Miracle-dz/UniFAR-FTUs huggingface dataset:
A Feasibility Study for Predicting 3D Radiotherapy Dose Distribution of Lung VMAT Patients

Accurate dose prediction has been proved to be able to improve radiotherapy planning efficiency. Recently, deep neural networks have been used in this area and made some progress. However, existing deep-learning-based methods could not predict dose distribution accurately for tumors at various locations, i.e. lung cancer. This article proposes a new deep neural network CAD-UNet that combines 3D U-net, dense connection, and SE-net architecture. Spatial distance information is used as a special input channel in addition to contour information. Dice similarity coefficients of planning target volume (PTV) region was added to the mean squared error (MSE) loss function. A cohort of 192 VMAT plans for lung cancer patients was selected for this study. The trained CAD-UNet and HD-UNet were tested on the test cases. The dose parameters derived form predicted dose distribution were used to generate new plans in the treatment planning system (TPS). The results showed that CAD-UNet can successfully predict dose distribution of lung cancer cases in VMAT, outperforming HD-UNet in PTV region homogeneity. Regenerated plans based on predicted dose showed improvements in DVHs of organs-at-risk (OAR). Those improvements showed that CAD-UNet has the potential to guide dosimetrist in the radiotherapy planning stage.
Optimization of Regenerator Placement in Optical Networks Using Deep Tensor Neural Network

Responding to the rapid growth of network traffic, the development of new backbone network technologies is essential. One of the solutions is to use Elastic Optical Networks (EONs) instead of traditional Wavelength Division Multiplexing (WDM). EON provides the means to allocate spectrum bandwidth flexibly in highly congested networks. However, due to physical limitations, many EON nodes require the use of regenerators. To optimize the cost and the efficiency of signal regeneration, we propose the use of a deep learning model. We use it to calculate the best allocation of regenerators in the network so that even with limited resources, EONs can still efficiently route traffic. After numerous experiments, our approach shows significant improvement in reducing both spectrum and regenerator blockage rates.
Bidirectional Wireless Power Transfer System with Optimized Coil Geometry

Electric vehicles (EVs) offer the opportunity to use vehicle-to-grid (V2G) technology to utilize the vehicle battery as a grid connected energy storage device and deliver power to the grid and local loads. Recently, wireless power transfer (WPT) in EVs has been studied by numerous researchers due to advantages such as convenience of wireless operation and safety of inductive power transfer. Design challenges for WPT systems in EVs include high charging efficiency, a large air gap, and tolerance for misalignment. By optimizing the coupler coil geometry, the efficiency can be increased over a wide range of parking positions and ground clearances, while at the same time limiting the electrical component stress. With a new control method, the semiconductor stress is minimized and the losses are reduced compared to conventional control methods. A fully functional prototype with V2G capabilities was built, which reaches a DC-DC efficiency of almost 96 %.
Dynamic Graph Filters Networks: A Gray-box Model for Multistep Traffic Forecasting

Short-term traffic forecasting is one of the key functions in Intelligent Transportation System (ITS). Recently, deep learning is drawing more attention in this field. However, how to develop a deep learning based traffic forecasting model that can dynamically extract explainable spatial correlations from traffic data is still a challenging issue. The difficulty mainly comes from the inconsistency between static model structures and the dynamic evolution of traffic conditions. To overcome this difficulty, we proposed a novel multistep speed forecasting model, Dynamic Graph Filters Networks (DGFN). The major contribution is that the regular pixel-wise dynamic convolution is extended to graph topology. DGFN has a simple recurrent cell structure where local area-wide graph convolutional kernels are dynamically computed from varying inputs. Experiments on ring freeways show that DGFN is able to precisely predict short-term evolution of traffic speed. Furthermore, we theoretically explain why DGFN is not a pure “black-box”, but a “gray-box” model that actually reduces entangled spatial and temporal features into one component representing dynamic spatial correlations. It permits tracking real-time interactions among adjacent links. DGFN has the potential to relate trained parameters in deep learning models with physical traffic variables.
Thermodynamic and Kinetic Study on Lithium Bis(Oxalato)Borate Thermal Decomposition Reaction

Reported accidents such as fire and explosion on account of lithium-ion batteries remind us it is important to understand the thermal stability of battery materials. Lithium salt plays an important role in the safety of lithium ion batteries. Thermal decomposition reaction of a lithium salt-lithium bis (oxalato) borate (LiBOB) was studied using C80 calorimeter under argon atmosphere. The samples were heated at a 0.2 K min-1 heating rate from ambient temperature to 573 K in single scanning rate experiments, or at different constant temperatures (528 K, 533 K, 538 K, 543 K) in isothermal experiments. The results show that LiBOB decomposes near 433 K, and its decomposition reaction order was calculated to be n=1.43. In addition, thermodynamic parameters and kinetic parameters were obtained. The reaction heat associated with LiBOB decomposition process is 197.33 J g-1, the activation energy and pre-exponential factor were calculated to be E=177.23 kJ mol-1 and A=6.51×106 s-1, respectively. Nomenclature
The Emergence of User Requirement Risk

Many organizations require a more effective and powerful information system to support their daily business activities. To develop these systems, engineering process requirement needs and its respective risks must be properly. Gathering requirement risks are a challenging process as it involves stakeholders, users and developers. This paper reviews the current researches on risks assessment methods. We have analyze, characterized and classified these similar risks into knowledge, requirement volatility and documentation risks. We conclude this paper by discussing how the new risk classification can be applied to help organization in risk identification and finally, help reduce these risks.
Black box dynamic modelling of proton exchange membrane fuel cells with artificial neural networks

The fuel cells are energy sources which can play an important role in transition of the energy sector into broader use of renewable energy. Numerical modelling provides an easy way to investigate properties of the objects modelled. There are various ways to model dynamic behaviour of the PEM fuel cells including methods using artificial neural networks. There are no clear rules of how a neural network should be configured: how many neurons in the hidden layer and which training algorithm should be used. In a time series modelling task additional parameters including sampling frequency, learning data set duration and number of past data points used for training need to be determined. The paper presents results of research on the influence of various model parameters on the PEM fuel cell modelling accuracy.
General Method for the Study of Solute-Surfactant Association Equilibria of Volatile Solutes by Head Space Gas Chromatography

A general methodology for measurement of solute-surfactant associations for volatile solutes based on head-space gas chromatography has been developed. A general theoretical model is also developed to treat the experimental data that requires no analytical solute standard. The model is applicable to any volatile solutes, especially very hydrophobic solutes such as n-alkylbenzenes, for itch accurate aqueous standard solutions cannot be prepared. Parameters such as solute-surfactant association constant, solute-micelles association constant and partition coefficient, critical micelles concentration, intramicellar mole fraction, and intramicellar activity coefficient of n-alkylbenzenes are obtained as a function of aqueous surfactant concentrations
Effects of gibberellins and Promalin on the growth and development of fruit crops: A review

Gibberellins are the organic compounds which tend to regulate several metabolic processes in the plants. They play an important role in the enhancement of efficiency of fruit crops in terms of growth, quality and yield. GAs are naturally synthesized by the higher plants but in insufficient amounts. Therefore, the exogenous applications of GA at different concentrations and at different stages of growth drastically increase the seed germination, stem elongation, shoot initiation, flower induction, flower inhibition, fruit set, fruit development and modify several other vital processes in the fruit crops. Similarly, Promalin is a mixture of two naturally occurring plant growth regulators: gibberellic acid 4 and 7 (GA4+7), which causes cell enlargement and elongation, and 6-benzyladenine (6-BA) which promotes cell division. Promalin has been reported very effective especially in temperate fruit crops like apple, pear, cherry etc. where it has proven beneficial for increased fruit set.
Implementing Approximate Mobile Computing

While approximate computing has shown potential to reach an acceptable trade-off between result accuracy and computational cost in desktop and server environments, its application in the mobile computing field is still lagging. In this position paper we first identify opportunities for approximate mobile computing (AMC), by observing how users’ expectations with respect to the result accuracy vary as the context in which a mobile application is used varies. We assess the energy savings enabled by closing this gap be-tween the expected and the actually delivered result quality, propose a general framework for AMC, and discuss a road towards its realization.
Anomaly Detection using Machine Learning for Data Quality Monitoring in the CMS Experiment

Reliable, robust and fast-turnaround monitoring of the quality of the data is a key asset to deliver high-quality data for physics analysis for any modern High Energy Physics experiment. The current paradigm of the quality assessment in the CMS collaboration is based on the scrutiny of a large number of histograms by detector experts comparing them with a reference. The project aims at applying recent progress in Machine Learning techniques to the automation of this process allowing the check of large volumes of data in real-time and improving the ability to detect unexpected features. A test implementation using an unsupervised machine learning model focused on the data of one of the CMS muon detectors has been developed and bench-marked on real and fake data.
The Connection Machine' Message Router

: The Connection Machine message router is described. A Connection Machine computer system with 65536 processors physically fits within a 2 meter cube. The Connection Machine interconnection network is designed to deliver ten thousand to ten million short (about 32 bit) messages at a time with a throughput of about 2 • 108 messages per second. The network is wired as a variant of a hypercube. The routing of messages through the network is performed on-line, i
Forecaster Inattention: Measurement, Determinants and Policy Implications

Using individual forecasts from the U.S. Survey of Professional Forecasters during 1968-2014 and Consensus Economics for G7 countries during 1990-2014, we provide direct econometric estimates of time varying inattention, defined as the common component in forecasters’ inattentiveness when predicting many economic variables. Based on this measure, we find that professional forecasters update their information sets every four months on average, and they are less inattentive in periods of recession and high economic uncertainty. Through the time varying structural vector autoregression model, we show that the same sized monetary shock has more persistent real effects when the degree of inattention is high. Our findings contribute to the literature on the transmission of monetary policy shocks and suggest inattention as an additional explanation why policy might become less effective during recessions. JEL Classification: E3, E5
PATHWAYS OF GASTRO PROTECTIVE POTENTIAL OF CHAMOMILE IN INDOMETHACIN INDUCED ULCEROGENIC IN RATS

Indomethacin (IND) is an extensively used drug. However, it provokes aggressive ulcerogenic potential. Chamomile is a widely used as aims to assess the effects of chamomile flowers extract (CFE) in IND induced peptic ulcer (PU). Fifty rats classified to five groups; control, ulcer, and pretreated groups with CFE (200 mg/kg), with ranitidine RAN (50 mg/kg), and pretreated with CFE+RAN for 14 days pre Pretreatment with CFE and/or RAN significantly decreased ulcer index, gastric acidity, pepsin activity, gastric oxidative stress biomarkers, serum anti histopathological changes induced by IND. The pretreated groups significantly elevated gastric enzyme antioxidant activity, mucin content and gastric mucosal prostaglandin E2 with comparison to IND group. Pretreatment with CFE+R pretreated with either CFE or RAN alone. Collectively, the antisecretory and cytoprotective effects of antioxidant and anti Copyright © 2019, Jamilah Hashemi. This is an open access distribution, and reproduction in any medium, provided
Market Capacity Framework: An Approach for Identifying Thin Markets in the NDIS

Markets are fundamental to the success of the National Disability Insurance Scheme (NDIS). It is through diverse and robust disability markets that participants can exercise choice and control, thereby delivering on the goals of the scheme. Following the introduction of the NDIS we have seen unprecedented investment in growing and developing disability markets across the country. However, as the scheme is being rolled out, we are seeing markets emerge that do not have enough providers or sufficient capacity to meet demand and ensure competition takes place. This has led to debates about thin markets – a term used in various ways by different stakeholders to describe market deficiencies ranging from low numbers of providers, to immature markets, to market failure. Observation and foresight of the various market issues in the NDIS has drawn attention to the importance of effective market stewardship within the NDIS. <br> <br>To date, questions of how and who will steward NDIS markets has been raised by a number of high profile reviews into the scheme (1,2). These reviews have consistently called for clarity over who is responsible for market stewardship (e.g. Commonwealth Government and/or the National Disability Insurance Agency), and what actions or activities should be deployed in response to thin market problems. However, there is little evidence, locally or internationally, to guide policymakers on how to effectively steward markets within a scheme such as the NDIS. This is concerning, because without robust markets the NDIS will fail to deliver on its goals of choice and control for all participants. <br>
USING THE SIMPLETREAT MODEL TO ESTIMATE POLLUTANTS REMOVAL IN WASTEWATER TREATMENT PLANTS

In order to analyze organic chemicals fate and removal by primary and secondary treatments in a wastewater treatment plant, the SimpleTreat model is used. The model estimates pollutants concentration in different basins of the treatment plant (primary clarifier, aeration tank and secondary clarifier). It is presented a comparative analysis of two categories of organic pollutants, the results in graphical form also indicating the removal efficiency, in %.
ADVANCED OXIDATION DEGRADATION OF DICLOFENAC

Advanced oxidation/reduction processes (AO/RPs), utilize free radical reactions to directly degrade chemical contaminants as an alternative to traditional water treatment. This study reports the absolute rate constants for reaction of diclofenac sodium and the model compound (2, 6-dichloraniline) with the two major AO/RP radicals; the hydroxyl radical (•OH) and hydrated electron (e aq). The bimolecular reaction rate constants (M -1 s -1 ) for diclofenac for •OH was (9.29 ± 0.11) x 10 9 , and, for e aq was (1.53 ± 0.03) x10 9 . Preliminary degradation mechanisms are suggested based on product analysis using 60 Co -irradiation and LC-MS for reaction by-product identification. The toxicity of products was evaluated using the Vibrio fischeri luminescent bacteria method.
A VALIDATED STABILITY-INDICATING HPLC-PDA METHOD FOR ANALYSIS OF DESMODIUM GANGETICUM: AN IMPORTANT INGREDIENT OF AYURVEDIC DRUG “DASHMOOL”

A stability-indicating reversed-phase liquid chromatographic (RPLC) method has been established for analysis of rutin (1), kaempferol-3-O-robinobioside (2), and nicotiflorin (3) in Desmodium gangeticum. The study was performed in the presence of the degradation products generated in the study of forced degradation. Marker compounds were subjected to stress by hydrolysis (acidic and basic), oxidation, photolysis, and thermal treatment. Under the optimized conditions, well-resolved separation of pure compounds from the degradation products with significantly different Rt values was achieved on a Spherisorb ODS2 column (250 mm × 4.6 mm, 10 µm) using isocratic elution of methanol and water (0.5% acetic acid); with acceptable validation results such as linearity, sensitivity, and recovery in terms of RSD (%). The calibration curves were linear in the concentration range of 20–100 µg/mL. Method was validated as per ICH guidelines. The reproducible and robust method may be applied for assays and stability tests of D. gangeticum and phytopreparations containing D. gangeticum.
Significant relationship between spring AO and the summer rainfall along the Yangtze River

The influence of spring AO on the summer rainfall along the Yangtze River is investigated. The long-term rainfall observations are filtered to remove the low-frequency variations longer than 10 years. The inter-annual components show a high correlation to AO in the last hundred years. The strongest correlation appears for May AO and summer rainfall with a value of −0.39, significant above the 99% confidence level. Associated with one standard deviation stronger May AO index, the rainfall over the Yangtze River to the southern Japan decreases by about 3%-9%, while, at the same time increases by about 3%–6% in the northern China and far-eastern Russia. The coherent changes in rainfall are significantly related to the East Asian summer jet stream in the upper troposphere. When there is stronger AO in spring, the jet stream tends to move polarward in summer, and leads the rainfall-belt to move northward too. That gives rise to a drier condition in the Yangtze River valley, wetter anomalies in northern China. This signal would be helpful for the summer rainfall prediction in China.
SANS STUDIES INSIGHT INTO IMPROVING OF YIELD OF BLOCK COPOLYMER‐STABILIZED GOLD NANOPARTICLES

Triblock copolymer poly(ethylene oxide)‐poly(propylene oxide)‐poly(ethylene oxide) (PEO‐PPO‐PEO) are well known as dispersion stabilizers. It has also been recently found that they can act as reducing agents along with stabilizers and these two properties of block copolymers together have provided a single‐step synthesis and stabilization of gold nanoparticles at ambient temperature. We have studied the synthesis of stable gold nanoparticle solutions using block copolymer P85. Gold nanoparticles are prepared from 1 wt% aqueous solution of P85 mixed with varying concentration of HAuCl4.3H2O salt in the range 0.001 to 0.1 wt%. Surface plasmon resonance (SPR) band in UV‐visible absorption spectra confirm the formation of the gold nanoparticles and the maximum yield of the nanoparticles is found to be quite low at 0.005 wt% of the salt solution. Small‐angle neutron scattering (SANS) measurements in these systems suggest that a very small fraction of the block copolymers (<1%) is only associated with the gold .
Single Image Super Resolution

Single Image Super Resolution (SISR) is an important task of computer vision, in which we seek to improve resolution of images. In recent years, due to the increase in computing power, signiﬁcant progress has been made in the ﬁeld of deep learning. Convolutional neural networks form the basis of many machine vision algorithms including SISR. Generating realistic high resolution images not only requires deployment of very deep convolutional architectures, but also requires adversarial training approach. In this project we use a very deep residual generator network to generate realistic high resolution images and train it in an adversarial manner. We propose a new fully convolutional discriminator architecture which has fewer parameters than regular CNN discriminators and is more efﬁcient to train. We show that FCN discriminators are capable of outperforming traditional discriminators with much lower parameter count. We also add DCT loss function to the perceptual loss to enhance high frequency reconstruction ability of the generator.
Institutional Quality, Tax Avoidance, and Analysts' Forecast: International Evidence

Research Question: This study examines the joint-effect of tax avoidance and institutional quality on analysts forecast. Motivation: The aspects of tax are important in the valuation by financial analysts in the capital markets, but the extent to which the analysts incorporate tax avoidance in their forecasts is uncertain. The complexity associated with tax avoidance may lead to more efforts for appropriate forecast, but such complexity may also reduce the ability of the analysts in forecasting. Further, when institutional theory is considered, the strength of country-level institutional environment may influence the role of the analysts in factoring tax avoidance in their forecast. Idea: This study hypothesized that tax avoidance is associated with analyst forecast, and the strength of institutional quality jointly affects the association between tax avoidance and analyst forecast. Data: The dataset consists of 22,690 firm-year observations from 36 countries over the period 2007-2016. Data were gathered from Institutional Brokers' Estimate System I/B/E/S, Thomson Reuters Fundamentals, the World Governance Indicators, OSIRIS and ownership data reported in La Porta et al.(2006). Method/Tools: The regression models employ two measures of analyst forecasts as the dependent variables; forecast dispersion and accuracy. The test variables are tax avoidance that is proxied by firms’ effective tax rate, institutional quality that is an index from Kaufmann et al.(2009) to proxy for country-level institutional environment, and the interaction between tax avoidance and institutional quality. Findings: Findings indicate that high tax avoidance is associated with high forecast dispersion but more accurate forecast. There are evidence that the effect of tax avoidance on properties of analyst forecast is weakened for firms in high institutional quality countries. The results are robust even after employing the two-stage least square regression to address endogeneity issue, and the weighted least square in overcoming issue of differences in sample size between countries. Contributions: The findings corroborate evidence on tax avoidance and analyst forecast, and enrich the international accounting literature. This study provides insights to policy makers on the role of institutional quality in reducing information asymmetry, specifically on tax and forecast activities.