Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Diagnosis of Bearing Based on Cauchy Kernel Relevance Vector Machine Classifier with SIWPSO

Bearing is an important component of mechanical system; any defects of bearing will lead to serious damage for the entire mechanical system. In this paper, Cauchy kernel relevance vector machine with stochastic inertia weight particle swarm optimization algorithm (SIWPSO-CauchyRVM) is proposed to fault diagnosis for bearing. As the selection of the Cauchy kernel parameter has a certain influence on the diagnosis result of relevance vector machine, stochastic inertia weight PSO is used to select the Cauchy kernel parameter. The relative energies of 16 wavelet coefficients of the forth layer of vibration signal of bearing can be used as the diagnosis features of bearing. The experimental results indicate that fault diagnosis method of bearing based on SIWPSO-CauchyRVM has excellent diagnosis ability.
Learning Dynamics from Kinematics: Estimating Foot Pressure from Video

Human pose stability analysis is the key to understanding locomotion and control of body equilibrium, with numerous applications in the fields of kinesiology, medicine and robotics. We propose and validate a novel approach to learn dynamics from kinematics of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure from a human pose derived from video. We have collected and utilized a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized motion capture, foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named "PressNET". Cross-validation results show promising performance of PressNET, significantly outperforming the baseline method under reasonable sensor noise ranges. We also show that the center of pressure locations computed from our regressed foot pressure maps are more accurate than those obtained from the baseline approach.
Labeling Ecological Niche Models

The ongoing biodiversity crisis is pushing ecologists and conservation biologists to develop models to foretell the effects of human-induced transformation of natural resources on the distribution of species, although ecology and biogeography still lacks a paradigmatic body of theory to fully understand the drivers of biodiversity patterns. Two decades of research on ecological niche models and species distributions have been characterized by technical development and discussions on a plethora of methods or algorithms to infer and predict species distributions. Here we suggest a metaphorical classification scheme for some of the most popular models based on their complexity, interpretability and suitability for specific applications in ecology and conservation biology. Our purpose is not to compare methods by their capacity to accurately predict the observed distribution of species, nor to criticize how they are commonly used in applied studies. Instead, we believe that a simple classification scheme can potentially highlight how some methods are more suited for specific applications in ecology and conservation biology. Envelope and distance-based models are grouped into the “fish bowl” category, for their transparency and simplicity. Statistical models are classified as “turbine” models, because of their hidden complexity and general applicability. Finally, machine-learning models are classified as “vault” models, for their high complexity and lack of interpretability of fit parameters. We conclude that the diversity of species distribution models used today is expected for a young research field, but the choice of modeling strategy depends on the purpose of the study. We provide some general guidelines for choosing models for studies of conservation planning and climate change mitigation and suggest models of intermediate complexity for conservation planning and forecast of climate change effects on biodiversity as they provide a good balance between interpretability, predictive power and robustness to model over-fit.
Fuzzy Hammerstein Model Based States Space Identification Approach of Nonlinear Dynamics Systems

This paper presents a novel methodology for evolving fuzzy identification of nonlinear systems in state space based on Hammerstein models. The nonlinear static characteristic is approximated by an evolving TakagiSugeno fuzzy model and the linear dynamics by a state space model. The recursive estimation of the linear model in state space is performed based on the system Markov parameters applied to the algorithm of minimum realization ERA. Computational results illustrate the effectiveness of the proposed method in the online identification of nonlinear systems
A cross sectional investigation of the relationship between mindfulness, emotion regulation and eating disorder symptoms amongst female university students

Recently it has been established that individuals with eating disorders have difficulties regulating their emotions and have low levels of mindfulness. The current study has employed undergraduate female participants (N = 119) with no prior diagnosis of an eating disorder in order to explore the relationship between mindfulness, emotion regulation and eating disorder symptoms in a non-clinical setting. The aim of this research is to increase the understanding of how these psychological constructs are possible risk factors for young female students in developing anorexia nervosa. Participants completed a cross sectional survey which included the Five Facet Mindfulness Questionnaire – Short form (FFMQ-SF), the Difficulties in Emotion Regulation Scale (DERS) and the Eating Disorder Inventory-3 (EDI-3). Results indicate how individuals with more symptoms typical of eating disorders had lower levels of mindfulness and greater difficulties regulating their emotions. The relationship between mindfulness and eating disorders was found to be mediated by emotion regulation. This research emphasises the importance of understanding the psychological aspects of eating disorders as it has been demonstrated that low mindfulness and poor emotion regulation could be possible risk factors in developing an eating disorder. Further research is warranted to explore this using a prospective design.
Genetic variation and correlation of agronomic traits in meadow bromegrass (Bromus riparius Rehm) clones

Meadow bromegrass (Bromus riparius Rehm.) is a recently introduced pasture grass in western Canada. Its leafy production and rapid regrowth have made it a major grass species for pasturing beef animals in this region. As relatively little breeding work has been done on this species, there is little information on its breeding behaviour. The main objective of this study was to estimate total genetic variability, broad-sense heritability, phenotypic and genetic correlations. Forty-four meadow bromegrass clones were evaluated for agronomic characters. Genetic variation for dry matter yield, seed yield, fertility index, harvest index, plant height, plant spread, crude protein, neutral detergent fiber and acid detergent fiber, was significant. Broad-sense heritability estimates exceeded 50% for all characters. Heritability estimates were at least 3.5 times greater than their standard errors. Phenotypic and genetic correlation between all possible characters were measured. There was general agreement in both sign and magnitude between genetic and phenotypic correlations. Correlations between the different characters demonstrated that it is possible to simultaneously improve seed and forage yield. Based on the results, it appears that the development of higher yielding cultivars with higher crude protein, and lower acid and neutral detergent fibers concentration should be possible.
Antioxidant Activity of Acalypha wilkesiana var. lace-acalypha (Muell &Arg.)

Background: The incidence of free-radicals has become a major concern in health issues such as wounds, respiratory dysfunctions, heart troubles, cancers and inflammations amongst many others. The search for natural antioxidant drug templates led to the screening of Acalypha wilkesiana var. lace- acalypha for potential antioxidant activity. Objectives of study . The crude extract, butanol fraction and four compounds previously obtained from the plant were to be screened for antioxidant activity. Methodology: The extract, butanol fraction and four compounds designated as 5A-II (3), 5B (1), 5C (2) and 5D (H-2) obtained from silica-gel column chromatography of the butanol fraction were to be screened for antioxidant activity using DPPH reagent. Results and Discussion : The extract and butanol fraction of A. wilkesiana var. lace-acalypha demonstrated marginal antioxidant activity (IC 50 ) of at 0.79 and 0.77 μg mL -1 respectively. However, 5B (1) and 5C (2) (both polyphenols) gave moderate antioxidant activities of 0.47 and 0.56 μg mL- 1 respectively which compare favourably with the IC 50 values obtained with vitamins C and A at 0.46 and 0.58 μg mL- 1 respectively. However, 5D (H-2) demonstrated a relatively similar antioxidant activity as vitamin E at 0.66 and 0.60 μg mL- 1 respectively. The antioxidant activities demonstrated by the plant extract/ fraction/compounds were not surprising because the phytochemical investigations carried out on the plant revealed the presence of terpenes and tannins which have been reported in previous studies to exhibit antioxidant activities. Conclusion: The results of the antioxidant assays have revealed that compounds isolated from the plant especially 5B (1) and 5C (2) could serve as potential antioxidant templates which could be further investigated in detailed in-vitro and in-vivo studies with the aim of improving on their activities for the fights against the destructive free-radical oxygenated species. Keywords : Free-radicals; antioxidant activity; DPPH; assay; Acalypha wilkesiana
Oil spill classification from multi-spectral satellite images: exploring different machine learning techniques

This work describes the potential of oil spill classification from optical satellite images, as investigated by applying different machine learning techniques to a dataset of more than 300 oil spill candidates, which have been detected from multi-spectral satellite sensors during the years 2008 and 2009, over the entire area of the Mediterranean Sea. A set of geometrical and grey level features from Synthetic Aperture Radar (SAR) literature has been extracted from the regions of interest in order to characterize possible oil spills and feed the classification system. Results obtained by applying different machine learning classifiers to the dataset, and the achieved performance are discussed. In particular, as a first approach to oil spill classification, simple statistical classifiers and neural networks were used. Then, a more interpretable fuzzy rule-based classifier was employed, and performance evaluation was refined by exploiting Receiver Operating Characteristic (ROC) analysis. Finally, since oil spill dataset collection happens incrementally, a suitable technique for online classification was proposed, encompassing at the same time cost-oriented classification, in order to allow for a dynamic change of the misclassification costs. This latter goal has been achieved by building an ensemble of cost-oriented, incremental and decremental support vector machines, exploiting the concept of the ROC convex hull.
An efficient classification of flower images with convolutional neural networks

Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.
GARCH Time Series Models: An Application to Retail Livestock Prices

Traditional time series models assume a constant conditional variance. Realizing the implausibility of this assumption, Bollerslev proposed Generalized Autoregressive Conditional Heteroscedasticity (GARSH) processes, which are characterized by nonconstant conditional variances. In this paper, GARCH (1,1) processes were applied to model livestock prices. Results indicate that GARCH processes adequately describe retail meat price behavior.
IMPROVING SKILLS IN PRODUCT DESIGN: EXPLORING SOLUTION SPACE AND THE IMPACT OF APPLIED MENTAL SCALING

This paper explores how mental scaling may constitute a creative tool in product design education, in order to improve design solutions through different methodological approaches. The increasing expectations from industry on optimization within product design are clearly evidenced today. These demands are often defined by specific user groups or corporate product managers, putting a pressure on designer’s propositions. How can we as educators prepare our students for these requirements? In a real-life product design project, the creativity potential is reflected through a requirement specification framing a certain solution space which the design has to comply with. In academia, our challenge is to encourage our students to question the rigidity and limitations given by this requirement specification and its inherent solution space. Experience gained from practicing design methods enable design students into building sufficient courage and self-awareness in order to challenge this creativity potential. Our experience from teaching design methods indicates that practicing mental scaling - or mental elasticity - enables the students to do so. When applied to a design project, the ability to mentally fluctuate between abstraction and concretization builds a thematic divergence which generates an open and sensitive mind-set. This mind-set enables the student to utilize this creativity potential through individual procedural diversity. These abilities have been observed and assessed through practical assessments. The thematic divergence in these practical exercises has produced a valuable body of experience, which contributes to articulate a firmer understanding of the effect of these methodological approaches.
Novel Technique for Density Based Clustering Using Neural Networks

I n order to separate similar and dissimilar type of data into different clusters, the clustering mechanism is used. This helps in analyzing the data that is given as input in more efficient manner. The EPS is computed in case when DBSCAN algorithm is applied on the data that is to be processed. The Euclidean distance is computed from the central point which is mainly the EPS point calculated. This helps in further defining the similar and dissimilar type of data present. The accuracy of clustering is minimized in cases where there is a dynamic calculation of EPS and static computation of Euclidean distance. The back propagation algorithm is used in this paper that computes the Euclidean distance in a dynamic manner. The DBSCAN algorithm has less complexity as compared to the other algorithms. It can also identify any type of shaped cluster which is difficult with in other algorithms. The identification of specific types of clusters is difficult in cases where objects are circulated in heterogeneous manner. As per the various experiments conducted here, it is seen that the accuracy of the algorithm is enhanced and the execution time is minimized.
'Raits blong mere'? Framing Human Rights and Gender Relations in Solomon Islands

In Solomon Islands, the language of human rights has become central to national legislation, policy making and NGO activism, focusing in particular on women's rights and violence against women. This is partly the result of trying to deal with the aftermath of a devastating conflict (1998–2003), which was due to growing anxieties between Malaitans and Guadalcanal people with regard to development equity and cultural respect. [1] During the civil conflict, which is by Solomon Islanders commonly referred to as 'the ethnic tension,' Solomon Islands experienced increasing civil unrest and instability, leading to a breakdown in law and order and a failure of essential government services. Moreover, gender-based violence against women and girls seems to have increased, and has become entrenched. [2] In 2003, after the Solomon Islands government requested assistance, Australia intervened through the Regional Assistance Mission to the Solomon Islands (RAMSI), which stopped most of the violence. 1.
On the Triebel-Lizorkin space boundedness of Marcinkiewicz integrals along compound surfaces

In this paper the author present the boundedness of Marcinkiewicz integral operators associated to compound surfaces with rough kernels given by h ∈ Δγ(R+) and Ω ∈ L(log+ L)1/2(Sn−1)∪(∪1<r<∞B r (Sn−1)) on Triebel-Lizorkin spaces and Besov spaces. The main results of this paper represent improvements as well as natural extensions of many previously known results. Mathematics subject classification (2010): 42B20, 42B15, 47G10.
MOJAVE. XV. VLBA 15 GHz Total Intensity and Polarization Maps of 437 Parsec-scale AGN Jets from 1996 to 2017

We present 5321 mas-resolution total intensity and linear polarization maps of 437 active galactic nuclei (AGNs) obtained with the VLBA at 15 GHz as part of the MOJAVE survey, and also from the NRAO data archive. The former is a long-term program to study the structure and evolution of powerful parsec-scale outflows associated with AGNs. The targeted AGNs are drawn from several flux-limited radio and γ-ray samples, and all have correlated VLBA flux densities greater than ∼50 mJy at 15 GHz. Approximately 80% of these AGNs are associated with γ-ray sources detected by the Fermi LAT instrument. The vast majority were observed with the VLBA on 5–15 occasions between 1996 January 19 and 2016 December 26, at intervals ranging from a month to several years, with the most typical sampling interval being six months. A detailed analysis of the linear and circular polarization evolutions of these AGN jets is presented in the other papers in this series.
Ferramenta de Gestão de Processos de Desenvolvimento de Software

Information systems tend to last several years. But they have to be updated over time to meet the new business needs, originating new requirements. Maintenance plays an important role in the software's life cycle, incorporating the new needs into the existing product, through a controlled, defined and effective development process. Unfortunately this is not an easy task. Most of the projects slipped, exceeding their initial budgets and schedules. Additionally, maintenance costs are rising, with estimated cost from 60% to 80% of the total software budget. Maintenance is an extended subject. This study is focused only in the perfective maintenance process. A few techniques (such as IEEE Standard 1219, CMM, Scrum and ITIL) are being used in order to reduce the costs and gain control over the process. Besides that it is necessary an excellent knowledge of the perfective maintenance process, as well a management tool with good control mechanisms, aligned with the process. With this study we propose to elicit the perfective maintenance process and to create a management tool which reflects the process needs and integrates the old systems used by OutSystems. Working close to OutSystems
Effect of Fiber Waviness on Tensile Properties of Sliver-Based Natural Fiber Composites

Glass and carbon fiber-reinforced composite materials have been applied for the high demand in industrial use to date, because their advantages are light weight, high strength, and corrosion resistance. However, the disposal problem after the use of these materials has also surfaced as a serious environmental problem. As a measure to solve this problem, many researchers have tried to investigate the potential of plant-based natural fibers instead of artificial fibers. When we use natural fibers as a long fiberreinforcement, the negative point is irregular fiber waviness inherent in a sliver form. This is because such fiber waviness often decreases the mechanical properties. The purpose of this study is thus to clarify the relation between irregular fiber waviness and the composite’s tensile strength. The clarification was performed from two points of view: One is quantification of irregular fiber waviness, based on spatial analysis such as Local Moran’s I and Geary’s c. Result shows that quantified parameters were correlated well with tensile strengths of sliver-based natural fiber composites. Another is a 3-D finite element analysis in which the fiber waviness was treated as an orthotropic body. Finally, the relation of the tensile strengths with maximum stress and Tsai-Hill criterions was discussed.
Quantification of arm joint motion for monitoring badminton performance using MEMS based wearable sensor

Recent advancement in wearable sensor technology has made a significant impact in the field of sports performance analysis, as it dramatically changes the conventional ways of training athletes. Sensor based systems help coaches to monitor, interpret and analyse athlete’s performance, where technology is used to train professionals. This study aims to develop a Wearable Sensor System (WSS) to monitor and quantify a badminton players arm motion, especially the wrist and elbow joint when performing serve and drive strokes. Current monitoring systems are unable to classify player’s competency level, due to insufficient technology to quantify joint variables and subsequent analysis on related badminton movement. However, previous studies suggest the vision-based systems, either using high-speed cameras or pre-recorded video analysis; but both systems are costly and are inconsistent in monitoring a badminton player due to environmental conditions (excessive lighting). Therefore, two monitoring modules are developed for the WSS. First, Hand Wrist Monitoring Module (HWMM) that uses flex sensors to measure player’s finger flexion and Inertia Measurement Units (IMU) to measure wrist rotation angle. Second, Elbow Monitoring Module (EMM) that used one IMU sensor to measure elbow joint rotation angle. Additionally, Real Time Operating System (RTOS) is implemented to manage simultaneous sensor data acquisition for synchronisation between monitoring modules. As a result, the WSS provides reliability and accuracy up to 95% for static joint motion measurements. Encouragingly, 6 out of 19 players were found to have improvements in their serve technique, whereas 12 players were able to perform a drive stroke with more than 80% similarities with the coach. Therefore, these findings emphasise the importance of a monitoring tool to help improve a badminton player’s performance, where it contributes by enhancing the coaching process by providing statistical and quantified movement data.