Miracle-dz/UniFAR-FTUs huggingface dataset:
The Application of the Minimum Forest Graph in Centroid Updating Stage of K-Means Algorithm

K-Means is a well known algorithms of clusteing. It generates some groups based on degree of similarity. Simplicity of implementation, ease of interpretation, adaptability to sparse data, linear complexity, speed of convergence, and versatile in almost every aspect are noble characteristics of this algorithm. However, this algorithm is very sensitive on defining initial centroids process. Giving a bad initial centroid always produces a bad quality output. Due to this weakness, it is recommended to make some runs with different initial centroids and select the initial centroid that produces cluster with minimum error. However, this procedure is hard to achieve a satisfying result. This paper introduces a new approach to minimize the initial centroid problem of K-Means algorithm. This approach focus on centroid updating stage in K-Means algorithm by applying minimum forest graph to produce better new centroids. Based on gain information and Dunn index values, this approach provided a better result than Forgy method when this approach tested on both well distributed and noisy dataset. Moreover, from the experiments with two dimentional data, the proposed approach produced consisten members of each cluster in every run, where it could not be found in Forgy method.
Similar event-related potentials to music and language : A replication of Patel , Gibson , Ratner , Besson , & Holcomb ( 1998 )

https://doi.org/10.15626/MP.2018. 1481 Article type: Original Article Published under the CC-BY4.0 license Open data: Yes Open materials: Yes Open and reproducible analysis: Yes Open reviews and editorial process: Yes Preregistration: Yes Edited by: Henrik Danielsson Reviewed by: Stefan Kölsch and Carine Signoret Analysis reproduced by: Martina Sladekova All supplementary files can be accessed at the OSF project page: https://doi.org/10.17605/OSF.IO/EPD42
Plant Curl Disease Detection And Classification Using Active Contour And Fourier Descriptor

: Automatic plant leaf curl detection is an important step towards the development of Computer-aided crop damage analysis systems. It helps in analyzing the health condition of the plants through leaf images. Image processing techniques are recently being used to analyze the condition of the leaf and identify the disease that inflicted the crop. Leaf curl disease can be identified by analyzing the edges of the leaf. This paper presents a procedure to identify the curl disease occurring in plant leaves using active contour, Fourier feature descriptor, and deep learning. Active contour is used to identify the shape of the leaf. The edge contour of the leaf is then given to the Fourier feature descriptor. The feature extracted using the Fourier descriptor is invariant to the angle and size of the leaf. The same feature vector is produced in any given angle and size of the leaf in the image. The features are trained using 1D CNN. The model can then be used to classify new images and automatically identify the leaf have curl disease or not. The experimental results prove that the proposed algorithm produces good results in identifying the leaf curl disease.
COVID-19: Naming Process and the Mechanism Behind Stigmatisation

This paper considers the development of naming process of COVID-19 pandemic and its interaction with the mechanism of stigmatisation formed toward certain groups. The majority of data collected were from online newspapers, mass social medias and Chinese we-medias. I have applied discourse analysis throughout the research to examine the intention, forming process and consequence of stigmatisation. Naming of the coronavirus is closely associated with the stigmatisation at each phase. Stigmatisation exists at any time and could be especially highlighted by specific opinion formers. Furthermore, the stigmatised targets could also change and switch quickly along with the development of the pandemic. One driving force of such changes in the case of COVID-19 is thought to be the various naming of the virus. Therefore, it can be concluded that an appropriate naming is a fair tool to modify stigmatisation.
Changes in carbon stocks in Eucalyptus globulus Labill. plantations induced by different water and nutrient availability

Changes in the carbon stocks under different soil water and nutrient conditions were studied in Eucalyptus globulus Labill. stands in a field experiment, at Óbidos (central Portugal). The treatments were irrigation plus a complete fertiliser solution to simulate ‘near optimal’ nutrition (IF), irrigation only (I), and fertilisers added to rain-fed plots (F). The control (C) received neither water nor fertilisers (except a small amount at planting). The production of biomass (aboveground), the litterfall and the soil chemical composition were evaluated regularly during the experiment. Root biomass was estimated at the end of the experiment. Carbon in biomass, litterfall and soil, increased significantly when water and/or nutrients were supplied, in comparison to the control. The amount of carbon accumulated in the system, 6 years after planting, was 8.22, 10.22, 11.23 and 13.76 kg C m 2 in the control, F, I and IF treatments, respectively. The increase of carbon in the system during the same period was 5.86, 7.86, 8.87 and 11.40 kg C m 2 in the control, F, I and IF treatments, respectively. This rise in carbon resulted from the accumulation of long-lived woody biomass, which represented between 77.7 (in IF) and 82.9% (in the control) of the total rise in carbon. Although water was the main limiting factor for biomass accumulation, the allocation of carbon to the soil was mainly related to nutrient supply, irrespective of water availability. The amount of carbon stored belowground, i.e. soil and forest floor, plus stumps and roots, reached 4.2, 4.7, 4.8 and 6.3 kg C m 2 in the control, F, I and IF treatments, respectively. The increase in C in the mineral soil regarding the initial state was, in the same order as above, 0.21, 0.75, 0.58 and 1.21 kg C m . These values were 3.6, 9.6, 6.6 and 10.6% of the C accumulated in the whole system, during the experimental period. # 2002 Elsevier Science B.V. All rights reserved.
NUTRITIONAL DEFICIENCIES AND HYPOTHYROIDISM

Introduction: Several micronutrients are essential for thyroid hormones metabolism, as iodine, selenium, zinc, iron, vitamin A, magnesium and vitamin D, among others. These elements deficiency in body compromises the function of thyroid gland, which can lead to thyroid disorders, with hypothyroidism being the most frequent. Objective: The aim of this article is to provide a literature review describing how some nutrients are involved in synthesis, secretion and thyroid hormones metabolism, and the influence of their deficiencies on the pathophysiology of hypothyroidism. Methodology: The literature review was performed using the descriptors Thyroid, Hypothyroidism, Micronutrients, Thyroid Dysfunction and Levothyroxine, associated with 48 sources. Discussion: Thyroid is dependent on iodine to synthesize its hormones, and its deficiency leads to thyroid changes. Selenium is a cofactor of deiodinases, which convert T4 to T3, while zinc increases its activity and contributes to hormonal binding, and together they act in the immune, antioxidant and anti-inflammatory defense of the gland. Deiodinases and thyroid peroxidase are iron dependent, and their deficiency impairs hormones synthesis and metabolism. Vitamin A has its role in hormonal production, acting on the pituitary, thyroid and also on the periphery. Magnesium, despite little evidence of influence on the thyroid, when in severe deficiency generates inflammation and oxidative stress, which damage the gland. Vitamin D acts in the immune system, and its deficiency is associated with Hashimoto’s thyroiditis. Final considerations: According to the studies, it was observed that nutritional deficiencies can be the main cause of thyroid dysfunction, mainly hypothyroidism.
A CROSS-SECTIONAL RESEARCH ON THE ASSOCIATION OF DIABETES AND DIABETIC PERIODONTITIS WITH INCREASED LEVELS OF CRP (C-REACTIVE PROTEIN)

: Objective: Our research aimed to assess the level of plasma “ C ” reactive protein in the population of Pakistan and its association with diabetes and periodontitis. Subjects & Methods: Our cross-sectional study was carried out at Mayo Hospital, Lahore (Medicine Department) in the timeframe of February 2016 to April 2017 on a total of eighty patients enrolled in the research study. Four groups were made out of the total research population which were named as Group – I, II, III and IV. Group I consisted of normal healthy participants, Group – II had periodontitis cases, Group – III had diabetic patients and Group – IV had both diabetic and periodontitis patients. Every group was given twenty patients having about fifteen teeth and the age was in the age bracket of (30 – 60) years. We did not include all pregnant women, rheumatoid arthritis, chronic cardiac disease, chronic renal failure, smokers, thyroid disease, acute infection and chronic infection cases in this research study. We also measured the periodontal disease status with the help of CPIN (Community Periodontal Index of Treatment Needs). Quantitative determination was made through the Immune Assay Test Kit which is used for the measurement of the concentration of C-Reactive protein in the serum samples of a human. Samples, reference and controls were also calculated for the values of mean absorbance. CRP outcomes were taken mu multiplying the obtained values with dilution factor for each patient ’ s sample and outcomes were shown in (mg/l). Statistical analysis was made through SPSS software. Results : Research sample included a total of eighty patients, further sub-division of the patients was also carried out and patients were divided into four subgroups. These groups included healthy controls, diabetics, periodontitis and a mixture of diabetic and periodontitis cases. Male to the female distribution shown a small dominance of females over males as males were 46% and females were 65% in the total research sample. The range of age started to form 30 years and culminated on 45 years; about thirty-five percent of the total research sample was in the age bracket of 45 – 60 years with a mean age factor of (48.80 ± 6.96) years. Level of serum in diabetic periodontitis and diabetic patients was more than the control group. Conclusion: If diabetes and periodontitis are present in the patients than CRP (C Reactive Protein) levels are increased and further increase as both have their presence in the human body.
H∞ STATE-FEEDBACK CONTROL FOR DISCRETE-TIME MARKOV JUMP LUR’E SYSTEMS

We propose LMI conditions for H∞ analysis and state-feedback control of discrete-time Markov Jump Lur’e systems. We will also approach two possible situations, either mode-dependent and modeindependent state-feedback control. For the first approach, we consider that the controller has access to the Markov mode θ(k), on the other hand, for the second approach, we consider that the Markov parameter θ(k) cannot be read by the controller. A numerical example illustrates the obtained results. Keywords— Markov jump systems, circle criterion, H∞ control, linear matrix inequalities.
Rethinking Software Runtimes for Disaggregated Memory Extended

Disaggregated memory addresses resource provisioning inefficiencies in current datacenters by improving memory utilization and decreasing the total memory over-provisioning necessary to avoid out-of-memory errors or swapping [3]. In addition, disaggregated memory enables independent scaling of memory and compute, and it disentangles hardware failures and replacements from the monolithic server. Finegrain microsecond-latency networking technologies, such as Remote Direct Memory Access (RDMA) [8, 14, 15] and GenZ [6] are key enablers for hardware disaggregated memory and make the technology feasible in the near future. Unfortunately, enabling applications to efficiently adopt disaggregated memory is not straightforward. Many software systems [10, 4, 2, 7, 12] have been proposed to enable applications to transparently, without code changes, use remote memory – the memory of another host in the rack or memory that has been physically disaggregated from the compute. These systems use various kernel subsystems [7, 2, 12] or redesign the kernel altogether [11]. Fundamentally, they all rely on the core virtual memory mechanism for three essential functions: 1) fetching remote data by detecting remote accesses using page faults, and caching the remote pages in a local DRAM cache; 2) dirty data tracking the cached data by write-protecting pages and causing page faults on the first write of each page; and 3) evicting cached pages from the local DRAM cache, which requires marking the pages as not present and flushing the translation look-aside buffers (TLBs). Virtual memory provides application transparency, but results in high overhead and causes a significant drop in application performance, even when the amount of remote data accessed is small. Page faults latencies exceed network latencies, creating a bottleneck in the system software stack. Moreover, virtual memory requires moving and tracking data at page-granularity, with a page size of 4KB or higher. In contrast, throughout their lifetimes, applications often access only a small part of each page, causing large amplification and poor network utilization, by re-writing data that is already in remote memory. For example, we typically see applications modifying only 1-8 cache-lines in a 4KB page, but the entire page is marked dirty and transferred over the network. Overall, there is a mismatch between applications requirements for remote memory—low latency, fine-grain access, transparency—and the mechanism used to implement these requirements because virtual memory has not been designed to provide low-latency or fine-grain access. We need a different, more efficient mechanism to realize practical memory disaggregation.
Optimizing Nitrogen Fertilizer–Use Recommendations for Winter Wheat in a Mediterranean‐Type Environment using Tissue Nitrate Testing

Abstract Although nitrogen (N) fertilizer use is essential for economic yields of modern field crops, whether rainfed or irrigated, the underlying concern is to maximize nutrient‐use efficiency and avoid excessive N use that results in deterioration of water quality from the environmental standpoint as well in the interest as human and animal health. Soil testing for N forms and total plant N analysis can contribute to the efficient use of N, but both strategies have limitations. Accordingly, tissue testing of the growing crop has been promoted as an alternative approach. Although such tests have been widely used in the West to monitor the N status of crops, they are rarely used in developing countries. A 2‐year field trial examined the potential of tissue testing of rainfed wheat in a typical Mediterranean environment in the Cukurova region of southern Turkey, because observations from wells in the region had suggested increasing and excessive nitrate levels, most probably from fertilizer N use. The trial, in essence, compared different levels of N fertilizer application based on farmers' traditional application in addition to considering soil mineral N with tissue N based on a color index to refine N application recommendations. The measurements were taken in conjunction with observations on wheat yields. The study showed that using the combined approach of soil and tissue nitrate resulted in lowering the N fertilizer recommendations without any lowering of crop yields, clear evidence that farmers had been using excessive amounts of fertilizer N in the absence of limited or no official guidelines. Thus, such tests should be promoted to replace traditional fertilizer application practices as a simple and easily used tool to improve N‐use efficiency for the farmer, in addition to reducing negative environmental impacts of such use. The quick tests can help promote the awareness of the potential negative effects of overuse of N fertilizer.
17IND03 LaVA

This project targets improved, accurate, traceable measuring systems for operation as Large Volume Metrology (LVM) tools and integration of these tools into a factory coordinate metrology network. The network and tools will be suitable for operation in typical factory environments or for permanent inclusion inside manufacturing systems such as large machine tools, industrial robots, etc., in accordance with ISO Geometrical Product Specification (GPS) standards. The new tools and technologies will offer better accuracy than existing systems, enhanced uncertainty calculation and budgeting, improved compensation methods for air refractive index, and the ability to interface with production and assembly process control, resulting in traceability, efficiency and cost improvements in industries & science facilities relying on LVM.
BRAND IMAGE IS NOT POWERFUL BUT STILL DEMANDED BY MANY CONSUMERS

This paper discusses the results of a study on the relationship between customer satisfaction, brand image, and trust on consumer loyalty. Respondents from this study were 100 Go-Jerk customers (the largest new mode of application-based transportation at smart phones) in Yogyakarta, Indonesia. Recreational analysis revealed that consumer satisfaction, brand image and trust significantly affected customer loyalty. But among the three independent variables, the brand image contributes the least influence to customer loyalty. Because the brand image has a very important issue in marketing construct, the discussion in this paper is more emphasized on the role and how the company should build and manage its brand image so as to win the competition.
Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volume of paste and containing cement replacement materials such as slag, natural pozzolana and silica fume. Cement replacement materials provide a wide variety of benefits such as lower cost, reduced consumption of natural resources, reduced carbon dioxide emissions and improved fresh and hardened properties. SCC is used in many applications such as sections with congested reinforcement and high rise shear walls and there is a need for the prediction of the performance of SCC used. Artificial Neural networks (ANN) are widely used in civil engineering for the prediction of the performance of some engineering materials such as compressive strength and durability. However, currently, studies on SCC containing silica fume are very rare. In this paper, an artificial neural networks (ANN) model is developed to predict the compressive strength of SCC with silica fume using the Levenberg-Marquardt back propagation algorithm based on a database from 366 experimental studies. The model developed was correlated with a nonlinear relationship between the constituents (input) and the compressive strength of SCC (output). To evaluate the predictive ability and generalize the developed model, other researchers’ experimental results were compared with the model prediction and good agreements are found. A parametric study was conducted to study the sensitivity of the ANN proposed model to some parameters such as water/binder ratio and superplasticizer content. The model developed in this study can potentially be used for SCC compressive strength prediction with very acceptable results and a high correlation coefficient R2=0.93. The developed model is practical, easy to use and user friendly. Doi: 10.28991/cej-2021-03091642 Full Text: PDF
POLYCYSTIC IN OVARIAN SYNDROME AND ITS VARIOUS TREATMENT STRATEGIES

: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder, leading to several health complications, including menstrual dysfunction, infertility, hirsutism, acne, obesity, and metabolic syndrome. Polycystic ovary syndrome (PCOS) is a complex condition characterized by elevated androgen levels, menstrual irregularities, and/or small cysts on one or both ovaries. PCOS affects as many as 10% of reproductive-age women when using the NIH criteria for diagnosis, and up to 18% of reproductive-age women are diagnosed with PCOS as per the Rotterdam criteria. The exact pathophysiology of PCOS is complex and remains largely unclear. Genetic and environmental contributors to hormonal disturbances combine with other factors, including obesity, ovarian dysfunction and hypothalamic pituitary abnormalities to contribute to the etiology of PCOS. PCOS is a highly prevalent heterogeneous syndrome of clinical and/or biochemical androgen excess, ovulatory dysfunction and polycystic ovaries (PCO). Despite it being one of the most common reproductive health problems of women, its effective treatment remains a significant challenge to medical profession. Further research is needed to find the exact etiology, methods of prevention and proper management of this endocrine disorder.
The role of Entrepreneurship Education and Economic Development

Entrepreneurship is a key driver of our economy. Wealth and a high majority of jobs are created by small businesses started by entrepreneurially minded individuals, many of whom go on to create big businesses. Entrepreneurship education should be regarded as a critical component of our educational curriculum because of the obvious need to generate alternative sources of employment. Entrepreneurship if given the requisite encouragement and boost can solve our unemployment issue. A summary of the questionnaire shows that 69.3% are of the opinion that introduction of entrepreneurship education is a welcome development capable of boosting the economy. Of note is that students with entrepreneurial ability should be identified early and encouraged. Keywords—Entrepreneurship education; Entrpreneur; Economny; Questionnaire
The Relationship between Demographic Characteristic of Age and Instructional Leadership Style-A Kenyan Perspective

Education is the vital instrument for economic and social mobility at the personal level, an instrument for transforming a society and a veritable means of effecting development at the national level. Consequently, leadership exhibited at any institution of learning is widely deemed to be an important aspect. As such, there are several reasons in literature suggesting that Instructional leadership (IL) has a strong influence on the success of a school. However, from literature, studies on the relationship between instructional leadership behavior and demographic characteristic of age are very few. As such, the essence of this study was to fill that gap by establishing whether there is significant relationship between instructional leadership behavior and age of head-teachers in primary schools in Kiambu County, Kenya. The study adopted a quantitative method, used a stratified random sampling technique and utilized Instructional Leadership Inventory (ILI) instrument to survey 198 head-teachers of public and private primary schools in Kiambu County. Descriptive and inferential statistics were used to determine the relationship between IL behavior and age of the head teacher. On the overall, the study revealed that there was no significant relationship between IL behavior and demographic characteristic of age as measured in the dimensions of: Managing curriculum, supervising teaching, monitoring student progress, defining mission and promoting instructional development. The study is in agreement with various studies that demonstrate that age has no strong relationship with leadership