Miracle-dz/UniFAR-FTUs huggingface dataset:
Long term stability of White-necked Picathartes population in south-east Sierra Leone

Summary White-necked Picathartes Picathartes gymnocephalus is a globally ‘Vulnerable’ bird endemic to the highly threatened Upper Guinea forests in West Africa. In an environment under a high level of threat, the high breeding site fidelity (or breeding site persistence) of this species enables long term monitoring of colony site occupancy, colony size and other breeding parameters, which provide multiple indicators of population status. We surveyed known colony sites and searched for new sites in three recent breeding seasons in order to assess the current population status in the most important part of their range in Sierra Leone, the Gola Forest. We found 157 active nests at 40 colonies, equating to at least 314 adult birds. Less than half of the known colonies were protected by the Gola Forest Reserve. Colonies outside the reserve tended to be confined to larger rocks and subject to disturbance from human activities in close proximity, but did not have fewer active nests in them. Colonies outside the reserve were also more likely to be inactive in a given year whereas all colonies inside the reserve were active in every survey year. A predictive distribution model indicated that the survey region could have as many as 234 nests equating to at least 468 breeding birds. There was no evidence that mean colony size had declined since surveys undertaken in the 1980s and 1990s but it was not possible to compare colony abandonment rates inside and outside the reserve over that time period. Clutch and brood sizes were similar in each year, though brood size appeared slightly lower in the third survey year possibly because of a slightly later survey date. Mean clutch and brood sizes reported during the study period were similar to those found in the 1980s and 1990s. We conclude that the population of White-necked Picathartes in the Gola Forest area has been relatively stable over the last two decades, reflecting both the efficacy of protection afforded by the Gola Forest Reserve and presumably low pressure to farm new areas in the nearby community forest. However, regular monitoring of colonies both inside and outside the reserve is required to detect any systematic impact on the birds as pressure for land increases.
Nanolabyrinthine ZrAlN thin films by self-organization of interwoven single-crystal cubic and hexagonal phases

Self-organization on the nanometer scale is a trend in materials research. Thermodynamic driving forces may, for example, yield chessboard patterns in metal alloys [Y. Ni and A. G. Khachaturyan, Nature Mater. 8, 410–414 (2009)]10.1038/nmat2431 or nitrides [P. H. Mayrhofer, A. Horling, L. Karlsson, J. Sjolen, T. Larsson, and C. Mitterer, Appl. Phys. Lett. 83, 2049 (2003)]10.1063/1.1608464 during spinodal decomposition. Here, we explore the ZrN-AlN system, which has one of the largest positive enthalpies of mixing among the transition metal aluminum nitrides [D. Holec, R. Rachbauer, L. Chen, L. Wang, D. Luefa, and P. H. Mayrhofer, Surf. Coat. Technol. 206, 1698–1704 (2011)10.1016/j.surfcoat. 2011.09. 019; B. Alling, A. Karimi, and I. Abrikosov, Surf. Coat. Technol. 203, 883–886 (2008)]10.1016/j.surfcoat. 2008.08. 027. Surprisingly, a highly regular superhard (36 GPa) two-dimensional nanolabyrinthine structure of two intergrown single crystal phases evolves during magnetron sputter thin film synthesis of Zr0.64A.
Genome‐wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci

Dear Editor, We performed a metabolome-based genome-wide association study (metaboGWAS) in a Chinese cohort with coronary artery disease (CAD). We identified seven novel metabolite quantitative trait loci (metaboQTLs) and revealed the possible clinical significance of these metaboQTLs. Our studymay add novel biological insights into the metabolic background of disease-related variants and contribute to the development of risk prediction and treatment of CAD. CAD is the most common type of cardiovascular disease worldwide, with high morbidity and mortality.1 To date, genetic variants can explain approximately 10.6% of CAD heritability,2 whereas the internal biological information is often limited. Plasma metabolites represent a functional intermediate for genetic makeup and environmental exposure to the end phenotype that can be used to uniquely identify individuals.3 Understanding the genetic regulation of the levels of circulating metabolites in CAD can illustrate pathophysiological signals and guide the tool development for risk prediction and therapies. metaboGWAS is an effective way to discover important metaboQTLs,4 which can provide deep insights into the biological links and molecular mechanisms between genes and metabolites. To date, hundreds of metaboQTLs have been uncovered.5–7 The reportedmetaboQTLs exhibit greater effect size and stronger association with metabolic traits than with complex traits. However, most metaboGWASs were conducted on European populations, and a limited number were performed on Chinese populations, especially in CAD cohorts. Here, we present the first large-scale metaboGWAS of a Han Chinese population to identify effective metaboQTLs. Figure 1 depicts the study flow, and Figure S1 presents an overview of the patients’ enrollment. This work is a two-stage study and involved 1551 subjects with CAD from three clinical centers (Supporting Information Methods).
Sponge‐Like Piezoelectric Polymer Films for Scalable and Integratable Nanogenerators and Self‐Powered Electronic Systems

Y. C. Mao, Prof. X. D. Wang Department of Materials Science and Engineering University of Wisconsin-Madison Madison , WI , 53706 , USA E-mail: xudong@engr.wisc. edu Y. C. Mao, H. Yang, Prof. Y. X. Tong MOE Laboratory of Bioinorganic and Synthetic Chemistry KLGHEI of Environment and Energy Chemistry School of Chemistry and Chemical Engineering Sun Yat-sen University Guangzhou , 510275 , China Prof. P. Zhao Department of Mechanical and Industrial Engineering University of Minnesota-Duluth Duluth , MN , 55812 , USA G. McConohy Department of Engineering Physics University of Wisconsin-Madison Madison , WI , 53706 , USA
Improving mosquito population models over the Greater Toronto Area using MSI and SAR data

As West Nile Virus (WNV) and St. Louis Encephalitis (SLE) become more prevalent across North America, there is an increased risk of fatal neuroinvasive cases. In order for public health officials to prepare for these cases and potentially intervene, the ability to forecast mosquito borne disease outbreaks is paramount. In practice, however, such vector borne diseases are notoriously difficult to predict due to their seemingly sporadic spatial and temporal outbreak patterns. Recent research has demonstrated that mosquito abundance is causally related to WNV/SLE prevalence, providing a practical starting point for developing mosquito-borne disease forecasting systems. When focusing on building mosquito population models, understanding the reproduction environment of Culex mosquitos (WNV and SLE's primary vectors) is key: they rely on warmth, water, and vegetation to reproduce. Previous work has shown that global-coverage multispectral imagery (MSI) (i.e., Landsat 8, Sentinel- 2) is a valuable resource for characterizing vegetation health as a predictor of mosquito population, but it is limited in that it may not provide the spatial resolution necessary to distinguish between, e.g., a well-fertilized lawn (poor Culex habitat) and a stand of trees (good Culex habitat). The backscatter information collected by synthetic aperture radar (SAR) imagery provides opportunity to distinguish between broader categories of vegetation type, potentially helping to fill this gap. This research uses publicly available global-coverage MSI and SAR imagery (Landsat 8, Sentinel-2, and Sentinel-1) to explore if vegetation type, in tandem with vegetation health, improves our ability to forecast mosquito populations. Vegetation characterization is done over the Greater Toronto Area from 2014 to 2017, and we derive weekly time series from MSI, spectral indices, and SAR for this time period. We then quantify the strength of vegetation health and type as a predictor of Culex abundance.
Strengthening e-Education in India using Machine Learning

e-Education has developed as one of the most encouraging territories. The Indian Government is investing all amounts of energy to improve education among the residents of the nation. School and graduate understudies are focused on, however the stage is being created for all the residents seeking to learn. Without a doubt, the objective is to build the quantity of literates with advanced education. To accomplish the equivalent, propels in Data and Correspondence innovation are being utilized in the education division, which has cleared route for e-Training in India as well. To help educators in concentrating more on more current viewpoints, their excess work can be disposed of utilizing Machine Learning (ML). Difference to programming, ML deals with information and answers to create rules. In the event that Machine Learning is tackled effectively, it can setup the training division and contribute essentially to the development of the country. Hence, the work presented in this paper fortifiews e-Education in India utilizing Machine Learning. For the most part, three concerns are focused to be tended to: Personalized recommendation of course and Customized teaching methodology. The work proposes utilizing developmental methodology of hereditary calculations for improving conventional procedures. Implementation and experiments presented in the paper verify the viability of proposed calculations. Keywords​e-Education, Data mining, e-learning, Regression, Machine learning
Enhanced Thermoelectric Performance in 18‐Electron Nb0.8CoSb Half‐Heusler Compound with Intrinsic Nb Vacancies

Typical 18‐electron half‐Heusler compounds, ZrNiSn and NbFeSb, are identified as promising high‐temperature thermoelectric materials. NbCoSb with nominal 19 valence electrons, which is supposed to be metallic, is recently reported to also exhibit thermoelectric properties of a heavily doped n‐type semiconductor. Here for the first time, it is experimentally demonstrated that the nominal 19‐electron NbCoSb is actually the composite of 18‐electron Nb0.8+δCoSb (0 ≤ δ < 0.05) and impurity phases. Single‐phase Nb0.8+δCoSb with intrinsic Nb vacancies, following the 18‐electron rule, possesses improved thermoelectric performance, and the slight change in the content of Nb vacancies has a profound effect on the thermoelectric properties. The carrier concentration can be controlled by varying the Nb deficiency, and the optimization of the thermoelectric properties can be realized within the narrow pure phase region. Benefiting from the elimination of impurity phases and the optimization of carrier concentration, thermoelectric performance is remarkably enhanced by ≈100% and a maximum zT of 0.9 is achieved in Nb0.83CoSb at 1123 K. This work expands the family of half‐Heusler thermoelectric materials and opens a new avenue for searching for nominal 19‐electron half‐Heusler compounds with intrinsic vacancies as promising thermoelectric materials.
Sentiment Classification of News Text Data Using Intelligent Model

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h. ” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.
Credit Scoring Through Data Mining Approach: A Case Study of Mortgage Loan In Indonesia

Non-performing loans has been one of the biggest problems in the banking sector. One alternative to minimize credit risk is to improve the evaluation of the applicant's credibility. Credit scoring is an evaluation of the feasibility of credit requests. For financial institution, poor credit may lead to an increase in non-preforming loans that may reduce bank productivity even in the event of financial crises and financial institutions bankruptcy Previously, credit scoring is based on the conventional statistics such as logistic regression and discriminant analysis. those techniques produce a good accuracy, some of the assumptions aren't accomplished by the data. Along the development of information technology, more advance approach named data mining has been developed. Therefore, this study used the Data Mining approach to solve NPL percentage problems in Indonesian bank, specifically in its mortgage loan. The classifiers used are decision tree C4.5 and random forest. Classifier with the best accuracy is random forest with Adaboost with 72.95%, on the other hand the worst accuracy performed by C4.5 with 68,7%. The best sensitivity performed by random forest complemented by Ada-boost with 0,730. It is considered as the best model in terms of prevent the type II error which could impact to the increase of non-performing loan in a bank. By the end of the research, it can be concluded that all types of random forest model out-perform the C4.5 decision tree.
Issues and Optimization of UMTS Handover

UMTS is an emerging cell phone technology and it is basically another name for 3G mobile communication systems. It provides an enhanced range of multimedia services like video conferencing and high speed internet access. Sometimes UMTS is marketed as 3GSM emphasizing the both 3G nature and GSM standards which it was designed to succeed. UMTS is also European term for wireless systems based on the IMT-2000 standards. To utilize various merits in mobile telecommunication system which consist of various radio access networks, UMTS as Third Generation wireless technology utilizes a wideband CDMA or TD/CDMA transceiver and also cover large area. Handover is basically a function which continues the communication between users without any gaps when the hardware goes to a place where it finds no network coverage. When we talk in terms of cellular communications systems, handover is a process which is referred to the transfer of a connection from one cell to another. Handover time is generally between 200 and 1,200 milliseconds (ms), which accounts for the delay. In this thesis we are going to find the reasons for these factors which affect the Quality of service of handover. The main focus of this research is to study the some factors which really affect the handover phenomenon in UMTS that basically affect the overall quality of mobile network. For this we intend to find the solution for problems which born during the handover. Handover provides the mobility to users which are the main theme of wireless technology and it is also make the interoperability between different network technologies.
APPROPRIATING VALUE FROM INFORMATION TECHNOLOGY IN HEALTHCARE

Title: APPROPRIATING VALUE FROM INFORMATION TECHNOLOGY IN HEALTHCARE Jie Mein Goh, Ph.D., 2011 Directed By: Professor Ritu Agarwal, Decision, Operations & Information Technologies The value potential of information technology (IT) in healthcare settings has generated considerable optimism yet, significant questions remain unanswered. This dissertation employs the lens of social structure to investigate the value of information technology in healthcare situated in two distinct contexts: hospitals, that exemplify the traditional institutional form for the delivery of healthcare services, and online health communities that represent new organizational forms enabled by IT. It seeks to address the following fundamental research questions “What is the impact of information technology in healthcare settings? How does social structure influence the appropriation of the value of information technology in healthcare? ” Each of the two contexts is investigated in a separate essay, drawing upon distinct bodies of literature and using both qualitative and quantitative analytical methods. Essay 1: Evolving Work Routines: Adaptive Routinization of Information Technology in Healthcare The first essay investigates the impact of healthcare technologies such as electronic medical record systems in the traditional hospital environment. It traces the development of changes in social structure before and after an IT implementation. Using a longitudinal field study, the process of how information technology and routines interact is deconstructed. A theory of the co-evolution of routines and technology is proposed and described.
Integrative analysis reveals multiple modes of LXR transcriptional regulation in liver

Significance LXRs are critical regulators of hepatic metabolism and function, but their mechanisms of action at the genome level are incompletely understood. We performed integrated analysis of genome-wide chromatin accessibility, gene expression, and transcription factor binding. We reveal distinct mechanisms of LXR transcriptional regulation of both metabolic and nonmetabolic genes in liver. We show that LXR can both activate and repress genes and that LXR binding impacts the activity of other transcription factors. The nuclear receptors liver X receptor (LXR) α and β play crucial roles in hepatic metabolism. Many genes induced in response to pharmacologic LXR agonism have been defined; however, the transcriptional consequences of loss of LXR binding to its genomic targets are less well characterized. Here, we addressed how deletion of both LXRα and LXRβ from mouse liver (LXR double knockout [DKO]) affects the transcriptional regulatory landscape by integrating changes in LXR binding, chromatin accessibility, and gene expression. Many genes involved in fatty acid metabolism showed reduced expression and chromatin accessibility at their intergenic and intronic regions in LXRDKO livers. Genes that were up-regulated with LXR deletion had increased chromatin accessibility at their promoter regions and were enriched for functions not linked to lipid metabolism. Loss of LXR binding in liver reduced the activity of a broad set of hepatic transcription factors, inferred through changes in motif accessibility. By contrast, accessibility at promoter nuclear factor Y (NF-Y) motifs was increased in the absence of LXR. Unexpectedly, we also defined a small set of LXR targets for direct ligand-dependent repression. These genes have LXR-binding sites but showed increased expression in LXRDKO liver and reduced expression in response to the LXR agonist. In summary, the binding of LXRs to the hepatic genome has broad effects on the transcriptional landscape that extend beyond its canonical function as an activator of lipid metabolic genes.
Estimating city-wide hourly bicycle flow using a hybrid LSTM MDN

Cycling can reduce greenhouse gas emissions and air pollution and increase public health. With this in mind, policy-makers in cities worldwide seek to improve the bicycle mode-share. However, they often struggle against the fear and the perceived riskiness of cycling. Efforts to increase the bicycle's mode-share involve many measures, one of them being the improvement of cycling safety. This requires the analysis of the factors surrounding accidents and the outcome. However, meaningful analysis of cycling safety requires accurate bicycle flow data that is generally sparse or not even available at a segment level. Therefore, safety engineers often rely on aggregated variables or calibration factors that fail to account for variations in the cycling traffic caused by external factors. This paper fills this gap by presenting a Deep Learning based approach, the Long Short-Term Memory Mixture Density Network (LSTMMDN), to estimate hourly bicycle flow in Copenhagen, conditional on weather, temporal and road conditions at the segment level. This method addresses the shortcomings in the calibration factor method and results in 66-77\% more accurate bicycle traffic estimates. To quantify the impact of more accurate bicycle traffic estimates in cycling safety analysis, we estimate bicycle crash risk models to evaluate bicycle crashes in Copenhagen. The models are identical except for the exposure variables being used. One model is estimated using the LSTMMDN estimates, one using the calibration-based estimates, and one using yearly mean traffic estimates. The results show that investing in more advanced methods for obtaining bicycle volume estimates can benefit the quality, mitigating efforts by improving safety analyses and other performance measures.
New Compact Dual-band Filter Using Common Resonator Sections and Double-diplexing Structure

In this paper, a new compact size dual-band fllter based on double-diplexing struc- ture and shared common resonator sections is proposed. The basic conflguration is composed of two shared stepped-impedance resonators (SIRs), four separate SIRs, and two T-junction cir- cuits for two passbands. The shared SIRs can adjust fundamental resonant frequency and second harmonic resonant frequency thereof by designing the impedance ratio carefully, and therefore the fundamental resonant frequency and the second harmonic resonant frequency can be used to indicate the required frequencies of the two passbands, respectively. This property will be uti- lized to design the shared SIRs in the proposed structure. In conventional dual-band fllters using single impedance ratio SIRs for dual passbands, the external quality factors of the two passbands cannot be extracted separately due to obstacles such as designing two external quality factors simultaneously with only one feed point. Thus, by adding the SIRs of two difierent impedance ratios near input port or output port, the fundamental frequencies thereof are designed at the center frequencies of two passbands, respectively, and the two feed points near input port or out- put port can be utilized to design the external quality factors of two passbands independently. Finally, the double-diplexing structure, i.e., the two T-junction circuits utilized in conventional diplexers, are located near the input and output ports and connected via the SIRs in a back-to- back fashion, and can achieve the required matching conditions at the input and output ports as well as mutual isolation between the two passbands concurrently. Speciflcally, a fourth-order dual-band fllter with a Chebyshev response and 0.1-dB equal ripple is implemented on a substrate with a thickness of 0.508mm, a relative dielectric constant of 3.55, and a loss tangent of 0.0027. The designed center frequencies of two passbands are 2.4GHz and 4.1GHz, respectively. The flrst and second fractional bandwidths are 11% and 9%, respectively. In conclusion, agreement between measured and simulated results is achieved and the proposed design concept is thereby verifled.