Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Pressure-Controlled Ventilation with Volume Guarantee and Volume-Controlled Ventilation mode in Patients under Laparoscopic Surgery: A Meta-Analysis of Randomized Controlled Trials

Background：In traditional Volume-Controlled Ventilation (VCV) mode, the creation of pneumoperitoneum during laparoscopic surgery may lead to Ventilator-Associated Lung Injury (VALI). Pressure-Controlled Ventilation with Volume Guarantee (PCV-VG) mode ensures providing adequate oxygen supply to patients while reducing the risk of lung injury. Methods：Eligible randomized clinical trials (RCTs) were searched in Medline, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), Sino Med, China National Knowledge Infrastructure (CNKI) and Wan-Fang MED without language restriction up to March 2019. The primary outcome of this meta-analysis was airway peak pressure (Ppeak) at 30, 60 or 90 mins after complete CO2 insufflation. This meta-analysis was followed the recommendations of the PRISMA statement. Results：Finally, 9 articles were included. The Ppeak in the PCV-VG group was lower than that in the VCV group, and the difference was statistically significant at 30mins [Mean Difference (MD)= -3.55, 95% Confidence Interval (CI)= -5.13 to -1.98, I2=83%], 60mins [MD= -5.76, 95%CI= -8.15-3.36, I2=93%], 90 mins [MD= -4.59, 95%CI= -5.43-3.74, I2=30%] after complete CO2 insufflation. Meanwhile, PCV-VG mode could effectively reduce airway mean pressure (Pmean) and improve dynamic compliance (Cdyn) of patients after complete CO2 insufflation in laparoscopic surgery compared to VCV mode. However, no significant difference was found in PetCO2, HR, MAP, PH, PaO2, and PaCO2 between the two-ventilation modes. Conclusions: PCV-VG mode are superior to VCV mode in providing adequate oxygenation at lower airway peak pressure and greater dynamic compliance in patients under laparoscopic surgery.
MATHEMATICAL MODELS FOR THE COMPUTATION OF VEHICLE DYNAMIC BEHAVIOUR DURING DEVELOPMENT

This paper describes the use of mathematical models for forecasting vehicle dynamic behaviour. Three different models are discussed. The first is a Linear Single Track Model (LST) with a nonlinear tyre model. The second is a Two Track Model (LTT) with a linear tyre description. The third model is a non-linear Two Track Model (NLTT) which includes nonlinear tyre behaviour. The results obtained using these models were compared with measurements taken from real vehicles. The following aspects were forecast: a) over/understeer behaviour; b) transfer functions; and c) the step function response. For the covering abstract see IRRD 870290.
Sea Level Rise Drivers and Projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Paris Climate Targets: Global and around the Korea Peninsula

Estimating future sea level rise (SLR) projections is important for assessing coastal risks and planning of climate-resilient infrastructure. Therefore, in this study, we estimated the future projections of SLR from Coupled Model Intercomparison Project phase 6 (CMIP6) models for three climate targets (1.5 °C (T15), 2.0 °C (T20), and 3.0 °C (T30)) described by the Paris Agreement. The global SLR projections are 60, 140, and 320 mm for T15, T20, and T30, respectively, relative to the present-day levels. Similarly, around the Korean Peninsula, SLR projections become more intense with continuous global warming (20 mm (T15), 110 mm (T20), and 270 mm (T30)). Ocean variables show a slow response to climate change. Therefore, we developed the Emergence of Climate Change (EoC) index for determining the time when the variable is not following the present climate trend. The EoC of SLR appears after the EoC of sea-ice melting near the time of T15 warming. Moreover, the EoC of thermal expansion appears around the 2040s, which is similar to the time of the maximum of the T15 warming period and the median of the T20 warming period. Overall, our analysis suggests that the T15 warming may act as a trigger and SLR will accelerate after the T15 warming.
ACCIDENT ANALYSIS AND PREVENTION FROM THE CHAIN PERSPECTIVE AND REVISION REPORT

One of the most effective approaches interpreting accident causality is from the chain perspective. However, this approach has been applied only to measuring safety improvement for specific countermeasures. This paper emphasizes that the chain concept, which reflects the generating process of an accident, should be borne in mind when analyzing accidents and preventions. A safety framework of the driving behavior is constructed from the chain perspective in support of this argument. Also, a two-stage approach is proposed to adopt the idea in analyzing accident data. The approach includes applying classification techniques at the first stage and causal inference models at the second stage. The relevant methodologies are introduced and possible issues are discussed. It is believed that the proposed framework and approach would help further studies to analyze and interpret accident data in a more thorough perspective.
Time-Efficient Fire Detection Convolutional Neural Network Coupled with Transfer Learning

The detection of fires in surveillance videos are usually done by utilizing deep learning. In Spite of the advances in processing power, deep learning methods usually need extensive computations and require high memory resources. This leads to restriction in real time fire detection. In this research, we present a time-efficient fire detection convolutional neural network coupled with transfer learning for surveillance systems. The model utilizes CNN architecture with reasonable computational time that is deemed possible for real time applications. At the same time, the model will not compromise accuracy for time efficiency by tuning the model with respect to fire data. Extensive experiments are carried out on real fire data from benchmarks datasets. The experiments prove the accuracy and time efficiency of the proposed model. Also, validation of the model in fire detection in surveillance videos is proved and the performance of the model is compared to state-of-the-art fire detection models.
Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: A Case Study for Rice Phenology Characterization

Information on rice phenological stages from Synthetic Aperture Radar (SAR)images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. In particular, the degree of polarization attributes to scattering randomness from a target. The scattering randomness in crops increases with advancements in its growth stages due to the development of branches and foliage. Hence, the degree of polarization varies with changes in the crop growth stages. Besides, the elements of the coherency matrices are directly related to the crop geometry as well as soil and crop water content. There-fore, this complementarity information captures the scattering randomness at each crop growth stage while taking into account diverse crop morphological characteristics. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages. (Submitted to ISPRS journal)
A Comparative Analysis of Machine Learning Techniques For Foreclosure Prediction

The current decline in the U.S. economy was accompanied by an increase in foreclosure rates starting in 2007. Though the earliest figures for 2009 - 2010 indicate a significant decrease, foreclosure of homes in the U.S. is still at an alarming level (Gutierrez, 2009a). Recent research at the University of Michigan suggested that many foreclosures could have been averted had there been a predictive system that did not only rely on credit scores and loan-to-value ratios (DeGroat, 2009). Furthermore, Grover, Smith & Todd (2008) contend that foreclosure prediction can enhance the efficiency of foreclosure mitigation by facilitating the allocation of resources to areas where predicted foreclosure rates will be high. The primary goal of this dissertation was to develop a foreclosure prediction model that builds upon established bankruptcy and credit scoring models. The study utilized and compared the predictive accuracy of three supervised machine learning (ML) techniques when applied to mortgage data. The selected ML techniques were: ML1. Classification Trees; ML2. Support Vector Machines (SVM); ML3. Genetic Programming. The data used for the study is comprised of mortgage data, demographic metrics and certain macro-economic indicators that are available at the time of the inception of the loan. The hypothesis of the study was based on the assumption that foreclosure rates, and associated actions, are dependent on critical demographic (age, gender), economic (per capita income, inflation) and regional variables (predatory lending, unemployment index). The task of the machine learning techniques was to identify a function that well approximates the relationship between these explanatory variables and the binary outcome of interest (mortgage status in +3 years from inception). The predictive accuracy of ML1 through ML3 was significantly better than expected given the size of the recordset (1000) and the number of input variables (∼110). Each ML technique achieved classification accuracy better than 75%, with ML3 scoring in the upper 90s. Given such high scores, it was concluded that the hypothesis was satisfied and that ML techniques are suitable for prediction tasks in this problem domain.
Location-aware active signage

Three-dimensional route maps, which depict a path from one location to another, can be powerful tools for visualizing and communicating directions. This thesis presents a client-server architecture for generating and displaying accurate, usable route maps between locations on MIT's campus. Two exemplary clients of this architecture MITquest, a web based Java applet, and locationaware active signage demonstrate the flexibility and power of this model for route generation. Additionally, we provide a framework for displaying a set of campus-wide, public events of interest to an MIT visitor, including methods for inferring events from public sources and automatically selecting events of interest. Thesis Supervisor: Seth Teller Title: Associate Professor of Computer Science and Engineering
Indonesian Sarcasm Detection Using Convolutional Neural Network

In sentiment analysis, sarcasm is considered to be one of the most difficult problems to solve. Sarcasm or verbal irony is a linguistic phenomenon where the wording of a speaker signifies the opposite of the literal meaning of what they are saying. On social media, Indonesians tend to use sarcastic remarks to criticize something and this behavior can pose a problem when a sentiment analysis is performed where the analysis yields false-positive results. To solve this potential problem, the detection of sarcasm is necessary. A convolutional neural network model is used in this paper to handle the automated identification of sarcasm in Indonesian. Although it is mainly used in computer vision, this model has shown promising results on specific kinds of sentiment analysis and classification problems compared to other neural network models. Using this model can increase detection accuracy compared to discrete models. There have been many studies conducted on sarcasm detection, although the majority is in English. Other languages such as Indonesian are slow in improvement compared to English. This fact can be seen by the lack of publications on Indonesian sarcasm detection studies. This study is conducted to contribute to other Indonesian sarcasm detection studies.
Anaerobic and Aerobic Submerged Bio-Filter System for Small Scale On-Site Domestic Sewage Treatment

In order to prevent water pollution and satisfy the demands for flush toilets in regions where a public sewerage system has not been in service, some on-site small scale domestic wastewater treatment processes called GAPPEI JOHKASOU have been developed to treat black water and gray water together in an individual house. Prior to its technical development, the characteristics of wastewater discharged from individual houses should be measured. The possibility of application of anaerobic filter is examined for processing organic wastewater at low concentration like in domestic wastewater, and an anaerobic filteris successfully developed to lengthen the sludge removal interval, since the flow rate of wastewater from individual houses is characterized by a large variation. It is difficult to serve the centralized treatment systems in many regions. Thus, a small GAPPEI JOHKASOU has been developed and propagated for by individual houses in such regions.
Study on Extraction and Purifiction Process of Total Flavonoid and Baicalin in Scutellaria baicalensis

[Objective] This paper aimed to determine the optimum process condition of extraction and purifiction of total flavonoid and baicalin in Scutellaria baicalensis. [Method] Taking the extraction rate of total flavonoid and baicalin in Scutellaria baicalensis as evaluation index,single factor and orthogonal test were used for optimal selection. [Result] The oprimum process was as follows: 10 times volume of water was added into coarse powder of Scutellaria baicalensis.The mixture was decocted for 2 times with 1.0 h / times,and then centrifuged.The supernatants were adjusted to pH 1.5,and stored at 60 ℃ for 30 min.After blending the precipitation with water,the mixture was adjusted to neutral pH and 50% alcohol was used as alcohol-precipitation.The supernatants were adjusted to pH 1.5 again and stored at 60 ℃ for 30 min.The standing precipitation was centrifuged,and then washed to neutral pH.Scutellaria baicalensis extraction was obtained by vacuum drying at 60 ℃. The contents of total flavonoid and baicalin in Scutellaria baicalensis were 94.15% and 80.15%,respectively. [Conclusion] This process was rational,practical and adapted to industrialized production of total flavonoid and baicalin in Scutellaria baicalensis.
Invest in Innovation or Not? How Managerial Cognition and Attention Allocation Shape Corporate Responses to Performance Shortfalls

ABSTRACT This study investigates the influence of managerial cognition and attention allocation on firms’ responses to negative performance feedback. We explore how managerial cognition, as shaped by managers’ experiences, connections, positions, and industry environments, affects underperforming firms’ attention allocation and, consequently, their decisions to invest in innovation. Utilizing a longitudinal sample of Chinese high-tech firms from 2009 to 2017, we find that firms increase investment in research and development (R&D) when performance falls below aspiration levels. We also document that underperforming firms are associated with an even larger R&D investment increase when their CEOs have an R&D or engineering background, serve simultaneously as the board chair, or are not politically connected. In addition, we highlight the moderating effects of industry competition and industry norms on the relationship between firm underperformance and R&D intensity. We conclude that managerial cognition affects firms’ allocation of attention to innovation as a solution for closing performance gaps and shapes corporate responses to negative performance feedback. 摘要 本文研究高管认知与注意力分配对企业面临负面绩效反馈时所采取的反应。 具体而言，本文探讨了高管的认知能力—由其经历、关系、职位和行业环境所塑造的-- 对绩效下滑企业的管理者注意力分配以及企业创新投资行为的影响。 作者对2009年至2017年期间在深圳创业板上市的高新技术企业数据进行了分析，发现当业绩低于期望水平时，企业会增加研发投入。 而且，创新研发投入在CEO本人兼任董事长、有工程或研发背景、或者没有政治联系时，幅度更大。 此外，行业竞争强度以及行业研发投入强度对企业在业绩下滑时的研发投入也具有调节作用。 总而言之，高管认知会直接影响企业是否把注意力放在创新上作为应对企业业绩下滑的解决方法。
Infiltration heat gains through cold storage room doorways

The exchange of warm and cold air masses through doorways due to natural convection is a well-known phenomenon observed in the operation of cold storage rooms. This process results in an increase in refrigeration load for these facilities as a result of sensible and latent heat entering with the warm air; the problem has been studied to some extent by previous researchers, but analytical models developed to predict doorway infiltration heat gain still lack adequate verification. The primary objectives of this research project were to develop a better understanding of the natural convection process and to determine the predictive ability of existing analytical models. This study was accomplished through field experimental measurements made on full-size, working cold storage rooms. A preliminary investigation of doorway infiltration protection equipment was also conducted.
Evaluation of Gas and Oil Dispersion during Subsea Blowouts

The global demand for hydrocarbons is high and is also believed to be high in the future. Much of todays oil and gas exploration is carried out offshore and consequently, there is a risk if oil and gas blowouts at the seabed. Major concerns from a subsea oil and gas release are fire and toxic hazard to people working on offshore installations and loss of buoyancy of ships and floating installations. In addition, oil spills will result in both immediate and long-term environmental damage. Risk assessments are a very useful tool to pinpoint the risks of offshore oil and gas exploration and production. In terms of blowouts, these assessments require knowledge of the qualitative behavior and reliable quantitative estimates for where and when the oil and gas will surface. Since execution of underwater test releases of hydrocarbons is extremely costly, computer models are interesting research subjects. In this thesis, a simulation concept for forecasting oil and gas blowouts is presented. ANSYS FLUENT 15.0. 0, a commercial Computational Fluid Dynamics (CFD) package, is used to obtain both the qualitative behavior and the quantitative estimates. The model accounts for variation in bubble size and bubble density. In addition the model allows for the presence of ocean currents and gas dissolution. The released oil droplets and the natural gas bubbles are tracked while they rise towards the ocean surface in order to estimate the effect of ambient ocean currents. The general model set-up is first validated against experimental data, for which air-bubbles are released in a 7 m deep basin. The primary simulations are based upon a field experiment conducted in Norwegian waters during June 2000, known as DeepSpill. Four discharges of oil and gas from a water depth of 844 m was carried out under controlled circumstances. Extensive observations and documentation were acquired during the experiments, in addition chemical and biological samples were collected along the water column. In the present work, simulation results are presented, discussed and compared with chosen field data obtained from the DeepSpill experiment. The overall simulation results are found to correspond quite good with the results from the DeepSpill experiment. The mean path of oil corresponds favorably with the overall shape of the echo-sound images taken during the experiments. The point of complete gas dissolution is found to match the field data, as long as a mass transfer reduction factor is employed. However, the rise time of oil droplets are somewhat over-predicted, which may indicate a need for denser grid in the release zone and/or a reconsideration of the oil droplet size distribution.
On modeling and mitigating new breed of DoS attacks

ON MODELING AND MITIGATING NEW BREED OF DOS ATTACKS by Amey Bhaskar Shevtekar Denial of Service (DoS) attacks pose serious threats to the Internet, exerting in tremendous impact on our daily lives that are heavily dependent on the good health of the Internet. This dissertation aims to achieve two objectives: 1) to model new possibilities of the low rate DoS attacks; 2) to develop effective mitigation mechanisms to counter the threat from low rate DoS attacks. A new stealthy DDoS attack model referred to as the "quiet" attack is proposed in this dissertation. The attack traffic consists of TCP traffic only. Widely used botnets in today's various attacks and newly introduced network feedback control are integral part of the quiet attack model. The quiet attack shows that short-lived TCP flows used as attack flows can be intentionally misused. This dissertation proposes another attack model referred to as the perfect storm which uses a combination of UDP and TCP. Better CAPTCHAs are highlighted as current defense against botnets to mitigate the quiet attack and the perfect storm. A novel time domain technique is proposed that relies on the time difference between subsequent packets of each flow to detect periodicity of the low rate DoS attack flow. An attacker can easily use different IP address spoofing techniques or botnets to launch a low rate DoS attack and fool the detection system. To mitigate such a threat, this dissertation proposes a second detection algorithm that detects the sudden increase in the traffic load of all the expired flows within a short period. In a network rate DoS attacks, it is shown that the traffic load of all the expired flows is less than certain thresholds, which are derived from real Internet traffic analysis. A novel filtering scheme is proposed to drop the low rate DoS attack packets. The simulation results confirm attack mitigation by using proposed technique. Future research directions will be briefly discussed.
The Effect of Market and Entrepreneurship Orientation on SME’s Business Performance: The Role of Entrepreneurial Marketing in Indonesian Batik Industries

Although several researchers have studied entrepreneurial Marketing (EM), comprehensive testing of the EM integrative model has not been studied by previous researchers. Previous researchers were limited to examining the effect of antecedents on EM or business performance. The study is to discuss the influence of market and entrepreneurial orientation on the performance of SMEs. Entrepreneurial Marketing (EM) is a mediator. The sample is 285 managers of Batik SMEs in the Special Province of Yogyakarta and Central Java. Data were analyzed using Structural Equation Modelling (SEM) and to analyze the measurement models using the one-congeneric approach. The results of this study explain that market orientation and entrepreneurial orientation have a positive and significant effect on EM. Market orientation has an indirect effect on the performance of SMEs through EM as a mediator. Entrepreneurial orientation directly affects the performance of SMEs. The novelty in this study shows that EM plays an essential role in mediating market orientation and SME performance.