Deep Cellular Recurrent Neural Architecture for Efficient Multidimensional Time-Series Data Processing

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the-art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed

Exploring the Impact of NF- KB1 Gene Polymorphism

This review article extensively explores the influence of NF-κB1 gene polymorphism on a diverse range of health issues. The NF-κB pathway, a crucial controller of immune response, is closely associated with numerous disease mechanisms. The NF-kB1 gene has undergone significant genetic changes, and these changes have shown strong connections with the onset and development of numerous disorders. This article investigates the intricate relationship between mutations in the NF-kB1 gene and a wide range of disorders through a thorough study of the literature. These conditions encompass inflammatory disorders, cancer, cardiovascular diseases (CVD), and various other medical ailments. The notable discoveries emphasized within this review underscore the essential role of NF-κB1 gene polymorphism in the development of a range of diseases. Furthermore, these discoveries have important ramifications that could help develop more specialized, successful treatment approaches. To sum up, this work sheds light on the different ways in which NF-kB1 gene variation influences the progression of disorders and highlights the urgent need for more research in this area

Resolution of Inflammation in Periodontitis: A Comprehensive Review

Inflammation, a natural defence mechanism against injury or infection, can become problematic when it fails to resolve, as observed in conditions like periodontitisThis review explores how inflammation is resolved in periodontitis and seeks potential treatments for this chronic condition, which damages the periodontium, including the gingival tissue, periodontal ligament, and alveolar bone. The pathogenesis of this disease is initiated by the inflammatory response triggered by resident leukocytes and endothelial cells upon exposure to bacterial biofilms, resulting in vasodilation and immune cell recruitment. The review stresses the importance of researching targeted approaches for periodontitis treatment, such as inducing neutrophil apoptosis, shifting from M1 to M2 macrophages, and exploring M2-based tissue engineering. Additionally, investigating lymphangiogenesis and Treg cell recruitment at the inflammation site offers promising avenues. In conclusion, further studies are needed to refine lymphangiogenesis and assess the potential of pro-resolving lipid mediators and anti-inflammatory cytokines in managing periodontitis. Ongoing research aims to uncover the underlying biomolecular mechanisms governing immune cells and resolving mediators, with the ultimate goal of restoring tissue equilibrium and promoting healing

Geodynamics of the Andaman - Sumatra - Java Trench - Arc System Based on Gravity and Seismotectonic Study

This work aims to study the variation in subduction zone geometry along and across the arc and the fault pattern within the subducting plate. Depth of penetration as well as the dip of the Benioff zone varies considerably along the arc which corresponds to the curvature of the fold- thrust belt which varies from concave to convex in different sectors of the arc. The entire arc is divided into 27 segments and depth sections thus prepared are utilized to investigate the average dip of the Benioff zone in the different parts of the entire arc, penetration depth of the subducting lithosphere, the subduction zone geometry underlying the trench, the arctrench gap, etc.The study also describes how different seismogenic sources are identified in the region, estimation of moment release rate and deformation pattern. The region is divided into broad seismogenic belts. Based on these previous studies and seismicity Pattern, we identified several broad distinct seismogenic belts/sources. These are l) the Outer arc region consisting of Andaman-Nicobar islands 2) the back-arc Andaman Sea 3)The Sumatran fault zone(SFZ)4)Java onshore region termed as Jave Fault Zone(JFZ)5)Sumatran fore arc silver plate consisting of Mentawai fault(MFZ)6) The offshore java fore arc region 7)The Sunda Strait region.As the Seismicity is variable,it is difficult to demarcate individual seismogenic sources.Hence, we employed a moving window method having a window length of 3—4° and with 50% overlapping starting from one end to the other. We succeeded in defining 4 sources each in the Andaman fore arc and Back arc region, 9 such sources (moving windows) in the Sumatran Fault zone (SFZ), 9 sources in the offshore SFZ region and 7 sources in the offshore Java region. Because of the low seismicity along JFZ, it is separated into three seismogenic sources namely West Java, Central Java and East Java. The Sunda strait is considered as a single seismogenic source.The deformation rates for each of the seismogenic zones have been computed. A detailed error analysis of velocity tensors using Monte—Carlo simulation method has been carried out in order to obtain uncertainties. The eigen values and the respective eigen vectors of the velocity tensor are computed to analyze the actual deformation pattem for different zones. The results obtained have been discussed in the light of regional tectonics, and their implications in terms of geodynamics have been enumerated.ln the light of recent major earthquakes (26th December 2004 and 28th March 2005 events) and the ongoing seismic activity, we have recalculated the variation in the crustal deformation rates prior and after these earthquakes in Andaman—Sumatra region including the data up to 2005 and the significant results has been presented.ln this chapter, the down going lithosphere along the subduction zone is modeled using the free air gravity data by taking into consideration the thickness of the crustal layer, the thickness of the subducting slab, sediment thickness, presence of volcanism, the proximity of the continental crust etc. Here a systematic and detailed gravity interpretation constrained by seismicity and seismic data in the Andaman arc and the Andaman Sea region in order to delineate the crustal structure and density heterogeneities a Io nagnd across the arc and its correlation with the seismogenic behaviour is presented.Department of Marine Geology & Geophysics, Cochin University of Science and Technolog

Seismicity, gravity anomalies and lithospheric structure of the Andaman arc, NE Indian Ocean

The Andaman arc in the northeastern Indian Ocean defines nearly 1100 km long active plate margin between the India and Burma plates where an oblique Benioff zone develops down to 200 km depth. Several east-trending seismologic sections taken across the Andaman Benioff Zone (ABZ) are presented here to detail the subduction zone geometry in a 3-D perspective. The slab gravity anomaly, computed from the 3-D ABZ configuration, is a smooth, long-wavelength and symmetric gravity high of 85 mGal amplitude centering to the immediate east of the Nicobar island, where, a prominent gravity "high" follows the Nicobar Deep. The Slab-Residual Gravity Anomaly (SRGA) and Mantle Bouguer Anomaly (MBA) maps prepared for the Andaman plate margin bring out a double-peaked SRGA "low" in the range of -150 to -240 mGal and a wider-cum-larger MBA "low" having the amplitude of -280 to -315 mGal demarcating the Andaman arc-trench system. The gravity models provide evidences for structural control in propagating the rupture within the lithosphere. The plate margin configuration below the Andaman arc is sliced by the West Andaman Fault (WAF) as well as by a set of sympathetic faults of various proportions, often cutting across the fore-arc sediment package. Some of these fore-arc thrust faults clearly give rise to considerably high post-seismic activity, but the seismic incidence along the WAF further east is comparatively much less particularly in the north, although, the lack of depth resolution for many of the events prohibits tracing the downward continuity of these faults. Tectonic correlation of the gravity-derived models presented here tends to favour the presence of oceanic crust below the Andaman-Nicobar Outer Arc Ridge. (c) 200

Temperature error in digital bathythermograph data

234-236Simultaneous Digital Bathythermograph (DBT) and Nansen Cast data collected during two cruises of R.V. Gaveshani (GV-117 and GV-118) and archived in Indian Oceanographic Data Centre (IODC) are used to determine existing temperature errors in DBT. The resulting mean error for DBT data from the GV-117 cruise varies from -0.5 to - 1 oC, while it varied between -0.3 and -0.6 oC for data from cruise GV-118. For both the data sets, the error shows consistently negative bias from surface to 800 m depth, however there is no apparent or measurable systematic dependence of the error on depth. Considering the given temperature accuracy of 0.05 oC, the observed DBT error, varying from -0.3 to -1 oC, is significant and such offsets should be removed from DBT archives. It is found that a corrective measure of +0.5 oC, equivalent to the mean surface offset obtained from two cruises, can considerably reduce the temperature error at all DBT depths

Capacitive sensor based 2D subsurface imaging technology for non-destructive evaluation of building surfaces

Understanding the underlying structure of building surfaces like walls and floors is essential when carrying out building maintenance and modification work. To facilitate such work, this paper introduces a capacitive sensor based technology which can conduct non-destructive evaluation of building surfaces. The novelty of this sensor is that it can generate a real-time 2D subsurface image which can be used to understand structure beneath the top surface. Finite Element Analysis (FEA) simulations are done to understand the best sensor head configuration that gives optimum results. Hardware and software components are custom-built to facilitate real-time imaging capability. The sensor is validated by laboratory tests, which revealed the ability of the proposed capacitive sensing technology to see through common building materials like wood and concrete. The 2D image generated by the sensor is found to be useful in understanding the subsurface structure beneath the top surface

Investigating Effects of Reverse Osmosis-Treated Water on the Corrosion Rate of Chains in Armoured Face Conveyor for Longwall Mining

Premature failure of armoured face conveyor (AFC) chains due to corrosion is a significant proportion of the unplanned downtime experienced on longwall equipment. The AFC chains are constantly in contact with water and wet coal. The premature failure issue of AFC chains has become more prominent since the introduction of longwall top-coal caving with its additional AFC. Reverse osmosis (RO) is a popular water treatment method for reducing salinity and dissolved solids, but its impact on the corrosion of the AFC chains is unclear. This study has been commissioned to investigate the direct effects of RO water on the corrosion of AFC chains. An immersion test was carried out using AFC chain steel submerged in two water samples: untreated dam water and treated water from an RO treatment plant. Elemental analysis was conducted for both water samples, and four corrosion indices were measured for both water samples. The RO water more vigorously dissolves calcium carbonate scales leading to increased corrosion of the AFC chains although the dam water has much higher levels of calcium, chloride, sulphate, sodium, and magnesium. Bicarbonate ions are the main alkaline factor of water that provides the buffering capacity to acids. Decreased alkalinity without balancing other ions in water causes high corrosivity and decreased scaling tendencies