. The committee selected two largescale themes Equitable and Resilient Infrastructure Investments, and Compounding and Cascading Events. The Equitable and Resilient Infrastructure Investments theme yielded research topics for hazard mitigation and resilience - 1. Partnerships for equitable infrastructure development, 2. Systemic change toward resilient and equitable infrastructure investment, and 3. Innovations in finance and financial analysis. As we seek to understand the challenges of natural disasters like the hurricane IAN had on the East Coast and in Puerto Rico, we must also consider cascading factors such as how mining for rare earth elements can be a cascading factor on communities of color which often are located in areas that are both negatively impacted by natural hazards and man-made activities. Emerging technologies in relationship with existing technologies such as AI, unmanned land and air vehicles, and ultrasonic and infrared sensors can be used to inform, connect, and make plans that mitigate these challenges. Integrating these emerging technologies will go to support green mining or rare earth materials, recycling efforts, and building stronger infrastructures for reportable water, fires, and earthquakes. Technologies can support the investing in ESG technologies that allow for net neutral green supply chain mining, manufacturing, and transportation that will create the next generation of jobs.

Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach

The evolution of VDTN routing came to solve the problem of routing in sparse Vehicular Ad-hoc Networks (VANETs). Recently, the implication of Unmanned Aerial Vehicles (UAVs) in VANET communication has gathered quicker information dissemination than Roadside Units (RSUs) in urban VANETs, where ground vehicles’ mobility is restricted by the topology of city roads. Gradually, UAVs are emerging for VDTNs: UAV-aided VDTN (UVDTN) routing field has been introduced for sparse areas in order to reduce the network gaps between Vehicle-to-Vehicle (V2V) connections. Meanwhile, the conventional UVDTN routing protocols expose the short contact opportunity time in the case of Vehicle-to-UAV (V2U) and UAV-to-Vehicle (U2V) connections. This constraint entails more congested bundles’ flooding rates and a higher number of lost bundle copies compared to V2V-based forwarding. In this paper, a bio-inspired UVDTN routing protocol is conceived to improve the bundles’ forwarding between ground vehicles and UAVs in terms of flooding optimization and Quality-of-Service (QoS) performances for urban VANET environments. For this purpose, a swarm-inspired approach called the Social Spider Optimization (SSO) is proposed. SSO seeks to find a better selection of next Store-Carry-and-Forward (SCF) relay nodes specifically for V2U and U2V connections. In order to control the bundle replication rate and enhance bundles’ delivery ratio and delay, the proposed bio-inspired UVDTN protocol adjusts the SCF selection decision. The latter is based on predefined historical knowledge-based forwarding parameters and urban geographic topology information. The proposed bio-inspired routing protocol is simulated using the Opportunistic Network Environment (ONE) simulator and compared to a few VDTN and UVDTN routing protocols.

Flow Characteristics of Preliminary Shutdown and Startup Sequences for a Model Counter-Rotating Pump-Turbine

Pumped Hydropower Storage (PHS) is the maturest and most economically viable technology for storing energy and regulating the electrical grid on a large scale. Due to the growing amount of intermittent renewable energy sources, the necessity of maintaining grid stability increases. Most PHS facilities today require a geographical topology with large differences in elevation. The ALPHEUS H2020 EU project has the aim to develop PHS for flat geographical topologies. The present study was concerned with the initial design of a low-head model counter-rotating pump-turbine. The machine was numerically analysed during the shutdown and startup sequences using computational fluid dynamics. The rotational speed of the individual runners was decreased from the design point to stand-still and increased back to the design point, in both pump and turbine modes. As the rotational speeds were close to zero, the flow field was chaotic, and a large flow separation occurred by the blades of the runners. Rapid load variations on the runner blades and reverse flow were encountered in pump mode as the machine lost the ability to produce head. The loads were less severe in the turbine mode sequence. Frequency analyses revealed that the blade passing frequencies and their linear combinations yielded the strongest pulsations in the system.

Adaptability Analysis of 6LoWPAN and RPL for Healthcare applications of Internet-of-Things

Ubiquitous Networks powered by Wireless Sensor Networks (WSN) is cutting across many technologies assisting day-to-day human activities. This technology confers the ability to sense and surmise the external environmental factors of various ecologies. Interconnection of these sensing devices for Machine to Machine (M2M) communication leads to the origination of Internet-of-Things (IoT). Recent advancements in the technology of Internet-of-Things guides the production of smart objects that can accomplish location, identification, connection and measurement of external factors. This leads to a new type of communication paradigm between objects and humans. One of the important problem due to the population explosion that can be addressed by IoT is the Healthcare of individual human beings. Remote health monitoring is one of the greatest technology exploited in medical professionals to keep a check on the patient’s important health factors periodically. This was done in a smaller geographical area before the era of IoT. As IoT can communicate to other Internet, This remote healthcare monitoring can now be applied over a wider geographical topology. This paper extensively analyses the performance of 6LoWPAN and RPL IoT for healthcare applications. This paper especially focuses on monitoring an athlete's thermoregulation. Also, a new technique to identify and train marathon athletes to the race topography has been proposed. In this technique, each athlete is fitted with wearable sensors in their body in the training session to monitor and analyze the thermoregulation process occurring during training. After a detailed analysis of the athletes’ thermoregulation process, personal training schedules are charted down according to variation in the thermoregulation process in each athlete. This technique helps to monitor each athlete’s progress personally with less number of coaches and medical professionals leading to the prevention of unexpected death of a healthy athlete.

Reconfigurable Cellular Base Station Antenna Consisting of Parasitic Radiators

In this article, a beam-shaping technology, based on arrays of parasitic elements, is presented for the modulation of radiation patterns of existing 4G or 5G base station antennas. First, based on the cell's geographic topology and the requirements of different radiation power exposure, and using ray-tracing simulation, the desired antenna radiation patterns are determined and represented with spherical harmonics. Those patterns are implemented in a prototype in the 1800 MHz band using parasitic radiators, loaded with different impedances, to be placed in front of the existing cellular base station antenna. A smaller prototype demonstrates the electronic control of parasitic loads using varactor diodes. The developed prototypes have been tested in the laboratory and the measurements are in a good agreement with the simulation results.

Complexity and periodicity of daily mean temperature and dew-point across India

The complexity of temperature and dew-point fluctuations across India are being investigated and analyzed with the help of recurrence plots (RP) and recurrence quantification analysis (RQA). The results firmly state that both data sets is non-linear, non-stationary and deterministic. Hilbert–Huang transform and an efficient peak detection algorithm (integral method) have been used to detect the underlying periodicity (above 95% CL) within these two signals. The nature of the complexity and the derived periods of these two weather variables show that there are significant impact of the local geographical topology and global atmospheric fluctuations on the overall pattern and fluctuation of the temperature and dew-point profile across India.

SAFE-MAC: Speed Aware Fairness Enabled MAC Protocol for Vehicular Ad-hoc Networks.

Highly dynamic geographical topology, two-direction mobility, and varying traffic density can lead to fairness issues in Vehicular Ad-hoc Networks (VANETs). The Medium Access Control (MAC) protocol plays a vital role in sharing the common wireless channel efficiently between vehicles in a VANET system. However, ensuring fairness between vehicles can be a challenge in designing MAC protocols for VANET systems. The existing protocol, IEEE 802.11 DCF, ensures that the packet transmission rate for a particular vehicle is directly proportional to the amount of time a vehicle spends within a service area, but it does not guarantee that faster vehicles will be able to send the minimum number of packets. Other existing MAC protocols based on IEEE 802.11 are able to provide a minimum amount of data transmission regardless of velocity, but are unable to provide an amount of data transmission that is more proportionate to the time a vehicle spends in the service area. To address the above limitations, we propose a Speed Aware Fairness Enabled MAC (SAFE-MAC) protocol that calculates the residence time of a vehicle in a service area by using mobility metrics such as position, direction, and speed to synthesize the transmission probability of each individual vehicle with respect to its residence time. This is achieved by dynamically altering the values of parameters such as minimum contention window, maximum backoff stage, and retransmission limit in the MAC protocol. We then develop an analytical model to compare the performance of our proposed protocol with contemporary MAC protocols. Numerical analysis results show that our proposed protocol significantly improves fairness among the speed-varying vehicles in VANET.

Crowds in or crowds out? The effect of foreign direct investment on domestic investment in Chinese cities

This study investigates the empirical relationship between foreign direct investment (FDI) and domestic investment (DI) in China using a comprehensive city-level panel over the period from 2003 to 2011. System-generalized method-of-moment estimation reveals mixed results. At the national level, FDI neither crowds in nor crowds out DI, indicating a neutral FDI–DI nexus. However, when the full sample is segmented by geographical topology, a positive and significant FDI–DI nexus can be found in eastern and, to a lesser extent, central cities. A negative, although insignificant, association is reported among western cities. Further, the empirical nexus is conditional on several local absorptive capacities including human capital, financial development, and institutional quality. These findings suggest that a region-based FDI strategy in general and local governments should strengthen their absorptive capacities to fully internalize FDI spillovers.

Designing logistics systems for home delivery in densely populated urban areas

To deliver to customers in densely populated urban areas, companies often employ a two-echelon logistics system. In a two-echelon logistics system, the starting point for goods to be delivered in the urban area is a fulfillment center or city distribution center. From there, the goods are first transported to a satellite, from where they are delivered to their final destinations. To simplify operations, companies, in practice, often restrict the route choices at one or both of the echelons. We study the impact on the delivery cost of such restrictions and conclude that when the number of orders to be delivered is large and the location density of delivery addresses is high, the impact is likely to be small, and the operational benefits probably outweigh the cost increases. To more easily accommodate delivery volume growth and to more effectively handle day-to-day delivery volume variations, we introduce a simple aggregation concept, which leads to quality improvements without affecting operational simplicity. We provide further insight by means of a worst-case analysis for specific geographic topologies.

Experimental analysis of operating characteristics of a direct injection diesel engine fuelled with Cleome viscosa biodiesel

The continues and tireless researches on the development of biodiesel is still going on because of the demand and the expectation of the biodiesel is not meet out to replace the environmental hazardous fossil fuel. India the country with rich flora and fauna and great geographical topology for growing all types of plants and with 80% of the population practicing agriculture for their livelihood has not satisfactorily meet out the requirements of biodiesel production. The researches were repeatedly carried out in limited biodiesel feedstock and new feedstocks are neither tried nor tried in a reluctant manner. If studies of biodiesel with all available plant oils were carried out extensively and exclusively, the rural employability and the production would have been boomed up and if found suitable for replacing diesel the demand to supply gap will be filled. In this experimental study the biodiesel was prepared from Cleome viscosa plant oil a new feedstock for biodiesel production which is a seasonal plant growing almost in all parts of India. The biodiesel produced from Cleome viscosa methyl ester (CVME) was subjected to various testing in conventional direct injection CI engine using different blends B100, B80, B60, B40, B20 and diesel for performance, combustion and emission analysis to prove its suitability for substituting diesel. It has been observed that a reduction in green house gases like CO and HC to an extent of 30% and 20% respectively, but the BSFC was increased by 5.4% and a reduction in BTE of 4.7% was noted. The biodegradability with the environmental sustainable nature of Cleome viscosa will prefers it as a suitable biodiesel to substitute diesel.

A computational investigation of the hydrodynamics of the Badush dam in northern Iraq

A computational analysis of the hydrodynamics of the Badush dam in Iraq is presented, which is planned to be reconstructed as a repulse dam, to prevent the Mosul city, in case of a failure of the Mosul dam. Computational Fluid Dynamics (CFD) is applied in combination with Geometric Information System (GIS) and Digital Elevation Model (DEM). In the first part of the study, a hydrologic study of a possible Mosul dam failure is performed, predicting the important parameters for a possible flooding of Mosul city. Here, a two-dimensional, depth-averaged shallow water equations are used to formulate the flow. Based on GIS and DEM, the required reservoir size and the water level of the Badush dam are predicted, for its acting as a repulse dam. Subsequently, a computational model of the reconstructed Badush dam is developed, combining the proposed construction with the local geographic topology to achieve a perfect fit. Finally, the water flow through the bottom outlets and stilling basin of the proposed dam is calculated by an unsteady, three-dimensional CFD analysis of the turbulent, free-surface flow. The CFD model is validated by comparing the predictions with measurements obtained on a physical model, where a quite satisfactory agreement is observed.

Innovative planning method for the construction of electrical distribution network master plans

This paper proposes a method to optimize the feeders’ routes for medium-voltage distribution grids. The study aims to provide a static model for the construction of master plans and can also be applied to expansion planning. The optimization of the network is performed by a three-stage methodology. The first stage minimizes the total length of the conductors with an adapted simulated annealing algorithm. The secured feeder architecture is directly modelled in the structure of the solution. The second stage performs the balancing of the loads between the feeders in order to respect the different technical constraints. The last stage is the allocation of remote-controlled switches according to the objectives of supply quality. The methodology is applied to a real distribution network of 136 nodes. The complexity of the example is significant because there are no preselected possible routes. Instead of that, all the connections are considered, taking into account the geographical topology of urban areas.

Achieving a 100% Renewable Grid: Operating Electric Power Systems with Extremely High Levels of Variable Renewable Energy

What does it mean to achieve a 100% renewable grid? Several countries already meet or come close to achieving this goal. Iceland, for example, supplies 100% of its electricity needs with either geothermal or hydropower. Other countries that have electric grids with high fractions of renewables based on hydropower include Norway (97%), Costa Rica (93%), Brazil (76%), and Canada (62%). Hydropower plants have been used for decades to create a relatively inexpensive, renewable form of energy, but these systems are limited by natural rainfall and geographic topology. Around the world, most good sites for large hydropower resources have already been developed. So how do other areas achieve 100% renewable grids? Variable renewable energy (VRE), such as wind and solar photovoltaic (PV) systems, will be a major contributor, and with the reduction in costs for these technologies during the last five years, large-scale deployments are happening around the world.

Low-Complexity Energy Efficient Base Station Cooperation Mechanism in LTE Networks

Currently Energy-Saving (ES) methods in cellular networks could be improved, as compensation method for irregular Base Station (BS) deployment is not effective, most regional ES algorithm is complex, and performance decline caused by ES action is not evaluated well. To resolve above issues, a low-complexity energy efficient BS cooperation mechanism for Long Time Evolution (LTE) networks is proposed. The mechanism firstly models the ES optimization problem with coverage, resource, power and Quality of Service (QoS) constraints. To resolve the problem with low complexity, it is decomposed into two sub-problems: BS Mode Determination (BMD) problem and User Association Optimization (UAO) problem. To resolve BMD, regional dynamic multi-stage algorithms with BS cooperation pair taking account of load and geographic topology is analyzed. And then a distributed heuristic algorithm guaranteeing user QoS is adopted to resolve UAO. The mechanism is simulated under four LTE scenarios. Comparing to other algorithms, results show that the mechanism can obtain better energy efficiency with acceptable coverage, throughput, and QoS performance.

Self-configuring indoor localization based on low-cost ultrasonic range sensors.

In smart environments, target tracking is an essential service used by numerous applications from activity recognition to personalized infotaintment. The target tracking relies on sensors with known locations to estimate and keep track of the path taken by the target, and hence, it is crucial to have an accurate map of such sensors. However, the need for manually entering their locations after deployment and expecting them to remain fixed, significantly limits the usability of target tracking. To remedy this drawback, we present a self-configuring and device-free localization protocol based on genetic algorithms that autonomously identifies the geographic topology of a network of ultrasonic range sensors as well as automatically detects any change in the established network structure in less than a minute and generates a new map within seconds. The proposed protocol significantly reduces hardware and deployment costs thanks to the use of low-cost off-the-shelf sensors with no manual configuration. Experiments on two real testbeds of different sizes show that the proposed protocol achieves an error of 7.16∼17.53 cm in topology mapping, while also tracking a mobile target with an average error of 11.71∼18.43 cm and detecting displacements of 1.41∼3.16 m in approximately 30 s.