Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction

Demystifying the delay propagation mechanisms among multiple airports is fundamental to precise and interpretable delay prediction, which is crucial during decision-making for all aviation industry stakeholders. The principal challenge lies in effectively leveraging the spatiotemporal dependencies and exogenous factors related to the delay propagation. However, previous works only consider limited spatiotemporal patterns with few factors. To promote more comprehensive propagation modeling for delay prediction, we propose SpatioTemporal Propagation Network (STPN), a space-time separable graph convolutional network, which is novel in spatiotemporal dependency capturing. From the aspect of spatial relation modeling, we propose a multi-graph convolution model considering both geographic proximity and airline schedule. From the aspect of temporal dependency capturing, we propose a multi-head self-attentional mechanism that can be learned end-to-end and explicitly reason multiple kinds of temporal dependency of delay time series. We show that the joint spatial and temporal learning models yield a sum of the Kronecker product, which factors the spatiotemporal dependence into the sum of several spatial and temporal adjacency matrices. By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation. Furthermore, a squeeze and excitation module is added to each layer of STPN to boost meaningful spatiotemporal features. To this end, we apply STPN to multi-step ahead arrival and departure delay prediction in large-scale airport networks. To validate the effectiveness of our model, we experiment with two real-world delay datasets, including U.S and China flight delays; and we show that STPN outperforms state-of-the-art methods. In addition, counterfactuals produced by STPN show that it learns explainable delay propagation patterns.Comment: 14 pages,8 figure

Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch under Cyber-Attacks

False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation

Total metabolic tumor volume as a survival predictor for patients with diffuse large B-cell lymphoma in the GOYA study

This retrospective analysis of the phase III GOYA study investigated the prognostic value of baseline metabolic tumor volume parameters and maximum standardized uptake values for overall and progression-free survival (PFS) in treatment-naïve diffuse large B-cell lymphoma. Baseline total metabolic tumor volume (determined for tumors >1 mL using a threshold of 1.5 times the mean liver standardized uptake value +2 standard deviations), total lesion glycolysis, and maximum standardized uptake value positron emission tomography data were dichotomized based on receiver operating characteristic analysis and divided into quartiles by baseline population distribution. Of 1,418 enrolled patients, 1,305 had a baseline positron emission tomography scan with detectable lesions. Optimal cut-offs were 366 cm3 for total metabolic tumor volume and 3,004 g for total lesion glycolysis. High total metabolic tumor volume and total lesion glycolysis predicted poorer PFS, with associations retained after adjustment for baseline and disease characteristics (high total metabolic tumor volume hazard ratio: 1.71, 95% confidence interval [CI]: 1.352.18; total lesion glycolysis hazard ratio: 1.46; 95% CI: 1.15-1.86). Total metabolic tumor volume was prognostic for PFS in subgroups with International Prognostic Index scores 0-2 and 3-5, and those with different cell-of-origin subtypes. Maximum standardized uptake value had no prognostic value in this setting. High total metabolic tumor volume associated with high International Prognostic Index or non-germinal center B-cell classification identified the highest-risk cohort for unfavorable prognosis. In conclusion, baseline total metabolic tumor volume and total lesion glycolysis are independent predictors of PFS in patients with diffuse large B-cell lymphoma after first-line immunochemotherapy

End-of-treatment PET/CT predicts PFS and OS in DLBCL after first-line treatment: results from GOYA

GOYA was a randomized phase 3 study comparing obinutuzumab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) vs standard-of-care rituximab plus CHOP in patients with previously untreated diffuse large B-cell lymphoma (DLBCL). This retrospective analysis of GOYA aimed to assess the association between progression-free survival (PFS) and overall survival (OS) with positron emission tomography (PET)-based complete response (CR) status. Overall, 1418 patients were randomly assigned to receive 8 21-day cycles of obinutuzumab (n 5 706) or rituximab (n 5 712) plus 6 or 8 cycles of CHOP. Patients received a mandatory fluoro-2-deoxy-D-glucose-PET/computed tomography scan at baseline and end of treatment. After a median follow-up of 29 months, the numbers of independent review committee-assessed PFS and OS events in the entire cohort were 416 (29.3%) and 252 (17.8%), respectively. End-of-treatment PET CR was highly prognostic for PFS and OS according to Lugano 2014 criteria (PFS: hazard ratio [HR], 0.26; 95% confidence interval [CI], 0.19-0.38; P , .0001; OS: HR, 0.12; 95% CI, 0.08-0.17; P , .0001), irrespective of international prognostic index score and cell of origin. In conclusion, the results from this prospectively acquired large cohort corroborated previously published data from smaller sample sizes showing that end-of-treatment PET CR is an independent predictor of PFS and OS and a promising prognostic marker in DLBCL. Long-term survival analysis confirmed the robustness of these data over time. Additional meta-analyses including other prospective studies are necessary to support the substitution of PET CR for PFS as an effective and practical surrogate end point

The Electric Vehicle Lithium Battery Monitoring System

With the global increase in the number of vehicles, environmental protection and energy issues had become increasingly prominent. People paid more and more attention to the electric vehicle as the future direction of the vehicle, but because the battery technology was relatively backward, it had become the bottleneck in the development of electric vehicles. So in the existing conditions, a perfect battery Monitoring technology had become more and more important. This paper firstly analyzed the characteristics of lithium battery residual capacity and effect factors, then put forward to a set of solutions according to the actual situation. The solution of the lithium battery Monitoring system adopted distributed structure, including detection of voltage, current, temperature and measurement module and the realization of monomer battery equalizer module. Using a single bus device DS2438 produced by DALLAS on the battery voltage, current, temperature, power and other parameters, the system controlled DS2438 by the STC89C52 single-chip in data acquisition. Then it used the algorithm to predict state of charge(SOC) and displayed the battery status in the LCD1602. This solution of the lithium battery Monitoring system  was reliable, economy, strong anti-interference ability. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.254

The Emission Mechanism of Gold Nanoclusters Capped with 11-Mercaptoundecanoic Acid, and the Detection of Methanol in Adulterated Wine Model

The absorption and emission mechanisms of gold nanoclusters (AuNCs) have yet to be understood. In this article, 11-Mercaptoundecanoic acid (MUA) capped AuNCs (AuNC@MUA) were synthesized using the chemical etching method. Compared with MUA, AuNC@MUA had three obvious absorption peaks at 280 nm, 360 nm, and 390 nm; its photoluminescence excitation (PLE) peak and photoluminescence (PL) peak were located at 285 nm and 600 nm, respectively. The AuNC@MUA was hardly emissive when 360 nm and 390 nm were chosen as excitation wavelengths. The extremely large stokes-shift (>300 nm), and the mismatch between the excitation peaks and absorption peaks of AuNC@MUA, make it a particularly suitable model for studying the emission mechanism. When the ligands were partially removed by a small amount of sodium hypochlorite (NaClO) solution, the absorption peak showed a remarkable rise at 288 nm and declines at 360 nm and 390 nm. These experimental results illustrated that the absorption peak at 288 nm was mainly from metal-to-metal charge transfer (MMCT), while the absorption peaks at 360 nm and 390 nm were mainly from ligand-to-metal charge transfer (LMCT). The PLE peak coincided with the former absorption peak, which implied that the emission of the AuNC@MUA was originally from MMCT. It was also interesting that the emission mechanism could be switched to LMCT from MMCT by decreasing the size of the nanoclusters using 16-mercaptohexadecanoic acid (MHA), which possesses a stronger etching ability. Moreover, due to the different PL intensities of AuNC@MUA in methanol, ethanol, and water, it has been successfully applied in detecting methanol in adulterated wine models (methanol-ethanol-water mixtures)