Miracle-dz/UniFAR-FTUs huggingface dataset:
Re-evaluating supply chain integration and firm performance: linking operations strategy to supply chain strategy

Purpose This paper aims to explore the performance implications of supply chain integration (SCI) taking a strategic perspective. Thus, this paper is set to provide answers to the following research questions: Does a higher degree of SCI always lead to greater firm performance improvements? As the answer to this question is likely to be no, the authors explore the performance implications from a strategic perspective: Is the SCI–performance relationship contingent on a company’s competitive priorities (i.e. operations strategy)? Design/methodology/approach The authors explore their questions through multiple quasi-independent data sets to test the impact of SCI on firm performance. Furthermore, the authors provide a more nuanced conceptual and empirical view to explore the previously uncovered contradictory results and contingent relationship challenging the “more integration equals higher firm performance” proposition. Findings The results only provide partial support for the proposition that more integration is always beneficial in the supply chain context. The authors also identified that the impact of SCI on financial performance is contingent on a company’s competitive priorities. Originality/value This study provides a much-needed comprehensive assessment of the SCI–performance relationship through critically re-evaluating one of the most popular propositions in the field of supply chain management. The results can be extrapolated beyond the dyad, as the authors conceptualise integration simultaneously from an upstream and downstream perspective.
Test-day or 305-day milk yield for genetic evaluation of Gir cattle

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.
Efficacy Boost Clustering on Big Data for Association Rule Mining

Extremely large data sets are analyzed computationally to reveal patterns, trends and associations, especially relating to human behavior and interactions. Since the databases are extremely large in their volume, large numbers of frequent itemsets are constructed for the generation of association rules and many more rules are generated. Parallelism with the distribution of tasks into clusters to different processors in a distributed environment with local and global minimum support thresholds may be helpful to minimize the number of frequent itemsets to be used for the construction of association rules. Thus, it is worthwhile to group the transactions into clusters and discover frequent itemsets using local and global minimum supports in order to minimize the number of frequent itemsets to be considered in turn to generate moderate number of strong association rules. Thus, this proposed paper focuses on clustering of the transactions among multiple parallel processors with local as well as global minimum supports are used to find frequent itemsets resulting in minimization of frequent itemsets and association rules. It speeds up the discovery of associations by minimizing the processing time and reduces the required
A cognitive network management system to improve QoE in stereoscopic IPTV service

New Internet Protocol Television (IPTV) services are including new technologies such as Stereoscopic TV and three‐dimensions (3D) HDTV. As well, increased ubiquitous networking and promoting in smart devices have led to high demand IPTV (over networks). Stereoscopic content required higher data flow to support these emerging TV services, and there are higher requirements at the network layer to provide good quality of service and quality of experience to the end users in delivering stereoscopic IPTV. In this paper, we propose a new concept of cognitive network management algorithm and protocol based on 3D coding techniques for delivering of stereoscopic IPTV service. The proposed approach explains how the management algorithm observes the network performance to guarantee the quality of the stereoscopic IPTV services, by measuring the performance of quality of service (QoS) parameters (delay, jitter, and packets loss) and quality of experience (QoE) metrics such as Peak Signal‐to‐Noise Ratio (PSNR), Moving Image Videography (MIV), and Mean Opinion Score (MOS). Those parameters are monitored in order to take appropriate codification decision for IPTV service provider. Moreover, the codification decision uses K‐mean classification to select the better codification for the end users. Therefore, both kinds of 3D coding formats such as Stereo Video Coding (SVC) format and 2D + Z Coding format (2D‐plus‐Depth) are selected in our experiments. As a result, our proposal successfully ensures the appropriate quality of service and quality of experience to the end users when the service of stereoscopic IPTV is being delivered.
Chagas Disease and Heart Failure: An Expanding Issue Worldwide

Chagas disease, originally a South American endemic health problem, is expanding worldwide because of people migration. Its main impact is on the cardiovascular system, producing myocardial damage that frequently results in heart failure. Pathogenic pathways are mainly related to inmunoinflamatory reactions in the myocardium and, less frequently, in the gastrointestinal tract. The heart usually shows fibrosis, producing dilatation and damage of the electrogenic cardiac system. These changes result in cardiomyopathy with heart failure and frequent cardiac arrhythmias and heart blocks. Diagnosis of the disease must include a lab test to detect the parasite or its immune reactions and the usual techniques to evaluate cardiac function. Therapeutic management of Chagas heart failure does not differ significantly from the most common treatment for dilated cardiomyopathy, with special focus on arrhythmias and several degrees of heart block. Heart transplantation is reserved for end-stage cases. Major international scientific organisations are delivering recommendations for prevention and early diagnosis. This article provides an analysis of epidemiology, prevention, treatment and the relationship between Chagas disease and heart failure.
Analysis of Markers for Combustion Mode and Heat Release in MILD Combustion Using DNS Data

ABSTRACT Various commonly used markers for heat release are assessed using direct numerical simulation (DNS) data for Moderate or Intense Low-oxygen Dilution (MILD) combustion to find their suitability for non-premixed MILD combustion. The laser-induced fluorescence (LIF) signals of various markers are synthesized from the DNS data to construct their planar (PLIF) images which are compared to the heat release rate images obtained directly from the DNS data. The local OH values in heat releasing regions are observed to be very small compared to the background level coming from unreacted mixture diluted with exhaust gases. Furthermore, these values are very much smaller compared to those in burnt regions. This observation rises questions on the use of OH-PLIF for MILD combustion. However, the chemiluminescent image obtained using is shown to correlate well with the heat release. Two scalar-based PLIF markers, and , correlate well with the heat release. Flame index (FI) and chemical explosive mode analyses (CEMA) are used to identify premixed and non-premixed regions in MILD combustion. Although there is some agreement between the CEMA and FI results, large discrepancies are still observed. The schlieren images deduced from the DNS data showed that this technique can be used for a quick and qualitative identification of MILD combustion before applying expensive laser diagnostics.
Exploring short food supply chains from triple bottom line lens: A comprehensive systematic review

Growing awareness towards the sustainability has compelled supply chain domain experts to explore its relevance in this context. As a result, a number of studies in recent years have focused on investigating sustainable supply chain practices across the globe. Short food supply chains (SFSCs) have emerged as a promising sustainable alternative to the industrialized agro-food supply systems. However, academic literature hasn’t fully explored the linkage between SFSCs and sustainability. This study therefore aims to explore how SFSCs conforms to the dimensions of sustainability using the sustainability framework (social, economic, and environmental). The findings are based on a systematic literature review of 44 articles published between 2000 and 2018 selected from six electronic databases was used for the analysis. All items were properly analyzed by the researchers, seeking to identify the relationship or proximity of the information found in the papers with the SFSC concept. Our studies highlight the societal, environmental and cultural benefits of SFSC in addition to the associated economic and safety benefits. Our study thus, adds to the scant literature on SFSCs and shows a clear linkage between SFSCs and five-dimensional sustainability framework. We also propose a set of research questions that sets direction for future research.
Real-Time Distracted Drivers Detection Using Deep Learning

In the last few years, the number of road accidents is increasing worldwide. According to the World Health Organization the most common cause behind these accidents is driver’s distraction and in many cases is caused by the use of a mobile phone. An attempt to develop a system for detecting distracted drivers and warn the responsible person against it was done. The system is a CNN based system that detects and identifies the cause of distraction. The base architecture for the CNN is VGG-16 and is modified for this task. Various activation functions (Leaky ReLU, DReLU, SELU) were used in order to investigate performance. Also, the performance of a lightweight attention module (squeeze-and-excitation) was evaluated. Experimental results show that the system outperforms earlier lightweight models in literature achieving an accuracy of 95.82%.
An Exploration of Gender and Disability in the Workplace

Intro: Intersectionality argues that individuals who have multiple minority identities, such as gender and disability, have been found to experience unique difficulties in socio-cultural environments, such as employment. Statistics show that women with disabilities have been found to experience more difficulties in securing employment and more disadvantages once in employment compared to non-disabled women. This study aims to further our understanding of the intersection between gender and disability in terms of both recruitment and mainstream employment in the United Kingdom. Method: This cross sectional, qualitative study was based on semi-structured interviews with seven women, who had various physical disabilities and had experience of various types of mainstream employment. Findings: Thematic analysis identified numerous themes based on the experiences discussed in the interview data: 1) discrimination in recruitment processes, 2) career changes as a result of individual impairments, 3) accessibility to work environment, and 4) support, which was a central theme to emerge split between: a) support given which included accommodations and co-worker support, and b) support needed which centralised on the current lack of understanding and empathy the disabled women experienced in employment. Discussion: The specific work environment can impact on the type of experiences disabled women face and the difficulties experienced resulted from a combination of accessibility issues, lack of support and the limitations of the women’s disabilities. Difficulties attributed to disabilities in the workplace were found to be enhanced by experiences specific to women, such as pregnancy. Conclusion: Overall, it appears that although there is evidence that the intersection of disabled women’s combined identities affected their experiences, disability was arguably the more salient identity. Furthermore, it appears that the disabled women's self-identity was often in conflict with projected stereotypical social-identities, especially in certain socio-cultural employment environments. Career changes due to impairment, accessibility to workplace and a lack of support were the most shared experiences across the interviews.
The Preposition Corpus in Sketch Engine

Corpora from the Pattern Dictionary of English Prepositions (PDEP) provide the basis for examining the behavior of 304 English prepositions, with 1040 senses (patterns) describing in 20 fields. The PDEP corpora comprise dependency parses for 81,509 sentences in CoNLL-X format, previously used in several studies, particularly used for disambiguation modeling. We have now put these parse data into Sketch Engine (SE), using its mechanisms for further perspectives of preposition behavior. In the process, we have also annotated each parse with additional information that provides an even richer set of data to examine preposition behavior. For each sentence, Sketch Engine identifies the sense number and the direct link location to the PDEP sense description; these references can be displayed for each concordance line. Sketch Engine data for each sentence also includes the PDEP class, the subclass, and supersense tags for the preposition complements and governors (i.e., semantic relations using the WordNet lexicographer file class). We describe in detail how the corpora were prepared for SE, involving several scripts used in PDEP to access its databases. Some of these scripts provide additional entry points into the PDEP data, particularly for the class and subclass. We describe the use of WordNet noun, verb, adjective, and adverb supersenses to tag the complements and governors, i.e., semantic word sketches for the prepositions. The resultant preposition data within SE provides a perspective different from its usual focus on the main parts of speech, so we describe the unique aspects enabled for the PDEP corpora in considerable detail. We describe several corpus query language (CQL) queries that provide useful perspectives on preposition behavior, particularly showing preposition collocations, (semantic) word sketches, preposition thesauruses, and preposition sketch differences.
Orographic effect and multiscale interactions during an extreme rainfall event

Interaction of multiple oscillations of different time scales may result in severe weather events. The presence of orography can modulate the intensity of these events even further. Kerala witnessed one such heavy rainfall event in August, 2018, claiming 483 lives and damages worth INR 200 billion. This study focuses on the peak rainfall duration (13–17 August) when the departure from normal was 42%. Segregating moisture transport into its mean and perturbation terms show that an anomalous moisture channel over the Arabian Sea supplied continuous moisture to the Western Ghats (WG), whereas anomalous wind due to a monsoon depression advected moisture towards the southern peninsula. It is evident in the form of Moisture Flux Convergence (MFC) towers traversing along the Eastern Ghats before merging with the semi-permanent MFC feature over the WG. The presence of positive quasi bi-weekly oscillations and of Intra Seasonal Oscillations (ISO) aggravated the event as they complemented the anomalous moisture transport, with ISO constantly providing winds of the order of 2–3 ms−1. In addition, shedding of MFC towers by the depression is accredited to the synoptic scale oscillation.
A Survey of Topic Model Inference Techniques

Latent Dirichlet Allocation (LDA) is a probabilistic topic model that aims at organizing, visualizing, summarizing, searching, predicting and understanding the content of any given text data. The model enables users to discover themes in text, annotate, organize and summarize documents. LDA inference involves estimating the parameters and posterior distribution of a formulated mathematical relationship. This paper investigates topic modeling literature based on LDA and presents discoveries and state of the art in the topic. Presented also are challenges and popular tools. In conclusion, the paper identifies Gibbs sampling as a popular inference mechanism and notes that the method is limited for application in big data settings. Keywords-Topic Modelling; Latent Dirichlet Allocation; Sampling; Inference Techniques.
Formation of a crystal nucleus from liquid

EVOLUTION Correction for “Genome sequences of the human body louse and its primary endosymbiont provide insights into the permanent parasitic lifestyle,” by Ewen F. Kirkness, Brian J. Haas, Weilin Sun, Henk R. Braig, M. Alejandra Perotti, John M. Clark, Si Hyeock Lee, Hugh M. Robertson, Ryan C. Kennedy, Eran Elhaik, Daniel Gerlach, Evgenia V. Kriventseva, Christine G. Elsik, Dan Graur, Catherine A. Hill, Jan A. Veenstra, Brian Walenz, José Manuel C. Tubío, José M. C. Ribeiro, Julio Rozas, J. Spencer Johnston, Justin T. Reese, Aleksandar Popadic, Marta Tojo, Didier Raoult, David L. Reed, Yoshinori Tomoyasu, Emily Krause, Omprakash Mittapalli, Venu M. Margam, Hong-Mei Li, Jason M. Meyer, Reed M. Johnson, Jeanne Romero-Severson, Janice Pagel VanZee, David Alvarez-Ponce, Filipe G. Vieira, Montserrat Aguadé, Sara Guirao-Rico, Juan M. Anzola, Kyong S. Yoon, Joseph P. Strycharz, Maria F. Unger, Scott Christley, Neil F. Lobo, Manfredo J. Seufferheld, NaiKuan Wang, Gregory A. Dasch, Claudio J. Struchiner, Greg Madey, Linda I. Hannick, Shelby Bidwell, Vinita Joardar, Elisabet Caler, Renfu Shao, Stephen C. Barker, Stephen Cameron, Robert V. Bruggner, Allison Regier, Justin Johnson, Lakshmi Viswanathan, Terry R. Utterback, Granger G. Sutton, Daniel Lawson, Robert M. Waterhouse, J. Craig Venter, Robert L. Strausberg, May R. Berenbaum, Frank H. Collins, Evgeny M. Zdobnov, and Barry R. Pittendrigh, which appeared in issue 27, July 6, 2010, of Proc Natl Acad Sci USA (107:12168–12173; first published June 21, 2010; 10.1073/pnas.1003379107). The authors note that the author name Emily Krause should have appeared as Emily Kraus. The corrected author line appears below. The online version has been corrected.
Carbon Dioxide Injection for Hypervelocity Boundary Layer Stability

An approach for introducing carbon dioxide as a means of stabilizing a hypervelocity boundary layer over a slender bodied vehicle is investigated through the use of numerical simulations. In the current study, two different test bodies are examined. The first is a fivedegree-half-angle cone currently under research at the GALCIT T5 Shock Tunnel with a 4 cm porous wall insert used to transpire gas into the boundary layer. The second test body is a similar cone with a porous wall over a majority of cone surface. Computationally, the transpiration is performed using an axi-symmetric flow simulation with wall-normal blowing. The effect of the injection and the transition location are gauged by solving the parabolized stability equations and using the semi-empirical e N method. The results show transition due to the injection for the first test body and a delay in the transition location for the second test body as compared to a cone without injection under the same flight conditions. The mechanism for the stabilizing effect of carbon dioxide is also explored through selectively applying non-equilibrium processes to the stability analysis. The results show that vibrational non-equilibrium plays a role in reducing disturbance amplification; however, other factors also contribute.
Analysis of microlayer defects of a functionally graded titanium alloy by electrical discharge machining

This paper reports on the surface characteristics of a functionally graded Titanium (Ti-6Al-4V) alloy during Electrical Discharge Machining (EDM). The electrical discharge input parameters employed for this work are current (I), on-time (Ton), off-time (Toff) and spark gap voltage (V). The electrode used for EDM of the titanium workpieces is an electrolytic copper electrode. The two machining processes employed are at room temperature and sub-zero temperature using liquid nitrogen at -1600C as electrode coolant during the process. EDM machining was designed for all the input parameters and the materials machined with three different currents, i.e., 4.5A, 5.5A and 7.5A, while the workpieces were examined for microlayer defects using scanning electron microscopy (SEM). The result showed an average micro-layer thickness of 24.47 μm with current of 7.5A when machined at -1600C. Also an increase in crack formation with the highest current of 7.5A for the surfaces machined at subzero temperature was recorded for both the workpieces and electrode materials.
THE INTERACTION OF COMMUNITY CONFLICTS AND URBAN DEVELOPMENT IN WEST KALIMANTAN

Violence conflicts occurred in Indonesia, in particular, West Kalimantan, were associated with ethno-communal motives. At some points, the incidents linked to the role of the built environment. A study in the local context conflicts could provide an opportunity for a better understanding. This paper aims to present the story of conflict in West Kalimantan, which mainly happened in Sambas, from the urban development perspective. It attempts to show the interplay between the conflicts and the urban/spatial development. Using case study approach, the research utilised three data collection techniques: deep guided interview, observation, and survey. It interviewed fifteen key persons representing four different stakeholders: government, planning practitioners, academics, and local leaders. It documents the built environment elements of the location. It distributed 200 questionnaires to check the perception of people toward conflicts and the built environment. The finding demonstrates that somehow communal conflicts have linked to four elements: ethnicity, land ownership, wealth, and social status. The built environment could promote a positive urban environment in mitigating potential conflicts in the future.
“Roger Broke His Tooth. However, He Went to the Dentist”: Why Some Readers Struggle to Evaluate Wrong (and Right) Uses of Connectives

ABSTRACT Understanding discourse connectives is an important step to achieving effective verbal communication. Yet, the ability of adult native speakers to understand the broad range of connectives found in most Indo-European languages has seldom been assessed. In this article we demonstrate that some adults have difficulties recognizing correct and incorrect uses of connectives in their native language. In Experiment 1 we show that adults have a lower ability to understand less frequently used connectives from the written mode than more frequently used ones. We also report that such an ability depends on participants’ exposure to print (as measured by the Author Recognition Test), in turn correlating with higher general grammatical competence. In Experiment 2 we find that the performance of an adult increases with connectives that are used in both speech and writing, although differences are still present between connectives and between participants differently exposed to print. We conclude that connectives represent an area of complexity for some adults with less exposure to print and poorer grammatical competences.