Search results for: sequence alignment-free model
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 17169

Search results for: sequence alignment-free model

17109 Physical Education Teacher's Interpretation toward Teaching Games for Understanding Model

Authors: Soni Nopembri

Abstract:

The objective of this research is to evaluate the implementation of teaching games for Understanding model by conducting action to physical education teacher who have got long teaching experience. The research applied Participatory Action Research. The subjects of this research were 19 physical education teachers who had got training of Teaching Games for Understanding. Data collection was conducted intensively through a questionnaire, in-depth interview, Focus Group Discussion (FGD), observation, and documentation. The collected data was analysis zed qualitatively and quantitatively. The result showed that physical education teachers had got an appropriate interpretation on TGfU model. Some indicators that were the focus of this research indicated this points; they are: (1) physical education teachers had good understanding toward TGfU model, (2) PE teachers’ competence in applying TGfU model on Physical Education at school were adequate, though some improvement were needed, (3) the influence factors in the implementation of TGfU model, in sequence, were teacher, facilities, environment, and students factors, (4) PE teachers’ perspective toward TGfU model were positively good, although some teachers were less optimistic toward the development of TGfU model in the future.

Keywords: TGfU, physical education teacher, teaching games, FGD

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17108 Clastic Sequence Stratigraphy of Late Jurassic to Early Cretaceous Formations of Jaisalmer Basin, Rajasthan

Authors: Himanshu Kumar Gupta

Abstract:

The Jaisalmer Basin is one of the parts of the Rajasthan basin in northwestern India. The presence of five major unconformities/hiatuses of varying span i.e. at the top of Archean basement, Cambrian, Jurassic, Cretaceous, and Eocene have created the foundation for constructing a sequence stratigraphic framework. Based on basin formative tectonic events and their impact on sedimentation processes three first-order sequences have been identified in Rajasthan Basin. These are Proterozoic-Early Cambrian rift sequence, Permian to Middle-Late Eocene shelf sequence and Pleistocene - Recent sequence related to Himalayan Orogeny. The Permian to Middle Eocene I order sequence is further subdivided into three-second order sequences i.e. Permian to Late Jurassic II order sequence, Early to Late Cretaceous II order sequence and Paleocene to Middle-Late Eocene II order sequence. In this study, Late Jurassic to Early Cretaceous sequence was identified and log-based interpretation of smaller order T-R cycles have been carried out. A log profile from eastern margin to western margin (up to Shahgarh depression) has been taken. The depositional environment penetrated by the wells interpreted from log signatures gave three major facies association. The blocky and coarsening upward (funnel shape), the blocky and fining upward (bell shape) and the erratic (zig-zag) facies representing distributary mouth bar, distributary channel and marine mud facies respectively. Late Jurassic Formation (Baisakhi-Bhadasar) and Early Cretaceous Formation (Pariwar) shows a lesser number of T-R cycles in shallower and higher number of T-R cycles in deeper bathymetry. Shallowest well has 3 T-R cycles in Baisakhi-Bhadasar and 2 T-R cycles in Pariwar, whereas deeper well has 4 T-R cycles in Baisakhi-Bhadasar and 8 T-R cycles in Pariwar Formation. The Maximum Flooding surfaces observed from the stratigraphy analysis indicate major shale break (high shale content). The study area is dominated by the alternation of shale and sand lithologies, which occurs in an approximate ratio of 70:30. A seismo-geological cross section has been prepared to understand the stratigraphic thickness variation and structural disposition of the strata. The formations are quite thick to the west, the thickness of which reduces as we traverse towards the east. The folded and the faulted strata indicated the compressional tectonics followed by the extensional tectonics. Our interpretation is supported with seismic up to second order sequence indicates - Late Jurassic sequence is a Highstand Systems Tract (Baisakhi - Bhadasar formations), and the Early Cretaceous sequence is Regressive to Lowstand System Tract (Pariwar Formation).

Keywords: Jaisalmer Basin, sequence stratigraphy, system tract, T-R cycle

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17107 Perceptual Organization within Temporal Displacement

Authors: Michele Sinico

Abstract:

The psychological present has an actual extension. When a sequence of instantaneous stimuli falls in this short interval of time, observers perceive a compresence of events in succession and the temporal order depends on the qualitative relationships between the perceptual properties of the events. Two experiments were carried out to study the influence of perceptual grouping, with and without temporal displacement, on the duration of auditory sequences. The psychophysical method of adjustment was adopted. The first experiment investigated the effect of temporal displacement of a white noise on sequence duration. The second experiment investigated the effect of temporal displacement, along the pitch dimension, on temporal shortening of sequence. The results suggest that the temporal order of sounds, in the case of temporal displacement, is organized along the pitch dimension.

Keywords: time perception, perceptual present, temporal displacement, Gestalt laws of perceptual organization

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17106 One-Shot Text Classification with Multilingual-BERT

Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao

Abstract:

Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.

Keywords: OSML, BERT, text classification, one shot

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17105 Effect of Weave Structure and Picking Sequence on the Comfort Properties of Woven Fabrics

Authors: Muhammad Umair, Tanveer Hussain, Khubab Shaker, Yasir Nawab, Muhammad Maqsood, Madeha Jabbar

Abstract:

The term comfort is defined as 'the absence of unpleasantness or discomfort' or 'a neutral state compared to the more active state'. Comfort mainly is of three types: sensorial (tactile) comfort, psychological comfort and thermo-physiological comfort. Thermophysiological comfort is determined by the air permeability and moisture management properties of the garment. The aim of this study was to investigate the effect of weave structure and picking sequence on the comfort properties of woven fabrics. Six woven fabrics with two different weave structures i.e. 1/1 plain and 3/1 twill and three different picking sequences: (SPI, DPI, 3PI) were taken as input variables whereas air permeability, wetting time, wicking behavior and overall moisture management capability (OMMC) of fabrics were taken as response variables and a comparison is made of the effect of weave structure and picking sequence on the response variables. It was found that fabrics woven in twill weave design and with simultaneous triple pick insertion (3PI) give significantly better air permeability, shorter wetting time and better water spreading rate, as compared to plain woven fabrics and those with double pick insertion (DPI) or single pick insertion (SPI). It could be concluded that the thermophysiological comfort of woven fabrics may be significantly improved simply by selecting a suitable weave design and picking sequence.

Keywords: air permeability, picking sequence, thermophysiological comfort, weave design

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17104 PMEL Marker Identification of Dark and Light Feather Colours in Local Canary

Authors: Mudawamah Mudawamah, Muhammad Z. Fadli, Gatot Ciptadi, Aulanni’am

Abstract:

Canary breeders have spread throughout Indonesian regions for the low-middle society and become an income source for them. The interesting phenomenon of the canary market is the feather colours become one of determining factor for the price. The advantages of this research were contributed to the molecular database as a base of selection and mating for the Indonesia canary breeder. The research method was experiment with the genome obtained from canary blood isolation. The genome did the PCR amplification with PMEL marker followed by sequencing. Canaries were used 24 heads of light and dark colour feathers. Research data analyses used BioEdit and Network 4.6.0.0 software. The results showed that all samples were amplification with PMEL gene with 500 bp fragment length. In base sequence of 40 was found Cytosine(C) in the light colour canaries, while the dark colour canaries was obtained Thymine (T) in same base sequence. Sequence results had 286-415 bp fragment and 10 haplotypes. The conclusions were the PMEL gene (gene of white pigment) was likely to be used PMEL gene to detect molecular genetic variation of dark and light colour feather.

Keywords: canary, haplotype, PMEL, sequence

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17103 Speech Detection Model Based on Deep Neural Networks Classifier for Speech Emotions Recognition

Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

Abstract:

Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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17102 A Fuzzy Mathematical Model for Order Acceptance and Scheduling Problem

Authors: E. Koyuncu

Abstract:

The problem of Order Acceptance and Scheduling (OAS) is defined as a joint decision of which orders to accept for processing and how to schedule them. Any linear programming model representing real-world situation involves the parameters defined by the decision maker in an uncertain way or by means of language statement. Fuzzy data can be used to incorporate vagueness in the real-life situation. In this study, a fuzzy mathematical model is proposed for a single machine OAS problem, where the orders are defined by their fuzzy due dates, fuzzy processing times, and fuzzy sequence dependent setup times. The signed distance method, one of the fuzzy ranking methods, is used to handle the fuzzy constraints in the model.

Keywords: fuzzy mathematical programming, fuzzy ranking, order acceptance, single machine scheduling

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17101 DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations

Authors: Xiao Zhou, Jianlin Cheng

Abstract:

A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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17100 Elemental Graph Data Model: A Semantic and Topological Representation of Building Elements

Authors: Yasmeen A. S. Essawy, Khaled Nassar

Abstract:

With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: building information modeling (BIM), elemental graph data model (EGDM), geometric and topological data models, graph theory

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17099 In-Depth Analysis on Sequence Evolution and Molecular Interaction of Influenza Receptors (Hemagglutinin and Neuraminidase)

Authors: Dong Tran, Thanh Dac Van, Ly Le

Abstract:

Hemagglutinin (HA) and Neuraminidase (NA) play an important role in host immune evasion across influenza virus evolution process. The correlation between HA and NA evolution in respect to epitopic evolution and drug interaction has yet to be investigated. In this study, combining of sequence to structure evolution and statistical analysis on epitopic/binding site specificity, we identified potential therapeutic features of HA and NA that show specific antibody binding site of HA and specific binding distribution within NA active site of current inhibitors. Our approach introduces the use of sequence variation and molecular interaction to provide an effective strategy in establishing experimental based distributed representations of protein-protein/ligand complexes. The most important advantage of our method is that it does not require complete dataset of complexes but rather directly inferring feature interaction from sequence variation and molecular interaction. Using correlated sequence analysis, we additionally identified co-evolved mutations associated with maintaining HA/NA structural and functional variability toward immunity and therapeutic treatment. Our investigation on the HA binding specificity revealed unique conserved stalk domain interacts with unique loop domain of universal antibodies (CR9114, CT149, CR8043, CR8020, F16v3, CR6261, F10). On the other hand, NA inhibitors (Oseltamivir, Zaninamivir, Laninamivir) showed specific conserved residue contribution and similar to that of NA substrate (sialic acid) which can be exploited for drug design. Our study provides an important insight into rational design and identification of novel therapeutics targeting universally recognized feature of influenza HA/NA.

Keywords: influenza virus, hemagglutinin (HA), neuraminidase (NA), sequence evolution

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17098 Speed Breaker/Pothole Detection Using Hidden Markov Models: A Deep Learning Approach

Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

Abstract:

A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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17097 Novel Coprocessor for DNA Sequence Alignment in Resequencing Applications

Authors: Atef Ibrahim, Hamed Elsimary, Abdullah Aljumah, Fayez Gebali

Abstract:

This paper presents a novel semi-systolic array architecture for an optimized parallel sequence alignment algorithm. This architecture has the advantage that it can be modified to be reused for multiple pass processing in order to increase the number of processing elements that can be packed into a single FPGA and to increase the number of sequences that can be aligned in parallel in a single FPGA. This resolves the potential problem of many FPGA resources left unused for designs that have large values of short read length. When using the previously published conventional hardware design. FPGA implementation results show that, for large values of short read lengths (M>128), the proposed design has a slightly higher speed up and FPGA utilization over the the conventional one.

Keywords: bioinformatics, genome sequence alignment, re-sequencing applications, systolic array

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17096 Exploring Simple Sequence Repeats within Conserved microRNA Precursors Identified from Tea Expressed Sequence Tag (EST) Database

Authors: Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das

Abstract:

Tea (Camellia sinensis) has received substantial attention from the scientific world time to time, not only for its commercial importance, but also for its demand to the health-conscious people across the world for its extensive use as potential sources of antioxidant supplement. These health-benefit traits primarily rely on some regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions is being worthwhile for studying the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea the trait-specific Simple Sequence Repeats (SSRs) are yet to be identified, which can be used for marker assisted breeding technique. MicroRNAs are endogenous, noncoding, short RNAs directly involved in regulating gene expressions at the post-transcriptional level. It has been found that diversity in miRNA gene interferes the formation of its characteristic hair pin structure and the subsequent function. In the present study, the precursors of small regulatory RNAs (microRNAs) has been fished out from tea Expressed Sequence Tag (EST) database. Furthermore, the simple sequence repeat motifs within the putative miRNA precursor genes are also identified in order to experimentally validate their existence and function. It is already known that genic-SSR markers are very adept and breeder-friendly source for genetic diversity analysis. So, the potential outcome of this in-silico study would provide some novel clues in understanding the miRNA-triggered polymorphic genic expression controlling specific metabolic pathways, accountable for tea quality.

Keywords: micro RNA, simple sequence repeats, tea quality, trait specific marker

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17095 Prediction and Identification of a Permissive Epitope Insertion Site for St Toxoid in cfaB from Enterotoxigenic Escherichia coli

Authors: N. Zeinalzadeh, Mahdi Sadeghi

Abstract:

Enterotoxigenic Escherichia coli (ETEC) is the most common cause of non-inflammatory diarrhea in the developing countries, resulting in approximately 20% of all diarrheal episodes in children in these areas. ST is one of the most important virulence factors and CFA/I is one of the frequent colonization factors that help to process of ETEC infection. ST and CfaB (CFA/I subunit) are among vaccine candidates against ETEC. So, ST because of its small size is not a good immunogenic in the natural form. However to increase its immunogenic potential, here we explored candidate positions for ST insertion in CfaB sequence. After bioinformatics analysis, one of the candidate positions was selected and the chimeric gene (cfaB*st) sequence was synthesized and expressed in E. coli BL21 (DE3). The chimeric recombinant protein was purified with Ni-NTA columns and characterized with western blot analysis. The residue 74-75 of CfaB sequence could be a good candidate position for ST and other epitopes insertion.

Keywords: bioinformatics, CFA/I, enterotoxigenic E. coli, ST toxoid

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17094 Nucleotide Based Validation of the Endangered Plant Diospyros mespiliformis (Ebenaceae) by Evaluating Short Sequence Region of Plastid rbcL Gene

Authors: Abdullah Alaklabi, Ibrahim A. Arif, Sameera O. Bafeel, Ahmad H. Alfarhan, Anis Ahamed, Jacob Thomas, Mohammad A. Bakir

Abstract:

Diospyros mespiliformis (Hochst. ex A.DC.; Ebenaceae) is a large deciduous medicinal plant. This plant species is currently listed as endangered in Saudi Arabia. Molecular identification of this plant species based on short sequence regions (571 and 664 bp) of plastid rbcL (ribulose-1, 5-biphosphate carboxylase) gene was investigated in this study. The endangered plant specimens were collected from Al-Baha, Saudi Arabia (GPS coordinate: 19.8543987, 41.3059349). Phylogenetic tree inferred from the rbcL gene sequences showed that this species is very closely related with D. brandisiana. The close relationship was also observed among D. bejaudii, D. Philippinensis and D. releyi (≥99.7% sequence homology). The partial rbcL gene sequence region (571 bp) that was amplified by rbcL primer-pair rbcLaF-rbcLaR failed to discriminate D. mespiliformis from the closely related plant species, D. brandisiana. In contrast, primer-pair rbcL1F-rbcL724R yielded longer amplicon, discriminated the species from D. brandisiana and demonstrated nucleotide variations in 3 different sites (645G>T; 663A>C; 710C>G). Although D. mespiliformis (EU980712) and D. brandisiana (EU980656) are very closely related species (99.4%); however, studied specimen showed 100% sequence homology with D. mespiliformis and 99.6% with D. brandisiana. The present findings showed that rbcL short sequence region (664 bp) of plastid rbcL gene, amplified by primer-pair rbcL1F-rbcL724R, can be used for authenticating samples of D. mespiliforformis and may provide help in authentic identification and management process of this medicinally valuable endangered plant species.

Keywords: Diospyros mespiliformis, endangered plant, identification partial rbcL

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17093 Unveiling the Chaura Thrust: Insights into a Blind Out-of-Sequence Thrust in Himachal Pradesh, India

Authors: Rajkumar Ghosh

Abstract:

The Chaura Thrust, located in Himachal Pradesh, India, is a prominent geological feature that exhibits characteristics of an out-of-sequence thrust fault. This paper explores the geological setting of Himachal Pradesh, focusing on the Chaura Thrust's unique characteristics, its classification as an out-of-sequence thrust, and the implications of its presence in the region. The introduction provides background information on thrust faults and out-of-sequence thrusts, emphasizing their significance in understanding the tectonic history and deformation patterns of an area. It also outlines the objectives of the paper, which include examining the Chaura Thrust's geological features, discussing its classification as an out-of-sequence thrust, and assessing its implications for the region. The paper delves into the geological setting of Himachal Pradesh, describing the tectonic framework and providing insights into the formation of thrust faults in the region. Special attention is given to the Chaura Thrust, including its location, extent, and geometry, along with an overview of the associated rock formations and structural characteristics. The concept of out-of-sequence thrusts is introduced, defining their distinctive behavior and highlighting their importance in the understanding of geological processes. The Chaura Thrust is then analyzed in the context of an out-of-sequence thrust, examining the evidence and characteristics that support this classification. Factors contributing to the out-of-sequence behavior of the Chaura Thrust, such as stress interactions and fault interactions, are discussed. The geological implications and significance of the Chaura Thrust are explored, addressing its impact on the regional geology, tectonic evolution, and seismic hazard assessment. The paper also discusses the potential geological hazards associated with the Chaura Thrust and the need for effective mitigation strategies in the region. Future research directions and recommendations are provided, highlighting areas that warrant further investigation, such as detailed structural analyses, geodetic measurements, and geophysical surveys. The importance of continued research in understanding and managing geological hazards related to the Chaura Thrust is emphasized. In conclusion, the Chaura Thrust in Himachal Pradesh represents an out-of-sequence thrust fault that has significant implications for the region's geology and tectonic evolution. By studying the unique characteristics and behavior of the Chaura Thrust, researchers can gain valuable insights into the geological processes occurring in Himachal Pradesh and contribute to a better understanding and mitigation of seismic hazards in the area.

Keywords: chaura thrust, out-of-sequence thrust, himachal pradesh, geological setting, tectonic framework, rock formations, structural characteristics, stress interactions, fault interactions, geological implications, seismic hazard assessment, geological hazards, future research, mitigation strategies.

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17092 Solving Single Machine Total Weighted Tardiness Problem Using Gaussian Process Regression

Authors: Wanatchapong Kongkaew

Abstract:

This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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17091 Opaque Mineralogy of the Late Precambrian Ophiolites from Bou Azzer Area, Anti-atlas, Morrocco

Authors: Yaser Maher Abdelaziz Hawa

Abstract:

The Basic-ultrabasic rocks of Bou Azzer ophiolite complex in the Anti-atlas , Morrocco enclose some oxide and sulfide minerals as dissiminated traces. The oxide minerals show a wide variation in composition ranging from Cr-free. Titanomagnetite and ilmenite in the chilled margin gabbro of the upper part of the ophiolite sequence to Al-rich chromian spinel and pure magnetite enclosed in the serpentinized peridotite in the lower part of the sequence. Five mineral assemblages have been distinguished depending on the rock type of the ophiolite sequence. 1-Gersodorfite + Chalcopyrite + Al-Mg rich chromian spinel + pure magnetite, hosted by serpentinized peridotite. 2- Pyrite + Chalcopyrite, enclosed in metagabbro and overlying the ultrabasic cumulates. 3- Al-Fe rich Chromian spinel with rims of Al –rich chromian magnetite enclosed in wherlite. 4- Titanomagnetite replaced by sphene enclosed in marginal Gabbro. 5- Pyrrhotite exsolving Pentlandite + ilmenite + Ilmenite + Al- rich Chromian spinel + magnetite enclosed in fresh olivine olivine in the upper part of the ophiolite sequence.

Keywords: opaques, ophiolites, anti-atlas, morrocco

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17090 Life Prediction Method of Lithium-Ion Battery Based on Grey Support Vector Machines

Authors: Xiaogang Li, Jieqiong Miao

Abstract:

As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines.For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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17089 Analytical Modeling of Globular Protein-Ferritin in α-Helical Conformation: A White Noise Functional Approach

Authors: Vernie C. Convicto, Henry P. Aringa, Wilson I. Barredo

Abstract:

This study presents a conformational model of the helical structures of globular protein particularly ferritin in the framework of white noise path integral formulation by using Associated Legendre functions, Bessel and convolution of Bessel and trigonometric functions as modulating functions. The model incorporates chirality features of proteins and their helix-turn-helix sequence structural motif.

Keywords: globular protein, modulating function, white noise, winding probability

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17088 The Various Legal Dimensions of Genomic Data

Authors: Amy Gooden

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When human genomic data is considered, this is often done through only one dimension of the law, or the interplay between the various dimensions is not considered, thus providing an incomplete picture of the legal framework. This research considers and analyzes the various dimensions in South African law applicable to genomic sequence data – including property rights, personality rights, and intellectual property rights. The effective use of personal genomic sequence data requires the acknowledgement and harmonization of the rights applicable to such data.

Keywords: artificial intelligence, data, law, genomics, rights

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17087 The Influence of Directionality on the Giovanelli Illusion

Authors: Michele Sinico

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In the Giovanelli illusion, some collinear dots appear misaligned, when each dot lies within a circle and the circles are not collinear. In this illusion, the role of the frame of reference, determined by the circles, is considered a crucial factor. Three experiments were carried out to study the influence of directionality of the circles on the misalignment. The adjustment method was used. Participants changed the orthogonal position of each dot, from the left to the right of the sequence, until a collinear sequence of dots was achieved. The first experiment verified the illusory effect of the misalignment. In the second experiment, the influence of two different directionalities of the circles (-0.58° and +0.58°) on the misalignment was tested. The results show an over-normalization on the sequences of the dots. The third experiment tested the misalignment of the dots without any inclination of the sequence of circles (0°). Only a local illusory effect was found. These results demonstrate that the directionality of the circles, as a global factor, can increase the misalignment. The findings also indicate that directionality and the frame of reference are independent factors in explaining the Giovanelli illusion.

Keywords: Giovannelli illusion, visual illusion, directionality, misalignment, the frame of reference

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17086 Exploring the Impact of Input Sequence Lengths on Long Short-Term Memory-Based Streamflow Prediction in Flashy Catchments

Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

Abstract:

Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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17085 A Sentence-to-Sentence Relation Network for Recognizing Textual Entailment

Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

Abstract:

Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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17084 Unraveling the Puzzle of Out-of-Sequence Thrusting in the Higher Himalaya: Focus on Jhakri-Chaura-Sarahan Thrust, Himachal Pradesh, India

Authors: Rajkumar Ghosh

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The study examines the structural analysis of Chaura Thrust in Himachal Pradesh, India, focusing on the activation timing of Main Central Thrust (MCT) and South Tibetan Detachment System (STDS), mylonitised zones, and the characterization of box fold and its signature in the regional geology of Himachal Himalaya. The research aims to document the Higher Himalayan Out-of-Sequence Thrust (OOST) in Himachal Pradesh, which activated the MCTL and in between a zone south of MCTU. The study also documents the GBM-associated temperature range and the activation of Higher Himalayan Out-of-Sequence Thrust (OOST) in Himachal Pradesh. The findings contribute to understanding the structural analysis of Chaura Thrust and its signature in the regional geology of Himachal Himalaya. The study highlights the significance of microscopic studies in documenting mylonitized zones and identifying various types of crenulated schistosity. The study concludes that Chaura Thrust is not a blind thrust and details the field evidence for the OOST. The study characterizes the box fold and its signature in the regional geology of Himachal Himalaya. The study also documents the activation timing and ages of MCT, STDS, MBT, and MFT and identifies various types of crenulated schistosity under the microscope. The study also highlights the significance of microscopic studies in the structural analysis of Chaura Thrust. Finally, the study documents the activation of Higher Himalayan Out-of-Sequence Thrust (OOST) in Himachal Pradesh and the expectations for strain variation near the OOST.

Keywords: Chaura Thrust, Higher Himalaya, Jhakri Thrust, Main Central Thrust, Out-of-Sequence Thrust, Sarahan Thrust

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17083 Academic Staff Recruitment in Islamic University: A Proposed Holistic Model

Authors: Syahruddin Sumardi, Indra Fajar Alamsyah, Junaidah Hashim

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This study attempts to explore and presents a proposed recruitment model in Islamic university which aligned with holistic role. It is a conceptual paper in nature. In turn, this study is designed to utilize exploratory approach. Literature and document review that related to this topic are used as the methods to analyse the content found. Recruitment for any organization is fundamental to achieve its goal effectively. Staffing in universities is vital due to the importance role of lecturers. Currently, Islamic universities still adopt the common process of recruitment for their academic staffs. Whereas, they have own characteristics which are embedded in their institutions. Furthermore, the FCWC (Foundation, Capability, Worldview and Commitment) model of recruitment proposes to suit the holistic character of Islamic university. Further studies are required to empirically validate the concept through systematic investigations. Additionally, measuring this model by a designed means is appreciated. The model provides the map and alternative tool of recruitment for Islamic universities to determine the process of recruitment which can appropriate their institutions. In addition, it also allows stakeholders and policy makers to consider regarding Islamic values that should inculcate in the Islamic higher learning institutions. This study initiates a foundational contribution for an early sequence of research.

Keywords: academic staff, Islamic values, recruitment model, university

Procedia PDF Downloads 138
17082 Isolation and Characterization of Cotton Infecting Begomoviruses in Alternate Hosts from Cotton Growing Regions of Pakistan

Authors: M. Irfan Fareed, Muhammad Tahir, Alvina Gul Kazi

Abstract:

Castor bean (Ricinus communis; family Euphorbiaceae) is cultivated for the production of oil and as an ornamental plant throughout tropical regions. Leaf samples from castor bean plants with leaf curl and vein thickening were collected from areas around Okara (Pakistan) in 2011. PCR amplification using diagnostic primers showed the presence of a begomovirus and subsequently the specific pair (BurNF 5’- CCATGGTTGTGGCAGTTGATTGACAGATAC-3’, BurNR 5’- CCATGGATTCACGCACAGGGGAACCC-3’) was used to amplify and clone the whole genome of the virus. The complete nucleotide sequence was determined to be 2,759 nt (accession No. HE985227). Alignments showed the highest levels of nucleotide sequence identity (98.8%) with Cotton leaf curl Burewala virus (CLCuBuV; accession No. JF416947) No. JF416947). The virus in castor beans lacks on intact C2 gene, as is typical of CLCuBuV in cotton. An amplification product of ca. 1.4 kb was obtained in PCR with primers for betasatellites and the complete nucleotide sequence of a clone was determined to be 1373 nt (HE985228). The sequence showed 96.3% nucleotide sequence identity to the recombinant Cotton leaf curl Multan betasatellite (CLCuMB; JF502389). This is the first report of CLCuBuV and its betasatellite infecting castor bean, showing this plant species as an alternate host of the virus. Already many alternate host have been reported from different alternate host like tobacco, tomato, hibiscus, okra, ageratum, Digera arvensis, habiscus, Papaya and now in Ricinus communis. So, it is suggested that these alternate hosts should be avoided to grow near cotton growing regions.

Keywords: Ricinus communis, begomovirus, betasatellite, agriculture

Procedia PDF Downloads 489
17081 Depositional Facies, High Resolution Sequence Stratigraphy, Reservoir Characterization of Early Oligocene Carbonates (Mukta Formation) Of North & Northwest of Heera, Mumbai Offshore

Authors: Almas Rajguru, Archana Kamath, Rachana Singh

Abstract:

The study aims to determine the depositional facies, high-resolution sequence stratigraphy, and diagenetic processes of Early Oligocene carbonates in N & N-W of Heera, Mumbai Offshore. Foraminiferal assemblage and microfacies from cores of Well A, B, C, D and E are indicative of facies association related to four depositional environments, i.e., restricted inner lagoons-tidal flats, shallow open lagoons, high energy carbonate bars-shoal complex and deeper mid-ramps of a westerly dipping homoclinal carbonate ramp. Two high-frequency (4th Order) depositional sequences bounded by sequence boundary, DS1 and DS2, displaying hierarchical stacking patterns, are identified and correlated across wells. Vadose zone diagenesis effect during short diastem/ subaerial exposure has rendered good porosity due to dissolution in HST carbonates and occasionally affected underlying TST sediments (Well D, C and E). On mapping and correlating the sequences, the presence of thin carbonate bars that can be potential reservoirs are envisaged along NW-SE direction, towards north and south of Wells E, D and C. A more pronounced development of these bars in the same orientation can be anticipated towards the west of the study area.

Keywords: sequence stratigraphy, depositional facies, diagenesis petrography, early Oligocene, Mumbai offshore

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17080 Approximation of Convex Set by Compactly Semidefinite Representable Set

Authors: Anusuya Ghosh, Vishnu Narayanan

Abstract:

The approximation of convex set by semidefinite representable set plays an important role in semidefinite programming, especially in modern convex optimization. To optimize a linear function over a convex set is a hard problem. But optimizing the linear function over the semidefinite representable set which approximates the convex set is easy to solve as there exists numerous efficient algorithms to solve semidefinite programming problems. So, our approximation technique is significant in optimization. We develop a technique to approximate any closed convex set, say K by compactly semidefinite representable set. Further we prove that there exists a sequence of compactly semidefinite representable sets which give tighter approximation of the closed convex set, K gradually. We discuss about the convergence of the sequence of compactly semidefinite representable sets to closed convex set K. The recession cone of K and the recession cone of the compactly semidefinite representable set are equal. So, we say that the sequence of compactly semidefinite representable sets converge strongly to the closed convex set. Thus, this approximation technique is very useful development in semidefinite programming.

Keywords: semidefinite programming, semidefinite representable set, compactly semidefinite representable set, approximation

Procedia PDF Downloads 353