Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Safe Weight of Lift: A New Paradigm for Setting Design Limits in Manual Lifting Tasks Based on the Psychophysical Approach

The main objective of this study was to introduce a new concept for setting design limits in manual lifting tasks, i.e., the notion of the maximum safe weight of lift (MSWL), and to examine the effects of different sets of subject instructions for load selection on the amount of weight determined by male subjects in the psychophysical experiment. The results revealed that the MSWL values were significantly lower than the maximum acceptable weight of lift (MAWL) values. It was proposed that the classical approach to setting design limits in manual lifting tasks, based on the psychophysical method introduced by S. Snook, be modified to emphasize the subjects' safety and to reduce the risk of musculoskeletal injury.
ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification

We aim to design classifiers that have the interpretability of association rules yet match the predictive power of top machine learning algorithms for classification. We propose a novel mixed integer optimization (MIO) approach called Ordered Rules for Classification (ORC) for this task. Our method has two parts. The first part mines a particular frontier of solutions in the space of rules, and we show that this frontier contains the best rules according to a variety of interestingness measures. The second part learns an optimal ranking for the rules to build a decision list classifier that is simple and in-sightful. We report empirical evidence using several different datasets to demonstrate the performance of this method. 1. Introduction. Our goal is to develop classification models that are on par in terms of accuracy with the top classification algorithms, yet are interpretable, or easily understood by humans. This work thus addresses a dichotomy in the current state-of-the-art for classification: On the one hand, there are algorithms such as support vector machines (SVM) [Vapnik, 1995] that are highly accurate but not interpretable; for instance, trying to explain a support vector kernel to a medical doctor is not likely to persuade him to use an SVM-based diagnostic system. On the other hand, there are algorithms such as decision trees [Breiman et al., 1984, Quinlan, 1993] that are interpretable, but not specifically optimized to achieve the highest accuracy. For applications in which the user needs an accurate model as well as an understanding of how it makes predictions, we develop a new classification model that is both intuitive and optimized for accuracy. Our models are designed to be interpretable from multiple perspectives. First, the models are designed to be convincing: for each prediction, the algorithm also provides the reasons for why this particular prediction was made, highlighting exactly which data were used to make it. To achieve this, we use " association rules " to build " decision lists, " that is, ordered sets of rules.
Review of theory in model abstraction

Abstraction is key to model construction for simulation. While traditionally considered to be a black art, the importance of methodological support for abstraction is becoming increasingly evident. Complex distributed simulation systems, supporting multiresolution model sharing, presuppose effective ways to develop and correlate the underlying abstractions. Although a conceptual and computational framework grounded in mathematical systems theory has been around for some time, discussions of abstraction issues still often proceed through individual anecdotal experiences with little cumulative impact. This paper reviews theory and concepts available for working with abstraction in model construction.
Technical note 30: Young Lives rounds 1 to 3 constructed files

This Technical Note accompanies the Constructed Files of Young Lives data which have been deposited with the UK Data Service to facilitate analysis of the household and child surveys across the first three rounds of data collected. The constructed files are combined sub-sets of selected variables from Round 1, Round 2 and Round 3 of the Young Lives survey, collected in 2002, 2006 and 2009 respectively when the Younger Cohort children were aged 1, 5 and 8 years, and the Older Cohort children aged 8, 12 and 15 years. The files contain about 200 original and constructed variables, most of them comparable across the three rounds, presented in a panel format and classified in four broad groups: panel information, general characteristics, household characteristics, and child characteristics.
Inaugural Article: Parallel information processing channels created in the retina

In the retina, several parallel channels originate that extract different attributes from the visual scene. This review describes how these channels arise and what their functions are. Following the introduction four sections deal with these channels. The first discusses the “ON” and “OFF” channels that have arisen for the purpose of rapidly processing images in the visual scene that become visible by virtue of either light increment or light decrement; the ON channel processes images that become visible by virtue of light increment and the OFF channel processes images that become visible by virtue of light decrement. The second section examines the midget and parasol channels. The midget channel processes fine detail, wavelength information, and stereoscopic depth cues; the parasol channel plays a central role in processing motion and flicker as well as motion parallax cues for depth perception. Both these channels have ON and OFF subdivisions. The third section describes the accessory optic system that receives input from the retinal ganglion cells of Dogiel; these cells play a central role, in concert with the vestibular system, in stabilizing images on the retina to prevent the blurring of images that would otherwise occur when an organism is in motion. The last section provides a brief overview of several additional channels that originate in the retina.
Clustering of large time-series datasets using a multi-step approach / Saeed Reza Aghabozorgi Sahaf Yazdi

Various data mining approaches are currently being used to analyse data within different domains. Among all these approaches, clustering is one of the most-used approaches, which is typically adopted in order to group data based on their similarities. The data in various systems such as finance, healthcare, and business, are stored as time-series. Clustering such complex data can discover patterns which have valuable information. Time-series clustering is not only useful as an exploratory technique but also as a subroutine in more complex data mining algorithms. As a result, time-series clustering (as a part of temporal data mining research) has attracted increasing interest for use in various areas such as medicine, biology, finance, economics, and in the Web. Several studies which focus on time-series clustering have been conducted in said areas. Many of these studies focus on the time complexity of time-series clustering in large datasets and utilize dimensionality reduction approaches and conventional clustering algorithms to address the problem. However, as is the case in many systems, conventional clustering approaches are not practical for time-series data because they are essentially designed for static data and not for time-series data, which leads to poor clustering accuracy. Adequate clustering approaches for time-series are therefore lacking. In this thesis, the problem of the low quality in existing works is taken into account, and a new multi-step clustering model is proposed. This model facilitates the accurate clustering of time-series datasets and is designed specifically for very large time-series datasets. It overcomes the limitations of conventional clustering algorithms in dealing with time-series data. In the first step of the model, data is pre-processed, represented by symbolic aggregate approximation, and grouped approximately by a novel approach. Then, the groups are refined in the second step by using an accurate clustering method, and a representative is defined for each cluster. Finally, the representatives are merged to construct the ultimate clusters. The model is then extended as an interactive model where the results garnered by the user increase in accuracy over time. In this work, the accurate clustering based on shape similarity is performed. It is shown that clustering of time-series does not need to calculate the exact distances/similarity between all time-series in a dataset; instead, by using prototypes of similar time-series, accurate clusters can be obtained. To evaluate its accuracy, the proposed model is tested extensively by using published time-series datasets from diverse domains. This model is more accurate than any existing work and is also scalable (on large datasets) due to the use of multi-resolution of time-series in different levels of clustering. Moreover, it provides a clear understanding of the domains by its ability to generate hierarchical and arbitrary shape clusters of time-series data.
Syntax and first entropic approximation of L(MT)

Any mathematical model underlying a system of the complex reality (for example, in ecology, socioeconomy, etc.) defines a text written in a given language which belongs to a literature. A text‐model based language, L(MT) is developed, which focuses on its syntax. Three generative grammars are built for its alphabet (lexic unities). Further, the linguistic entropy of this language is analyzed from the Vakar’s point of view. Finally, this theory is applied to a particular ecological model, as an example of a text written in L(MT).
High ― Level Synthesis: Introduction to Chip and System Design

Preface. 1. Introduction. 2. Architectural Models in Synthesis. 3. Quality Measures. 4. Design Description Languages. 5. Design Representation and Transformations. 6. Partitioning. 7. Scheduling. 8. Allocation. 9. Design Methodology for High-Level Synthesis. Bibliography. Index.
Constrained principal component analysis via an orthogonal learning network

The regular principal components (PC) analysis of stochastic processes is extended to the so-called constrained principal components (CPC) problem. The CPC analysis involves extracting representative components which contain the most information about the original processes, The CPC solution has to be extracted from a given constraint subspace. Therefore, the CPC solution may be adopted to best recover the original signal and simultaneously avoid the undesirably noisy or redundant components. A technique for finding optimal CPC solutions via an orthogonal learning network (OLN) is proposed. The underlying numerical analysis for the theoretical proof of the convergency of OLN is discussed. The same numerical analysis provides a useful estimate of optimal learning rates leading to very fast convergence speed. Simulation and application examples are provided. <<ETX>>
Development of an online database system for remote monitoring of artificial heart patient

An online database system for remote monitoring of artificial heart patient has been developed. It is important for the patient with artificial heart implant to be discharged from the hospital after proper stabilization period for better and fast recovery and quality of life. The patients with artificial heart or mechanical circulatory support device are currently discharged after 1-2 months of hospitalization period. But, reliable and practical continuous remote monitoring systems for this kind of patients with life support device are still under study. The authors have developed a database system for this purpose that consists of a portable monitoring terminal, a database for continuous recording of patient and device status, and a web-based data access system with which the clinicians can access the real-time registering of patient data and past history data as well. The system has been tested with data generation emulators installed on remote sites for simulation study and in 2 cases of actual animal experiment conducted at remote facilities. The system showed acceptable functionality and reliability. The results are expected to be a good base for practical use of the system. A machine intelligence based automatic data analysis module is to be included for better practicality.
VLSI based design of a battery-operated digital hearing aid

A two-chip design is proposed wherein one chip is responsible for data acquisition and reconstruction while a second chip is dedicated to the digital signal-processing circuitry. A custom digital signal processor, potentially capable of performing over 3*10/sup 6/ multiply accumulate operations per second while consuming less than a fraction of a milliwatt, that is required to implement a four-channel hearing aid, is presented. Power consumption is minimized while maintaining a wide dynamic range through the use of sign/logarithm arithmetic. <<ETX>>
Circular RNA Signature in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. Circular RNAs (circRNAs) are a new class of endogenous functional non-coding RNAs (ncRNAs), and have been demonstrated to play important roles in the development of HCC. This study aimed to explore the significance of circRNAs in HCC progression. HCC-associated circRNA expression profiles GSE94508 and GSE97332 were downloaded from the Gene Expression Omnibus database (GEO), and 87 differentially expressed circRNAs (DECs) between HCC tissues and paired non-cancer tissues were identified, including 76 up-regulated and 11 down-regulated circRNAs. Gene ontolog (GO) and pathway analyses of the host genes of these DECs suggested that these host genes were enriched in cell adhesion, cytosol, and protein binding, and were associated with tight junction and Wnt signaling pathways. CircRNA-miRNA interaction prediction identified 20 miRNAs that predispose to interact with DECs. Among these, four essential miRNAs, hsa-miR-7-5p, hsa-miR-145-5p, hsa-miR-203a-3p, and hsa-miR-192-5p, were reported to play pivotal roles in HCC progression by targeting multiple genes. Pathway analysis suggested that putative target genes of these essential miRNAs were involved in HCC-associated signaling pathways, such as Wnt, TGF-β, and Ras; whereas protein-protein network (PPI) analysis demonstrated that some validated target genes of these miRNAs, such as PIK3CA, AKT1, MYC, JUN, SMAD4, and SRC, were hub target genes as they have more counts of interacting protein. In the meantime, the deregulation of some DECs was validated in HCC cell line HepG2 compared with normal liver cell line L02 by quantitative real-time polymerase chain reaction (qRT-PCR) and the Sanger sequencing. This study identified a set of DECs in HCC, and provided a comprehensive understanding of the roles of these DECs in HCC progression.
A method to extract additional biomedical text data using Support Vector Machine and PubMed

텍스트 마이닝은 자연언어로 된 문서를 분석하여 사 용자가 원하는 정보를 선별하고, 그 결과를 정제되고 가 공된 형태로 제시하는 것이다. 1980 년대에 처음 소개되 어, 1990 년대에 접어들며 급격하게 발전하기 시작했다 [1][2]. 텍스트 마이닝의 발전에 따라 생물학적 문헌에 대한 연구도 같이 진행되었다[3]. 이뿐만 아니라 1990 년 부터 진행된 인체 유전연구 프로젝트(Human Genome Project)는 유전자에 대한 다양한 연구를 가능케 했으며, 이 프로젝트의 가시적인 결과가 나타나기 시작한 1995 년 경부터 방대한 자료를 다루는 분자생물학과 전산학 의 결합인 생물정보학(Bioinformatics)이라는 분야가 성장 하기 시작하였다[4]. 이러한 연구의 발전에 따라, 생물학 분야에서의 텍스 트 마이닝은 매년 급격하게 성장하고 있다. 그림 1 에서 생명과학(life science) 과 생의학(biomedicine)에 대한 데이 터베이스 검색 엔진인 PubMed[10]에서 “text mining” 또 는 “literature mining”으로 검색한 결과가 매년 상승하는 것을 볼 수 있다. 그림 1최근 10년간 생물학 텍스트 마이닝 관련 문헌 수의 변동 추이
Using Next-Generation GPS Technology for Fuel Efficient Flight Path Optimization

Air navigation serves as a method to ensure the safety and efficiency of aircrafts. Airlines must develop flight plans, which seek to optimize fuel usage. This is not a simple task as wind patterns affect the groundspeed of aircrafts in the sky. Historically, flight planning has been heavily regulated by the Federal Aviation Administration by limiting paths to airways (highways in the sky) and waypoints (checkpoints in the sky) to prevent collisions between aircrafts due to limits of radar-based tracking technology. However, new next-generation GPS technology is anticipated to become the standard in aviation, which allows for new techniques for optimizing flight paths. This research attempts to take advantage of such technology by designing nextgeneration flight paths that follow the rules set in place by air traffic control. The focus of our methodology is to develop a model that optimizes fuel utilization by interpolating wind data and overlaying it on the various possible flight paths. Using a shortest-path formulation between origin and destination, this model designs a path that flies through waypoints, but is not constrained to traditional airways. This allows to create paths that are not dramatically different than historical paths. Our model showed a possible 8-9% decrease in fuel usage, which corresponds to huge environmental and economic savings. When scaled globally, these savings can have a huge impact for both carbon emissions and cost savings. These results demonstrate the potential of new optimization techniques for flight planning.
Atmospheric Retrieval using Deep Learning

Atmospheric retrievals on exoplanets involve usually computationally intensive Bayesian methods. The choice of the fitting parameters bounds are often leaded by physical constraints and and the user experience. We introduce ExoGAN, a new generation artificial intelligence able to recognise molecular features, abundances and atmospheric physical parameters using unsupervised learning. ExoGAN will return a probability distribution for each parameters that can be used either as a final atmospheric analysis or as prior distribution for a subsequent Bayesian model.
AN ALGORITHM TO FIND NESTED TANDEM REPEATS

1.1. Motivation: We introduce the algorithm NTRFinder to find a complex repetitive structure in DNA we call a nested tandem repeat (NTR). An NTR is a recurrence of two distinct tandem motifs interspersed with each other. We propose that nested tandem repeats can be used as phylogenetic and population markers. A major issue is the parsing problem, which is the problem of finding the optimal boundaries of the respective motifs. There has been little discussion about determining the best boundaries of the repeated motifs in tandem repeats. For nested tandem repeats, parsing is a significant issue.
UNIVERSITY OF VAASA FACULTY OF BUSINESS STUDIES DEPARTMENT OF ACCOUNTING AND FINANCE

The general theory of market efficiency assumes that the markets are efficient and deviations, in other words anomalies, do not exist. The theory of behavioural finance takes different approach because inefficiencies and deviations are appearing in the real world. This thesis concentrates on a seasonal anomaly called the Seasonal Affective Disorder (SAD). When the daylight is decreasing, SAD effect causes seasonal depression for people in different locations and the impact seems to be more powerful when moving away from equator to up north. The main aims are to solve significance of controversial weather variables and examine the inefficiencies and the implication in some Nordic stock markets, which can be caused by the SAD during the years 2000-2016. In this thesis, the efficient market section goes through the principles of asset pricing, dynamics of the efficient market theory and limits of arbitrage. The behavioural finance section explains by means of psychological biases, why investors could act irrationally. The topic introduces fundamental and calendar anomalies, including the SAD phenomenon. The empirical part run regression for Finnish indices with and without weather variables. The aim is to solve necessity of weather variables due to contradictory conclusions of recent research work about the SAD with weather variables. The regressions are run without weather variables also for all Nordic indices because the second aim is to find out the seasonal impact of the SAD to stock returns. Daily log returns are used in the regression analysis with seasonal dummies and SAD variables, which tells the length of the night in specific latitude. A Significant seasonal pattern of average stock returns and a weak SAD phenomenon are appearing among all indices during 2000-2016. However, the weather variables seem to be unnecessary for empirical analysis. The SAD is asymmetrical for several indices but in opposite direction than a known theory assumes. ______________________________________________________________________
DNA in a twist? How topoisomerases solve topological problems in DNA

DNA topoisomerases have been described as ‘the magicians of the DNA world’, somehow allowing DNA strands to pass through each other. These ingenious enzymes are both essential and potentially dangerous, as any interruption in their DNA breakage-reunion reactions can lead to chromosome breaks and cell death. This has led to their development as important targets for antibacterial and anti-cancer chemotherapy.
Perbandingan Algoritma K-Means dan EM untuk Clusterisasi Nilai Mahasiswa Berdasarkan Asal Sekolah

Dari beberapa fungsionalitas data mining, digunakan clustering untuk mengelompokkan mahasiswa berdasarkan nilai. Cluster dilakukan dengan menggunakan algoritma yang sudah ada yaitu K-Means dan EM (Expectation Maximation). Setelah sebelumnya melakukan proses pembersihan data dengan menggunakan aplikasi SQL Server 2008, kemudian data dalam bentuk tabel diolah dengan aplikasi WEKA (Waikato Environment for Knowledge Analysis) untuk mendapatkan hasilnya. Hasil dari penelitian berupa clustering informasi sekolah mana yang berpotensi menghasilkan lulusan dengan nilai yang baik. Pengelompokan terdiri atas 3 cluster dengan kategori nilai tinggi, sedang dan rendah. Pengelompokan tersebut juga berdasarkan lokasi yang disebut sebagai spatial clustering. Kemudian dilakukan analisis hasil setelah mendapatkan data yang sudah terkelompok. Informasi yang didapat selanjutnya dapat dimanfaatkan untuk pengambilan keputusan di bidang pendidikan bagi mahasiswa dan manajemen STMIK MDP. Bagi pihak manajemen STMIK MDP informasi berguna untuk mengetahui sekolah mana yang memberikan kontribusi mahasiswa dengan nilai tertinggi. From some of the functionality of data mining, clustering is used to group students based on the value. Clusters is done by using existing algorithms namely K-Means and EM (Expectation Maximation). Having previously done the cleaning process data using SQL Server 2008 applications, then the data in tabular form is processed by the WEKA (Waikato Environment for Knowledge Analysis) to get the result. Results from the study of clustering information which school has the potential to produce graduates with good grades. The grouping consists of three clusters with the category of high value, medium and low. Grouping is also referred to as a location based spatial clustering. Then performed the analysis of results after getting the data is already grouped. The information obtained can then be utilized for decision making in the field of education for students and management STMIK MDP. For the STMIK MDP management information useful to know which schools contribute to student with the highest score.