Filaments

The filament yarns have much more smoother surface and stronger as compare to staple yarn. The low twist multi-filament yarns create problems: if a single filament breaks during weaving. It will make fuzz ball, float or skip that will ultimately cause the loom to stop. It order to avoid these kinds of situations, it is necessary to size the yarn.

We have studied the relationship between filament disappearances with CMEs during solar period 1996–2010. We used the observed disappearing filaments in Ha data from Meudon given in NOAA, and coronal mass ejections data (CMEs) from SOHO/LASCO. We obtained 278 CME events (14%) contemporary filament disappearances and CME ejections (from a total of 2018 filament disappearance events and 15,874 CME events during 1996–2010). We found that the number of associated CME–filament disappearance events increased with the increase of the solar activity and significantly decreased with quiet sun. The longer filament disappearances have activity and ability to contemporary association with CMEs more than shorter filament disappearances. The filament disappearance powers the associated CMEs. CMEs which are associated with filament disappearance are ejected with higher speeds, massive, more energetic, and smaller angular width compared to non-associated CME events. In addition, the associated filament disappearance CMEs have two types depending on their duration; short-lived (<9 h), and long-lived (>9 h).

In activated sludge systems, the sludge settling ability is considered a critical step where filamentous bulking and biomass deflocculation are the most common problems, causing the reduction of the effluent quality. Furthermore, in recent years image processing techniques have been successfully used to elucidate the activated sludge morphology. Keeping that in mind, a program was developed for the characterization of activated sludge collected from eight wastewater treatment plants comprising both good and poor settling ...

The Galaxy and Mass Assembly (GAMA) survey provides an unprecedented level of deetail for studying the large scale structure of the nearby Universe, providing up to 10 times the target density of SDSS over an area of 144 square degrees. By using an adapted minimal spanning tree algorithm, we are able to use this data to identify and classify the distribution of groups and galaxies into filaments and voids; including coherent substructures composed entirely of galaxies. By running this algorithm on simulated GAMA catalogues, we compare the characteristics of large scale structure between observed and simulated data. This provides a probe of higher order distribution statistics not captured by the popularly used two-point correlation function.

Did you notice 'filaments' and 'spider webs' in the cosmos? Metaphors have been good for science. They are even more useful if we question the physical meaning of their words. The night sky is a permanent invitation to imagine what is, where it came from, and where it is heading. In antiquity, with naked eyes and open minds our ancestors imagined the Gods and 'signs' of what is about to happen to mankind. After Galileo Galilei, with telescopes, scientists observed movement and imagined relations among forces and bodies suspended in space. Today, with much more powerful photography and extraterrestrial probes, we are mesmerized by images of faraway galaxies. Celestial formations are out of reach. For lack of direct contact, we describe them in common terms such as spider webs and filaments. If we question the words, we may learn the cause of the natural configurations. First, the web of the spider is static. It is static because it is attached rigidly (in many points, without slip) to a much larger structure that is not moving in the frame of reference of the observer. What is the stationary structure that supports a celestial web? The spider web is a net, with loops, like the net for catching fish, and the net that holds the baker's hair. That web is not flowing along its threads like the water in river channels.

—Additive manufacturing is becoming more affordable and in recent years new techniques on implementing this emerging technology into microwave circuits are being developed. Therefore, understanding the propagation behavior of printed microstrip transmission lines implemented with these manufacturing techniques is needed. In this paper, the use of flexible and semi-rigid filaments to create substrates using additive manufacturing for printed microstrip transmission lines is presented. In particular, NinjaFlex, PLA and Armadillo filaments are used to manufacture 1.57 mm thick grounded substrates for 50Ω microstrip transmission lines. The dielectric properties (i.e., complex permittivity) of these newly manufactured substrates are determined by comparing the following three results: (1) measurements with a Keysight N1501A-101 high temperature dielectric probe; (2) measurements of a known transmission line problem and (3) full-wave simulations. The results in this paper show a good comparison between the three aforementioned techniques and that a 50Ω printed microstrip transmission line can be created on substrates manufactured using the NinjaFlex, PLA and Armadillo filaments up to 3.0 GHz.

In this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The support vector machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the US National Geophysical Data Center (NGDC) filament catalogue and the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph (SOHO/LASCO) CME catalogue are processed to associate filaments with CMEs. In the previous studies, which classified CMEs into gradual and impulsive CMEs, the associations were refined based on the CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data-mining system is created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that can have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type, and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14 000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance.

We studied the association between the filament disappearances and solar flares during 1996–2010; we listed 639 associated filament disappearances with solar flares under temporal and spatial condition, those particular 639 filament disappearance were associated with 1676 solar flares during the period 1996–2010. The best angular distance between filament disappearances and associated solar flares ranged between 30 and 60. The number of the associated events increased with increasing solar activity and decreased with quiet sun. The location of filament disappearances ranges between latitude ±50 and longitude ±70. We found that longer filament disappearances have activity and ability of contemporary association with flares more than shorter filament disappearance, filament disappearance powers the associated flares more than non-associated flares events. The associated flares have higher solar flux, longer
duration, and higher importance compared to non-associated flares with filament disappearance. In addition the associated filament disappearance with flares have two types depending on their duration, short-lived (<9 h), and long-lived (>9 h).