ANALISIS PERBANDINGAN ARUS JENUH PADA PENDEKAT SIMPANG TERLINDUNG DAN TERLAWAN DENGAN METODE MKJI DAN METODE TIME SLICE (STUDI KASUS: SIMPANG SUBITA DAN SIMPANG WARIBANG)

Sebanyak 33% kepemilikan kendaraan di Provinsi Bali pada tahun 2019 berada di Kota Denpasar, ditambah dengan melintasnya kendaraan dari luar kota untuk bekerja maupun berekreasi ikut membebani jaringan jalan di Kota Denpasar. Hal tersebut menimbulkan permasalahan pada sistem transportasi yaitu mempengaruhi kinerja jaringan jalan khususnya kinerja simpang bersinyal di Kota Denpasar. Kinerja simpang bersinyal dipengaruhi oleh kapasitas dari pendekatnya dimana salah satu faktor yang mempengaruhinya adalah arus jenuh. Penelitian ini bertujuan untuk mengetahui perbandingan arus jenuh pada pendekat terlindung dan terlawan antara Metode MKJI dengan Time Slice. Hasil arus jenuh pada pendekat terlindung di Simpang Subita berdasarkan MKJI adalah 3,629 smp/jam hijau lebih besar 71.18% dibandingkan dengan Metode Time Slice yaitu 2,120 smp/jam hijau, sedangkan hasil arus jenuh pada pendekat terlawan di Simpang Waribang berdasarkan MKJI adalah 1,857 smp/jam hijau lebih kecil 37.49% dibandingkan dengan Metode Time Slice yaitu 2,971 smp/jam hijau.

Auswirkungen von Halogen-induzierten stratosphärischen Ozonänderungen auf den effektiven Strahlungsantrieb und das Oberflächenklima

<p>   Emissionen von anthropogenen FCKW wurden infolge des Montrealer Abkommens von 1987 stark reduziert und entsprechend wird für das 21. Jahrhundert eine Erholung der Ozonschicht erwartet. Eine Änderung der Ozonkonzentration in der Stratosphäre verändert die Energiebilanz der Erde und wird, im Vergleich zum heutigen Tag, zu einem positiven Strahlungsantrieb führen. Daraus resultieren sowohl global als auch regional erwärmende oder abkühlende Einflüsse auf das Klima.</p> <p>    Der effektive Strahlungsantrieb (Effective radiative forcing - ERF) ist definiert als Änderung der Strahlungsflüsse am oberen Rand der Atmosphäre durch bestimmte „Treiber“, unter Berücksichtigung von Rückkopplungen des Klimasystems auf kurzen Zeitskalen, während Rückkopplungen auf langen Zeitskalen (insbesondere die Meeresoberflächentemperaturen) konstant gehalten werden.Einige Studien untersuchten bereits das ERF von troposphärischen Ozonäderungen, nur wenig ist aber bekannt über den Einfluss von stratosphärischem Ozonänderungen auf den effektiven Strahlungsantrieb und dessen Auswirkung auf das Oberflächenklima.</p> <p>    Um die Entwicklung der Ozonschicht und damit einhergehende Klimaänderung in die Zukunft zu projizieren führten wir so genannte Time-Slice Simulationen mit dem Klimamodell ICON-ART durch. Stratosphärische Ozonänderungen wurden mit dem linearisierten Ozonschema (LINOZ) berechnet, mit einem zusätzlichen Verlustterm, um die katalytische Ozonzerstörung in Polarregionen zu berücksichtigen. Das modellierte Ozon war interaktiv und mit der Strahlung gekoppelt.</p> <p>   In der Standardsimulation werden Meeresoberflächentemperatur und Meer-Eisbedeckung, Aerosole und Treibhausgase entsprechend für das Jahr 2000 fixiert. Für Sensitivitätssimulationen verwenden wir den gleichen Modellaufbau wie in der Standardsimulation, aber mit stratosphärischen Halogenkonzentrationen entsprechend dem 1960 Niveau. Unsere Ergebnisse zeigen, dass die Ozonabnahme zwischen den Jahren 1960 und 2000 zwar global zu keinem signifikantem ERF geführt hat. Regional, auf der Südhemisphäre, vor allem über der Antarktis, ist das ERF der Ozonabnahme aber durchaus signifikant. Unsere Ergebnisse zeigen, dass in hohen südlichen Breiten über der Antarktis das negative ERF durch die Ozonabnahme zwischen 1960 und 2000 zu einem wesentlichen Teil das positive ERF durch den CO2 Anstieg kompensiert hat.</p> <p> </p>

The art of modelling climate change in time slice integrations: The ICON-ART experience

<p>Climate change is happening in a transient manner – with continuously increasing greenhouse gases in the atmosphere, humans have started a radiative imbalance that leads to rising near-surface temperatures. However, there are good reasons why it makes sense to look at quasi-equilibrium climate change simulations. In such simulations, we approximate climate change by “fixing” the amount of long-lived greenhouse gases and use recurring boundary conditions that are representative of a particular year - past, present or future. With such a setup any climate model should simulate a stable climate (after a spin-up phase) that reveals internal variability and does not show any trends. It is a necessary condition for the validity of the model - if no transience is provided in the boundary conditions – that the model does not drift. With such a model configuration, it is possible to estimate probability density functions, because each year of a multi-annual integration is an equally valid realisation for the meteorology of the pre-selected year.</p> <p>Using such a time-slice approach, sensitivities to well-specified individual changes can be assessed. Here, we provide a range of examples using the ICON-ART modelling system to investigate (idealised) climate change scenarios with respect to different threshold temperatures, jet variability and the climatic impact of the ozone hole. We illustrate how such integrations allow the unambiguous attribution of certain climate change effects, e.g. the change of jet stream variability under global warming or the contribution of the ozone hole to regional surface warming. However, we caution against a strict causality chain of processes in explaining the response, because given the nature of the quasi-equilibrium modelled, consistency might not always imply causality.</p>

Time-series data dynamic density clustering

In many clustering problems, the whole data is not always static. Over time, part of it is likely to be changed, such as updated, erased, etc. Suffer this effect, the timeline can be divided into multiple time segments. And, the data at each time slice is static. Then, the data along the timeline shows a series of dynamic intermediate states. The union set of data from all time slices is called the time-series data. Obviously, the traditional clustering process does not apply directly to the time-series data. Meanwhile, repeating the clustering process at every time slices costs tremendous. In this paper, we analyze the transition rules of the data set and cluster structure when the time slice shifts to the next. We find there is a distinct correlation of data set and succession of cluster structure between two adjacent ones, which means we can use it to reduce the cost of the whole clustering process. Inspired by it, we propose a dynamic density clustering method (DDC) for time-series data. In the simulations, we choose 6 representative problems to construct the time-series data for testing DDC. The results show DDC can get high accuracy results for all 6 problems while reducing the overall cost markedly.

EDHA: Event-Driven High Accurate Simulator for Spike Neural Networks

In recent years, spiking neural networks (SNNs) have attracted increasingly more researchers to study by virtue of its bio-interpretability and low-power computing. The SNN simulator is an essential tool to accomplish image classification, recognition, speech recognition, and other tasks using SNN. However, most of the existing simulators for spike neural networks are clock-driven, which has two main problems. First, the calculation result is affected by time slice, which obviously shows that when the calculation accuracy is low, the calculation speed is fast, but when the calculation accuracy is high, the calculation speed is unacceptable. The other is the failure of lateral inhibition, which severely affects SNN learning. In order to solve these problems, an event-driven high accurate simulator named EDHA (Event-Driven High Accuracy) for spike neural networks is proposed in this paper. EDHA takes full advantage of the event-driven characteristics of SNN and only calculates when a spike is generated, which is independent of the time slice. Compared with previous SNN simulators, EDHA is completely event-driven, which reduces a large amount of calculations and achieves higher computational accuracy. The calculation speed of EDHA in the MNIST classification task is more than 10 times faster than that of mainstream clock-driven simulators. By optimizing the spike encoding method, the former can even achieve more than 100 times faster than the latter. Due to the cross-platform characteristics of Java, EDHA can run on x86, amd64, ARM, and other platforms that support Java.

Tracking patients healthcare experiences during the COVID-19 outbreak: Topic modeling and sentiment analysis of doctor reviews

Emerging voices of patients in the form of opinions and expectations about the quality of care can improve healthcare service quality. A large volume of patients’ opinions as online doctor reviews (ODRs) are available online to access, analyze, and improve patients’ perceptions. This paper aims to explore COVID-19-related conversations, complaints, and sentiments using ODRs posted by users of the physician rating website. We analyzed 96,234 ODRs of 5,621 physicians from a prominent health rating website in the United Kingdom (Iwantgreatcare.org) in threetime slices (i.e., from February 01 to October 31, 2020). We employed machine learning approach, dynamic topic modeling, to identify prominent bigrams, salient topics and labels, sentiments embedded in reviews and topics, and patient-perceived root cause and strengths, weaknesses, opportunities, and threats (SWOT) analyses to examine SWOT for healthcare organizations. This method finds a total of 30 latent topics with 10 topics across each time slice. The current study identified new discussion topics about COVID-19 occurring from time slice 1 to time slice 3, such as news about the COVID-19 pandemic, violence against the lockdown, quarantine process and quarantine centers at different locations, and vaccine development/treatment to stop virus spread. Sentiment analysis reveals that fear for novel pathogen prevails across all topics. Based on the SWOT analysis, our findings provide a clue for doctors, hospitals, and government officials to enhance patients’ satisfaction and minimize dissatisfaction by satisfying their needs and improve the quality of care during the COVID-19 crisis.

A dictionary learning method with atom splitting for seismic footprint suppression

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises two components, i.e., useful signals and acquisition footprint. Useful signals describe the spatial distributions of geological structures with local piecewise smooth morphological features. However, acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in the marine seismic acquisition appear as stripes. As useful signals and acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two sub-dictionaries to reconstruct these two components. We propose an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D post-stack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D post-stack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that the proposed method can effectively suppress the acquisition footprint with fidelity to the original data.

Impact of climate change on monthly streamflow in the Verde River Basin using two hydrological models

This study assessed the impact of climate change on monthly streamflow in the Verde River Basin, located in the Grande River Basin headwater. For this purpose, the SWAT and VIC hydrological models were used to simulate the monthly streamflow under RCP4.5 and RCP8.5 scenarios, obtained by Regional Climate Models Eta-HadGEM2-ES, Eta-CanESM2 and Eta-MIROC5 in the baseline period (1961-2005) and three time-slice (2011-2040, 2041-2070, and 2071-2099) inputs. At the end of the century, the Eta-HadGEM2-ES showed larger decrease of precipitation in both radiative scenarios, with an annual reduction of 17.4 (RCP4.5) and 32.3% (RCP8.5), while the Eta-CanESM2 indicated major warming, with an annual increase of 4.7 and 10.2°C under RCP4.5 and RCP8.5, respectively. As well as precipitation changes, the Eta-HadGEM2-ES also showed greater impacts on streamflow under RCP4.5 for the first time-slice (2011-2040), with an annual decrease of 58.0% for both hydrological models, and for the RCP8.5 scenario by the end the century (2071-2099), with an annual reduction of 54.0 (VIC model) and 56.8% (SWAT model). Regarding monthly streamflow, the Eta-HadGEM2-ES and Eta-CanESM2 inputs indicated decrease under the RCP8.5 scenario by the end the century, varying from 7.2 to 66.3 % (VIC model) and 37.0 to 64.7% (SWAT model). In general, Eta-MIROC5 presented the opposite in terms of direction in the simulations with both hydrological models at the end of the century. Combined effects of climate models, hydrological model structures and scenarios of climate change should be considered in assessments of uncertainties of climate change impacts.

Community Detection Based on Graph Representation Learning in Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.