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Stream gauging stations are important in hydrology and water science for obtaining water-related information, such as stage and discharge. However, for efficient operation and management, a more accurate grouping method is needed, which should be based on the interrelationships between stream gauging stations. This study presents a grouping method that employs community detection based on complex networks. The proposed grouping method was compared with the cluster analysis approach, which is based on statistics, to verify its adaptability. To achieve this goal, 39 stream gauging stations in the Yeongsan River basin of South Korea were investigated. The numbers of groups (clusters) in the study were two, four, six, and eight, which were determined to be suitable by fusion coefficient analysis. Ward’s method was employed for cluster analysis, and multilevel modularity optimization was applied for community detection. A higher level of cohesion between stream gauging stations was observed in the community detection method at the basin scale and the stream link scale within the basin than in the cluster analysis. This suggests that community detection is more effective than cluster analysis in terms of hydrologic similarity, persistence, and connectivity. As such, these findings could be applied to grouping methods for efficient operation and maintenance of stream gauging stations.

In recent years several multipath data transport mechanisms, such as MPTCP and XMP, have been introduced to effectively exploit the path diversity of data center networks (DCNs). However, these multipath schemes have not been widely deployed in DCNs. We argue that two key factors among others impeded their adoption: TCP incast and minimum window syndrome . First, these mechanisms are ill-suited for workloads with a many-to-one communication pattern, commonly found in DCNs, causing frequent TCP incast collapses. Second, the syndrome we discover for the first time, results in 2-5 times lower throughput for single-path flows than multipath flows, thus severely violating network fairness. To effectively tackle these problems, we propose AMP: an adaptive multipath congestion control mechanism that quickly detects the onset of these problems and transforms its multipath flow into a single-path flow. Once these problems disappear, AMP safely reverses this transformation and continues its data transmission via multiple paths. Our evaluation results under a diverse set of scenarios in a fat-tree topology with realistic workloads demonstrate that AMP is robust to the TCP incast problem and improves network fairness between multipath and single-path flows significantly with little performance loss.

This study has been carried out to evaluate the applicability of the pilot scale hybrid type of stormwater runoff treatment system for treatment of combined sewer overflow. Also, to determine the optimum operation parameter such as coagulation dosage concentration, effectiveness of coagulant usage, surface loading rate and backwashing conditions. The pilot scale stormwater filtration system (SFS) was installed at the municipal wastewater plant serving the city of Cheongju (CWTP), Korea. CWTP has a capacity of 280,000 m3/day. The SFS consists of a hydrocyclone coagulation/flocculation with polyaluminium chloride silicate (PACS) and an upflow filter to treat combined sewer overflows. There are two modes (without PACS use and with PACS use) of operation for the SFS. In case of no coagulant use, the range of suspended solids (SS) and turbidity removal efficiency were 72.0–86.6% (mean 80.0%) and 30.9–71.1% (mean 49.3%), respectively. And, the recovery rate of filter was 79.2–83.6% (mean 81.2%); the rate of remaining solid loading in filter media was 16.4–20.8% (mean 18.8%) after backwashing. The influent turbidity, SS concentrations were 59.0–90.7 NTU (mean 72.0 NTU), 194.0–320.0mg/L (mean 246.7mg/L), respectively. The range of PACS dosage concentration was 6.0–7.1mg/L (mean 6.7mg/L). The range of SS and turbidity removal efficiency was 84.9–98.2 (mean 91.4%) and 70.7–96.3 (mean 84.0%), respectively. It was found that removal efficiency was enhanced with PACS dosage. The recovery rate of filter was 92.0–92.5% (mean 92.3%) the rate of remaining solid loading in filter media was 6.1–8.2% (mean 7.2%) after backwashing. In the case of coagulant use, the particle size of the effluent is bigger than influent particle size. The results showed that SFS with PACS use more effective than without PACS use in SS and turbidity removal efficiency and recovery rate of filter.

The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change has become more severe. In this study, small hydropower (SHP) was evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. The results show that SHP potential for the near future (2021 to 2040) shows a tendency to be increased, and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP to expand the use of renewable energy. In addition, a methodology presented in this study could be used for the future prospect of the SHP potential.

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808&ndash, 3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.

Polyaromatic dye molecules employed in photovoltaic and electronic applications are often processed in organic solvents. The aggregation of these dyes is key to their applications, but a fundamental molecular understanding of how the solvent environment controls the stacking of polyaromatics is unclear. This study reports initial results from Monte Carlo simulations of how various acene molecule dimers stack when they are dissolved in different solvents. Free energies computed using full dispersion interactions versus those with sterics only suggest that solvent entropy alone accounts for the majority of the stacking free energy in solvents with compact molecular geometries such as carbon tetrachloride. However, in contrast with carbon tetrachloride, we also observe significant variations in the stacking free energies of naphthalene, anthracene, and tetracene across other solvents such as toluene and cyclohexane. The weak attractive dispersion interactions between the acene solutes and planar and near-pla...

The influences of microstructure and inclusion on cold cracking were studied in high-strength steel welds of YS 600 MPa grade using the Y-groove test. The weld microstructure showed a mixture of acicular ferrite, bainite and predominantly martensite. Cold crack fractography identified intergranular fracture (IF) and quasi-cleavage fracture (QCF). Bainite and martensite predominated on the surface and subsurface of IF while the QCF showed inclusions that nucleated acicular ferrite. The influence of inclusions on cold cracking can be described as follows: (i) the inclusions acted as nucleation sites of QCF, (ii) the inclusions contributed to the nucleation and growth of micro-cracks and (iii) new cracks were produced from inclusions when a crack impinged on the inclusions. Inclusions smaller than 2 μm increased the cold crack resistance by contributing to the nucleation of acicular ferrite. Inclusions larger than 2 μm increased the cold crack susceptibility by inducing crack nucleation in welds.

IoT-enabled devices continue to generate a massive amount of data. Transforming this continuously arriving raw data into timely insights is critical for many modern online services. For such settings, the traditional form of data analytics over the entire dataset would be prohibitively limiting and expensive for supporting real-time stream analytics.In this work, we make a case for approximate computing for data analytics in IoT settings. Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic tradeoff between the output accuracy and computation efficiency.This motivated the design of APPROXIOT— a data analytics system for approximate computing in IoT. To realize this idea, we designed an online hierarchical stratified reservoir sampling algorithm that uses edge computing resources to produce approximate output with rigorous error bounds. To showcase the effectiveness of our algorithm, we implemented APPROXIOT based on Apache Kafka and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. Our results show that APPROXIOT achieves a speedup 1.3×—9.9× with varying sampling fraction of 80% to 10% compared to simple random sampling.

Part 4: Software Security / Attacks; International audience; Attackers can exploit covert channels, such as timing side-channels, to transmit information without data owners or network administrators being aware. Sneak-Peek is a recently considered data centre attack, where, in a multi-tenant setting, an insider attacker can communicate with colluding outsiders by intentionally adding delays to traffic on logically isolated but physically shared links. Timing attack mitigations typically introduce delays or randomness which can make it difficult to understand the trade-off between level of security (bandwidth of the covert channel) and performance loss. We demonstrate that formal methods can help. We analyse the impacts of two Sneak-Peek mitigations, namely, noise addition and path hopping. We provide a precise mathematical model of the attack and of the effectiveness these defences. This mathematical analysis is extended by two tool-based stochastic formal models, one formalized in Uppaal and the other in Carma. The formal models can capture more general and larger networks than a paper-based analysis, can be used to check properties and make measurements, and are more easily modifiable than conventional network simulations. With Uppaal, we can analyse the effectiveness of mitigations and with Carma, we can analyse how these mitigations affect latencies in typical data centre topologies. As results, we show that using a selective strategy for path hopping is better than a random strategy, that using the two defences in conjunction may actually be worse than using a single defence, and we show the connection between hop frequency and network latency.

As many-to-one traffic patterns prevail in data center networks, TCP flows often suffer from severe unfairness in sharing bottleneck bandwidth, which is known as the TCP outcast problem. The cause of the TCP outcast problem is the bursty packet losses by a drop-tail queue that triggers TCP timeouts and leads to decreasing the congestion window. This paper proposes TCPRand, a transport layer solution to TCP outcast. The main idea of TCPRand is the randomization of TCP payload size, which breaks synchronized packet arrivals between flows from different input ports. Based on the current congestion window size and the CUBIC’s congestion window growth function, TCPRand adaptively determines the proper level of randomness. With extensive ns-3 simulations and experiments, we show that TCPRand guarantees the superior enhancement of TCP fairness by reducing the TCP timeout period noticeably even in an environment where serious TCP outcast happens. TCPRand also minimizes the total goodput loss since its adaptive mechanism avoids unnecessary payload size randomization. Compared with DCTCP, TCPRand performs fairly well and only requires modification at the transport layer of the sender which makes its deployment relatively easier.

The water-level time series of a tidal river is influenced by various factors and has a complex structure, which limits its use as hydrological forecast data. This study proposes a methodology for decomposing the water-level time series of a tidal river into various components that influence the water level. To this end, the tide, wave, rainfall-induced runoff and noise components were selected as the main components that affect the water-level time series. The tide component and the wave component were first separated through wavelet analysis and curve fitting and then they were removed from the water-level data. A high-pass filter was then applied to the resulting time series to separate the rainfall-induced runoff component and the noise component. These methods made it possible to determine the rate of influence that each component has on the water level of a tidal river. The results could be used as a basis for calibrating a rainfall-runoff model and issuing flood forecasts and warnings for a tidal river.

The effect of martensite&ndash, austenite (M&ndash, A) constituents and simulated microstructure on low-temperature toughness was investigated in YS 500 MPa grade structural steel welds. The specimens were fabricated using a direct quenching and tempering process. After simulated weld thermal cycles, the coarse-grained heat-affected zone (CGHAZ) and intercritically reheated coarse-grained heat-affected zone (IRCGHAZ) were produced using a Gleeble tester and real welded joint to support the simulation results. The largest low-temperature toughness was observed in the fine-grained heat-affected zone (FGHAZ) owing to the fine-ferrite microstructure. However, the toughness decreased in the IRCGHAZ because of the slender morphology of the M&ndash, A constituents that formed primarily along the prior austenite grain boundaries in the IRCGHAZ.

This study investigates the effects of Mo content on the low-angle misorientation microstructure and the mechanical properties of high strength steels of YS 550 MPa grade. Steels of various Mo contents (0.08, 0.15, and 0.25 wt%) are fabricated by controlled rolling and accelerated cooling. The Mo steels consist of acicular ferrite (AF), granular bainite (GB), a low-angle misorientation microstructure, that is, bainitic ferrite (BF), and a secondary phase of martensite-austenite (MA), regardless of the Mo content. Degenerated pearlite (DP) is observed only in the 0.08 wt% Mo steel. As the Mo content increases from 0.08 to 0.25 wt%, the microstructure and mechanical properties are described as follows: (i) the volume fraction of AF decreases and that of BF increases, (ii) the yield and tensile strengths increase and the elongation decreases, and (iii) the Charpy toughness at −60 °C decreases owing to the decrease in AF and increase in BF.

This study investigates the fracture behavior during hole expansion testing of a 980 MPa hot-rolled Fe–Cr–Ti–B steel, with particular focus on analyzing the microvoid initiation and crack propagation during plastic deformation. The microstructure of the steel consists of a tempered martensite matrix and granular bainite, with each constituent phase being identified by field emission scanning electron microscopy, transmission electron microscopy, micro Vickers hardness, and nanoindentation. Microvoids are observed at the intergranular boundaries, transgranular regions, and at the interface of precipitates/matrix and non-metallic inclusions/matrix. The main crack is generated at the intergranular interface between the tempered martensite and granular bainite, an area exhibiting a large deviation in hardness and elastic modulus. The tempered martensite has a greater hardness of 385 Hv and elastic modulus of 287 GPa, as opposed to the 326 Hv and 257 GPa of the granular bainite. Due to this difference in elastic modulus, the granular bainite plastically deforms at a lower applied stress than the tempered martensite. The main crack obtained from the hole expansion test generates a fractography mixed with dimple and quasi-cleavage fracture pattern. Therefore, the crack initiated at the interface of the tempered martensite and granular bainite is propagated along the transgranular regions.