Miracle-dz/UniFAR-FTUs huggingface dataset:
Hidden or missing outflows in highly obscured galaxy nuclei?

Context. Understanding the nuclear growth and feedback processes in galaxies requires investigating their often obscured central regions. One way to do this is to use (sub)millimeter line emission from vibrationally excited HCN (HCN-vib), which is thought to trace warm and highly enshrouded galaxy nuclei. It has been suggested that the most intense HCN-vib emission from a galaxy is connected to a phase of nuclear growth that occurs before the nuclear feedback processes have been fully developed. Aims. We aim to investigate if there is a connection between the presence of strong HCN-vib emission and the development of feedback in (ultra)luminous infrared galaxies ((U)LIRGs). Methods. We collected literature and archival data to compare the luminosities of rotational lines of HCN-vib, normalized to the total infrared luminosity, to the median velocities of 119 μm OH absorption lines, potentially indicating outflows, in a total of 17 (U)LIRGs. Results. The most HCN-vib luminous systems all lack signatures of significant molecular outflows in the far-infrared OH absorption lines. However, at least some of the systems with bright HCN-vib emission have fast and collimated outflows that can be seen in spectral lines at longer wavelengths, including in millimeter emission lines of CO and HCN (in its vibrational ground state) and in radio absorption lines of OH. Conclusions. We conclude that the galaxy nuclei with the highest LHCN − vib/LIR do not drive wide-angle outflows that are detectable using the median velocities of far-infrared OH absorption lines. This is possibly because of an orientation effect in which sources oriented in such a way that their outflows are not along our line of sight also radiate a smaller proportion of their infrared luminosity in our direction. It could also be that massive wide-angle outflows destroy the deeply embedded regions responsible for bright HCN-vib emission, so that the two phenomena cannot coexist. This would strengthen the idea that vibrationally excited HCN traces a heavily obscured stage of evolution before nuclear feedback mechanisms are fully developed.
Ketamine Administration Prior Regional Anesthesia for Fractured Femur

DOI: 10.21276/sjams.2018. 6.5. 52 Abstract: Femur fracture is a common, possibly overwhelming event for patients particularly geriatric with nearly 1.6 million hip fractures occurring yearly worldwide. Regional anesthesia compared to general anesthesia has many advantages in terms of decreased morbidity and mortality. This study will be conducted to explore the effect of administration a small dose of ketamine in avoiding pain at the fracture location when administered prior executing spinal anesthesia in the sitting position for patients suffering from femur bone fracture. This is a retrospective observational cross-sectional study. Data will be collected from patients’ medical profiles and anesthesia sheet. It will be performed on patients including males and females, who were presented for elective orthopedic operations for different forms of fractured femur (fracture head, neck and shaft). All the operations that performed under spinal anesthesia in the sitting position by using bupivacaine 0.5% in a dose of 10-20 mg will be screened. Patients will be divided into two groups; group (1) the control group and group (2) the ketamine group. Seventy two patients were included in this study and divided patients into two equal groups. Group (1) the control group and group (2) the ketamine group. Patients who experienced pain at the fracture site in the ketamine group were 30%, while those in control group were 76.7%. P value 0.023. A small dose of ketamine before performing spinal anaesthesia in sitting position for patients having fracture femur has a valuable effect in reducing patient pain at the site of fracture. It facilitates the procedure, reducing its time, reducing the number of trials of spinal injection and requires no local infiltration at site of injection.
“ IJ-CIS-D19-00025 _ proof ” — 2019 / 2 / 14 — 9 : 58 — page 284 — # 3 284

The object of software defect prediction (SDP) is to identify defect-prone modules. This is achieved through constructing prediction models using datasets obtained by mining software historical depositories. However, data mined from these depositories are often associated with high dimensionality, class imbalance, and mislabels which deteriorate classification performance and increase model complexity. In order to mitigate the consequences, this paper proposes an integrated preprocessing framework in which feature selection (FS), data balance (DB), and noise filtering (NF) techniques are fused to deal with the factors that deteriorate learning performance. We apply the proposed framework on three software metrics, namely static code metric (SCM), object orientedmetric (OOM), and combinedmetric (CombM) and buildmodels based on four scenarios (S): (S1) original data; (S2) FS subsets; (S3) FS subsets after DB using random under sampling (RUS) and synthetic minority oversampling technique (SMOTE); (S4) FS subsets after DB (RUS and SMOTE); and NF using iterative partitioning filter (IPF) and iterative noise filtering based on the fusing of classifiers (INFFC). Empirical results show that 1. the integrated preprocessing of FS, DB, and NF improves the performance of all the models built for SDP, 2. for all FS methods, all the models improve performance progressively from S2 through to S4 in all the software metrics, 3. model performance based on S4 is statistically significantly better than the performance based on S3 for all the software metrics, and 4. in order to achieve optimal model performance for SDP, appropriate implementation of the proposed framework is required. The results also validate the effectiveness of our proposal and provide guidelines for achieving quality training data that enhances model performance for SDP.
Modelling and analysis of grid-connected solar-PV system through current-mode controlled VSC

Recently, solar-PV energy becomes one of the most vital renewable resources of electrical energy as it is utilized in all life applications. In case of connecting the solar-PV system with the utility grid a voltage-sourced converter (VSC) is required to convert the extracted solar-PV array’s DC power into AC. There are many methods to dominate the active and reactive power produced from the VSC. In the following model we use the current mode control as it has some features such as highly output accuracy, protection against over current troubles, robustness against AC side voltage and load variations. As the produced power from the solar cells is intermittent, the point of maximum power has to be tracked using an MPPT technique. Also, in order to reduce the system harmonics a filter must be implemented in the model. In this paper, a complete model of 50KW grid-connected solar-PV system using current-mode controlled two-level three-phase VSC (grid imposed frequency VSC) is implemented. Also, the Incremental Conductance model to track the point of maximum available power (MPPT) and LCL filter has been provided into the system with total harmonic distortion (THD) analysis in PSCAD/EMTDC
Stability Blind-Area-Free Control Design for Microgrid-Interfaced Voltage Source Inverters Under Dual-Mode Operation

Voltage-controlled voltage source inverters (VSIs) are generally adopted to achieve seamless transfer between islanded mode and grid-connected mode in microgrid applications. However, these two operation modes for VSIs feature different output filters, i.e., LC filters for islanded mode while LCL filters for grid-connected mode, thus leading to different stability performance. In this article, the traditional control strategy based on an outer capacitor voltage loop cascaded with an inner capacitor current active damping loop is investigated in details. The analysis indicates that there exists an inevitable blind area of stability design, where the system cannot be stable under two operation modes at the same time. Therefore, in order to raise available space for system design, this article proposes a stability blind-area-free control design method. The analysis indicates that the proposed stability design is only affected by LC filter parameters under islanded mode. Therefore, the blind area of the traditional control method can be well eliminated, and meanwhile the system robustness against the grid impedance variation under grid-connected mode is improved. The detailed design method is also presented. Simulation and experimental results are finally provided to verify the effectiveness of the analyses and the feasibility of the method.
Automated Factorization of Security Chains in Software-Defined Networks

Software-defined networking (SDN) offers new perspectives with respect to the programmability of networks and services. In particular in the area of security management, it may serve as a support for building and deploying security chains in order to protect devices that may have limited resources. These security chains are typically composed of different security functions, such as firewalls, intrusion detection systems, or data leakage prevention mechanisms. In previous work, we suggested the use of techniques for learning automata as a basis for generating security chains. However, the complexity and the high number of these chains induce significant deployment and orchestration costs. In this paper, we propose and evaluate algorithms for merging and simplifying these security chains in software-defined networks, while keeping acceptable accuracy. We first describe the overall system supporting the generation and factorization of the security chains. We then present the different algorithms supporting their merging, and finally we evaluate the solution through an extensive set of experiments.
Planck 2013 results. XX. Cosmology from Sunyaev-Zeldovich cluster counts

We present constraints on cosmological parameters using number counts as a function of redshift for a sub-sample of 189 galaxy clusters from the Planck SZ (PSZ) catalogue. The PSZ is selected through the signature of the Sunyaev-Zeldovich (SZ) effect, and the sub-sample used here has a signal-to-noise threshold of seven, with each object confirmed as a cluster and all but one with a redshift estimate. We discuss the completeness of the sample and our construction of a likelihood analysis. Using a relation between mass M and SZ signal Y calibrated to X-ray measurements, we derive constraints on the power spectrum amplitude sigma(8) and matter density parameter Omega(m) in a flat Lambda CDM model. We test the robustness of our estimates and find that possible biases in the Y-M relation and the halo mass function are larger than the statistical uncertainties from the cluster sample. Assuming the X-ray determined mass to be biased low relative to the true mass by between zero and 30%, motivated by comparison of the observed mass scaling relations to those from a set of numerical simulations, we find that sigma(8) = 0.75 +/- 0.03, Omega(m) = 0.29 +/- 0.02, and sigma(8)(Omega(m)/0.27)(0.3) = 0.764 +/- 0.025. The value of sigma(8) is degenerate with the mass bias; if the latter is fixed to a value of 20% (the central value from numerical simulations) we find sigma(8)(Omega(m)/0.27)(0.3) = 0.78 +/- 0.01 and a tighter one-dimensional range sigma(8) = 0.77 +/- 0.02. We find that the larger values of sigma(8) and Omega(m) preferred by Planck's measurements of the primary CMB anisotropies can be accommodated by a mass bias of about 40%. Alternatively, consistency with the primary CMB constraints can be achieved by inclusion of processes that suppress power on small scales relative to the Lambda CDM model, such as a component of massive neutrinos. We place our results in the context of other determinations of cosmological parameters, and discuss issues that need to be resolved in order to make further progress in this field.
The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics

This paper introduces a learning analytics policy and strategy framework developed by a cross-European research project team — SHEILA (Supporting Higher Education to Integrate Learning Analytics), based on interviews with 78 senior managers from 51 European higher education institutions across 16 countries. The framework was developed adapting the RAPID Outcome Mapping Approach (ROMA), which is designed to develop effective strategies and evidence-based policy in complex environments. This paper presents four case studies to illustrate the development process of the SHEILA framework and how it can be used iteratively to inform strategic planning and policy processes in real world environments, particularly for large-scale implementation in higher education contexts. To this end, the selected cases were analyzed at two stages, each a year apart, to investigate the progression of adoption approaches that were followed to solve existing challenges, and identify new challenges that could be addressed by following the SHEILA framework.
STUDENTS’ CRITICAL THINKING IN DETERMINING COEFFICIENTS OF ALGEBRAIC FORMS

This paper reports the study of the critical thinking skills of 7th-grade students in junior high school on determining the coefficients of algebraic operation. Data were collected using worksheet and interviews. 34 students were involved during the study. The study reveals that there were 35% students who performed algebraic operations correctly. Students’ difficulty is mainly on determining the coefficients associated with the algebraic forms. In general, errors occur in the process of finding algebraic solutions, explaining the reasons for the stages of strategies taken and drawing conclusions. This is due to the fact that the main attention was given to the final result of the process of working on algebraic form problems, and less focus was paid on giving reasons for the process of working on problems that stimulate the students to think critically.
Multispectral Thermal Imaging with a LWIR + VIS Camera

Thermal imaging, operating at long-wave infrared (LWIR) wavelengths, is important for a wide range of room temperature applications. However, cameras at this wavelength are expensive compared typical visible (VIS) cameras. We built a multispectral camera that captures a high-resolution visible image to enhance a low-resolution thermal image at a fraction of the cost of higher-end thermal cameras.
Exploring MOOC User Behaviors Beyond Platforms

MOOC user behavior is generally studied using the data collected within platform interactions in the learning system or via outside social media platforms. It is important to understand the root causes of anomalies in MOOCs, such as the 80% attrition, less interactions within platforms and what causing the reflected behaviors beyond platforms. We study MOOC student behaviors outside the platform using ethnographic methods, mainly focusing on diary study and interviews. Two groups, 11 extreme users who have completed many MOOCs and 10 who never completed MOOC have been used to collect data. The log sheets data and interviews were analyzed using the Epistemic Network Analysis (ENA) method to explore if there is a significance between these 2 groups and other qualitative comparisons to explore behavioral patterns. Our results indicated 4 behavioral patterns with insights into a significant level of learner's habits between extreme and novice users’ behaviors leading to completion or dropping. This reflects the design gaps of MOOC platforms and based on the behavioral patterns, we provide recommendations to meet the learners' needs.
The road and its influence on bicycle accidents in Denmark

The National accident database is often used as basis when designing and prioritizing safety initiatives for cyclists. Due to the very low reporting rate this is not optimal. The purpose of this study is to get a better understanding of factors influencing the occurrence of cyclist accidents with a particular focus on the influence of the condition of the road. The study is based on data on cyclist injuries reported to the hospital and merged with road data, including information on road condition and existence of bicycle lane. The data is analyzed using a Latent Class Clustering approach for pattern recognition. The analysis uncovers patterns of road maintenance and cyclists accidents and reveals 11 clusters. The results identify the road condition as a significant factor for many of the accidents, especially for accidents involving less experienced cyclists. In addition, the analysis confirms that the use of medical records together with road maintenance data leads to new insight of the occurrence of bicycle accidents, which is relevant for the prioritization of preventive efforts.
Value-driven Collaborative Privacy Decision Making

Multiparty privacy conflicts (MPCs) occur when the privacy of a group of individuals is affected by the same piece of information, yet they have different (possibly conflicting) individual privacy preferences. One of the domains in which MPCs manifest strongly is online social networks, where the majority of users reported having suffered MPCs when sharing photos in which multiple users were depicted. Previous work on supporting users to make collaborative decisions to decide on the optimal sharing policy to prevent MPCs share one critical limitation: they lack transparency in terms of how the optimal sharing policy recommended was arrived at, which has the problem that users may not be able to comprehend why a particular sharing policy might be the best to prevent a MPC, potentially hindering adoption and decreasing the chance for users to accept or influence the recommendations. In this paper, we report our work in progress towards an AI-based model for collaborative privacy decision making that can justify its choices and allows users to influence them based on human values. In particular, the model considers both the individual privacy preferences of the users involved as well as their values to drive the negotiation process to arrive at an agreed sharing policy. We formally prove that the model we propose is correct, complete and that it terminates in finite time. We also provide an overview of the future directions in this line of research.
Scalable Computing: Practice and Experience

Intermittently connected mobile networks are sparsely connected wireless ad-hoc networks where there is no endto-end path from a source device to a destination. Generally, these paths do not exist. Hence, these devices use intermittent paths using the concept of store-and-forward mechanism to successfully accomplish the communication. These networks are featured by long delay, dissimilar data rates, and larger error rates. Hence, we look into the analysis of several delay-tolerant routing protocols, e.g., epidemic, spray-and-wait, prophet, maxprop, rapid, and spray-and-focus using opportunistic network environment simulator. At first, the investigations of the above considered routing protocols are done across three mobility models namely random direction, random walk, and shortest path map based (SPMB) movement model for node impact only. Then, we evaluate these routing protocols against the impacts of message copy, buffer, and time-to-live using SPMB movement model considering the results of node impact. We use three metrics, and the results show that spray-and-focus yields good performance for showing higher delivery, lower latency, and lower overhead among all routing techniques, while epidemic is poor.
A Survey on Explainability: Why Should We Believe the Accuracy of A Model?

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability. '' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.
Creative Nostalgia: Social and Psychological Benefits of Scrapbooking

Abstract Nostalgic memories are highly social and revisiting them increases social connectedness and motivation. The present study offers a naturalistic approach to studying the social experience of nostalgia. Participants created scrapbooks in groups across three sessions over a period of six weeks. Nostalgia proneness, meaning, and social connection were measured prior to the first and after the final session. A follow-up survey was also administered. Participants reported higher levels of nostalgia proneness and greater connectedness to their scrapbooking group following the sessions. The more participants reported experiencing negative psychological states, the more they indicated planning to use their scrapbooks as a source of psychological comfort. The present work has important implications for art therapists and arts in health practitioners.
Feature Based Sentiment Analysis of Mobile Product Reviews using Machine Learning Techniques

With the advent of Web technology and its rapid growth led many people to express their views or opinions on the web related to the products, the services, the events or any social issues. Due to the substantial extent of data which is increasing day by day on the web, it is obvious that the reviews provided by the people on the e-commerce sites is also huge and such reviews are in unstructured form. Hence, it is challenging for the online users, customers and manufacturers to make a proper decision about opinions on these reviews. During the last two decades, many researchers including corporation are working consistently on sentiment analysis, also recognized as, opinion analysis or mining, to derive the opinions expressed by various end users as reviews. In this paper, we study the fine-grained investigation in identifying the sentiments (or opinions) expressed on various aspects of the entity considering the aspects as explicit one over various brands of mobile product reviews and classify these opinions based on some machine learning algorithms. Exploratory outcomes on the different datasets manifest the promising results with respect to the accuracy of classifying the opinions.