Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic Material Management Practice on Supply Chain Performance of Sugar Processing Firms in Kenya

The objective this study was to assess the influence of electronic material management practice on supply chain performance. The study intended to how electronic material management can support or impair the supply chain function in the process of sourcing, receiving, inventory management and order distribution to customers’ satisfaction The study applied a mixed research design with a survey of 12 sugar processing firms with a population of 7,584 employees and a sample of 379 respondents drawn from management staff. Data was gathered by a questionnaire, interviews and observation. Pearson’s correlation was applied to determine the relationship between electronic material management practice and Supply chain performance and regression analysis to test hypothesis. The results of model reveals that electronic material management practice has a positive influence on supply chain performance with a beta value of r=0.551, p= 0.000. The study recommends that sugar processing firms should provide their suppliers access credentials to company electronic procurement portal to increase buyer and supplier access to information to enhance electronic material management.
Development of YAC (Yangbajing Air shower core array)to be used for next phase of Tibet experiment

Y.Katayose , M.Shibata , K.Kimura , L.K. Ding , N.Hotta , H.B. Hu , J.Huang , K.Kawata , M.Ohnishi , H.Onuma , T.Ouchi , T.Y. Saito , T.Saito , A.Shiomi , H.Sugimoto , M.Takita , Y.H. Tan , H.Tsuchiya and T.Yuda (a) Department of Physics, Yokohama National University, Yokohama 240-8501, Japan (b) Institute of High Energy Physics, Chinese Academy of Sciences , Beijing 100049, China (c)Faculty of Education, Utsunomiya University, Utsunomiya 321-8505, Japan (d)Institute for Cosmic Ray Research, University of Tokyo, Kashiwa 277-8502, Japan (e)Department of Physics, Saitama University, Saitama 338-8570, Japan (f)Faculty of Engineering, Kanagawa University, Yokohama 221-8686, Japan (g)Tokyo Metropolitan College of Aeronautical Engineering, Tokyo 116-0003, Japan (h)Shonan Institute of Technology,Fujisawa 251-8511, Japan (i)RIKEN, Wako 351-0198, Japan Presenter: Y.Katayose(katayoes@ynu.ac.jp)jap-katayose-Y-abs1-he15-poster
Analyzing the Capacity of Distributed Vector Representations to Encode Spatial Information

Vector Symbolic Architectures belong to a family of related cognitive modeling approaches that encode symbols and structures in high-dimensional vectors. Similar to human subjects, whose capacity to process and store information or concepts in short-term memory is subject to numerical restrictions, the capacity of information that can be encoded in such vector representations is limited and one way of modeling the numerical restrictions to cognition. In this paper, we analyze these limits regarding information capacity of distributed representations. We focus our analysis on simple superposition and more complex, structured representations involving convolutive powers to encode spatial information. In two experiments, we find upper bounds for the number of concepts that can effectively be stored in a single vector only depending on the dimensionality of the underlying vector space.
Mixed formulations (formulations using mixed elements) in electromagnetics via the finite element method: The case of modeling tokamak plasmas in a computational electromagnetic environment

This paper tends to extend the scope of this conference towards the direction of continuum physics. Ordinary matter consists of material points. On the one hand, each material point is so small, that it has no further structure. On the other hand, each material point is made up of so many particles, that the laws of thermodynamics for infinitely large systems apply. It makes sense to speak of content, flow and production of certain quantities like mass, charge, momentum, energy and entropy. The corresponding balance equations are valid irrespective of the special material under consideration. They include precise versions of the first and second law of thermodynamics. Additional constitutive equations define entire branches of physics like fluid dynamics, electromechanics, heat transport, diffusion, reaction kinetics and so forth. The Navier – Stokes equation for Newtonian fluids, Hooke’s law for elastic media, Fourier’s law of heat and Ohm’s law of charge conduction are examples. More precisely the paper aims to demonstrate the usefulness of mixed formulations (formulations using mixed elements) in electromagnetics via the finite element method. Key-Words: computational electromagnetism, Maxwell's equations, differential forms, Hodge operator, finite element method, modelling, tokamak, plasma.
Atmospheric rivers in the Australia-Asian region: a BoM–CMA collaborative study

The name ‘atmospheric river’ (AR) could easily be misinterpreted to mean rivers flowing in the sky. But, ARs actually refer to narrow bands of strong horizontal water vapour transport that are concentrated in the lower troposphere. These bands are called ‘atmospheric rivers’ because the water vapour flux they carry is close to the volume of water carried by big river systems on the ground. ARs can cause heavy rainfall events if some physical mechanisms, such as orographic enhancement, exist to set up the moisture convergence and vertical motions necessary to produce condensation. In recent decades, these significant moisture plumes have attracted increasing attention from scientific communities, especially in North America and western Europe, to further understand the connections between ARs and extreme precipitation events which can trigger severe natural disasters such as floods, mudslides and avalanches. Yet very limited research has been conducted in the Australia-Asian (A-A) region, where the important role of atmospheric moisture transport has long been recognised for its rainfall generation and variations. In this paper, we introduce a collaborative project between the Australian Bureau of Meteorology and China Meteorological Administration, which was set up to explore the detailed AR characteristics of atmospheric moisture transport embedded in the A-A monsoon system. The project in China focused on using AR analysis to explore connections between moisture transport and extreme rainfall mainly during the boreal summer monsoon season. In Australia, AR analysis was used to understand the connections between the river-like Northwest Cloud Band and rainfall in the region. Results from this project demonstrate the potential benefits of applying AR analysis to better understand the role of tropical moisture transport in rainfall generation in the extratropics, thus achieve better rainfall forecast skills at NWP (Numerical Weather Prediction), sub-seasonal and seasonal time scales. We also discuss future directions of this collaborative research, including further assessing potential changes in ARs under global warming.
Minority Oversampling Using Sensitivity

The Synthetic Minority Oversampling Technique (SMOTE) is effective to handle imbalance classification problems. However, the random candidate selection of SMOTE may lead to severe overlap between classes and introduce new noise factors. Many variants of SMOTE have been proposed to relieve these problems by generating new examples in safe regions. Most of these methods generate new examples with existing minority examples without considering the negative impact that class imbalance have brought on these examples. In this paper, we handle the imbalance classification using Bayes’ decision rule and propose a novel oversampling method, the Minority Oversampling using Sensitivity (MOSS). Candidates for new example generations are selected considering their sensitivity with respect to class imbalance. New examples are then generated by interpolating the candidate and one of its adjacent examples. Experiments on 30 datasets confirm the superiority of the MOSS against one baseline method and seven oversampling methods.
The P-class and Q-class functions on symmetric cones

In this paper, we investigate the functions of P-class and of Q-class associated with symmetric cones. We provide the characterizations for P-class functions on symmetric cones, and discuss the relationship between P-class functions and monotone functions in the setting of symmetric cones. In addition, we also discuss the sufficient conditions for Q-class functions being monotone in the setting of symmetric cones.
Evaluating the Credibility of Downscaling: Integrating Scale, Trend, Extreme, and Climate Event into a Diagnostic Framework

Because downscaling tools are needed to support climate change mitigation and adaptation practices, the guarantee of their credibility is of vital importance. To evaluate downscaling results, one needs to select a set of effective and nonoverlapping indices that reflect key system attributes. However, this subject is still insufficiently researched. With this study, we propose a diagnostic framework that evaluates the credibility of precipitation downscaling using five different attributes: spatial, temporal, trend, extreme, and climate event. A daily variant of the bias-corrected spatial downscaling approach is used to downscale daily precipitation from the GFDL-ESM2G climate model at 148 stations in the Yangtze River basin in China. Results prove that this framework is effective in systematically evaluating the performance of downscaling across the Yangtze River basin in the context of climate change and exacerbating climate extremes. Moreover, results also indicate that the downscaling approach adopted in this study yields good performance in correcting spatiotemporal bias, preserving trends, approximating extremes, and characterizing climate events across the Yangtze River basin. The proposed framework can be beneficial to planners and engineers facing issues relevant to climate change assessment.
Chiral Lipid Tubules

Recent fundamental research has revealed the possibility to rationally form supramolecular aggregates. These self-assembled structures range in size from several nanometers (containing a few molecules) to hundreds of microns (containing billions of molecules). The ability to control both the morphology and size of these aggregates through modifications of both molecular structure and solution conditions suggests the possibility that they might be exploited to solve important problems in the development of advanced material applications. It is also possible that the lesson learned for the study of chiral behavior in lipid may have some relevance to the study of the ways in which polypeptide tapes self assemble in to fibers. Tubules formed from phospholipids constitute a unique self-assembled microstructure and show promise in a number of biotechnological applications [1]. This article will provide an outline of the talk that was given in June 1999 at the International Meeting on Polypeptide self-assembly held in Crete. It will discuss advances that have led to the understanding of chiral behavior and the subsequent ability to control the structure of lipid-derived tubules and the resulting impact of this on materials applications. Modifications to the molecular structure of lipids have been made to increase the versatility of liposomes as delivery agents for pharmaceutical agents [2]. Phospholipids with photopolymerizable diacetylenic moieties in the acyl chains were synthesized to increase the durability of liposomes [3-5]. However, Yager and Schoen found that some polymerizable lipids selfassemble to form hollow, cylindrical structures known as tubules [6]. Remarkably, tens of billions of molecules self-organize to form these
ARABIC NAMED ENTITY RECOGNITION BASED ON TREE-BASED PIPELINE OPTIMIZATION TOOL

Named Entity Recognition (NER) is a clue task to improve automatic text processing, which is needed in a diverse variety of applications. NER techniques vary from hand-craft rules to machine learning approaches. As a human opting for a supervised learning algorithm, it is often difficult to choose the optimal machine learning (ML) model for a classification problem. Automated ML (AutoML) aims at automatically selecting, composing, and parameterizing machine learning algorithms in order to obtain optimal performance for a given task (dataset). In this paper, we apply an approach based on Tree-based Pipeline Optimization Tool (TPOT). This method uses genetic programming based on the tree structure to find the model and its hyperparameters that more closely predicts the class of Arabic named entities in the text comes from social media. The structure and parameters are fine-tuned to achieve the optimum performance of the machine's learning pipeline. Our method outperforms strong baselines. It reaches a novel state-of-the-art in the NER task.
Quantum dots genosensor for Her2/Neu oncogene - a breast cancer biomarker

The human epidermal growth factor receptor (HER)-family of receptor tyrosine kinases; human epidermal growth factor receptor 1, human epidermal growth factor receptor 2, human epidermal growth factor receptor 3 and human epidermal growth factor receptor 4 (EGFR/HER1, ErbB2/HER2, ErbB3/HER3, and ErbB4/HER4) plays a major role in the pathogenesis of many solid tumours, in approximately 25 30% of breast cancers. Breast cancer is the second most common type of cancer and affects around 3000 women annually in South Africa alone. While the benefits of treatment and cancer progress to enhance therapeutic effectiveness for the patient are well documented, it is also important to employ or fabricate methods in which cancer can be screened at an early stage. A number of gene and protein based biomarkers have shown potential in the early screening of cancer. One specific biomarker that is over-expressed in 20 30% of human breast cancers is the human epidermal growth factor receptor 2 (Her2/neu). Several methods have been developed for detection of Her2/neu oncogene including immunohistochemistry (IHC), enzyme-linked immunosorbant assay (ELISA), fluorescent in situ hybridisation (FISH) and polymerase chain reaction (PCR). However, these methods are subjected to interference problem. For these reasons an ultrasensitive, cheap and easy to use genosensor has been developed for early detection of the Her2/neu oncogene using electrochemical and spectroscopic methods. Due to their high surface-to-volume ratio, electro-catalytic activity as well as good biocompatibility and novel electron transport properties quantum dots are highly attractive materials for ultra-sensitive
Optimizing decision tree criteria for predicting COVID-19 mortality in South Korea dataset

SARS CoV 2 spreads very quickly When this research was written, there were 1,780,315 positive cases globally Countries in the world take various actions to curb the spread of this new virus China is taking strict lockdown steps, while other countries such as South Korea are using massive diagnoses While it might only be acceptable for countries with not too big populations, what the South Korean Government is doing is very interesting to research so that it can be recognized by other countries This study use data mining techniques to search for pattern or new information from the data of the Covid-19 patients in South Korea This study studies the use of decision trees to process the data in South Korea by finding the most optimal decision tree criteria This study contributes to the data mining related field by showing decision tree is good enough to analyze the data There are three best criteria for using a decision tree to predict deaths from this new virus The best decision tree metrics are the gain ratio, knowledge gain and the gini index In this analysis the gain-ratio is use for further analysis Another finding is the province or location of this virus is the important factor The implication of this finding supports the strategy of reducing virus transmission Sex and age also play an important role in the prediction model © 2020 Little Lion Scientific All rights reserved
Multicomponent Soliton Bound States in Coherently Coupled Optical Media

In this work, we consider the simultaneous propagation of multiple optical fields in Kerr-type nonlinear media described by a system of integrable multicomponent coherently coupled nonlinear Schrödinger equations. With the aid of explicit soliton solution, we investigate the soliton bound states for different choices of solution parameters. Especially, we explore the effects of coherent and incoherent nonlinearities on the dynamics of bound states. OCIS codes: (190.5530) Pulse propagation and temporal solitons, (060.4370) Nonlinear fiber optics, (190.4223) Nonlinear wave mixing.
A Description of a Humans Knowledge using Artificial Intelligence

There currently does not exist a way to easily view the relationships between a collection of written items (e.g. sports articles, diary entries, research papers). In recent years, novel machine learning methods have been developed which are very good at extracting semantic relationships from large numbers of documents. One of them is the (unsupervised) machine learning model Doc2Vec which constructs vectors for documents. The research project detailed in this paper uses this and other already existing algorithms to analyze the relationship between pieces of text. We set forth a broader ambition for this project before discussing the use and need of Doc2Vec. We set and evaluate criteria in order to examine the feasibility of Doc2Vec for accomplishing this broader ambition.
The role of the putative receptor-like cytoplasmic kinase CLR1 in chitin signalling

Plants detect potential pathogens by perception of conserved microbe-associated molecular patterns (MAMPs) through plasma membrane-localized receptors. Signalling initiated by these receptors is a key process of plant innate immunity. Typically, binding of MAMPs to the surface-exposed ectodomains of receptor components induces formation of homo- or heteromeric receptor complexes. These may consist of receptor-like kinases (RLKs), receptor-like proteins (RLPs) as well as receptor-like cytoplasmic kinases (RLCKs) which lack an extracellular ligand-binding domain. This study focuses on a potential heteromeric signalling complex involving the Arabidopsis lysin motif (LysM)-RLK CERK1 (Chitin Elicitor Receptor Kinase1), which mediates chitin-induced signalling and defence responses. In a preceding yeast two-hybrid screen the RLCK CERK1-INTERACTING LysM-RLK-LIKE RLCK1 (CLR1) was identified as a putative interactor of the CERK1 kinase domain. When taking a closer look at the amino acid sequence of CLR1, it becomes obvious that the sequence shares high homology with the kinase domains of Arabidopsis LysM-RLKs. Data obtained in this study suggest that the CLR1 sequence annotated by TAIR10 seems to be not correct and the protein likely starts 23 amino acids C terminal of the annotated start, thus exposing a predicted N-myristoylation motif. In vitro phosphorylation assays showed that the CERK1 kinase domain can directly phosphorylate CLR1 in vitro. This finding was supported by the fact that CLR1 fusion proteins stably expressed in Arabidopsis plants showed chitin-induced and CERK1-dependent phosphorylation. Thus, CLR1 represents a phosphorylation substrate of CERK1 in vitro and in vivo. This phosphorylation seemed to be independent of the N-terminal myristoylation of CLR1. Microsomal fractionations and subcellular localization studies in transgenic plants suggested that the majority of the CLR1 protein is soluble, but a membrane-associated CLR1 subpopulation is present in plant cells. Three independent T-DNA insertion lines were isolated and characterized with regard to chitin signalling and immunity to fungal and bacterial pathogens. The clr1 T-DNA lines showed reduced chitin-induced ROS generation, MAPK activation and defence gene expression, suggesting that CLR1 plays a role in chitin signalling. The severity of the phenotype depended on the position of the T-DNA. clr1 plants were not impaired in resistance against fungal pathogens, but showed a subtly enhanced sensitivity to bacterial infection. Since the CLR1 promoter showed high activity in hydathodes, CLR1 could be involved in selectively restricting pathogen entry through these constitutively open vents.
Alzheimer’s Disease, Early Diagnosis

Aim: In this review paper we propose a method to make an early diagnosis of the Alzheimer’s Disease (AD), the most common form of neurodegenerative dementia. Background: Glymphatic System (GS) is the main means of eliminating waste substances in the central nervous system (CNS); if it does not work properly, waste substances accumulate in CNS until to cause AD. Basal Forebrain is the most important component of a much broader system of cholinergic cells distributed throughout the Central Nervous System (CNS). This structure regulates attention, learning and memory and its destruction is considered responsible for the cognitive AD alterations. The characteristics of AD patients, that interest us most, are the lack of Acetylcholine, and the Orexin excess; we think that the hypothalamus produces more Orexin to stimulate cholinergic cells, indispensable for a correct CNS functioning. We want to identify these patients by detecting the Orexin excess. Early Diagnosis Model. Of course we could take a cerebrospinal fluid sample and dose Orexin but this method is risky and painful for the patient’s health, therefore unsuitable for large numbers of patients. We propose a fairly simple method for the early diagnosis of AD: if we temporarily eliminate the Orexin excess, with Dual Orexin Receptor Antagonist (DORA), i.e. Suvorexant, we can intercept the Orexin increase and demonstrate the decrease in Acetylcholine with a Functional Magnetic Resonance or a Polysomnography, many years before the AD symptoms occur.
An Experimental Study on Unconfined Compressive Strength of Soft Soil-Cement Mixtures with or without GGBFS in the Coastal Area of Vietnam

Soft soil is widely distributed in Vietnam, especially in the coastal area. In engineering practice, soft soil cannot be used to build any construction and needs to be improved or treated before building construction. In addition, Vietnam has many pig-iron or thermal power plants, which annually produce a huge amount of granulated blast furnace slag (GBFS). Thus, the use of this material for soft soil improvement needs to be considered. This paper presents experimental results on the unconfined compressive strength (UCS) of three Vietnam’s soft soils treated with Portland cement and Portland cement with ground granulated blast furnace slag (GGBFS). Binder dosage used in this study is 250, 300, and 350 kg/m3 with the three different water/cement ratios of 0.8, 0.9, and 1.0, respectively. The research results showed that the UCS of soil-cement mixtures depends on soil type, water/cement ratio, cement type, and binder content. Accordingly, the unconfined compressive strength increased with the increase of binder contents, the decrease of the natural water content of soft soil, water/cement ratios, and clay content. The highest value of UCS of treated soils was found for the soil at Site II with the Portland cement content, cement GGBFS, and water/cement ratio of 873 kg/m3, 2355 kg/m3, and 0.8, respectively. Besides, for all the three soils and two binder types, the water/cement ratio of 0.8 was found to be suitable to reach the highest UCS values of treated soil. The research results also showed that the UCS of treated soil with cement GGBFS was higher than that of treated soil with Portland cement. This indicated the effectiveness of the use of Portland cement with GGBFS in soft soil improvement. There is great potential for reducing the environmental problems regarding the waste materials from pig-iron plants in Vietnam and the construction cost as well.