Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Document Representations for Ranked Retrieval in the Legal Domain

In this work I detail the compilation of a unique corpus of Norwegian court decisions. I utilize this corpus to train several different machine learning models to produce dense semantic vectors for both words and documents. I use the document vectors to perform ranked document retrieval, and evaluate and demonstrate the performance of the vectors for this task using a purposely-built ranked retrieval model utilizing the document references in the documents. Furthermore, I explore the interplay between pre-trained semantic word vectors and convolutional neural networks and conduct several hyperparameter optimization experiments using convolutional neural networks to produce document vectors.
LITHIUM LINES IN SPECTRA OF C-GIANTS

The lithium abundance in stellar atmospheres provides information on various details of the stellar evolution and physical processes in stars. Among lithium-rich stars AGB, Li-rich (logN(Li) ≥ 1 in scale logN(H)= 12.0) and super Li-rich giants (SLR, logN(Li) ≥ 4) are of great cosmological interest, because they belong to the main suppliers of lithium into the interstellar medium. The resonance Li I line λ 670.8 nm is usually strong and sometimes very strong in spectra of cool C-stars. Besides, there are some subordinate Li I lines in optical and near IR regions that can also be strong in spectra of lithium-rich C-giants. Four lines formed by transitions from a level 2p are in the range λλ 450–820 nm, namely, λλ 812.6, 610.4, 497.2, and 460.3 nm. From the computations [5, 7] follows that only the Li I lines λλ 812.6, 670.8, 610.4, and 497.2 nm can be used as the lithium abundance indicators in spectra of C-giants. The line λ 460.3 nm is severely blended by the Fe I line at the same wavelength [6]. Different lithium lines are quite strong and sensitive to the lithium abundance for different values logN(Li). So, the Li I lines λλ 812.6 and 610.4 nm are saturated in spectra of SLR C-stars and, therefore, they are rather non sensitive to the lithium abundance. But the Li I line λ 497.2 nm is quite strong and sensitive to the lithium abundance for logN(Li)= 3–5 [5, 7]. We use for the calculations a model atmosphere and parameters of the spectra of the J-type star WZ Cas (C9.2J).
Correction model for rainfall forecasts using the LSTM with multiple meteorological factors

The goal of this study was to improve the accuracy of model forecasting, such that forecasters could use model products to make more efficient daily weather predictions. Historical data of the 12 hr following a given time for various meteorological factors from the control forecasts of the European Centre for Medium‐Range Weather Forecasting (ECMWF) between 20 ° and 40 ° N latitude and 110 °–130 ° E longitude were used to verify the performance of the proposed method. Eight major meteorological factors were selected via correlation analysis between control forecast meteorological factors and real‐time rainfall. The samples were divided into four types using the K‐means clustered method. Each type was respectively modelled by long short‐term memory (LSTM) in order to correct rainfall forecasts for eastern China. The eight major meteorological factors were used as the model input, and the differences between real‐time rainfall data and model‐forecast rainfall were used as the model output. The corrected results revealed that the root mean square error decreased by 0.65, and the threat scores of light rainfall and rainstorms were improved.
Educating Pre-Service School Librarians for the Instructional Partner Role: An Exploration into University Curricula.

In Empowering Learners: Guidelines for School Library Programs (2009), the American Association of School Librarians (AASL) identified the instructional partner role of school librarians as the most critical role for the future of the profession. To determine the extent to which school librarian preparation programs prepare pre-service school librarians for this role, this mixed-methods case study examined program ranking responses and questionnaires from nine universities and colleges that prepare these candidates for practice. Instructors who teach courses in school librarianship submitted a program of study document on which they rated the percentage of readings/viewings and assignments that focus on the instructional partner role in courses offered exclusively for pre-service school librarian candidates. Participants were invited to complete follow-up questionnaires that asked for details regarding readings, textbooks, and assignments. The findings of this case study demonstrate a tendency for school librarian preparation programs to assign different priorities to the five roles identified by AASL; developing the instructional partner role was not ranked first for most of the programs under study. These programs also integrate into their courses various textbooks, book chapters, articles, and other resources focused on instructional partnerships. The results of this case study
Position and Attitude Estimation for Lunar-Planetary Rovers

本論文では,月・惑星探査ローバのための Unscented Kalman Filter(UKF)を利用した自己位置・姿勢推 定システムについて述べる. このシステムは月・惑星探査ローバの自己位置・姿勢推定における特有の問題であ る,タイヤのスリップによる wheel odometryの観測誤差,低特徴環境における Visual Odometry (VO)の局所的推 定誤差,そして Gyroセンサのドリフトによる観測誤差の問題を解決するように設計されている. これらの問題を 解決するために,本システムでは,拡張された UKFを利用しスリップ率を推定する手法や,VOにおいて抽出さ れた特徴点の中のインライアの数を利用した VOの精度推定法,そして,Gyrodometryモデルを利用した自己位置 推定法などを取り入れた. 本論文では,これらの手法について述べる. そして本論文の最後において,実際の不 整地におけるローバの実験機を使用した自己位置推定の実験を行い,それらの結果から各手法と本システム全体 の有効性を示す. In this paper, we propose an efficient solution to 6 degrees of freedom (6DOF) localization using Unscented Kalman filter for planetary rovers. The solution is a technique augmented the Unscented Kalman filter for accurate 6DOF localization, named Augmented Unscented Kalman Filter (AUKF). The AUKF is designed to deal with problems which occur on other planets: wheel slip, visual odometry error, and gyro drift. To solve the problems, the AUKF estimates the slippage ratio in an augmented state vector, the accuracy of the visual odometry with the number of inliers among feature points, and sensor usefulness with Gyrodometry model. Experimental results of rover runs over rough terrain are presented, the effectiveness of the AUKF and its each component is shown.
Contraception: Knowledge, Attitude and Practice with Special Emphasis on Contraceptive Pills among Saudi Women at Al-Khobar City, Eastern Saudi Arabia

Objectives: to estimate the prevalence of different methods of contraception and to assess the level of knowledge, attitude and practices (KAP) of Saudi fertile women towards contraception in general and oral contraceptive pills (OCP) in particular and to find-out the possible associated factors Subjects and Methods: This was a cross sectional study conducted in Al-Khobar city, Saudi Arabia. Three hundred ant eighty eight married fertile Saudi women having living children in the catchments of two randomly selected primary health care centers represent the sample of the study. Data were collected by using an interview questionnaire which was composed of two main parts: socio-demographic and reproductive characteristics and the second part included questions to assess the level of KAP of the women concerning contraception. Logistic regression analysis was used to determine the independent variables that affect KAP of these methods. Results: The mean age of Saudi women was 32±8 years, and the prevalence of OCP users among Saudi women using contraceptive methods was 28.7%. The majority of Saudi women had poor knowledge, practice and negative attitudes towards OCP. The significant factors associated with knowledge of the studied Saudi women regarding OCP is number of living children; also socio-economic score and age at marriage are the significant factors associated with attitude toward OCP. Conclusion: This study highlights poor knowledge and negative attitude of Saudi women regarding OCP which are significantly associated with socio-demographic and number of living children. Effective counseling and education are needed for negatively disposed toward OCP use. Compliance strategies for longer continuation with OCP regimen must be provided for favorable users.
Crohn's and Colitis at the Crossroads: Part 1 Impact of New Strategies on Old Therapies— Focus on 5-ASA Responders

raditionally,inflammatory bowel disease (IBD) has been divided into 2 distinct entities:ulcerative colitis (UC) and Crohn’s disease (CD). An innovative and nuanced view presents IBD as an immunoinflammatory spectrum of chronic and recurring diseases of the intestines. This newly gained perspective holds the promise of moving treatment in a more proactive direction—toward targeting molecules, rather than treating symptoms. In the vast majority of patients, the traditional diagnosis of IBD is established by clinical history, examination, endoscopy, pathology, and radiology. Just as the advent of the purified protein derivative (PPD) initially defined IBD by distinguishing it from tuberculosis and chronic appendicitis, molecular diagnostic tests (antibody profiling) will redefine the pathobiology of IBD by characterizing the diversity of mucosal immune responses to luminal bacteria. The evolution of diagnostic antibody profiling offers physicians the ability to stratify patients and correlate the clinical and immunologic patterns of IBD based on the heterogeneity of mucosal inflammation. The foundation of this new correlative science is predicated on the idea that genetic, bacterial, and immunologic diversity may allow physicians to individualize therapy. The human genome is 99.9% identical in all people. Individual differences are determined by only 0.1% of the genome. This very small fraction of the genome displaying DNA variations (polymorphisms) is responsible for individual therapeutic response and disease susceptibility. The emerging role of pharmacogenomics is increasingly recognized in optimizing drug therapy. Serologic markers do not replace traditional diagnostic modalities; rather, they serve as clinical adjuvants,potentially enhancing our ability to optimize medical therapy. Crohn’s and Colitis At the Crossroads: Part 1 Impact of New Strategies on Old Therapies—Focus on 5-ASA Responders
Integrated imaging research based on lenticular lens and projection equipment

Stereoscopic display technology has a high sense of presence and can bring unparalleled stereoscopic enjoyment to the audience, which requires stereoscopic glasses, helmet display and other auxiliary visual tools in that greatly limits its application. Auto-stereoscopic display technology (especially based on lenticular lens ) enables the audience to feel the visual impact brought by stereoscopic images freely and clearly with the naked eye in multiple positions within the larger perspective, thus greatly promoting the development and application of stereoscopic display technology. In this paper, an integrated imaging technique based on lenticular lens (88 inches) and projection equipment is introduced. The parameters of lenticular lens grating are calculated firstly, then, we use the 3Ds Max software to acquire the parallax images of the object, integrating all the parallax image pixels together to obtain an image with parallax by using Matlab program. The integrated parallax image is matched by the optical transmission characteristics of the lenticular lens. Experiment show that, the integrated imaging technique based on lenticular lens (88 inches) and projection equipment can display a good stereo image and realize super large-size, true color, dynamic and smooth parallax display. A 3D display by using a large lenticular lens and a projector is achieved in this paper.
Recognizing Fine-Grained Contexts at Home with Image-based Cognitive API

With the rapid progress of ICT and IoT (Internet of Things) technologies, research and development of smart homes have been actively conducted. The key challenge in the smart home is how smartly the system is able to recognize various contexts at home, automatically. The home context refers to any situational information at home, including daily activities of residents, the environment in the house, and the status of the room. Moreover, we use the term fine-grained home context to represent a home context that is more concrete and is specifically defined by individual houses, residents, and environment, for a special purpose of application. The technologies for home context recognition have been studied for many years in the field of ubiquitous computing. The traditional ubiquitous computing employs ambient sensors (e.g., temperature, humidity, presence) [1], wearable sensors (e.g., accelerometer, heart rate), and indoor positioning systems [2]. They are installed at rooms or worn by residents to collect essential data characterizing various contexts. However, these existing technologies are yet far from practical use in general households, since they usually require expensive devices and resources at home. It is difficult for ordinary users to operate and maintain complex ubiquitous devices at home on a daily basis. The goal of research is to implement fine-grained home context recognition that can adapt to custom contexts in every single house, and can achieve accurate recognition with a small amount of resources affordable by general households. We extensively utilize the image-based cognitive API throughout the research. An image-based cognitive API receives an image from an
Development and mathematical modelling of affinity system based on novel matrix

A novel and fast flow regenerated cellulose based support matrix was investigated for use in affinity separations. Separation performance and some of the factors affecting it were studied experimentally and theoretically with an aim of establishing the criteria for process scale prediction and optimization of its performance. Two forms of the matrix support (membrane and sponge) to which Cibacron Blue 3GA has been attached (blue membrane and blue sponge) were used for separation of human serum albumin (HSA) from the human blood plasma. The performance of blue membrane (BM) and the sponge (BS) were found to be satisfactory in terms of capacity and product purity. Protein A-sponge capacity was comparable to other cellulosic matrix-protein A adsorbents. Transport and bed parameters of the sponge packed in a column were investigated by means of tracer pulse response technique. This showed that the sponge had a high voidage and indicated relatively high but tolerable axial dispersion. Mathematical modelling of the adsorption stage in column operation was carried out using non porous and axially dispersed plug flow models. It appeared from the comparison of experimental and predicted breakthrough curve (B.T.C.) that more than a single rate mechanism was involved in the determination of the shape of the B.T. C. CHAPTER
Diabetische Mikroangiopathie

Zusammenfassung Die diabetische Retinopathie ist die häufigste mikrovaskuläre Komplikation des Diabetes mellitus. Sie hängt im Wesentlichen von der Stoffwechselkontrolle durch den Patienten ab, gemessen am HbA1c. Die Schädigungskaskade bringt Veränderungen im zellulären Crosstalk der Retinakapillare mit sich, an deren Anfang der Verlust der Perizyten steht. Durch Untersuchungen an transgenen und diabetischen Modellen kann die relative Bedeutung dieses Phänomens eingeordnet werden. Neue Einblicke in die biochemischen und zellbiologischen Zusammenhänge zwischen Hyperglykämie und vaskulärem Zellschaden begründen metabolische Signalhemmer als neue Therapieansätze zur Behandlung der diabetischen Mikroangiopathie.
Random Walk Simulation of Fibroblast Cells through RyTrack and Lab VIEW Software Applications

Objectives: In this study, the theoretical results of the trajectories of a particle in Brownian motion are presented through simulation using LabVIEW. Methods/Statistical Analysis: Particle-tracking methods, which probe local mechanical properties has been employed. For a 2.0-micron polystyrene bead attached to the walls of fibroblast cells, its movements are tracked through time using video microscopy in which Rytrack software geared with algorithms is used to extract the trajectories of the particles. The time-lag dependence Mean Square Displacement (MSD) of each fibroblast cell is computed from the time-dependent coordinates of that cell. Findings: From the experiment and through simulations of random walk, it is found that as time increases, MSD also increases. It reveals the viscoelastic nature of the microenvironment in the immediate vicinity of each fibroblast cell. From the computed MSDs, the fibroblast cell possesses both having liquidlike and solid-like characteristics. The MSD graph gives characteristic curve that elucidates the nature of fibroblast cell in the system. The analyzed diffusion is of isotropic nature depicted by the linear correlation and the tracers are having sub-diffusive mean trajectories. The mechanical properties of the cytoplasm of fibroblast cells are spatially variable and depend on timescales. Application/Improvement: This study gives characteristic analysis on the mechanical properties of fibroblast cells which are essential for proper function and health.
Trajectory planning of a Pendulum-Driven Underactuated Cart

The control problem of the pendulum-driven cart has been well studied in several literatures. The challenge issue of control of such underactuated systems is to control the motions of the pendulum and the cart by applying only one control input. However, most literatures focused on addressing the dynamic modeling and the tracking control design. Few papers have considered the trajectory planning of the system which is still an open issue. This paper studies the optimization for the trajectory planning of the system by considering three questions: 1) the furthest cart displacement; 2) the fastest average cart speed; 3) the most efficient power consumption. The paper systematically studies the system dynamics, the system constraints, the tracking control method, some initializations for the optimization problems, and the solutions for the three optimal questions. Extensive simulation studies are given to identify the optimum trajectories of the system.
Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity

The powered exoskeleton promises substantial improvements on daily activities of the people who need robots provide assistance. In order to achieve flexible and stable control of a powered lower limb exoskeleton, in this paper, a hybrid control that combines a brain-computer interface (BCI) based on motor imagery (MI) with surface electromyogram (EMG) signals has been developed. We utilized the common spatial pattern (CSP) method to extract the variance of electroencephalogram (EEG) signals and back propagation (BP) neural network to recognize the imagery tasks. Moreover, we have used the strength of EMG signals obtained from upper forearms of subjects to adjust the gait of exoskeleton robots according to real stairs so that subjects can climb stairs easily and stably. The recognized results of EEG and the strength of EMG are used to drive the powered exoskeleton to help subjects climb the stairs by the designed gait synthesis which satisfies the environmental constraint and kinematic constraint. The developed hybrid control strategy has been verified by three healthy subjects, and all subjects can successfully fulfill steadily climbing the stairs, assisted by the powered exoskeleton. The results of the experiment have demonstrated the developed hybrid brain/muscle signals powered robot can effectively enhance human mobility.
Hybridisation of forklift trucks

In the past years, forklift manufacturers have started to hybridise their products under the worldwide interest toward electrification. This is generally made starting from existing standard products, and converting them into hybrid versions, rather than start from scratch and designing fully new vehicles. This approach involves several gaps from the inaccurate evaluation of the powertrain architecture and relative energy management strategy, up to a rough sizing of components. Therefore, this study suggests a structured methodology to follow, aimed to properly design hybrid forklift trucks. The first step is the analysis of the conventional working machine to hybridise, identifying one set of duty cycles. Then, the design of the new powertrain architecture, with a proper energy management strategy, and the definition of a numerical simulation model realised through a multiphysics object-oriented software package, to properly size the new powertrain components, and to verify energy and power flows. Finally, it is also presented a brief-cost–benefit analysis, as last verification of the proposed methodology, to verify the cost-effectiveness to bring the new products to the market. Results have shown the general feasibility of the operation since the significant reduction in fuel consumption is combined with sustainable costs of the new electrified components.
WOMEN'S AND CHILDREN'S HEALTH KNOWLEDGE HUB

Adolescents (aged 10 to 19 years) undergo rapid development characterized by an increasing physical capacity to be sexually active, but a less than fully developed psychological and emotional capacity to assess the unintended consequences of sexual activity and negotiate safe and consensual sex. Adolescents also experience unique barriers to accessing reproductive health information and care, and are unlikely to benefit from reproductive health interventions targeted at the overall population. As a result, adolescents experience a disproportionate burden of poor reproductive health outcomes including sexually transmitted infections (STIs) and unintended pregnancies.
Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective

Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.