Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Irrigation on Summertime Temperatures in the Pacific Northwest

Irrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.
Nexus between Green Intellectual Capital and Organizational Learning Capability. Evidence from Malaysian Manufacturing Sector

The environmental degradation have been concerned by many previous scholars claimed that the high demand for sustainable practices particularly in manufacturing sector due to the large amount and wide distribution of energy consumption. It is an urgent task for organizations to implement the innovation to cope with this issue. Apart from that, most scholars have suggested various possible solutions. Regrettably, very few studies have examined the importance of intellectual capital related to green protection known as green intellectual capital to achieve better performance. Furthermore, as the manufacturing is the main sector that focus on the transformation of low-carbon economy required organizations to keep themselves learned and updated about their business challenges. In spite of that, the role of management that facilitate learning to each member which under the concept of organizational learning capability is very important to obtain superior performance. Data were gathered from 168 manufacturing SMEs in Malaysia, and analysed using Partial Least Square approach. Results show that green relational capital has positive relationship with organizational learning capability while green human capital and green structural did not. Finally, the implications, limitations and future studies are discussed.
Pantropical Response to Global Warming and the Emergence of a La Niña‐Like Mean State Trend

A recent study by Zhang et al. (2019, https://doi.org/10.1029/2019GL084088) proposes “an interbasin ocean thermostat mechanism” to support the hypothesis that enhanced Indian Ocean warming during the past century has reduced the Pacific warming response to increasing greenhouse gases. In this commentary, we review the proposed mechanism in a broader perspective and discuss a potential role of Atlantic Ocean in modulating the future Pacific warming response to emphasize the interactive response of the pantropical atmosphere‐ocean to increasing greenhouse gases.
Financial Cycles in Asset Markets and Regions

Abstract This paper provides a comprehensive analysis of financial cycles in asset markets and regions. Using a large sample of 38 advanced and emerging economies to enable a comparative assessment, the analysis conforms with the prevailing literature pertaining to the characterization of financial cycles in advanced economies, but finds that equity market cycles in emerging market economies (EMEs) in Asia, Latin America, and Eastern Europe may be a more useful gauge of the financial cycle compared to cycles in credit and property markets. Similar to more advanced economies, it is found that financial and business cycles in emerging economies are synchronized, albeit partially and with some cross-country heterogeneity. This underscores the importance for policy makers to be vigilant of interlinkages between real and financial sectors, pointing toward a need for carefully designed macroprudential policies. Finally, it is found that financial cycles in emerging markets remain vulnerable to global risk aversion in financial markets and spillovers from the US, thereby reinforcing the importance of continuing to strengthen domestic macroeconomic fundamentals, and develop further local financial sectors through targeted structural reforms.
Trendrapport vraag naar arbeid 2006

Dit OSA Trendrapport Vraag naar Arbeid 2006 peilt hoe het Nederlandse bedrijfsleven in de afgelopen jaren de bakens heeft gezet en verzet op een aantal terreinen zoals omvang en samenstelling van het personeelsbestand, arbeidsvoorwaarden, personeelsbeleid, scholingsactiviteiten, arbeidsomstandigheden, ziekteverzuim, Research & Development. Het geeft ook aan hoe men de eigen toekomst ziet en welke knelpunten men ontwaart. De bevindingen zijn ontleend aan de negende golf van het OSA-Arbeidsvraagpanel die gehouden is in 2005 en het voorjaar van 2006. Deze meting vond dus plaats aan de vooravond van het economisch herstel in Nederland zoals dat vanaf begin 2006 inzette.
Imbalanced Big Data Classification using Feature Selection Under-Sampling

Imbalanced learning is the classification problem where the number of observations of one class, far surpasses the number of observations of another class. Different sampling approaches are proposed for paired and Multi-Class imbalanced classification. Paired Imbalanced classification encompasses two classes: one of them is majority, while the other one is a minority class. Multi-Class imbalanced classification contains more than two classes for classification. Under-sampling technique is the better sampling technique among conventional approaches. However, existing approaches may not work in the Big Data environment, as considering all the features might compromise the performance of the system. In this work, a novel method is presented which takes into account only the essential features, as well as, deals with massive data as in Big Data environment. In the proposed system, Feature Selection Under-Sampling technique is used for resampling the data. Feature selection is the vital step because it not only decreases the dimensionality of data but also helps classifier to run faster, and accuracy can also be improved. Over that, SVM learning classifier is adopted to construct the model and test the data. The proposed system is implemented using MapReduce framework by integrating statistical analytical tool R.
Tobacco Dependence in Medical Education in Countries of the Middle East and North Africa

ii Dedication. iii Acknowledgements. iv Vitae. v List of Tables. xiii List of Figures. xv Chapter 1: Introduction. 1 References. 6 Chapter 2: Background. 8 Origins of Waterpipe Smoking. 8 Prevalence and Popularity of Waterpipe Smoking. 10 The Waterpipe Epidemic in the Middle East . 10 Medical Schools Curriculum in Tobacco Cessation. 12 Reports on Tobacco Cessation Training in Medical Schools‘ Curricula around the World. 13 Reports on Smoking Prevalence among Medical Students in the World. 13
Fiber optic biosensor based on polydimethylsiloxane (PDMS) and bioactive lipids

In this work, we demonstrate an optical fiber biosensor based on Fabry-Perot (F-P) interferometers using polydimethylsiloxane (PDMS) as a support for bioactive lipids. The sensors are fabricated by dip-coating producing PDMS end-capped devices. For biosensing, the PDMS cap was functionalized with a previously characterized bioactive lipid antigen cocktail from Mycobacterium fortuitum, used as a surrogate source of antigens for tuberculosis diagnosis. The performance of the biosensor was evaluated upon monitoring the changes in the interference pattern associated to the interactions between the active lipids and the antibody-containing sera. Our results show that the proposed biosensor offers new and attractive possibilities for developing novel lipidomic analytical tools.
Model of rainfall erosion index for predicting the potential erosion rate by using a rainfall simulator

The intent of this research is to investigate the rainfall erosion index that has the highest correlation to the surface runoff volume and the amount of soil erosion. In addition, the aim of this research is to determine the effective rainfall erosion index to predict soil erosion based on observations. This research was conducted in a hydrology laboratory by using a rainfall simulator. The three methods that were used for analysing rainfall erosion were the formulas of EI30 by Bols, Utomo, and Lenvain. The result is expected to become a consideration in selecting the model of rainfall erosion index due to the rainfall factor as the input of soil erosion prediction. This research provides a temporary hypothesis for obtaining the value of the rain erosivity index with a different value from previous studies with EI30, which leads to the latest model for estimating and predicting from the Bols equation. Based on this research, the rain erosivity index was obtained with a predicting value and final equation of EI30 = 5.128 x (Rain)1.26 x (Days)0.47 x (Maxp)0.53 x (High)0.61 with the explained value from research analysis being EI30 = 5.128 x (50.002)1.35 x (23)0.47 x (622)0.53 x (12)0.61. Analysis was then performed with trial and error, and the conclusion of this research is a high rain erosivity index.
New development: Effective public sector performance—the reform cycle continues

ABSTRACT This article examines the demise of the 1980s ‘managing for results’ reform in Australia against the likely achievements of implementing the Public Governance, Performance and Accountability (PGPA) Act 2013—current legislation relating to public management and accountability. The PGPA Act mandates that non-financial performance be demonstrated, arguably re-introducing programme evaluation as an important element of accountability. The Australian Public Service (APS) is a large and geographically-dispersed organization, where maintaining reform momentum over the time required for change to be effective can be difficult. The author discusses issues in implementing effective reform, such as extended time, administrative amnesia, geographical factors and an absence of embedding in the management culture and associated processes.
Technology Acceptance Models and Theories in Library and Information Science Research

There is a growing interest of studying technology implementation, acceptance, and adoption as there are a lot of new innovations regarding potential technologies that can be implemented in information institutions. This study tries to explore the application of theories and models of acceptance models in library and information science research. This study employs a systematic literature review to discuss the historical development of theories and models of technological acceptance and their potential use in the library and information science research. Models and theories of acceptance models have been continuously developed to accommodate reasons explaining users would accept and adopt new systems and technologies. Through this study, we understand that the technology acceptance models and theories are useful to understand how users perceive the new technology and information systems. Through acceptance models and theories, we can formulate reasons the tendency towards either acceptance or rejection of new systems and technologies. Acceptance models and theories are highly relevant in information science to understand issues of the acceptance of the new technologies and systems in information institutions.
Effect of Toxoplasmosis on Lipid Profile and Thyroid Hormones in Aborted Women

This study addressed the effect of toxoplasmosis on levels of thyroid hormones (Triiodothyronine [T3], Thyroxine [T4] and Thyroid Stimulating Hormone [TSH], and lipid profile such as (Cholesterol, Triglycerides, High-Density Lipoprotein [HDL], Low-Density Lipoprotein [LDL] and very Low -Density Lipoprotein [vLDL] in 100 samples which included 80 aborted women, who had positive test for toxoplasmosis and 20 samples act as control group during the period from August 2015 to January 2016 which were taken from Al-Yarmouk Teaching Hospital in Baghdad City. Infected and control women had similar age that ranged between (17-47) years old. Serum samples were collected and tested by ELISA technique, lipid profile tests were performed by using commercial kits, in addition to measured thyroid hormones by using the Mini Vidus apparatus. The results revealed chronic infection [IgG] recorded the highest percentage (68.75%) and the acute infection [IgM] showed the lowest percentage (31.25%). There were significant decrease in the total cholesterol, Triglycerides, LDL and vLDL (110.96±1.55, 30.93±0.56, 41.38 ±1.55 & 6.40 ±0.18 mg/dl respectively) compared with control group (200.05 ±5.83, 56.05 ±1.98, 142.85±2.05 & 36.90±1.24 mg/dl respectively). On the other hand, a significant increase in HDL, T3 and TSH was recorded (64.51±1.27 mg/dl, 12.74 ±0.53 and 11.64±0.58 mlU/L respectively) compared with control group (53.45±1.57 mg/dl, 1.49 ±0.16 & 2.29 ±0.25 mlU/L respectively). A significant decline in T4 was estimated (2.81±0.12 mlU/L) compared with control group (8.35±0.80 mlU/L). So, we concluded that the Toxoplasma gondii had important role in changes of lipid profile levels & thyroid hormones in infected women.
Multitemporal Land Use and Land Cover Classification from Time-Series Landsat Datasets Using Harmonic Analysis with a Minimum Spectral Distance Algorithm

An understanding of historical and present land use and land cover (LULC) information and its changes, such as urbanization and urban growth, is critical for city planners, land managers and resource managers in any rapidly changing landscape. To deal with this situation, the development of a new supervised classification method for multitemporal LULC mapping with long-term reliable information is necessary. The ultimate goal of this study was to develop a new classification method using harmonic analysis with a minimum spectral distance algorithm for multitemporal LULC mapping. Here, the Jiangning District of Nanjing City, Jiangsu Province, China was chosen as the study area. The research methodology consisted of two main components: (1) Landsat data selection and time-series spectral reflectance reconstruction and (2) multitemporal LULC classification using HA with a minimum spectral distance algorithm. The results revealed that the overall accuracy and Kappa hat coefficients of the four LULC maps in 2000, 2006, 2011, and 2017 were 97.03%, 90.25%, 91.19%, 86.32% and 95.35%, 84.48%, 86.74%, 80.24%, respectively. Further, the average producer accuracy and user accuracy of the urban and built-up land, agricultural land, forest land, and water bodies from the four LULC maps were 92.30%, 90.98%, 94.80%, 85.65% and 90.28%, 93.17%, 84.40%, 99.50%, respectively. Consequently, it can be concluded that the newly developed supervised classification method using harmonic analysis with a minimum spectral distance algorithm can efficiently classify multitemporal LULC maps.
Application of Deep Convolutional Neural Network in Remote Protection of Transmission Lines

In order to alleviate the pressure of line operation and maintenance, it is necessary to use advanced image recognition methods to identify hidden line hazards. In this paper, we extract features from hidden targets and perform target recognition using deep convolutional neural network. Firstly, we collect a large number of photos with hidden dangers for transmission line. Secondly, we classify those samples according to the detection targets. Thirdly, the deep convolutional neural network model is trained and tuned by using the sample images of various detection objects. When deployed on the server, identification of hidden dangers such as cranes, tower cranes, excavators, and mountain fires reaches acceptable accuracy.
Integration of Cloud Technologies in Teaching Foreign Languages in Higher Education Institutions

This study was aimed at summarizing theoretical knowledge of students studying Economics, enhancing the educational process and training skills of independent long-term professional upgrade of students (sustainability training) through the use of an English-taught training program implemented through the use of MS Office 365 cloud services. The variational traits studied were identified: educational motivation, self-confidence, cognitive and creative skills. The study used methods that are recommended for quantitative research, namely: Rasch’s models for measuring academic motivation, methods for determining the level of academic confidence; methods of diagnosis of cognitive reflection and decision making, and a questionnaire to determine the level of creativity of thinking. The results of the diagnostic tests on academic motivation were processed using the Rasch Unidimensional Measurement Model (RUMM-2010) software, and the results of other tests were processed using SPSS 10.0. 5. , Statistica v. 5.5 A. , MathCAD 2000 Pro. It has been found that the problem of integrating cloud technologies into the teaching foreign languages has been of interest of scholars, as the former are gradually becoming a full-fledged educational tool, allowing all institutions to create their own online spaces. In terms of language teaching methods, the used in this study teaching approach proved to create the learning environment helping to deal with the students’ communication and interaction barriers, reluctance to being engaged resulted in the enhanced students’ learning activity, their motivation for learning. This approach addressed the students’ needs in developed logical (critical) thinking and creative tactic to problem-solving, student memory (cognitive component). The integration of cloud (education) services in general proved to increase the efficiency of the learning process. https://doi.org/10.26803/ijlter.19. 2.4
Early menopause: Implementation research using the experiences and perspectives of women and health professionals to translate evidence into practice

This thesis explores the information needs of women and healthcare providers regarding early menopause (menopause <45 years) and assesses the current evidence and guidelines on early menopause management. This research identifies women’s information and communication needs and health professionals’ knowledge gaps regarding early menopause and treatment options. Findings of this study show that women are supportive of a comprehensive co-designed eHealth website including a wide range of topics related to early menopause. This research program develops online early menopause resources to enhance knowledge and women-clinician communication, facilitate health care and improve outcomes.
Texture-based 3D Face Recognition using Deep Neural Networks for Unconstrained Human-machine Interaction

3D assisted 2D face recognition involves the process of reconstructing 3D faces from 2D images and solving the problem of face recognition in 3D. To facilitate the use of deep neural networks, a 3D face, normally represented as a 3D mesh of vertices and its corresponding surface texture, is remapped to image-like square isomaps by a conformal mapping. Based on previous work, we assume that face recognition benefits more from texture. In this work, we focus on the surface texture and its discriminatory information content for recognition purposes. Our approach is to prepare a 3D mesh, the corresponding surface texture and the original 2D image as triple input for the recognition network, to show that 3D data is useful for face recognition. Texture enhancement methods to control the texture fusion process are introduced and we adapt data augmentation methods. Our results show that texture-map-based face recognition can not only compete with state-of-the-art systems under the same preconditions but also outperforms standard 2D methods from recent years.
Postfault Operation of Cascaded H-Bridge Converter for Equal Power Handling by the Healthy Cells

This article propose a new strategy for the operation of a cascaded H-bridge converter under the postfault condition when the faulty cells are bypassed. During the postfault condition, the phase voltages of the converter become unbalanced. A zero sequence voltage is, therefore, injected into the converter phase voltages to make them balanced. However, this causes unequal power handling by each healthy power cell causing an uneven rise of temperature in the cells. In the proposed strategy, the zero sequence voltage not only ensures maximum line voltage availability from the converter but additionally the power handled by each cell remains equal. The expression for zero sequence voltage is derived when the converter supplies certain active and reactive power to the load. A closed-loop control along with a clamped pulsewidth modulation strategy is used to maximize the line voltage and ensuring equal power handing by each cell during the postfault condition. Simulation and experimental results are included to validate the proposed strategy.
Internalization of Character Values in Pesantren School: Efforts of Quality Enhancement

Educational institutions have sought a solution to improve quality, one of them is by making efforts to cultivate moral-based values in order to improve the effectiveness of education. The purpose of this study was to determine the internalization of character values in Pesantren school, and the implementation of character values in an effort of quality enhancement. Descriptive qualitative research methods will describe the internalization of character values and quality of education. Data collected through observations are processed through recording, typing, or rearranging. Formal schools in Islamic boarding schools have carried out character education through their curriculum and activities. In it there are efforts that are designed and implemented systematically to help students understand the values of human behavior. Moral knowing, moral feeling, moral action, habit and value are compositions of character values that are internalized to develop the quality of education. Keywords—character value; pesantren school; quality of