PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales

An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

Identifying Novel Inhibitors for Hepatic Organic Ani-on Transporting Polypeptides by Machine-learning based Virtual Screening

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity, drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were subsequently determined using dedicated in vitro assays, and guided the priori-tization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) inhibitors, were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7, H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

A Collaborative Cache Strategy in Satellite-Ground Integrated Network Based on Multiaccess Edge Computing

Multiaccess edge computing (MEC) provides users with a network environment and computing storage capacity at the edge of the network, ensuring a deterministic service with low delivery delay. This paper introduces a new satellite-ground integrated collaborative caching network architecture based on MEC and studies the caching strategy. On the ground side, the edge nodes (ENs) are deployed to the user side to form a hierarchical collaborative cache mode centered on the base station. On the satellite side, we utilize intelligent satellite ENs to precache and multicast the highly popular contents, reducing the initial content delivery delay. Under the constraints of the user demand and storage capacity, we study the deployment and cache scheme of ENs and establish the delivery delay minimization problem. To solve the problem, we propose a content update decision parameter for content cache update and transform the problem into improving the hit rate of ENs. Simulation results show that the proposed MEC network architecture and content caching scheme can increase the caching system hit rate to 64% and reduce the average delay by 32.96% at most.

On Location Privacy Protection Algorithm Based on Edge Computing and Caching

To resolve the communication overhead problem of anonymous users, we propose a location privacy protection method based on the cache technology. In particular, we first place the cache center on edge server nodes to reduce interaction between servers and users. In this way, the risk of privacy leaks can be reduced. Furthermore, to improve the caching hit rate, a prediction system based on Markov chain is designed to protect the trajectory privacy of mobile users. Simulations show that the algorithm can protect the privacy of users and reduce the transmission delay.

Benchmarking LHC background particle simulation with the CMS triple-GEM detector

In 2018, a system of large-size triple-GEM demonstrator chambers was installed in the CMS experiment at CERN's Large Hadron Collider (LHC). The demonstrator's design mimicks that of the final detector, installed for Run-3. A successful Monte Carlo (MC) simulation of the collision-induced background hit rate in this system in proton-proton collisions at 13 TeV is presented. The MC predictions are compared to CMS measurements recorded at an instantaneous luminosity of 1.5 ×1034 cm-2 s-1. The simulation framework uses a combination of the FLUKA and GEANT4 packages. FLUKA simulates the radiation environment around the GE1/1 chambers. The particle flux by FLUKA covers energy spectra ranging from 10-11 to 104 MeV for neutrons, 10-3 to 104 MeV for γ's, 10-2 to 104 MeV for e±, and 10-1 to 104 MeV for charged hadrons. GEANT4 provides an estimate of the detector response (sensitivity) based on an accurate description of the detector geometry, the material composition, and the interaction of particles with the detector layers. The detector hit rate, as obtained from the simulation using FLUKA and GEANT4, is estimated as a function of the perpendicular distance from the beam line and agrees with data within the assigned uncertainties in the range 13.7-14.5%. This simulation framework can be used to obtain a reliable estimate of the background rates expected at the High Luminosity LHC.

PENGGUNAAN DATA MINING DALAM HIT RATE IMPORTASI JALUR MERAH DENGAN MODEL DECISION TREE

ABSTRACT: Customs and Excise faces a big challenge to be able to increase the hit rate of red line imports by 40% in accordance with the Blueprint for the 2014-2025 Ministry of Finance Institutional Transformation Program and international benchmarks. Through a qualitative study, this study aims to determine the use of data mining that is applied to the risk engine based on import data, people's experiences, and research results of customs institutions of other countries. The data mining method used is CRISP-DM, classification method, and decision tree model, using data imported from the red line KPU BC Type A Tanjung Priok for the period September – December 2019 and January 2020. The results show that the use of data mining can increase the hit rate of red line importation. The most relevant attribute in classifying data is the sending country which is categorized as a root node, while the import duty tariff attribute does not provide information on data classification. This research is expected to provide a new perspective for the KPU BC Type A Tanjung Priok in an effort to improve the risk engine targeting and risk engine routing of Customs and Excise. Keywords: CRISP-DM, data mining, decision tree, hit rate, the red line import. ABSTRAK: Bea dan Cukai menghadapi tantangan besar untuk dapat meningkatkan capaian hit rate importasi jalur merah sebesar 40% sesuai dengan Cetak Biru Program Transformasi Kelembagaan Kementerian Keuangan Tahun 2014 – 2025 dan benchmark internasional. Melalui studi kualitatif, penelitian ini bertujuan untuk mengetahui penggunaan data mining yang diterapkan dalam risk engine berdasarkan data importasi, pengalaman orang, dan data hasil penelitian institusi kepabeanan negara lain. Metode data mining yang digunakan adalah CRISP-DM, metode klasifikasi, dan model decision tree, dengan menggunakan data importasi jalur merah Kantor Pelayanan Utama (KPU) Bea dan Cukai (BC) Tipe A Tanjung Priok periode September – Desember 2019 dan Januari 2020. Hasil penelitian menunjukkan bahwa penggunaan data mining dapat meningkatkan capaian hit rate importasi jalur merah. Atribut yang paling relevan dalam mengklasifikasikan data adalah negara pengirim yang dikategorikan sebagai root node (akar), sedangkan atribut tarif bea masuk tidak memberikan informasi dalam klasifikasi data. Penelitian ini diharapkan dapat memberikan pandangan baru bagi KPU BC Tipe A Tanjung Priok dalam upaya perbaikan risk engine targeting dan risk engine penjaluran Bea dan Cukai. Kata Kunci: CRISP-DM, data mining, decision tree, hit rate, importasi jalur merah.

Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world.

The Medium-to-Short-Term Earthquake Predictions in China and Their Evaluations Based on the R-Score

The earthquake tendency consultations in China, which have been carried out by the China Earthquake Administration for more than 40 yr, are really forward prediction of earthquakes. The results, experiences, and data accumulation are valuable for seismic researches. In this article, the annual, monthly, and weekly predictions produced by the regular earthquake tendency consultations and the rapid postearthquake tendency prediction derived from the irregular ones are presented systematically. In the regular predictions, the areas where earthquakes tend to occur are identified by specific space–time windows. To evaluate the efficiency of the predictions, we apply the R-score method to all the medium-to-short-term efforts. The R-score has been used as a routine tool to test annual predictions in China, in which the hit rate and the percentage of spatial alarms over the whole territory are taken into consideration. Results show that the annual R-scores, during the period of 1990–2020, increased gradually, with the average of 0.293. The examples in 2018 indicate that a considerable proportion of earthquakes with the Ms 5.0 and above were detected by the annual prediction; some earthquakes were detected by the monthly prediction, whereas just only a few earthquakes could be detected by the weekly prediction. The corresponding R-scores are 0.46, 0.11, and 0.002, decreasing obviously with reduction of the prediction time windows, and the smallest one, which is very close to zero, may suggest the minimum time scale for an effective earthquake prediction. We also evaluated efficiency of the irregular predictions by analyzing the practices of 29 Ms≥5.0 earthquakes since January 2019 and found that it is highly possible to do rapid postearthquake tendency prediction in China.

Effects of Moderate-to-Vigorous Acute Exercise on Visual Consciousness

Background the effect of acute exercise on cognition covers almost all stages of information processing, but few studies have focused on visual awareness. Subjective reports on the appearance of faint speed-changes in the perception of stimuli were used as an index for visual consciousness. Visual consciousness was assessed after exercise or rest. Aside from subjective index, objective speed-change discrimination was added as an index for the level of consciousness. Results: the results showed that subjective reports on the appearance of faint speed-changes in the perception of stimuli were affected by acute aerobic exercise. The hit rate for speed-change detection was marginally significantly higher after exercise than sedentary condition. Furthermore, the d’ index was higher after exercise. Analysis of the results obtained for the objective discrimination task showed that discrimination speed was boosted only when subjects were aware of the speed-change. Conclusions: these results suggest that acute exercise enhances visual consciousness.