Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of the World’s Scientific Production in the Area of Sustainable Development and Environmental Impact

Comparative Analysis of the World’s Scientific Production in the Area of Sustainable Development and Environmental Impact Henry A. Lambis-Miranda*, Alvaro J. Garzón, Guillermo E. Valencia a Process Engineering program. CIPTEC Research Group. Fundacion Universitaria Tecnológico Comfenalco. Cr44D N 30A, 91, Cartagena, Bolivar, Colombia. Affiliation ID: 60110627 b Mechanical Engineering Department, Universidad Del Atlántico, Barranquilla, Colombia c Mechanical Eng., Efficient Energy Management Research Group, Kaí, College of Engineering, Universidad del Atlántico. Barranquilla, Colombia hlambs@tecnocomfenalco.edu. co
O ct 2 01 9 Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification

Person re-identification addresses the problem of matching pedestrian images across disjoint camera views. Design of feature descriptor and distance metric learning are the two fundamental tasks in person re-identification. In this paper, we propose a metric learning framework for person re-identification, where the discriminative metric space is learned using Kernel Fisher Discriminant Analysis (KFDA), to simultaneously maximize the inter-class variance as well as minimize the intra-class variance. We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification. We also show how the efficiency of KFDA in metric learning can be further enhanced for person reidentification by using two simple yet efficient multiple kernel learning methods. We conduct extensive experiments on three benchmark datasets for person re-identification and demonstrate that the proposed approaches have competitive performance with state-of-the-art methods.
Beyond the typology : A Reinterpretation of the East Asian Welfare Regime

Some researchers have been convinced that welfare developments in East Asia, especially Japan and Korea, can be fitted into the existing three worlds of welfare model, while others have insisted that existing welfare regime theories are not able to explain East Asian welfare regimes. This article assumes that we need to go beyond both of these traditional explanations. In the welfare state research fields, welfare regime approaches tend to focus on specific contextual conditions and cross-national differences. As a result, they tend to overemphasize history at the expense of theory. This article tries to combine deductive causal modeling with an institutional–historical context by identifying the contingent rent political game model and deducing important characteristics of East Asian welfare regime from this model. This model opens out the possibility of change in East Asian welfare regimes following the processes of democratization and globalization. Details of this are given in the conclusion.
Information Criteria for Kernel Machines

We present kernel regularization information criterion (KRIC), which is a new criterion for tuning regularization parameters in kernel logistic regression (KLR) and support vector machines (SVMs). The main idea of the KRIC is based on the regularization information criterion (RIC). Although the RIC is a useful criterion for tuning regularization parameters in statistical regularization models, it cannot be directly applied to parameter tuning for the kernel machines because kernel functions define only inner products in feature spaces. We derive an eigenvalue equation to calculate the KRIC and solve the problem. The computational cost for parameter tuning by the KRIC is reduced drastically by using the Nyström approximation. The test error rate of SVMs or KLR with the regularization parameter tuned by the KRIC is comparable with the one by the cross validation or evaluation of the evidence. The computational cost of the KRIC is significantly lower than the one of the other criteria.
Carbon dynamics on a drained peatland forest with drainage and nutrition gradients

The carbon balance of pristine and drained boreal peatlands is one of the focal points of ecosystem research. Usually drained peatland forests are considered carbon sinks. However, on some site types the tree stands may suffer from nutrient imbalances. We present a hypothesis of the carbon balance of a drained peatland forest. The treatments of the experiment are control and NPK-fertilization. The estimates of the autotrophic and heterotrophic CO2-C-respiration fractions were based on the tree stand biomass and literature data. The NPK treatment had resulted in enhanced tree growth and stand. On most control plots the tree stand and growth were smaller and the concentrations of P and K in the pine needles were low. The range of the CO2-C respiration was roughly the same on both the fertilized and unfertilized plots. The estimated autotrophic fraction was higher on the NPK treatments while the hetrotrophic was higher on the controls.
Stresa , Italy , 26-28 April 2006 NEW INTERNAL STRESS DRIVEN ON-CHIP MICROMACHINES FOR EXTRACTING MECHANICAL PROPERTIES OF THIN FILMS

A new concept of micromachines has been developed for measuring the mechanical properties of thin metallic films. The actuator is a beam undergoing large internal stresses built up during the deposition process. Al thin films are deposited partly on the actuator beam and on the substrate. By etching the structure, the actuator contracts and pulls the Al film. Full stress strain curves can be generated by designing a set of micromachines with various actuator lengths. In the present study, the displacements have been measured by scanning electronic microscopy. The stress is derived from simple continuum mechanics relationships. The tensile properties of Al films of various thicknesses have been tested. A marked increase of the strength with decreasing film thickness is observed.
Yunis et al. ICT in HealthCare: E-health Country Prism Model DECISION SCIENCES INSTITUTE ICT in HealthCare: The E-health Country Prism Model

Evolutions in information and communications technologies (ICT) have remarkably contributed to momentous advancements in the way healthcare practices are managed and delivered. This paper conceptually highlights eHealth contribution in bringing about better healthcare outcomes in terms of quality, access, and efficiency at the country level. Nevertheless, the eHealth impact is promoted or impeded by a myriad of challenges. Drawing on contextualist theories and the theory of light, a country-prism eHealth model is presented. The model portrays the relationship between eHealth and healthcare outcomes as could be influenced by various country-level factors. The proposed model is validated, and implications are discussed.
Faster Transformers for text summarization

Lightweight Convolutions [2]: This model replaces self-attention layers by some kind of local convolutions where each filter only takes into account one dimension, via a matrix W ∈ Rd×k where k is the size of the convolution window. Oi,c = k ∑ j=1 W ′ c,j ·X(i+j−dk+1 2 ]),c where X ∈ Rn×d is the input and O ∈ Rn×d is the output. W’ is the matrix W with a softmax layer applied across each channel. Complexity: O(n× k × d).
cooperation between IDSs between adjacent nodes . IDS should always run on all nodes to monitor network activity

The importance of data communication in wireless network demands a complete belief system through which the data security and privacy can be preserved during communication. Even the increase of the utilization of wireless data sharing especially in short-range communication or ad hoc network suffers from impersonation and DoS attacks. It is highly important to identify the belief nodes which supports the sort range or hop based communication to have a secure and privacy routing. In this paper, we propose a Belief Forecasting Methodology (BFM) to forecast the routing node about the believing nodes to improvise the data privacy routing in MANET. The goal of the BFM to protect the network from the malicious intruders and secure the data privacy during routing. It will compute the Belief Weight (BW) prediction based on the node activity and trustiness to decide the level of privacy of a node to support routing. The experiment simulation was performed through intruding malicious node in the network to evaluate the improvisation, and the result analysis suggested the effectiveness of the BFM to preserve the routing privacy in comparison of different evaluation measures.
Identification of Concrete Poetry within a Modern-Poetry Corpus

This work aims to discern the poetics of concrete poetry by using a corpus-based classification focusing on the two most important techniques used within concrete poetry: semantic decomposition and syntactic permutation. We demonstrate how to identify concrete poetry in modern and postmodern free verse. A class contrasting to concrete poetry is defined on the basis of poems with complete and correct sentences. We used the data from lyrikline, which contain both the written as well as the spoken form of poems as read by the original author. We explored two approaches for the identification of concrete poetry. The first is based on the definition of concrete poetry in literary theory by the extraction of various types of features derived from a parser, such as verb, noun, comma, sentence ending, conjunction, and asemantic material. The second is a neural network-based approach, which is theoretically less informed by human insight, as it does not have access to features established by scholars. This approach used the following inputs: textual information and the spoken recitation of poetic lines as well as information about pauses between lines. The results based on the neural network are more accurate than the feature-based approach. The best results, calculated by the weighted F-measure, for the classification of concrete poetry vis-à-vis the contrasting class is 0.96. 96 Quantitative Approaches to Versification
Developing an implementation strategy for the use of objective adherence data in routine clinical practice: a case study in cystic fibrosis clinics (abstract only)

This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published version GIRLING, Carla, HIND, Daniel, ARDEN, Madelynne A and WILDMAN, Martin J (2019). Developing an implementation strategy for the use of objective adherence data in routine clinical practice: a case study in cystic fibrosis clinics (abstract only). Implementation Science, 14 (Supp2).
Evolution of the quaking gene

5 TERMINOLOGY . 6 INTRODUCTION . 8 The quaking gene . 9 Aim . 11 MATERIALS AND METHODS . 12 Retrieval of sequences . 13 Alignment . 13 Visualization, editing, analysis, conversion . 13 Phylogenetic Analysis . 14 RESULTS . 16 Preliminary analysis . 16 Analysis of quaking isoforms . 17 Sequences retrieval . 17 Alignment . 17 Phylogenetic tree of all the isoforms . 17 Phylogenetic tree of the QKI5 isoform . 19 QKI5 isoform and models of evolution. 21 Testing models of nucleotide substitution . 21 GTR and molecular clock model . 21 Validation of the topology with ML . 23 DISCUSSION . 24 Evolution of the qki genes revealed . 24 Methods and models of evolution, the importance of making the right choice . 25 Quaking family and future studies . 26 ACKNOWLEDGMENTS . 27 REFERENCES . 28 APPENDIX . 31
Aalborg Universitet Exploring Deep Recurrent Q-Learning for Navigation in a 3 D Environment

Learning to navigate in 3D environments from raw sensory input is an important step towards bridging the gap between human players and artificial intelligence in digital games. Recent advances in deep reinforcement learning have seen success in teaching agents to play Atari 2600 games from raw pixel information where the environment is always fully observable by the agent. This is not true for first-person 3D navigation tasks. Instead, the agent is limited by its field of view which limits its ability to make optimal decisions in the environment. This paper explores using a Deep Recurrent QNetwork implementation with a long short-term memory layer for dealing with such tasks by allowing an agent to process recent frames and gain a memory of the environment. An agent was trained in a 3D first-person labyrinth-like environment for 2 million frames. Informal observations indicate that the trained agent navigated in the right direction but was unable to find the target of the environment.
Management of expectations or answerability to mandate ?

European agencies are subject to many kinds of control mechanisms and analysts worry that they often suffer from an ‘accountability overload’. This paper argues that such diagnoses are typically set without any explicit demarcation criterion for identifying accountability practices and separating them from mere instruments of political control. The paper argues that the concept of an accountability practice cannot be reduced the ‘management of expectations’. Rather, it should be restricted to procedures that track the mandate of the agency. This requires defence of the idea that there is such a thing as a coherent mandate as opposed to a mere heap of conflicting standards. The concept of a ‘multi-directional mandate’ is therefore introduced as a hermeneutical device for thinking about the coherence of different kinds of considerations. It is argued that neither account-givers nor account-holders can treat different aspects of the mandate in isolation; there is no accountability without sensitivity to the intertwinement of the different dimensions.
Dynamical behavior of a nondiffusive scheme for the advection equation

We study the long time behaviour of a dynamical system strongly linked to the anti-diffusive scheme of Despres and Lagoutiere for the $1$-dimensional transport equation. This scheme is nondiffusive in the sens that discontinuities are not smoothened out through time. Numerical simulations indicates that the scheme error's is uniformly bounded with time. We prove that this scheme is overcompressive when the Courant--Friedrichs--Levy number is 1/2: when the initial data is nondecreasing, the approximate solution becomes a Heaviside function. In a special case, we also understand how plateaus are formed in the solution and their stability, a distinctive feature of the Despres and Lagoutiere scheme.
Transformers and Pointer-Generator Networks for Abstractive Summarization

Recently, transformers have outperformed RNNs on sequence to sequence tasks like machine translation. Nima Sanjabi [15] showed that transformers also succeed in abstractive summarization tasks. However, like vanilla RNNs, transformer models produce summarizations that are very repetitive and often factually inaccurate. We improve on the transformer model by applying variants used to successfully solve these problems on RNN summarization models. We use techniques such as n-gram blocking and coverage loss to reduce repetition. We also add a pointer-generator network to allow the model to copy words from source texts and thus reduce both unknown word tokens and incorrect factual representations. We explore whether these mechanisms improve performance on the summarization task and whether they reduce redundancy and factual error as they do for RNN models. We find that pointer-generator networks improve ROUGE scores and the fluidity of summaries. Coverage and n-gram blocking also improve ROUGE scores and reduce repetition, with n-gram blocking significantly outperforming coverage.
Utilisation of technology acceptance model (TAM) in determining user perceptions of hospital appointment system

The traditional method of appointment making was based on physical visits to hospital or through telephone calls. With the rapid development of computer hardware and network environment, hospital system is changing to digital environment for reducing time wasted and efforts. The system design and implementation shown is a process of patients' ability to book for a special appointment related to the digital data base belonging to the hospital. The objective of this study is to prepare a suitable hospital appointment system for the benefit of improved convenience for both the medical team and also the patients. Quantitative methods are used in this research in order to obtain ideas to develop the prototype of the system. This is followed by system development, by which the researcher developed a Patient Appointment System. This system was put to test by the potential users. The prototype system is used as a base to measure the feedback of the target respondents as to the acceptance of the patient appointment system. The findings of the study are used to identify the attitude, ease of use and usefulness of the proposed system. The three target groups including doctors, hospital staff and patients are not very different from each other in terms of the mean values obtained. However, the result clearly shows that the system is very useful. Overall, the hospital appointment system is an effective approach to reduce time, cost and effort to make hospital appointment. The system is conceived to be acceptable, friendly, easy to be accessed and culturally acceptable. The development and provision of quality healthcare could be promoted through the usage of ICT. The researcher believes that the prototype can be further improved to enable the hospital appointment system to be fully implemented in private hospitals as an ongoing development. The researcher considers capitalizing on quantitative methods in this study. In the stage leading to identifying the crucial contents for the proposed system, the researcher uses mainly the quantitative method. Based on the literature review and the analytical observation the researcher compiled a list of groups to be included in the proposed system. At this stage the researcher used the quantitative method to identify the responses and feedback of the potential users of the newly developed system. Upon identifying the relevant groups, the researcher was able to develop a prototype to be tested among the potential users. The questionnaire self administered questionnaire was used by the researcher to extract the feedback from the potential users of the system.
AN ALGORITHM TO CONSTRAINTS BASED MULTI-DIMENSIONAL DATA CLUSTERING AIDED WITH ASSOCIATIVE CLUSTERING 1

To address the clustering problem related to multi-dimensional data clustering, a number of techniques have been implemented. A constraint based multi-dimensional data-clustering algorithm is proposed in this paper which helped with associative clustering can find out the number of clusters optimally present in a multi-dimensional data set. Now, by bays factor computation process associative constraint based clustering process is executed. Moreover, genetic algorithm is applied to optimization process to discover the optimal cluster results. The constraints based proposed algorithm assists in recognizing the right data to be clustered and the knowledge considering the data regarded as a constraint which enhances the precision of clustering. The data constraints furthermore assist in indicating the data related to the clustering task. The result of the proposed optimal associative clustering algorithm is compared with an existing algorithm on two multi dimensional datasets. Experimental result demonstrates that the proposed method is able to achieve a better clustering solution when compared with one existing algorithm.
Inhibition of growth of U 87 MG human glioblastoma cells by Usnea longissima

Herbal medicines are efficient to reduce side effects in the fight against glioblastoma, which plays a critical role within brain cancer species. The recent studies designated for testing the effects of lichens that have shown numerous anticancer activities on glioblastoma so far. In the present study, different concentrations of water extract obtained from Usnea longissima Ach. were used in order to determine cytotoxic (via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and lactate dehydrogenase tests), antioxidant (via total antioxidant capacity test), pro-oxidant (via total oxidant status test) and genotoxic (via 8-hydroxy-2′-deoxyguanosine test) effects of them on human U87MG-glioblastoma cancer cell lines. Primary mixed glial-neuronal non-cancerous cells from Sprague-Dawley rats were also utilized to measure the effects of treatments on non-cancerous cells. Based on median inhibitory concentration values, the data belonged to non-cancerous cells (2486.71 mg/l) showed distinct towering compared to U87MG (80.93 mg/L) cells. The viability of non-cancerous and U87MG cells exposed to extract is decreased in a dose dependent manner. It was also showed that low concentrations of extract notably increased total antioxidant capacity on non-cancerous cells. In addition, various phenolic compounds in extract were detected through high-performance liquid chromatography. The recent results encourage that extract will be able to have therapeutic potential against glioblastoma.
THE DEVELOPMENT OF A RESEARCH AND DEVELOPMENT PLANNING SYSTEM IN EDUCATION

EDRS Price MP-$0.65 BC-53.29 *Decision Making, Educational Research, Mathematical Models, Objectives, *Planning, *Research and Development Centers, *Research Projects, *Resource Allocations, Systems Approach This paper presents an R S D planning system (RDPS) that consists of (1) a multistage model for formulating projects and (2) a "reward" model for selecting projects. The system is based on a combination of decisionmaking and operations research approaches. Using the system concept, the R E D process is defined in three interrelated terms -Aission, goals, and objectives -that serve as the radix for generating projects in ultistages. The project selection model is used i; select projects that maximize expected total "reward" within budgetary limitations. The proposed RDPS can be used to concentrate and coordinate research and development activities. (Author) O 5 DEPARTMENT OF HEALTH, EDLICATION18. WELFARE OF FICE OF EDSTDATITTc4 TITVE DOCUMENT Has BEZN RTFRO DUCE! ) EXACTLY AS RECW.ED FROM THE PERSON OR OF,GANIzATIoN ocic, [NA'ING POINTS CF VIEW C.R OpLN IONS 'STATED DO NOT NEC.ESSARILY REPRESENT 0 DAL OFTICE OF EDU CATION YOSIT 0,[ ,,:3POT)CY
Abortion access and state variation in measured unintended pregnancy

Comparing unintended pregnancy rates across states and within states across time could allow demographers to describe the impact of increasingly divergent state reproductive health policies. For example, California has greater access to subsidized contraception than Texas and comparing the two states’ unintended pregnancy rates could illuminate the impact of their divergent policies. Unexpectedly, California’s unintended pregnancy rate is estimated to be slightly higher. We identify and describe possible bias in the estimation of state unintended pregnancy rates arising from between-state variation in rates of induced abortion and the possibility of imperfect retrospective reporting of unintended pregnancies. We find that with imperfect (<90%) retrospective reporting of unintended pregnancy, the current method of estimating state unintended pregnancy rates could rank states inaccurately due differences in abortion rates. Between-state differences in rates of retrospective reporting of unintended pregnancy could also bias between-state comparisons of unintended pregnancy rates.