Cumulative Probability Distribution Research Articles

Multi-Sensor fusion and semantic map-based particle filtering for robust indoor localization

To enhance positioning accuracy and ensure long-term stability in large-scale indoor environments, this paper presents a robust particle filtering method that integrates IMU, magnetic field, Bluetooth, and semantic map data, implemented through a handheld portable device. In the particle prediction phase, based on peak candidates extracted in the time domain, the unintentional interference movement of the hand is removed through short-time Fourier transform in the frequency domain to reduce the noise of pedestrian dead reckoning. During the observation phase, we introduce a magnetic interference coefficient to model and quantify the degree of electromagnetic interference in different environments, thereby improving the accuracy of indoor magnetic headings and enhancing the alignment of particle swarms with real-world directional constraints. Furthermore, based on passable semantic information, the indoor map is segmented into distinct probability regions, allowing the particle filter to adaptively adjust the weight of the particle swarm when moving between regions, thus improving the robustness of the overall fusion system by preventing particles from entering infeasible areas. Experiments and validations were carried out in a large shopping mall and parking lot with multiple devices. Results demonstrate that the proposed method improves the average positioning accuracy by approximately 25% compared to the Bluetooth-only positioning system, with a 15% increase in cumulative distribution probability within 3 m.

Revisiting the Lee-Yang singularities in the four-dimensional Ising model: a tribute to the memory of Ralph Kenna

We have studied numerically the Lee-Yang singularities of the four dimensional Ising model at criticality, which is believed to be in the same universality class as the φ44 scalar field theory. We have focused in the numerical characterization of the logarithmic corrections to the scaling of the zeros of the partition function and its cumulative probability distribution, finding a very good agreement with the predictions of the renormalization group computation on the φ44 scalar field theory. To obtain these results, we have extended a previous study [R. Kenna, C. B. Lang, Nucl. Phys., 1993, B393, 461] in which there were computed numerically the first two zeros for L≤24 lattices, to the computation of the first four zeros for L≤64 lattices.

Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes.

The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.

Residual distribution and risk assessment of neonicotinoids in urban green space soils of the pearl river delta, South China: A socioeconomic analysis

Urban green spaces are the soil component in cities that interacts most closely with humans. This study investigated the residues of seven neonicotinoids (NEOs) in soils from urban green spaces within the Pearl River Delta (PRD) region and analyzed the correlation between the residue characteristics and the region's economic development. Notably, we introduced the Nemerow Index method, a comprehensive approach, to quantify the overall pollution level of NEOs in the soil of urban park green spaces and utilized this to assess the cumulative exposure probability risks for different populations in this scenario. We found that: (1) The soil of urban park green spaces exhibited varying degrees of NEOs contamination (Σ7NEOs: N.D.–137.31; 6.25 μg/kg), with imidacloprid and clothianidin constituting the highest proportions (89.46 % and 83.60 %); (2) The residual levels of NEOs in Children's Park were significantly higher than those in community parks within Guangzhou, with an average value of 13.30 μg/kg compared to 3.30 μg/kg; (3) The residual characteristics of NEOs exhibited a positive correlation with regional economic development; specifically, the per capita GDP well correlated with IMIRPF, a summation of seven NEOs in imidacloprid equivalents via relative potency factors (R2 =0.86). Regions with higher economic development typically exhibited elevated IMIRPF levels; (4) The fitted cumulative probability distributions for average daily exposure doses revealed that children's exposure was an order of magnitude higher than adults'. Despite this, the exposure risks for both groups remained within acceptable limits.

Contribution of anthropogenic influence to the 2022-like Yangtze River valley compound heatwave and drought event

In August 2022, an unprecedented compound heatwave and drought event (CHDE) lasting 24 days occurred in the Yangtze River valley (YRV), leading to a severe reduction of the crop, fresh water, and power supply. We constructed a joint cumulative probability distribution of heatwave and drought intensity, and found that the lowest probability-based index (PI) of 0.06 in 2022 was estimated as a 1-in-662-year event over the 1961–2022 climate. We then detected the fingerprint of greenhouse gas forcing to the observed PI in a generalized extreme value framework, but not the aerosol forcing, suggesting the leading contribution of greenhouse gas forcing on such extreme CHDE. Furthermore, anthropogenic influence had increased the probability of such CHDE by more than 10 times compared to the counterfactual climate. Also, the PI decreased from about 0.30 at the present climate to about 0.14 at the 3 °C global warming level, indicating that CHDE will become more extreme over YRV.

Assessing the effects of plant density and nitrogen on millet yield in Southern Niger using the CERES-millet model

BackgroundThe dryland production environments in Niger Republic (Niger) generate variable crop production risks that reduce crop yields and increase regional food insecurity. Optimal combinations of crop varieties and management are needed to maximize crop water-limited yields in these environments.MethodsIn this study, we calibrated and validated the CERES-Millet model using data from field experiments. Seasonal analysis (1984–2020) was carried out in 18 selected sites across the three agroecological zones (AEZs) to test the effects of plant density and N fertilization on grain yield. The treatment included five different plant densities (1.5, 3, 6, 9, and 12 plants m−2) and four N rates (0, 20, 40, and 60 kg N ha−1). Three millet varieties (CHAKTI, HKP, and SOSAT-C88) were compared. Millet production risk was assessed at each AEZ using cumulative probability distribution graphs. The acceptable grain yield required to compensate for the minimum production cost of millet in Niger was set to 975 kg ha−1 (75th percentile of the simulated data).ResultsThe CERES-Millet model reasonably reproduced number of days to flowering (d-index > 0.50; RMSE < 2 days), number of days to maturity (d-index > 0.50; RMSE < 2 days), and grain yield d-index > 0.78; RMSE < 100 kg ha−1) for all the three varieties. The results showed that there was significant response to N (40 – 110% yield increase following N application) and plant density (30–80% yield increase by increasing density above 1.5 plants m−2) in all the AEZs depending on variety. The SOSAT was the most responsive variety to N application and plant density in all AEZs. Under low N application (0–20 kg ha−1) and low (1.5—3 plants m−2) to moderate plant density (6 plants m−2), CHAKTI and HKP had the highest production risks. Increasing N application above 20 kg ha−1 mitigate these risks where grain yield was above the 975 kg ha−1 threshold representing the minimum production cost for millet in more than 50% of the years under all plant densities except in Sahel where this threshold was only achieved in < 20% of the years. In all AEZs, increasing plant density above 6 plants m−2 increases this risk under low to moderate N application, but the downside risk was mitigated when N was applied at high rates.ConclusionThis study demonstrated N application rate and plant density recommendations must be tailored to specific variety and AEZs to maximize grain yield and reduce volatility in Niger.

Investigation of Variability of Flaw Strength Distributions on Brittle SiC Ceramic

The present paper investigates flaw strength distributions established using various flexural tests on batches of SiC bar test specimens, namely four-point bending as well as three-point bending tests with different span lengths. Flaw strength is provided by the elemental stress operating on the critical flaw at the fracture of a test specimen. Fracture-inducing flaws and their locations are identified using fractography. A single population of pores was found to dominate the fracture. The construction of diagrams of p-quantile vs. elemental strengths was aimed at assessing the Gaussian nature of flaw strengths. Then, empirical cumulative distributions of strengths were constructed using the normal distribution function. The Weibull distributions of strengths are then compared to the normal reference distributions. The parameters of the Weibull cumulative probability distributions are estimated using maximum likelihood and moment methods. The cumulative distributions of flexural strengths for the different bending tests are predicted from the flaw strength density function using the elemental strength model, and from the cumulative distribution of flexural strength using the Weibull function. Flaw strength distributions that include the weaker flaws that are potentially present in larger test pieces are extrapolated using the p-quantile diagrams. Implications are discussed regarding the pertinence of an intrinsically representative flaw strength distribution, considering failure predictions. Finally, the influence of the characteristics of fracture-inducing flaw populations expressed in terms of flaw strength interval, size, dispersion, heterogeneity, and reproducibility with volume change is examined.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.

A Chronic Aquatic Hazard Assessment for the Perfume Raw Material Octahydro-tetramethyl-naphthalenyl-ethanone.

Octahydro-tetramethyl-naphthalenyl-ethanone (OTNE) is a high-production volume fragrance material used in various down-the-drain consumer products. To assess aquatic risk, the Research Institute for Fragrance Materials (RIFM) uses a tiered data-driven framework to determine a risk characterization ratio, where the ratio of the predicted-environmental concentration to the predicted-no-effect concentration (PNEC) of <1 indicates an acceptable level of risk. Owing to its high production volume and the conservative nature of the RIFM framework, RIFM identified the need to utilize a species sensitivity distribution (SSD) approach to reduce the PNEC uncertainty for OTNE. Adding to the existing Daphnia magna, Danio rerio, and Desmodesmus subspicatus chronic studies, eight new chronic toxicity studies were conducted on the following species: Navicula pelliculosa, Chironomus riparius, Lemna gibba, Ceriodaphnia dubia, Hyalella azteca, Pimephales promelas, Anabaena flos-aquae, and Daphnia pulex. All toxicity data were summarized as chronic 10% effect concentration estimates using the most sensitive biological response. Daphnia magna was the most sensitive (0.032 mg/L), and D. subspicatus was the least sensitive (>2.6 mg/L, the OTNE solubility limit). The 5th percentile hazardous concentration (HC5) derived from the cumulative probability distribution of the chronic toxicity values for the 11 species was determined to be 0.0498 mg/L (95% confidence interval 0.0097-0.1159 mg/L). A series of "leave-one-out" and "add-one-in" simulations indicated the SSD was stable and robust. Add-one-in simulations determined that the probability of finding a species sensitive enough to lower the HC5 two- or threefold was 1/504 and 1/15,300, respectively. Given the high statistical confidence in this robust SSD, an additional application factor protection is likely not necessary. Nevertheless, to further ensure the protection of the environment, an application factor of 2 to the HC5, resulting in a PNEC of 0.0249 mg/L, is recommended. When combined with environmental exposure information, the overall hazard assessment is suitable for a probabilistic environmental risk assessment. Environ Toxicol Chem 2024;43:1378-1389. © 2024 SETAC.

Quantifying the Inverter-Interfaced Renewable Energy Critical Integration Capacity of a Power Grid Based on Short-Circuit Current Over-Limits Probability

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system’s short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined.

Probabilistic CDF-based load forecasting model in a power distribution system

The study searches for a probabilistic forecasting model for electric power demand based on the correlation between weather temperature and electric power demand using hourly time series data spanning a year. The model is based on the temperature and power demand data's cumulative probability distribution functions or CDFs. In probabilistic forecasting, a range of potential values and their associated probabilities are predicted in addition to a single value for a given range. The qualities of CDF modeling allow it to cover these uncertainties. For scenario 1, case 2, and case 3, the mean absolute percentage error (MAPE)-based forecasting accuracy for the hourly load demand is 3.93, 4.64, and 2.56, respectively. The Gaussian distribution-based CDFs' capacity to forecast outside of the present data range is an extra advantage. The world's electric power networks are growing increasingly intricate and unpredictable through the integration of several distributed energy resources (DERs) and cutting-edge technology from renewable energy resources (RERs). Other variables, such as variations in weather patterns, shifts in the status of the economy, or adjustments to energy policies, can also add to uncertainty and not be foreseen. A body of literature demonstrates a shortage of research on LTLF and general-purpose forecasting methods. The proposed approach is flexible and might establish a standard for many different forecasting tasks. The proposed method is intended to be a flexible strategy that may be used for different utilities with varying time horizons.

Structural phase transition and resistive switching properties of Cu x O films during post-thermal annealing

We studied the phase change and resistive switching characteristics of copper oxide (Cu x O) films through post-thermal annealing. This investigation aimed to assess the material’s potential for a variety of electrical devices, exploring its versatility in electronic applications. The Cu x O films deposited by RF magnetron sputtering were annealed at 300, 500, and 700 °C in ambient air for 4 min by rapid thermal annealing (RTA) method, and then it was confirmed that the structural phase change from Cu2O to CuO occurred with increasing annealing temperature. Resistive random-access memory (ReRAM) devices with Au/Cu x O/p+-Si structures were fabricated, and the ReRAM properties appeared in CuO-based devices, while Cu2O ReRAM devices did not exhibit resistive switching behavior. The CuO ReRAM device annealed at 500 °C showed the best properties, with a on/off ratio of 8 × 102, good switching endurance of ∼100 cycles, data retention for 104 s, and stable uniformity in the cumulative probability distribution. This characteristic change could be explained by the difference in the grain size and density of defects between the Cu2O and CuO films. These results demonstrate that superior and stable resistive switching properties of RF-sputtered Cu x O films can be obtained by low-temperature RTA.

Evaluation of Subdaily Hydrological Regime Alteration Characteristics for Hydro–Photovoltaic Complementary Operation in the Upper Yellow River

The complementary operation of hydropower and photovoltaic power, aimed at meeting real-time demand, has led to frequent adjustments in power generation, causing significant fluctuations in hydrological systems and adversely affecting fish reproduction. The traditional hydrological regime alteration assessment index is based on index of hydrologic alternation (IHA) and mostly focuses on annual and daily runoff alterations. This study proposes a new set of indicators considering the characteristics of subdaily hydrological regime alterations, including magnitude, rate of change, duration, frequency, and timing. Using the hourly outflow from Longyangxia, an analysis of indicator redundancy was conducted. The alteration of the indicators before and after hydropower and photovoltaic operation was then analyzed using the cumulative probability distribution curve. Additionally, a concentration index was introduced to analyze the variations in hydrological impacts during different months. The results show that the hydro–photovoltaic complementary operation changed the subdaily natural flow regime, significantly increasing the rate of flow increase or decrease and the duration, with most indexes increasing by more than 100% compared with the natural flow regime. Furthermore, the concentration values of the indexes for the hydro–photovoltaic complementary operation were less than 10, indicating a more significant impact on the subdaily flow regime throughout the year. This research provides crucial data for mitigating ecological impacts under multi-source complementary scheduling.

Investigating paleoseismicity using high-resolution airborne LiDAR data - A case study of the Huangxianggou fault in the northeastern Tibetan plateau

Fault-offset landforms have long been recognized as holding important information about paleoseismic slip. Constructing an along-strike fault slip distribution could help reveal a fault's long-term rupture patterns and facilitate a more precise assessment of its future behavior. In this study, we documented the paleoseismic history of the Huangxianggou Fault, an oblique sinistral-thrust segment of the West Qingling Fault in the northeastern region of the Tibetan Plateau, using LiDAR-derived high-resolution DEM (0.1 m/pixel) and measurement of offset landforms. A total of 46 well-preserved and well-shaped landforms were chosen for horizontal and vertical displacement measurements, and the dense measurement data allow us to generate the cumulative offset probability distribution curves of horizontal and vertical displacements. Statistical analysis revealed at least seven surface-rupturing events, each with similar coseismic horizontal displacements of ∼7.0 m and vertical displacements of ∼0.7 m, in agreement with previous paleoseismic trench results. Using empirical relationships between moment magnitude and horizontal displacement, a plausible moment magnitude range of Mw7.3–7.7 was determined for these paleoearthquakes. Furthermore, by evaluating rupture parameters and fault slip rate, we determined a recurrence interval of 2.7–3.4 kyr for significant surface-rupturing events on this fault. The elapsed time since the last earthquake closely approaches this interval, suggesting a potential seismic risk for the Huangxianggou Fault. Our results emphasizes the importance of high-resolution topographic data in paleoseismic investigations, enabling the identification of numerous offset landforms and precise displacement measurements across faults. Paleoearthquake counts derived from horizontal and vertical displacement analyses were found to be consistent, highlighting the significance of vertical displacements in quantifying paleoearthquakes for strike-slip faults with a thrust component.

A Rapid-Accurate Fault Diagnosis Method Based on Cumulative Probability Distribution for Lithium-Ion Battery Packs

A Rapid-Accurate Fault Diagnosis Method Based on Cumulative Probability Distribution for Lithium-Ion Battery Packs

Experimental and numerical investigation on the bubble breakup and coalescence characteristics in the highly efficient static mixers

CFD-PBM and experiment methods were applied to study the bubble dispersion characteristics in the static mixers with three twisted blades (TKSM). The gas-liquid velocity ratio was selected as drag correction factors. The simulation data was compared with experimental values at different superficial gas velocity and the results revealed that the modified model could effectively enhance the calculation accuracy of gas holdup. Furthermore, bubble size distribution (BSD) was well predicted by Luo-Prince model and the mean relative error (MRE) of axial d32 between the prediction of Luo-Prince model and experimental value was 2.86 %. Meanwhile, the influences of bubble collision mechanisms and coalescence correctors on the numerical prediction were quantitatively analyzed. The effects of turbulent fluctuation, viscous shear, wake entrainment and buoyancy driven were considered for the prediction of bubble collision frequency in TKSM. The prediction precision of cumulative probability distribution (CPD) was promoted by 17.26 % when the correctors obtained from Energy-Minimization Multi-Scale (EMMS) method were used. In addition, empirical parameter CDas was evaluated and the results showed that the Das coalescence model could accurately describe the BSD when CDas was 0.01–0.1. The backmixing performance of the gas phase in the TKSM exceeds that of the liquid phase, which is distinct from that in vortex unit.

Fault slip behavior and segmentation revealed by geomorphic offset clusters: An example from the middle Riganpei Co fault, central Tibet

High-resolution topographic datasets are useful for revealing offset features of fault geomorphology and are thus important for reconstructing the evolution history of faults. The Riganpei Co fault, located in the central Tibetan Plateau, is an important active fault on the northern boundary of the Bangong-Nujiang suture zone. In this study, WorldView-2 stereo images were used to generate high-resolution topographic data in a 1 km range on both flanks of the middle section of the Riganpei Co fault. We interpreted the tectonic geomorphology along the fault, classified it into distinct units, and measured the horizontal displacement of each unit. Within approximately 70 km along the fault, we obtained 396 displacement measurements ranging from 0 to 60 m. Given the evident uneven distribution of displacement, the fault was divided into four segments. In each fault segment, the reconstructed slip distributions revealed a tendency of larger displacements in the center and smaller displacements toward the ends. The cumulative offset probability distribution (COPD) of displacements displays 4–5 offset clusters in the range of 0–30 m for each segment, suggesting that at least four large earthquakes ruptured this fault. The binned COPD demonstrated that there are differences in the displacement accumulation modes among these fault segments, where the middle segment follows a characteristic slip accumulation mode. Reconstructed cumulative slip profiles revealed a complex pattern of strong earthquake activities along the Riganpei Co fault.

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

Modelling the compound floods upon combined rainfall and storm surge events in a low-lying coastal city

Low-lying coastal cities are highly vulnerable to compound floods, which may result from a simultaneous occurrence of storm surges and heavy rainfall. Systematic numerical analysis is essential for projecting flood scenarios upon compound events. In this study, a coupled model that integrates the 1D drainage module with the 2D overland flow module is developed and validated by taking Macau as the study area. The joint cumulative probability distribution between rainfall and storm tide are derived based on the historical typhoon data. The flood hazards are projected under designed scenarios, and an investigation is conducted on the backflow processes through drainage system and effectiveness of installing tide gates. The flood zones are delineated and the contributions of rainfall and storm tide to floods are analyzed. Storm surge exerts a greater influence on the maximum inundation depth in the western Macau Peninsula than heavy precipitation does. As the peak tidal level increases from a 10-year to a 200-year return period, the increments of flooded area ratio to the total land area are approximately 19 %. The backflows through underground drainage system could expand the tide affected area during storm events, as indicated by the backflows in the pipe 1.4 km away from the coastal boundary. By installing tide gates, backflows could be greatly eliminated in the corresponding pipes. The inundation area in the compound flood scenario is not equal to the superposition of the extents by the individual components. The transition area that is affected by both storm tide and rainfall extends from coast to inland areas under the combined effects. This study is expected to furnish valuable insights for flood risk assessment, flood hazard prevention and mitigation in low-lying coastal cities such as Macau.

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding.

Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.