Miracle-dz/UniFAR-FTUs huggingface dataset:
THE MEDIATING ROLE OF COMPANY NON-FINANCIAL PERFORMANCE AND RELIGIOSITY AS A MODERATING VARIABLE BETWEEN LEARNING ORIENTATION AND COMPANY FINANCIAL PERFORMANCE

This study aims to see the mediating role of non-financial performance between learning orientation and financial performance. Furthermore religiosity has a moderating role between learning orientation and non-financial performance. No relevant profit theory and still limited research that saw the company's performance from both sides of the financial and non-financial to be a gap for doing research. The sample in this study is small and medium enterprises (SMEs) domiciled in Indonesia country. Research method using a Partial Least Square (PLS) 3. The results of this study indicate that the orientation of learning has a positive and significant impact on company financial performance and company non-financial companies. Religiosity has a moderating role strengthen the relationship learning orientation and non-financial performance. Furthermore, this study also gives results that company non-financial performance mediates some of the relationship between learning orientation to company financial performance. Keywords : learning orientation, company financial performance, company non-financial performance, religiosity, SMEs.
Improving healthcare provider knowledge in acute and primary transgender health needs: The implementation of a clinical education program with urgent care and emergency room staff and providers

The purpose of this project was to improve knowledge and identify personal bias and beliefs in the acute and primary healthcare needs of transgender persons, through the implementation of a clinical education program with healthcare providers and staff working within the urgent care and emergency department settings. The need for such a program exists due to the lack of suitable and accessible healthcare that may lead to misdiagnosis, delay of treatment and poor health outcomes. Including clinical education in the acute and primary healthcare needs of transgender persons that can be integrated into healthcare organizations may aid in the improvement of overall healthcare and healthcare experiences of transgender persons. Goals of the educational program included: increased healthcare provider and staff knowledge in the acute and primary care of transgender persons; identification of personal bias and beliefs in the healthcare of transgender persons; and the identification of factors limiting transgender persons seeking healthcare. One hundred and sixty-four participants were approached as potential participants within the program, only four elected to participate, with two completing the program. Some of the limitations of the program can be contributed to time requirements and personal beliefs. Future research may include the incorporation of a succinct program with minimal time requirement or the exploration of participant interest. Through the incorporation of an easily accessible, cost effective, electronically based education program, healthcare providers and staff in the urgent care and emergency room settings were able to obtain needed transgender inclusive healthcare knowledge. IMPROVING HEALTHCARE PROVIDER KNOWLEDGE IN ACUTE 5 Improving healthcare provider knowledge in acute and primary transgender health needs: The implementation of a clinical education program with urgent care and emergency room staff and providers
Well-Being and Social Media: A Systematic Review of Bergen Addiction Scales

Does social media addiction impair the well-being of non-clinical individuals? Despite the Internet being able to be considered as a promoting factor for individual empowerment, previous literature suggests that the current massive availability of Information and Communication Technologies (ICT) may be dangerous for users’ well-being. This article discusses the relationship between the most used social media addiction measures (i.e., the Bergen Facebook Addiction Scale—BFAS, the Bergen Social Media Addiction Scale—BSMAS) and well-being. A systematic review considering all the publications indexed by PsycInfo, PsycArticles, PubMed, Science Direct, Sociological Abstracts, Academic Search Complete, and Google Scholar databases was performed to collect the data. Ten of 635 studies were included in the qualitative synthesis. Overall, most of the included works captured a negative but small relationship between BFAS/BSMAS and well-being, across multiple definitions and measurement.
Optical pulling forces and their applications

Optical manipulations utilizing the mechanical effect of light have been indispensable in various disciplines. Among those various manipulations, optical pulling has emerged recently as an attractive notion and captivated the popular imagination, not only because it constitutes a rich family of counterintuitive phenomena compared with traditional manipulations but also due to the profound physics underneath and potential applications. Beginning with a general introduction to optical forces, related theories, and methods, we review the progresses achieved in optical pulling forces using different mechanisms and configurations. Similar pulling forces in other forms of waves, including acoustic, water, and quantum matter waves, are also integrated. More importantly, we also include the progresses in counterintuitive left-handed optical torque and lateral optical force as the extensions of the pulling force. As a new manipulation degree of freedom, optical pulling force and related effects have potential applications in remote mass transportation, optical rotating, and optical sorting. They may also stimulate the investigations of counterintuitive phenomena in other forms of waves.
Integrated Control of Coffee Bean Borer (Hypothenemus Hampei) on Sigararutang Coffee, Motung Village, Ajibata Sub-district, Toba Samosir District, Sumatera Utara

Integrated control Hypothenemus hampei in Saurdot farmer group, Motung Village, Ajibata District, Toba Samosir Regency, North Sumatra. The purpose of the activity was to increase the knowledge and skills of farmers about H.hampei and their management measures by improving cultivation techniques. The socialization of coffee borer pest management has been carried out by improving crop cultivation through spacing, crop pruning, chemical control and planting refugia plants. Partner problems are overcome by a number of technological solutions, which are carried out using technology transfer methods through education, training, demonstration plots, and mentoring. Socialization activities can increase farmers' knowledge about coffee berry borer (CBB) pests and its environmentally friendly control. The prototype of healthy coffee cultivation is very beneficial in increasing the knowledge and skills of Saurdot farmer group members in coffee cultivation. There is an increase in both knowledge and skills in the application of plant cultivation techniques. coffee specifically about the spacing of coffee seedlings increased by 15.38%, the distance of the shading trees around 50%, the selection of protective trees only about 50%, pruning coffee plants around 54%, pruning of shading trees around 25%, applying basic fertilizer and supplementary fertilizers around 50% in addition, there was also an increase in the group in the introduction of plant pests and control methods. There was an increase in the knowledge of partner groups about the type of pest of coffee plants by 11.11%, CBB pest by 51.11%, about CBB pest control by 50% and knowledge about the types of pesticides in controlling CBB by 50%
Anger, Affective Injustice, and Emotion Regulation

ABSTRACT:Victims of oppression are often called to let go of their anger in order to facilitate better discussion to bring about the end of their oppression. According to Amia Srinivasan (2018), this constitutes an affective injustice. In this paper, we use research on emotion regulation to shed light on the nature of affective injustice. By drawing on the literature on emotion regulation, we illustrate specifically what kind of work is put upon people who are experiencing affective injustice and why it is damaging. We begin by explaining affective injustice and how it can amount to a call for emotion regulation. Then we explain the various techniques that can be used to regulate emotions and explain how each might be harmful here. In the penultimate section of the paper, we explain how the upshot of this is that victims of affective injustice are left with a dilemma. Either they try to regulate their anger in a way that involves ignoring the fact of their oppression or they regulate it in a way that is likely to be harmful for them. Finally, we consider whether there are any good solutions to this dilemma, and how this issue opens up the possibility for further research into emotion regulation and moral philosophy.
Deep learning as a tool for improving efficiency of glial tumor diagnosis

espanolLa aplicacion de tecnicas basadas en Inteligencia Artificial como apoyo a la deteccion y diagnosis de cancer mediante imagen es una practica muy extendida hoy dia. Ademas, el reconocimiento por regiones de interes y otros algoritmos constituyen una rama de investigacion amplia que mejoran considerablemente la calidad de la clasificacion. En este trabajo, se propone como caso de estudio la identificacion del tumor glial con Imagenes por Resonancia Magnetica de pacientes sanos y enfermos mediante la combinacion de algoritmos de Deep Learning de deteccion de regiones que se basan en la extraccion de regiones de interes en la imagen utilizando una red Spatial Pyramid Pooling combinado con la modificacion de las imagenes de entrada con el algoritmo Fuzzy c-means. Obteniendo un acierto cercano al del personal sanitario. EnglishNowadays, existing Artificial Intelligent techniques are used as a support for cancer detection and diagnosis. Moreover, regional object interest and other related algorithms have become common place for improving the quality of the classification. Opening a wide field of interest and research. In this work, a deep neural network based on a new pooling strategy and image segmentation (Fuzzy c-means) [1] is proposed for glial tumor in Magnetic Resonance Imaging (MRI) images by using Region of Interests methods as Spatial Pyramid Pooling. The power of SPP-net is the possibility of working with feature maps from images with different sizes, and then subsampling these features to generate a fixed-length set and to implement finally a classification step.
YSL3-mediated copper distribution is required for fertility, grain yield, and size in Brachypodium

Addressing the looming global food security crisis requires the development of high yielding crops. In this regard, the deficiency for the micronutrient copper in agricultural soils decreases grain yield and significantly impacts a globally important crop, wheat. In cereals, grain yield is determined by inflorescence architecture, flower fertility, grain size and weight. Whether copper is involved in these processes and how it is delivered to the reproductive organs is not well understood. We show that copper deficiency alters not only the grain set but also flower development in both wheat and it’s recognized model, Brachypodium distachyon, We then show that a brachypodium yellow-stripe-like 3 (YSL3) transporter localizes to the phloem and mediates copper delivery to flag leaves, anthers and pistils. Failure to deliver copper to these structures in the ysl3 CRISPR/Cas9 mutant results in delayed flowering, altered inflorescence architecture, reduced floret fertility, grain number, size, and weight. These defects are rescued by copper supplementation and are complemented by the YSL3 cDNA. This new knowledge will help to devise sustainable approaches for improving grain yield in regions where soil quality is a major obstacle for crop production.
A Learning-based Iterative Method for Solving Vehicle Routing Problems

This paper is concerned with solving combinatorial optimization problems, in particular, the capacitated vehicle routing problems (CVRP). Classical Operations Research (OR) algorithms such as LKH3 (Helsgaun, 2017) are extremely inefficient (e.g., 13 hours on CVRP of only size 100) and difficult to scale to larger-size problems. Machine learning based approaches have recently shown to be promising, partly because of their efficiency (once trained, they can perform solving within minutes or even seconds). However, there is still a considerable gap between the quality of a machine learned solution and what OR methods can offer (e.g., on CVRP-100, the best result of learned solutions is between 16.10-16.80, significantly worse than LKH3's 15.65). In this paper, we present the first learning based approach for CVRP that is efficient in solving speed and at the same time outperforms OR methods. Starting with a random initial solution, our algorithm learns to iteratively refines the solution with an improvement operator, selected by a reinforcement learning based controller. The improvement operator is selected from a pool of powerful operators that are customized for routing problems. By combining the strengths of the two worlds, our approach achieves the new state-of-the-art results on CVRP, e.g., an average cost of 15.57 on CVRP-100.
A simple model of some possible long-run adverse effects of inflation targeting

ABSTRACT In Latin America, Brazil, Chile, Colombia, Mexico and Peru, have been using Inflation Targeting for at least two decades, and although there were significant improvements in social indicators, macroeconomic results have been mixed. In particular, the real exchange rate has been much more volatile and growth was significantly slower in Brazil and Mexico. In accordance with these facts, we sketch a simple New Consensus open economy model with hysteresis and we show that a policy that targets inflation disregarding the effects on the real exchange rate may have negative long-run effects.
Nonlocal Band-Weighted Iterative Spectral Mixture Model for Hyperspectral Imagery Denoising

Although efficient hyperspectral image (HSI) denoising relies on complete and accurate description and modeling the spatial–spectral signal in HSI, the current approaches do not fully account for key characteristics of HSI, i.e., the mixed spectra effect, the spatial nonstationarity effect, and noise variance heterogeneity effect. To address this issue, this article presents a linear spectral mixture model with nonlocal means constraint (LSMM-NLMC), with the following advantages. First, LSMM-NLMC can effectively learn the signal in mixed pixels in HSI by estimating clean endmembers and abundances for image restoration. Second, LSMM-NLMC can efficiently address nonstationary spatial correlation effect by imposing NLMC on the latent scene signal. Last, LSMM-NLMC provides accurate noise characterization by accounting for noise variance heterogeneity effect using a band-dependent noise model and a band-weighted Mahalanobis distance for similarity measurement. A novel optimization method based on the expectation–maximization (EM) algorithm and the purified means approach is used to efficiently solve the resulting maximum a posterior (MAP) problem. The experiments on both simulated and real HSI data sets demonstrate that the visual quality and denoising accuracy are significantly improved by the proposed LSMM-NLMC compared with previous methods.
Impact of school locality on teaching and learning: A qualitative inquiry

This research examined the impact of school location on teaching and learning through a case study of two urban, two rural and two remote Fijian secondary schools. A total of 48 semi-structured interviews were conducted: 16 from each category of urban, rural and remote. Each school was represented by three teachers, three heads of department and two administrators. The study established that rural and remote schools often face different challenges to their urban counterparts: geography, poverty and funding influence the quality of education. Leadership support and adequate resources are the key to breaking the overreliance on traditional methods of teaching and enhancing student classroom interest and participation. Finally, just as schools serve different communities, geographical location impacts on external links, cooperation and professional exchange and development. Understanding the impact of school locality on teaching and learning in Fiji should benefit other developing nations and the educational community at large.
Earnings Belief Risk and the Cross-Section of Stock Returns*

This paper examines whether the risk arising from stochastic fluctuations in the average belief of investors about future stock earnings is priced. We construct the earnings belief measure using analysts’ EPS forecasts and an EPS forecasting model, which primarily captures the subjective opinions of institutional investors and represents a random source of commonality. The average return on stocks with high exposure to earnings belief risk is 5.82%/year higher ⇤We thank Yakov Amihud, Tim Baldenius, Michael Brennan, Stephen Brown, Francois Degeorge, Jerome Detemple, Olivier Scaillet, Fabio Trojani, and Jeffrey Wurgler for helpful comments and discussions, as well as participants of the 2013 NFA Annual Conference and the finance seminar at Shanghai Jiao Tong University. The financial support of the Swiss National Science Foundation and the NCCR-Finrisk Project C1 “Credit Risk and Non-Standard Sources of Risk in Finance” is greatly acknowledged. All errors are ours. †Rajna Gibson Brandon is the Swiss Finance Institute (SFI) Chaired Professor of Finance at the Geneva Finance Research Institute, University of Geneva, Geneva, Switzerland. Email: rajna.gibson@unige.ch ‡Songtao Wang is Assistant Professor at the Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China. Email: wangsongtao@sjtu.edu. cn
Gender Prediction by Speech Analysis

Speech is one of the important methods of communication for humans. The speech signal itself contain linguistic information that can be used to identify the speaker information such as gender, emotions and many more. There are some problems that involve in detection gender of the speaker. In forensic analysis, the police need to detect criminal profile from any evidence such as voice from any calls and while in healthcare aspect, some vocal fold pathologies can be bias to a particular gender such as vocal fold cyst can be seen particularly in female patients and the patient will have problem with their voices. Three features are extract from the speech signal which are Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding (LPC) and Linear Prediction Coding Coefficient (LPCC). While for the classification, two classifier are used which are Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The recognition rate is higher for the combination of MFCC and LPCC compared to other features. SVM classifier had outperformed KNN classifier and obtained highest recognition rate of 97.45%. Lastly a graphical user interface system is develop that will record the voice of the speakers, pre-process the signal, extract MFCC and LPCC and classify it using SVM.
A scalable model for real‐time venue recommendations using MapReduce

The popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to mark the check‐ins when a venue is visited. The traditional venue recommendation systems have opted collaborative filtering to propose recommendations. However, collaborative filtering overlooked certain critical issues, including real‐time recommendations, cold start, and scalability, for venue recommendations. Moreover, real‐time physical factors such as distance from the venue are also not considered in traditional venue recommendation systems. Furthermore, parsing and processing of huge volume of unstructured data is the main challenge for conventional recommender systems, particularly when dealing with real‐time recommendations. For efficient scaling, significant computational and storage resources for recommendation systems are desired. This article proposes a Real‐Time Venue Recommendation (RTVR) model that utilizes cloud‐based MapReduce framework to process, compare, mine, and manage large data sets for generating recommendations. The results showed that the proposed model has improved accuracy for real‐time recommendations. The proposed RTVR is more scalable as it exploits a cloud‐based architecture. Moreover, the proposed techniques are verified using formal verification methods.
Effect of Fungi on the Destruction of Historical
Parchment and Paper Documents

Microorganisms (bacteria, archaea and fungi), together with lichens and insects, due to their biodeteriorative potential, damage items of cultural heritage. Fungi play an important role in archives and libraries where stored objects are made of paper or parchment. The main component of paper is cellulose, and of parchment it is inner collagen type I, with its typical fibre arrangement and external gelatin layers. Paper and parchment can be sources of energy, carbon and nitrogen for numerous microorganisms. Bacteria are the main contaminants of parchment due to their preferences for higher pH [1-6]. Filamentous fungi also play an important role in biodeterioration due to their preferences for lower temperature and higher humidity, often present in archives. Typical fungal deterioration is caused by the slowgrowing, often mitosporic, cellulolitic or proteolytic Pol. J. Environ. Stud. Vol. 29, No. 4 (2020), 2679-2695
Metabolic modelling of CAM cycling, CAM idling and the C3-CAM continuum

Abstract The evolution of Crassulacean acid metabolism (CAM) is thought to be along a C3-CAM continuum including multiple variations of CAM such as CAM cycling and CAM idling. Here, we applied large-scale constraint-based modelling to investigate the metabolism and energetics of plants operating in C3, CAM, CAM cycling and CAM idling. Our modelling results suggested that CAM cycling and CAM idling could be potential evolutionary intermediates in CAM evolution by establishing a starch-malate cycle. Our model analysis showed that by varying the day-time stomatal conductance, there exists a C3-CAM continuum with gradual metabolic changes, supporting the notion that evolution of CAM from C3 could occur solely through incremental changes in metabolic fluxes. Along the C3-CAM continuum, our model predicted changes in metabolic fluxes not only through the starch-malate cycle that is involved in CAM photosynthesis but also other metabolic processes including the mitochondrial electron transport chain and the tricarboxylate acid cycle at night. These predictions could guide engineering efforts in introducing CAM into C3 crops for improved water use efficiency. One sentence summary Metabolic modelling of CAM cycling, CAM idling and the C3-CAM continuum revealed the establishment of a starch-malate cycle in CAM evolution.