Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem

This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudogradient search for particle’s movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.
Generic fundamental polygons for Fuchsian groups

A Dirichlet fundamental polygon for a Fuchsian group is said to be generic if its combinatorial shape is stable under any small permutation of the center of the polygon. Almost all points in the hyperbolic plane are known to be centers of generic fundamental polygons. We prove that the same property holds for points in the boundary of the hyperbolic plane.
Nurses' Views on Infection Control in Long-Term Care Facilities in South Korea: A Focus Group Study

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Korean Journal of Adult Nursing Vol. 30 No. 6, 634-642, December 2018 pISSN 1225-4886 / eISSN 2288-338X https://doi.org/10.7475/kjan.2018. 30.6. 634
Conducting a Formative Evaluation of an Intervention Promoting Mammography Screening in an American Indian Community: The Native Women’s Health Project

ABSTRACT Background: Breast cancer is an important public health issue among American Indian/Alaska Native (AI/AN) women. However, there are very few published studies describing the evaluation of breast health promotion programs among AI/AN women. Purpose: To describe the formative evaluation of a multicomponent intervention to promote mammography screening in an AI community in rural Oklahoma. Methods: A comprehensive process evaluation plan with emphasis on context, reach, dose received, dose delivered, and fidelity was developed. Data collection included mixed research methodology and impact was assessed via one group pre/post research design. Data analysis consisted of descriptive statistical analysis and content analysis. The study utilized a community-based participatory research (CBPR) approach. Results: Process evaluation revealed a relatively high reach within the priority population for both components (clinic and community) and a moderate implementation. Focus group research showed that participants were overall satisfied with program implementation. The intervention was feasible to implement in real-world settings. Translation to Health Education Practice: Community-based evaluation of breast health promotion programs among AI communities is challenging, because one has to balance methodological rigor with practical constraints. An evaluation plan, mixed methods, and a collaborative approach are useful tools in conducting the evaluation.
Perancangan Sistem Pengontrolan Level Pada Steam Drum Waste Heat Boiler Berbasis Adaptive Network Fuzzy Inference System (ANFIS)

Unit boiler sangatlah penting pada industri pupuk seperti di PT. Petrokimia Gresik, khususnya dalam produksi steam. Kualitas steam yang dihasilkan steam sangat dipengaruhi oleh aliran gas buang panas yang dihasilkan dari pembakaran di dalam turbin gas dan juga dipengaruhi oleh level dari drum itu sendiri. Untuk bisa menghasilkan kualitas steam yang baik, maka berbagai parameter di dalamnya harus dapat dikontrol dengan baik, mengingat karakteristik dinamiknya yang sering berubah menyesuaikan beban proses yang ada. Salah satu komponen kontrol yang penting adalah High Pressure Steam Drum (HP drum). Parameter kontrol yang harus dijaga dengan baik adalah level air pada steam drum. Oleh karena itu dalam penelitian ini, dengan memperhatikan struktur agar tidak terlalu mengganggu sistem yang telah ada, maka ditawarkan solusinya menggunakan kontrol adaptif yaitu dengan merancang suatu sistem pengontrolan level pada steam drum waste heat boiler berbasis neuro fuzzy jenis Adaptive Neuro Fuzzy Inference System (ANFIS). Berdasarkan hasil simulasi dan pengujian, maka dapat diambil kesimpulan bahwa pemodelan sistem berbasis pada neuro fuzzy dengan 3 masukan, 1 keluaran serta model dinamika invers plant pada HP steam drum telah menunjukkan hasil yang cukup baik. Selain itu performansi pengontrol neuro fuzzy dalam kecepatan beradaptasi dan kemampuan belajar cukup baik dalam mengejar set point .
Interactive comment on “ Hypoxia and cyanobacterial blooms are not natural features of the Baltic Sea ” by L . Zillén and D . J .

The uncertainties associated with radiocarbon dating of bulk sediment samples are thoroughly discussed in Zillén et al. 2008 as well as the quality of the sediment descriptions of the cores used in that study. There is therefore no need to further discuss these issues in this manuscript. We do make the important statement that improved time control is needed to better constrain the periods of hypoxia in order to disentangle the effects of human and climate forcing on the Baltic Sea ecosystem.
FQWCOS: A Flexible Model for Measuring Customer Satisfaction on Software Based Products and Service

Many software products and services deployed in user environments at times fail to meet user needs satisfactorily. This may be due to the fact that the product or service failed to meet user requirements from the outset (inception) of the Information Systems (IS) project. This study proposes a Flexible Qualifier Weighted Customer Opinion with Safeguard Estimates (FQWCOS) model for measuring the satisfaction of users of software products and services. The FQWCOS model is a variant of the Qualifications Weighted Customer Opinion with Safeguard questions (QWCOS). The FQWCOS model was verified with empirical data using samples from 40 users of ASAS software product. Descriptive statistics were also used to obtain the frequencies, mean values, relative frequencies, standard error, and standard deviation. From these values, it was possible to compute the normalized score of customer opinion Oi and the external measures E for QWCOS and Ei (i=1-4) for FQWCOS were computed. Results from the study reveal that there was no difference between the external measures for QWCOS and FQWCOS. However, the result suggest that external measures were higher when standard error (SE) was used to obtain the measures at different levels 31.58, 19.79, 21.76, 35.69 and 31.06 than when external measure was computed using standard deviation (STD) which yielded the values 4.99, 3.13, 3.44, 5.64 and 4.07. We conclude that FQWCOS and QWCOS yield the same values probably due to small sample used. However, FQWCOS provides a flexible and simple approach, and reveals the need to use the standard error instead of standard deviation since this yields higher magnitude values appropriate for expressing external measures in percentages.
Analyzing the Savings-Investment Trend in a Panel of G-7 Countries

This study examined the relationship between savings and investment in the G-7 countries for the period of 1970 to 2015. The stationary analysis of the data has been done by adopting the panel Levin, Lin & Chu, Breitung, Pesaran & Shin, ADF-Fisher & PP-Fisher criteria while the long run relationship has been tested by employing the Pedroni residual test of co-integration. The results neglected the existence of a long run correlation between saving and investment in G-7 countries. Further, joint causality between the savings and investment has also been tested using the fixed effect VAR model. Wald test explains that the two consecutive lags i.e. S (-1) and S (-2) of savings is jointly causing savings in the current year in the G7 countries. While the same two lags of investment i.e. I (-1) and I (-2) does not jointly granger cause savings in the G7 economies. The results are in line with Feldstein and Horioka (1980) that there is a stable and significant association between the increasing rates of savings and investment in the short run while this relationship weaken in the long run.
Automated space/time scaling of streaming task graphs on field-programmable gate arrays

Parallel computing platforms with a high degree of parallelism provide good performance for streaming applications within a limited power budget. However, these platforms can be difficult to program because the tools they use rely upon explicit identification and management of parallelism and resources. Moreover, when the size of the computing platform target changes, users must manually reallocate resources and parallelism to match what is available in the new target. This thesis provides a framework to retarget applications described by a Streaming Task Graph (STG) for implementation on different platforms, where the framework can automatically scale the solution size to fit available resource or performance targets. First, we explore automated space/time scaling for STGs targeting a pipelined coarse-grained architecture. We produce a tool that analyzes the degrees of parallelism in a general stream application and finds possible bottlenecks. We introduce possible optimization strategies for STGs, and two algorithmic approaches for mapping these to the underlying architecture: a classical approach based upon Integer Linear Programming (ILP), and a novel heuristic approach that produces better results with a shorter run-time. The heuristic can achieve the same throughput targets with 30% less area compared to the ILP approach. Second, we explore automated space/time scaling for STGs targeting a finegrained architecture (Field-Programmable Gate Array (FPGA)). We propose a framework on top of a commercial High-Level Synthesis (HLS) tool which adds the ability to automatically meet a defined area budget or throughput target. Within the framework, we use similar ILP and heuristic approaches to find the best space/time trade-off. The heuristic uses an average of over 95% of the target area budget while
Magnetic Structure and Susceptibility of CoSe$_2$O$_5$: A Low Dimensional Antiferromagnet

CoSe$_2$O$_5$ has a crystal structure consisting of zig-zag chains of edge shared CoO$_6$ octahedra running along the c axis, with the chains separated by Se$_2$O$_5^{2-}$ units. Magnetic susceptibility measurements indicate a transition at 8.5 K to an ordered state. We investigate here the nature of this magnetic ordering using magnetization and specific heat measurements in addition to powder neuttron diffraction. A transition to long-range antiferromagnetic order is found below $T_N$ = 8.5 K as identified by magnetic susceptibility measurements and magnetic Bragg reflections, with a propagation vector $\mathbf{k}$ = 0. The magnetic structure shows that the moments align perpendicular to the c-axis, but with the spins canting with respect to the a axis by, alternately, $+8^\circ$ and $-8^\circ$. Interestingly, the low-field magnetic susceptibility does not show the anticipated cusp-like behavior expected for a well-ordered antiferromagnet. When the susceptibility is acquired under field-cooling conditions under a 10 kOe field, the the usual downturn expected for antiferromagnetic ordering is obtained. Heat capacity measurements at low temperatures indicate the presence of gapped behavior with a gap of 6.5 K.
Optimal Power Scheduling of Cooperative Microgrids in Electricity Market Environment

This paper proposes an optimal power scheduling strategy for cooperative operation of multiple-coupled microgrids, where the microgrid coalition makes day-ahead energy exchange commitments with the grid. Deviations from their commitments are limited by penalizing the microgrids. Various system intermittencies are captured via scenarios. The framework aims at maximizing the expected profit of each microgrid, while mitigating the distribution power losses by enabling local power trades between all microgrids within a coalition. The scheduling problem only utilizes the power exchange requests as the control signals between participating microgrids. Extensive numerical results are presented to corroborate the efficacy of the proposed approach.
Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition

Recent research on generative adversarial nets (GAN) for language identification (LID) has shown promising results. In this paper, we further exploit the latent abilities of GAN networks to firstly combine them with deep neural network (DNN)-based i-vector approaches and then to improve the LID model using conditional generative adversarial net (cGAN) classification. First, phoneme dependent deep bottleneck features (DBF) combined with output posteriors of a pre-trained DNN for automatic speech recognition (ASR) are used to extract i-vectors in the normal way. These i-vectors are then classified using cGAN, and we show an effective method within the cGAN to optimize parameters by combining both language identification and verification signals as supervision. Results show firstly that cGAN methods can significantly outperform DBF DNN i-vector methods where 49-dimensional i-vectors are used, but not where 600-dimensional vectors are used. Secondly, training a cGAN discriminator network for direct classification has further benefit for low dimensional i-vectors as well as short utterances with high dimensional i-vectors. However, incorporating a dedicated discriminator network output layer for classification and optimizing both classification and verification loss brings benefits in all test cases.
Inhibition effect of hydrocolloid irreversible alginate on soaking spray using aloe vera juice

Introduction: Alginate impression material is the most commonly used in dentistry to get a negative mold of the oral tissues. Alginate impression material has imbibition effect when in contact with the liquid during the disinfection process. This study aimed to observe the imbibition effect on alginate impression material soaked and sprayed using Aloe vera juice. Methods: The research method used was a laboratory experimental with pretest and posttest. Alginate was molded by using a mold with the size of 28 mm in diameter and 18 mm high. The results of molding alginate were soaked and sprayed using Aloe vera juice 100% for 5 minutes and 10 minutes. Calculation of imbibition was performed by weighing using digital scales. The normality tested by Shapiro-Wilk and the data were analyzed by Mann Whitney. Result: The results showed that there were imbibition effects on alginate molding soaked and sprayed for 5 minutes and 10 minutes, the test indicated that there were significant differences (p<0.05) between 10 minutes and 5 minutes soaking. Meanwhile, between 5 minutes and 10 minutes spraying, no significant difference was
A multiobjective fuzzy PI controller based on genetic algorithm

This paper presents a fuzzy proportional-integral (PI) controller, which is a discrete-time version of a conventional PI controller. The data base as well as the constant PI control gains are optimized by using a genetic algorithm according to the following design specifications: minimizing the overshoot, the settling time and smoothing of the output curve. Thus, the optimization problem close to a multiobjective optimization one, resulting in an optimal fuzzy PI controller. Simulation results are shown to demonstrate the improvement over a conventional one.
Genetic Study of Low-Density Lipoprotein Receptor Gene and Apolipoprotein B-100 Gene among Malaysian Patients with Familial Hypercholesterolaemia

AbstractBackground: Autosomal dominant hypercholesterolaemia (ADH) is a genetic disorder that is mainly resulted from defects in the low-density lipoprotein receptor (LDLR) and apolipoprotein B-100(APOB-100) genes. Few studies of familial hypercholesterolaemia (FH) have been conducted in Malaysia, that makes the underlying main defect remains not well understood.Objectives: This study was aimed to describe the molecular aspects of FH among Malaysian subjects.Methods and Findings: We studied a group of 74 familial hypercholesterolaemic patients and 77 healthy control subjects. The promoter region and the 18 exons of the low-density lipoprotein receptor gene were screened by denaturing high-performance liquid chromatography (DHPLC) to detect small deletions, insertions and nucleotide substitutions, while DNA sequencing was applied to look for gene variants in amplicons of exon 26 and 29 in the APOB -100 gene. A total of five gene sequence variants in the LDLR gene were reported in 54.1% of the studied patients. P.Arg471Arg variant has the highest frequency of 20.3% among the study subjects. One intronic mutation (c.313+1G>A) and one missense mutation (p.Arg 385Try) were found to be pathogenic, while the other three variants were reported to be non-pathogenic by the in silico analyses. Nine variants were reported in the APOB-100 gene among familial hypercholesterolaemic patients with a non-significant difference in their frequency from the control subjects.Conclusions: five LDLR gene sequence variants were reported. However, nine polymorphisms were stated in the APOB -100 gene. However they were not associated with FH phenotype among this cohort. These findings suggested that LDLR gene mutation is the major genetic cause for FH among Malaysians. The results offer information on the genetic spectrum of FH among Malaysian cohort which can serve as a platform for further genetic studies.
Modeling the Factors that Affect the Implementation of Knowledge Networks

Knowledge is increasingly recognized by organizations as an important source of lasting competitive advantage. Knowledge networks are a mechanism used to support knowledge sharing and creation. Variables important for implementing a knowledge network are described by numerous researchers, but few studies have been undertaken to understand the interaction between these variables and to develop a model of the hierarchy of variables in the knowledge network. This paper identifies a set of variables necessary for the operation of a knowledge network and then analyzes and ranks them using interpretive structural modeling (ISM). The study identified seven key factors affecting the implementation of knowledge networks and revealed contextual relationships among these variables. Some variables, the drivers, are little impacted by other factors, while others, the dependent variables, have limited potential to exert an influence on other factors and are furthermore determined by the drivers. The former includes such factors as an organization’s environmental factors, managerial processes, IT infrastructure, while the latter encapsulates the resultant effects such as knowledge, culture, organizational structures, and communication processes. The use of ISM in the development of a hierarchy and the categorization of variables affecting the implementation of knowledge networks provides one with a better understanding of causal relationships between said variables, thus allowing one to identify which elements to concentrate upon and how trade-offs between variables should be managed.
Trigeminal Amyloidoma: Case Report and Review of Literature

Amyloid is an abnormal insoluble protein that can deposit in extracellular space. It can involve nearly any organ system and may manifest as a systemic process or focal lesion (amyloidoma). We present a rare case of localized amyloidosis with trigeminal nerve being the only site of involvement and no evidence of systemic disease. We also review literature relevant to trigeminal amyloidoma and make recommendations for diagnosis and treatment.
Modeling and Analysis in Marine Big Data: Advances and Challenges

It is aware that big data has gathered tremendous attentions from academic research institutes, governments, and enterprises in all aspects of information sciences. With the development of diversity of marine data acquisition techniques, marine data grow exponentially in last decade, which forms marine big data. As an innovation, marine big data is a double-edged sword. On the one hand, there are many potential and highly useful values hidden in the huge volume of marine data, which is widely used in marine-related fields, such as tsunami and red-tide warning, prevention, and forecasting, disaster inversion, and visualization modeling after disasters. There is no doubt that the future competitions in marine sciences and technologies will surely converge into the marine data explorations. On the other hand, marine big data also brings about many new challenges in data management, such as the difficulties in data capture, storage, analysis, and applications, as well as data quality control and data security. To highlight theoretical methodologies and practical applications of marine big data, this paper illustrates a broad view about marine big data and its management, makes a survey on key methods and models, introduces an engineering instance that demonstrates the management architecture, and discusses the existing challenges.
A place to heal: a qualitative focus group study of respite care preferences among individuals experiencing homelessness*

ABSTRACT Homeless patients have longer hospital stays than housed persons due, in part, to a lack of safe discharge locations. Respite care for homeless individuals decreases hospital length of stay and readmissions rates. This study aimed to develop client-centered recommendations for medical respite care. We conducted four focus group interviews (n = 25 participants) between August 2010 and February 2013 of homeless adult residents in Minnesota. Four domains of respite care were identified from content analysis of interviews: physical, relational, facilitation, and amenities. Novel recommendations include (a) a facility equipped to address mental health and substance dependence needs, (b) a standardized training protocol for respite staff, (c) the creation of a patient advocate, and (d) the creation of a respite case manager to facilitate post-discharge instructions and connection to primary care and social services. These new and actionable recommendations could help inform policies and the development of future medical respite care facilities.