Search results for: taxonomic identification

Authors: Cemil Turan, Mohammad Shukri Salman

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The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms.

Keywords: adaptive filtering, sparse system identification, TD-LMS algorithm, VSSLMS algorithm

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Authors: Vinod C. Nayak

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In day-to-day forensic practice, identification is always a difficult task. Availability of anti-mortem and postmortem records plays a major rule in facilitating this tough task. However, the advent of digital forensic is a boon for forensic experts. This study has made use of digital forensics to establish identity by radiological dimensions of maxillary sinus using workstation software. The findings suggest a significant association between maxillary sinus dimensions and human gender. The author will be discussing the methods and results of the study in this e-poster.

Keywords: digital forensics, identification, maxillary sinus, radiology

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Authors: Paulus Maulana

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Forensic identification is to help investigators determine a person's identity. Personal identification is often a problem in civil and criminal cases. Orthodontists like all other dental professionals can play a major role by maintaining lateral cephalogram and thus providing important or vital information or can clues to the legal authorities in order to help them in their search. Radiographic lateral cephalometry is a measurement method which focused on the anatomical points of human lateral skull. Sex determination is one of the most important aspects of the personal identification in forensic. Lateral cephalogram is a valuable tool in identification of sex as reveal morphological details of the skull on single radiograph. This present study evaluates the role of lateral cephalogram in identification of sex that parameters of lateral cephalogram are linear measurement and angle measurement. The linear measurements are N-S ( Anterior cranial length), Sna-Snp (Palatal plane length), Me-Go (menton-gonion), N-Sna ( Midfacial anterior height ), Sna-Me (Lower anterior face height), Co-Gn (total mandibular length). The angle measurements are SNA, SNB, ANB, Gonial, Interincical, and facial.

Keywords: lateral cephalometry, cephalogram, sex, forensic, parameter

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: long-short-term memory, re-identification, security critical application, spatial-temporal awareness

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Authors: Priya Arora, Ashutosh Mishra

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Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method.

Keywords: disease gene identification, Parkinson’s disease, physicochemical properties of amino acid, protein sequences

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Kilari Nikhil, Ankur Tibrewal, Srinivas Kruthiventi S. S.

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Conventional speaker identification systems often fall short of capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN-based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net-inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.

Keywords: multi-scale feature extraction, squeeze and excitation, VoxCeleb1 speaker identification, mel-spectrograms, USENet

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Authors: Shaoying Guo, Yanyun Xu, Meng Zhang, Weiqing Huang

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The wireless communication network is developing rapidly, thus the wireless security becomes more and more important. Specific emitter identification (SEI) is an vital part of wireless communication security as a technique to identify the unique transmitters. In this paper, a SEI method based on multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The algorithms of MDE and RCMDE are used to extract features for identification of five wireless devices and cross-validation support vector machine (CV-SVM) is used as the classifier. The experimental results show that the total identification accuracy is 99.3%, even at low signal-to-noise ratio(SNR) of 5dB, which proves that MDE and RCMDE can describe the communication signal series well. In addition, compared with other methods, the proposed method is effective and provides better accuracy and stability for SEI.

Keywords: cross-validation support vector machine, refined com- posite multiscale dispersion entropy, specific emitter identification, transient signal, wireless communication device

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: David Pinzauti, Simon De Jaegher, Maria D'Aguano, Manuele Biazzo

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The COVID-19 pandemic has left an indelible mark on global health, leading to a pressing need for understanding the intricate interactions between the virus and the human microbiome. This study focuses on characterizing the nasal microbiota of patients affected by COVID-19, with a specific emphasis on the comparison with unaffected individuals, to shed light on the crucial role of the microbiome in the development of this viral disease. To achieve this objective, Nanopore technology was employed to analyze the bacterial 16s rRNA full-length gene present in nasal swabs collected in Malta between January 2021 and August 2022. A comprehensive dataset consisting of 268 samples (126 SARS-negative samples and 142 SARS-positive samples) was subjected to a comparative analysis using an in-house, custom pipeline. The findings from this study revealed that individuals affected by COVID-19 possess a nasal microbiota that is significantly less diverse, as evidenced by lower α diversity, and is characterized by distinct microbial communities compared to unaffected individuals. The beta diversity analyses were carried out at different taxonomic resolutions. At the phylum level, Bacteroidota was found to be more prevalent in SARS-negative samples, suggesting a potential decrease during the course of viral infection. At the species level, the identification of several specific biomarkers further underscores the critical role of the nasal microbiota in COVID-19 pathogenesis. Notably, species such as Finegoldia magna, Moraxella catarrhalis, and others exhibited relative abundance in SARS-positive samples, potentially serving as significant indicators of the disease. This study presents valuable insights into the relationship between COVID-19 and the nasal microbiota. The identification of distinct microbial communities and potential biomarkers associated with the disease offers promising avenues for further research and therapeutic interventions aimed at enhancing public health outcomes in the context of COVID-19.

Keywords: COVID-19, nasal microbiota, nanopore technology, 16s rRNA gene, biomarkers

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Authors: Sello Mokwena, Monyepao Thabang

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In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: camera network, convolutional neural network topology, person tracking, person re-identification, siamese

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Authors: Anish Shah, Steve A. Wakelin, Derrick Moot, Aurélie Laugraud, Hayley J. Ridgway

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A mixed pasture system of ryegrass-clover is used in New Zealand, where clovers are generally inoculated with commercially available strains of rhizobia. The community of rhizobia living in the soil and the way in which they interact with the plant are affected by different biotic and abiotic factors. In general, bacterial richness and diversity in soil varies by soil pH. pH also affects cell physiology and acts as a master variable that controls the wider soil physiochemical conditions such as P availability, Al release and micronutrient availability. As such, pH can have both primary and secondary effects on soil biology and processes. The aim of this work was to investigate the effect of soil pH on the genetic diversity and metabolic profile of Rhizobium leguminosarum strains nodulating clover. Soils were collected from 12 farms across New Zealand which had a pH(water) range of between 4.9 and 7.5, with four acidic (pH 4.9 – 5.5), four ‘neutral’ (5.8 – 6.1) and four alkaline (6.5 – 7.5) soils. Bacteria were recovered from nodules of Trifolium repens (white clover) and T. subterraneum (subterranean clover) grown in the soils. The strains were cultured and screened against a range of pH-amended media to demonstrate whether they were adapted to pH levels similar to their native soils. The strains which showed high relative growth at a given pH (~20% of those isolated) were selected for metabolic and taxonomic profiling. The Omnilog (Biolog Inc., Hayward, CA) phenotype array was used to perform assays on carbon (C) utilisation for selected strains. DNA was extracted from the strains which had differing C utilisation profiles and PCR products for both forward and reverse primers were sequenced for the following genes: 16S rRNA, recA, nodC, nodD and nifH (symbiotic).

Keywords: bacterial diversity, clover, metabolic and taxonomic profiling, pH adaptation, rhizobia

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Authors: Venkatesh Pulletikurthi, Karthik B. Ariyur, Luciano Castillo

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The system identification from the turbulence wakes will lead to the tactical advantage to prepare and also, to predict the trajectory of the opponents’ movements. A low-order modeling technique, POD, is used to predict the object based on the wake pattern and compared with pre-trained image recognition neural network (NN) to classify the wake patterns into objects. It is demonstrated that low-order modeling, POD, is able to predict the objects better compared to pretrained NN by ~30%.

Keywords: the bluff body wakes, low-order modeling, neural network, system identification

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Authors: Bin Liu

Abstract:

Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: S. B. Rathna Kumar, Sandya K. Varudhini, Aparna Ravichandran

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Telugu is a south central Dravidian language spoken in Andhra Pradesh, a southern state of India. The available speech identification tests in Telugu have been developed to determine the communication problems of individuals having a flat frequency hearing loss. These conventional speech audiometric tests would provide redundant information when used on individuals with high-frequency sloping hearing loss because of better hearing sensitivity in the low- and mid-frequency regions. Hence, conventional speech identification tests do not indicate the true nature of the communication problem of individuals with high-frequency sloping hearing loss. It is highly possible that a person with a high-frequency sloping hearing loss may get maximum scores if conventional speech identification tests are used. Hence, there is a need to develop speech identification test materials that are specifically designed to assess the speech identification performance of individuals with high-frequency sloping hearing loss. The present study aimed to develop speech identification test for individuals with high-frequency sloping hearing loss in Telugu. Individuals with high-frequency sloping hearing loss have difficulty in perception of voiceless consonants whose spectral energy is above 1000 Hz. Hence, the word lists constructed with phonemes having mid- and high-frequency spectral energy will estimate speech identification performance better for such individuals. The phonemes /k/, /g/, /c/, /ṭ/ /t/, /p/, /s/, /ś/, /ṣ/ and /h/are preferred for the construction of words as these phonemes have spectral energy distributed in the frequencies above 1000 KHz predominantly. The present study developed two word lists in Telugu (each word list contained 25 words) for evaluating speech identification performance of individuals with high-frequency sloping hearing loss. The performance of individuals with high-frequency sloping hearing loss was evaluated using both conventional and high-frequency word lists under recorded voice condition. The results revealed that the developed word lists were found to be more sensitive in identifying the true nature of the communication problem of individuals with high-frequency sloping hearing loss.

Keywords: speech identification test, high-frequency sloping hearing loss, recorded voice condition, Telugu

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Authors: Rickard Grassman

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In this paper, Lacanian theory will be used to illustrate the way discourses interact with the material by way of reifying antagonisms to shape our sense of identities in and around organizations. The ability to ‘sustain the loss’ is, in this view, the common structure here discerned in the very texture of a discourse, which reifies ‘lack’ as an ontological condition into something contingently absent (loss) that the subject hopes to overcome (desire). These fundamental human tendencies of identification are illustrated in the paper by examples drawn from history, cinema, and literature. Turning to a select sample of empirical accounts from a management consultancy firm, it is argued that this ‘sustaining the loss’ operates in discourse to enact identification in an organizational context.

Keywords: Lacan, identification, discourse, desire, loss

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Authors: Amandeep Kaur, N. S. Atri, Munruchi Kaur

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Herbivorous dung is a special substrate for the growth of fungi. Fungi growing thereon are known as coprophilous. These fungi are amongst the most abundant taxa in the ecosystem, which regulate the decomposition of dung organic matter, nutrient dynamics and maintenance of ecological balance on the earth. The coprophilous fungi represent a diverse group of saprobes, including taxa from most major fungal groups belonging to Zygomycota, Ascomycota and Basidiomycota. The present work, however, has been focused on the basidiomycetous coprophilous mushrooms belonging to the order Agaricales. The research work includes the results of eco-taxonomic studies of coprophilous mushrooms in Punjab, India, on the basis of a survey of dung localities of the state. The mushrooms were collected growing as saprobes on dung of various domesticated and wild herbivorous animals in pastures, grasslands, zoos, and on dung heaps in villages, etc. The present study observed the frequency of distribution of coprophilous mushrooms in different taxonomic categories in different regions of the state in various seasons on different dung types along with their growing habit. The paper also discusses their economic utility as edible, inedible, poisonous, medicinal and hallucinogenic species. The study has shown that animal dung is a good niche for the growth of mushrooms. However, the natural habitats with dung deposits are getting destroyed because of different developmental activities. Livestock in agriculture-based societies like Punjab state in India should be managed in a manner that favors their grazing in the wild places and thereby the growth of coprophilous mushrooms so that a significant role in ecological balance on the earth is established.

Keywords: herbivorous dung, psychoactive, seasonal availability, taxo-ecology

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Authors: Xuewen Yu, Danhui Dan

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The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Didier Auroux, Vladimir Groza

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This work is part of STEEP Marie-Curie ITN project, and it focuses on the identification of unknown parameters of the proposed generic Abrasive WaterJet Milling (AWJM) PDE model, that appears as an ill-posed inverse problem. The necessity of studying this problem comes from the industrial milling applications where the possibility to predict and model the final surface with high accuracy is one of the primary tasks in the absence of any knowledge of the model parameters that should be used. In this framework, we propose the identification of model parameters by minimizing a cost function, measuring the difference between experimental and numerical solutions. The adjoint approach based on corresponding Lagrangian gives the opportunity to find out the unknowns of the AWJM model and their optimal values that could be used to reproduce the required trench profile. Due to the complexity of the nonlinear problem and a large number of model parameters, we use an automatic differentiation software tool (TAPENADE) for the adjoint computations. By adding noise to the artificial data, we show that in fact the parameter identification problem is highly unstable and strictly depends on input measurements. Regularization terms could be effectively used to deal with the presence of data noise and to improve the identification correctness. Based on this approach we present results in 2D and 3D of the identification of the model parameters and of the surface prediction both with self-generated data and measurements obtained from the real production. Considering different types of model and measurement errors allows us to obtain acceptable results for manufacturing and to expect the proper identification of unknowns. This approach also gives us the ability to distribute the research on more complex cases and consider different types of model and measurement errors as well as 3D time-dependent model with variations of the jet feed speed.

Keywords: Abrasive Waterjet Milling, inverse problem, model parameters identification, regularization

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Authors: D. L. David, J. A. Wahedi, Q. T. Zaku

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A study was conducted at River Mayo Ranewo and River Lau, Taraba State Nigeria. The two rivers empty into the Upper Benue Basin. A survey of visual encounter was conducted within the two wetlands from June to August, 2014. The fish record was based entirely on landings of fishermen, number of canoes that land fish was counted, types of nets and baits used on each sampling day. Fishes were sorted into taxonomic groups, identified to family/ species level, counted and weighed in groups by species. Other aquatic organisms captured by the fishermen were scallops, turtles and frogs. The relative species abundance was determined by dividing the number of species from a site by the total number of species from all tributaries/sites. The fish were preserved in 2% formaldehyde solution and taken to the laboratory, were identified through keys of identification to African fishes and field guides. Shannon-Wieiner index of species diversity indicated that the diversity was highest at River Mayo Ranewo than River Lau. Results showed that at River Mayo Ranewo, the family Mochokidae recorded the highest (23.15%), followed by Mormyridae (22.64%) and the least was the family Lepidosirenidae (0.04%). While at River Lau, the family Mochokidae recorded the highest occurrence of (24.1%), followed by Bagridae (20.20%), and then Mormyridae, which also was the second highest in River Lau, with 18.46% occurrence. There was no occurrence of Malapteruridae and Osteoglossidae (0%) in River Lau, but the least occurrence was the family Gymnarchidae (0.04%). According to the result from the t-test, the fish composition was not significantly different (p≤0.05).

Keywords: Diversity Index, Lau, Mayo Ranewo, Wetlands

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Authors: Mehran Nasiri, Ardeshir Poornemat

Abstract:

The aim of this study was to investigate the current and ideal situations of the process of talent identification in Iranian football from the point of view of Iranian instructors of the Asian Football Confederation (AFC). This research was a descriptive-analytical study; in data collection phase a questionnaire was used, whose face validity was confirmed by experts of Physical Education and Sports Science. The reliability of questionnaire was estimated through the use of Cronbach's alpha method (0.91). This study involved 122 participants of Iranian instructors of the AFC who were selected based on stratified random sampling method. Descriptive statistics were used to describe the variables and inferential statistics (Chi-square) were used to test the hypotheses of the study at significant level (p ≤ 0.05). The results of Chi-square test related to the point of view of Iranian instructors of the AFC showed that the grass-roots scientific method was the best way to identify football players (0.001), less than 10 years old were the best ages for talent identification (0.001), the Football Federation was revealed to be the most important organization in talent identification (0.002), clubs were shown to be the most important institution in developing talents (0.001), trained scouts of Football Federation were demonstrated to be the best and most appropriate group for talent identification (0.001), and being referred by the football academy coaches was shown to be the best way to attract talented football players in Iran (0.001). It was also found that there was a huge difference between the current and ideal situation of the process of talent identification in Iranian football from the point of view of Iranian instructors of the AFC. Hence, it is recommended that the policy makers of talent identification for Iranian football provide a comprehensive, clear and systematic model of talent identification and development processes for the clubs and football teams, so that the talent identification process helps to nurture football talents more efficiently.

Keywords: current situation, talent finding, ideal situation, instructors (AFC)

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Maha Alaoui Ismaili, Bouchta Saidi, Mohamed Zahar, Abed Hamama

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112 lactic isolates were obtained from 15 samples of camel raw milk produced in Laayoune Boujdour Sakia-El Hamra region (South of Morocco). The main objective was the identification of species of lactic flora belonging to Lactococcus, Lactobacillus and Leuconostoc. Data obtained showed predominance of cocci among lactic isolates (86.6%) while lactic rods represented only 13.4%. With regard to genera identified, Enterococcus was the mostly found out (53.57%), followed by Lactococcus (28.57%), Lactobacillus (13.4%) and Leuconostoc (4.4 %). Identification of the lactic isolates according to their morphological, physiological, and biochemical characteristics led to differentiating 11 species with Lactococcus lactis ssp lactis biovar diacetylactis being the mostly encountered (24.1%) followed by Lactobacillus brevis (3.57%), Lactobacillus plantarum (3.57%), Lactobacillus delbrueckii subsp lactis (3.57%) and Lactococcus lactis subsp cremoris (2.67%).

Keywords: raw camel milk, south of morocco, lactic acid bacteria, identification

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: N. Jinesh, K. Shankar

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This article presents a method of using the one dimensional piezo-electric patch on beam model for structural identification. A hybrid element constituted of one dimensional beam element and a PZT sensor is used with reduced material properties. This model is convenient and simple for identification of beams. Accuracy of this element is first verified against a corresponding 3D finite element model (FEM). The structural identification is carried out as an inverse problem whereby parameters are identified by minimizing the deviation between the predicted and measured voltage response of the patch, when subjected to excitation. A non-classical optimization algorithm Particle Swarm Optimization is used to minimize this objective function. The signals are polluted with 5% Gaussian noise to simulate experimental noise. The proposed method is applied on beam structure and identified parameters are stiffness and damping. The model is also validated experimentally.

Keywords: inverse problem, particle swarm optimization, PZT patches, structural identification

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Authors: Eric Bonetto, Anthony Piermatteo, Fabien Girandola, Gregory Lo Monaco

Abstract:

The present contribution focuses on the effect of the structure of social representations on group identification. A social representation (SR) is defined as an organized and structured set of cognitions, produced and shared by members of a same group about a same social object. Within this framework, the central core theory establishes a structural distinction between central cognitions – or 'core' – and peripheral ones: the former are theoretically considered as more connected than the later to group members’ social identity and may play a greater role in SRs’ ability to allow group identification by means of a common vision of the object of representation. Indeed, the central core provides a reference point for the in-group as it constitutes a consensual vision that gives meaning to a social object particularly important to individuals and to the group. However, while numerous contributions clearly refer to the underlying role of SRs in group identification, there are only few empirical evidences of this aspect. Thus, we hypothesize an effect of the structure of SRs on group identification. More precisely, central cognitions (vs. peripheral ones) will lead to a stronger group identification. In addition, we hypothesize that the refutation of a cognition will lead to a stronger group identification than its activation. The SR mobilized here is that of 'studying' among a population of first-year undergraduate psychology students. Thus, a pretest (N = 82), using an Attribute-Challenge Technique, was designed in order to identify the central and the peripheral cognitions to use in the primings of our main study. The results of this pretest are in line with previous studies. Then, the main study (online; N = 184), using a social priming methodology, was based on a 2 (Structural status of the cognitions belonging to the prime: central vs. peripheral) x 2 (Type of prime: activation vs. refutation) experimental design in order to test our hypotheses. Results revealed, as expected, the main effect of the structure of the SR on group identification. Indeed, central cognitions trigger a higher level of identification than the peripheral ones. However, we observe neither effect of the type of prime, nor interaction effect. These results experimentally demonstrate for the first time the effect of the structure of SRs on group identification and indicate that central cognitions are more connected than peripheral ones to group members’ social identity. These results will be discussed considering the importance of understanding identity as a function of SRs and on their ability to potentially solve the lack of consideration of the definition of the group in Social Representations Theory.

Keywords: group identification, social identity, social representations, structural approach

Procedia PDF Downloads 191

Authors: A. Ballard

Abstract:

The ability to track goods and products en route in the delivery system, in the warehouse, and on the top floor is a huge advantage to shippers and retailers. Recently the emergence of radio frequency identification (RFID) technology has enabled this better than ever before. However, a significant problem exists in that RFID technology depends on the quality of the information stored for each tagged product. Because of the profusion of names for colours, it is very difficult to ascertain that stored values are recognised by all users who view the product visually. This paper reports the findings of a study in which 50 consumers and 50 logistics workers were shown colour swatches and asked to choose the name of the colour from a multiple choice list. They were then asked to match consumer products, including toasters, jumpers, and toothbrushes, with the identifying inventory information available for each one. The findings show that the ability to match colours was significantly stronger with the color swatches than with the consumer products and that while logistics professionals made more frequent correct identification than the consumers, their results were still unsatisfactorily low. Based on these findings, a proposed universal model of colour identification numbers has been developed.

Keywords: consumer preferences, supply chain logistics, radio frequency identification, RFID, colour preference

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Authors: Ayman El Behiry, Rasha Nabil Zahran, Reda Tarabees, Eman Marzouk, Musaad Al-Dubaib

Abstract:

Mastitis is one of the most economic disease affecting dairy cows worldwide. Its classic diagnosis using bacterial culture and biochemical findings is a difficult and prolonged method. In this research, using of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) permitted identification of different microorganisms with high accuracy and rapidity (only 24 hours for microbial growth and analysis). During the application of MALDI-TOF MS, one hundred twenty strains of Staphylococcus and Streptococcus species isolated from milk of cows affected by clinical and subclinical mastitis were identified, and the results were compared with those obtained by traditional methods as API and VITEK 2 Systems. 37 of totality 39 strains (~95%) of Staphylococcus aureus (S. aureus) were exactly detected by MALDI TOF MS and then confirmed by a nuc-based PCR technique, whereas accurate identification was observed in 100% (50 isolates) of the coagulase negative staphylococci (CNS) and Streptococcus agalactiae (31 isolates). In brief, our results demonstrated that MALDI-TOF MS is a fast and truthful technique which has the capability to replace conventional identification of several bacterial strains usually isolated in clinical laboratories of microbiology.

Keywords: identification, mastitis pathogens, mass spectral, phenotypical

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Authors: Mohamed Adnan Landolsi, Ali F. Almutairi

Abstract:

The paper addresses the problem of line-of-sight (LOS) vs. non-line-of-sight (NLOS) propagation link identification in ultra-wideband (UWB) wireless networks, which is necessary for improving the accuracy of radiolocation and positioning applications. A LOS/NLOS likelihood hypothesis testing approach is applied based on exploiting distinctive statistical features of the channel impulse response (CIR) using parameters related to the “skewness” of the CIR and its root mean square (RMS) delay spread. A log-normal fit is presented for the probability densities of the CIR parameters. Simulation results show that different environments (residential, office, outdoor, etc.) have measurable differences in their CIR parameters’ statistics, which is then exploited in determining the nature of the propagation channels. Correct LOS/NLOS channel identification rates exceeding 90% are shown to be achievable for most types of environments. Additional improvement is also obtained by combining both CIR skewness and RMS delay statistics.

Keywords: UWB, propagation, LOS, NLOS, identification

Procedia PDF Downloads 249