Search results for: hate speech detection

Authors: Kidane workelul, Li xu, Xiaoyang Pang, Jiaping Lv

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Dairy products are exceptionally ideal media for the growth of microorganisms because of their high nutritional content. There are several ways that milk might get contaminated throughout the milking process, including how the raw milk is transported and stored, as well as how long it is kept before being processed. Psychrotrophic bacteria are among the one which can deteriorate the quality of milk mainly their heat resistance proteas and lipase enzyme. For this research purpose 8 selected strains of Psychrotrophic bacteria (Entrococcus hirae, Pseudomonas fluorescens, Pseudomonas azotoformans, Pseudomonas putida, Exiguobacterium indicum, Pseudomonas paralactice, Acinetobacter indicum, Serratia liquefacients)are chosen and try to determine their characteristics based on the research methodology protocol. Thus, the 8 selected strains are cultured, plated incubate, extracted their genomic DNA and genome DNA was amplified, the purpose of the study was to identify their Psychrotrophic properties, lipase hydrolysis positive test, their optimal incubation temperature, designed primer using the noble strain P,flourescens conserved region area in target with lipA gene, optimized primer specificity as well as sensitivity and PCR detection for lipase positive strains using the design primers. Based on the findings both the selected 8 strains isolated from stored raw milk are Psychrotrophic bacteria, 6 of the selected strains except the 2 strains are positive for lipase hydrolysis, their optimal temperature is 20 to 30 OC, the designed primer specificity is very accurate and amplifies for those strains only with lipase positive but could not amplify for the others. Thus, the result is promising and could help in detecting the Psychrotrophic bacteria producing heat resistance enzymes (lipase) at early stage before the milk is processed and this will safe production loss for the dairy industry.

Keywords: dairy industry, heat-resistant, lipA, milk, primer and psychrotrophic

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Authors: Dimitra Das, Anuradha Mitra, Kalyan Kumar Chattopadhyay

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Organic polymers, constructed from light elements like carbon, hydrogen, nitrogen, oxygen, sulphur, and boron atoms, are the emergent class of non-toxic, metal-free, environmental benign advanced materials. Covalent triazine-based polymers with a functional triazine group are significant class of organic materials due to their remarkable stability arising out of strong covalent bonds. They can conventionally form hydrogen bonds, favour π–π contacts, and they were recently revealed to be involved in interesting anion–π interactions. The present work mainly focuses upon the development of a single-crystalline, highly cross-linked triazine-based nitrogen-rich organic polymer with nanodendritic morphology and significant thermal stability. The polymer has been synthesized through hydrothermal treatment of melamine and ethylene glycol resulting in cross-polymerization via condensation-polymerization reaction. The crystal structure of the polymer has been evaluated by employing Rietveld whole profile fitting method. The polymer has been found to be composed of monoclinic melamine having space group P21/a. A detailed insight into the chemical structure of the as synthesized polymer has been elucidated by Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopic analysis. X-Ray Photoelectron Spectroscopic (XPS) analysis has also been carried out for further understanding of the different types of linkages required to create the backbone of the polymer. The unique rod-like morphology of the triazine based polymer has been revealed from the images obtained from Field Emission Scanning Electron Microscopy (FESEM) and Transmission Electron Microscopy (TEM). Interestingly, this polymer has been found to selectively detect mercury (Hg²⁺) ions at an extremely low concentration through fluorescent quenching with detection limit as low as 0.03 ppb. The high toxicity of mercury ions (Hg²⁺) arise from its strong affinity towards the sulphur atoms of biological building blocks. Even a trace quantity of this metal is dangerous for human health. Furthermore, owing to its small ionic radius and high solvation energy, Hg²⁺ ions remain encapsulated by water molecules making its detection a challenging task. There are some existing reports on fluorescent-based heavy metal ion sensors using covalent organic frameworks (COFs) but reports on mercury sensing using triazine based polymers are rather undeveloped. Thus, the importance of ultra-trace detection of Hg²⁺ ions with high level of selectivity and sensitivity has contemporary significance. A plausible sensing phenomenon by the polymer has been proposed to understand the applicability of the material as a potential sensor. The impressive sensitivity of the polymer sample towards Hg²⁺ is the very first report in the field of highly crystalline triazine based polymers (without the introduction of any sulphur groups or functionalization) towards mercury ion detection through photoluminescence quenching technique. This crystalline metal-free organic polymer being cheap, non-toxic and scalable has current relevance and could be a promising candidate for Hg²⁺ ion sensing at commercial level.

Keywords: fluorescence quenching , mercury ion sensing, single-crystalline, triazine-based polymer

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Authors: Samaneh Nabavi, Hadi Shirzad, Somayeh Khanjani, Shirin Jalili

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Quantum dots (QDs) are semiconductor nanocrystals that exhibit intrinsic optical and electrical properties that are size dependent due to the quantum confinement effect. Quantum confinement is brought about by the fact that in bulk semiconductor material the electronic structure consists of continuous bands, and that as the size of the semiconductor material decreases its radius becomes less than the Bohr exciton radius (the distance between the electron and electron-hole) and discrete energy levels result. As a result QDs have a broad absorption range and a narrow emission which correlates to the band gap energy (E), and hence QD size. QDs can thus be tuned to give the desired wavelength of fluorescence emission.Due to their unique properties, QDs have attracted considerable attention in different scientific areas. Also, they have been considered for forensic applications in recent years. The ability of QDs to fluoresce up to 20 times brighter than available fluorescent dyes makes them an attractive nanomaterial for enhancing the visualization of latent fingermarks, or poorly developed fingermarks. Furthermore, the potential applications of QDs in the detection of nitroaromatic explosives, such as TNT, based on directive fluorescence quenching of QDs, electron transfer quenching process or fluorescence resonance energy transfer have been paid to attention. DNA analysis is associated tightly with forensic applications in molecular diagnostics. The amount of DNA acquired at a criminal site is inherently limited. This limited amount of human DNA has to be quantified accurately after the process of DNA extraction. Accordingly, highly sensitive detection of human genomic DNA is an essential issue for forensic study. QDs have also a variety of advantages as an emission probe in forensic DNA quantification.

Keywords: forensic science, quantum dots, DNA typing, explosive sensor, fingermark analysis

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Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

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OPEN-EmoRecII is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (mimic reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and mimic annotations.

Keywords: open multimodal emotion corpus, annotated labels, intelligent interaction

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Authors: Elaine Y. L. Ng

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Elizabeth Gilbert’s (2006) biography Eat, Pray, Love describes her travels to Italy, India, and Indonesia after a painful divorce. The author’s experiences with love, loss, search for happiness, and meaning have resonated with a huge readership. As regards the translation of Gilbert’s (2006) Eat, Pray, Love into Chinese, it was first translated by a Taiwanese translator He Pei-Hua and published in Taiwan in 2007 by Make Boluo Wenhua Chubanshe with the fairly catching title “Enjoy! Traveling Alone.” The same translation was translocated to China, republished in simplified Chinese characters by Shanxi Shifan Daxue Chubanshe in 2008 and renamed in China, entitled “To Be a Girl for the Whole Life.” Later on, the same translation in simplified Chinese characters was reprinted by Hunan Wenyi Chubanshe in 2013. This study employs Munday’s (2002) systemic model for descriptive translation studies to investigate the translation of Gilbert’s (2006) Eat, Pray, Love into Chinese by the Taiwanese translator Hu Pei-Hua. It employs an interdisciplinary approach, combining systemic functional linguistics and corpus stylistics with sociohistorical research within a descriptive framework to study the translator’s discursive presence in the text. The research consists of three phases. The first phase is to locate the target text within its socio-cultural context. The target-text context concerning the para-texts, readers’ responses, and the publishers’ orientation will be explored. The second phase is to compare the source text and the target text for the categorization of translation shifts by using the methodological tools of systemic functional linguistics and corpus stylistics. The investigation concerns the rendering of mental clauses and speech and thought presentation. The final phase is an explanation of the causes of translation shifts. The linguistic findings are related to the extra-textual information collected in an effort to ascertain the motivations behind the translator’s choices. There exist sets of possible factors that may have contributed to shaping the textual features of the given translation within a specific socio-cultural context. The study finds that the translator generally reproduces the mental clauses and speech and thought presentation closely according to the original. Nevertheless, the language of the translation has been widely criticized to be unidiomatic and stiff, losing the elegance of the original. In addition, the several Chinese translations of the given text produced by one Taiwanese and two Chinese publishers are basically the same. They are repackaged slightly differently, mainly with the change of the book cover and its captions for each version. By relating the textual findings to the extra-textual data of the study, it is argued that the popularity of the Chinese translation of Gilbert’s (2006) Eat, Pray, Love may not be attributed to the quality of the translation. Instead, it may have to do with the way the work is promoted strategically by the social media manipulated by the four e-bookstores promoting and selling the book online in China.

Keywords: chinese translation of eat pray love, corpus stylistics, motivations for translation shifts, systemic approach to translation studies

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: A. K. M. Kafi, S. N. Nina, Mashitah M. Yusoff

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In this work, we have described a new 3-dimensional (3D) network of cross-linked Horseradish Peroxidase/Carbon Nanotube (HRP/CNT) on a thiol-modified Au surface in order to build up the effective electrical wiring of the enzyme units with the electrode. This was achieved by the electropolymerization of aniline-functionalized carbon nanotubes (CNTs) and 4-aminothiophenol -modified-HRP on a 4-aminothiophenol monolayer-modified Au electrode. The synthesized 3D HRP/CNT networks were characterized with cyclic voltammetry and amperometry, resulting the establishment direct electron transfer between the redox active unit of HRP and the Au surface. Electrochemical measurements reveal that the immobilized HRP exhibits high biological activity and stability and a quasi-reversible redox peak of the redox center of HRP was observed at about −0.355 and −0.275 V vs. Ag/AgCl. The electron transfer rate constant, KS and electron transfer co-efficient were found to be 0.57 s-1 and 0.42, respectively. Based on the electrocatalytic process by direct electrochemistry of HRP, a biosensor for detecting H2O2 was developed. The developed biosensor exhibits excellent electrocatalytic activity for the reduction of H2O2. The proposed biosensor modified with HRP/CNT 3D network displays a broader linear range and a lower detection limit for H2O2 determination. The linear range is from 1.0×10−7 to 1.2×10−4M with a detection limit of 2.2.0×10−8M at 3σ. Moreover, this biosensor exhibits very high sensitivity, good reproducibility and long-time stability. In summary, ease of fabrication, a low cost, fast response and high sensitivity are the main advantages of the new biosensor proposed in this study. These obvious advantages would really help for the real analytical applicability of the proposed biosensor.

Keywords: redox enzyme, nanomaterials, biosensors, electrical communication

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Authors: B. T. Naveen Kumar, Anuj Tyagi, Niraj Kumar Singh, Visanu Boonyawiwat, A. H. Shanthanagouda, Orawan Boodde, K. M. Shankar, Prakash Patil, Shubhkaramjeet Kaur

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Early mortality syndrome (EMS)/Acute Hepatopancreatic Necrosis Disease (AHPND) has emerged as a major obstacle for the shrimp farming around the world. It is caused by a strain of Vibrio parahaemolyticus. The possible preventive and control measure is, early and rapid detection of the pathogen in the broodstock, post-larvae and monitoring the shrimp during the culture period. Polymerase chain reaction (PCR) based early detection methods are good, but they are costly, time taking and requires a sophisticated laboratory. The present study was conducted to develop a simple, sensitive and rapid diagnostic farmer level kit for the reliable detection of AHPND in shrimp. A panel of monoclonal antibodies (MAbs) were raised against the recombinant Pir B protein (rPirB). First, an immunodot was developed by using MAbs G3B8 and Mab G3H2 which showed specific reactivity to purified r-PirB protein with no cross-reactivity to other shrimp bacterial pathogens (AHPND free Vibrio parahaemolyticus (Indian strains), V. anguillarum, WSSV, Aeromonas hydrophila, and Aphanomyces invadans). Immunodot developed using Mab G3B8 is more sensitive than that with the Mab G3H2. However, immunodot takes almost 2.5 hours to complete with several hands-on steps. Therefore, the flow-through assay (FTA) was developed by using a plastic cassette containing the nitrocellulose membrane with absorbing pads below. The sample was dotted in the test zone on the nitrocellulose membrane followed by continuos addition of five solutions in the order of i) blocking buffer (BSA) ii) primary antibody (MAb) iii) washing Solution iv) secondary antibody and v) chromogen substrate (TMB) clear purple dots against a white background were considered as positive reactions. The FTA developed using MAbG3B8 is more sensitive than that with MAb G3H2. In FTA the two MAbs showed specific reactivity to purified r-PirB protein and not to other shrimp bacterial pathogens. The FTA is simple to farmer/field level, sensitive and rapid requiring only 8-10 min for completion. Tests can be developed to kits, which will be ideal for use in biosecurity, for the first line of screening (at the port or pond site) and during monitoring and surveillance programmes overall for the good management practices to reduce the risk of the disease.

Keywords: acute hepatopancreatic necrosis disease, AHPND, flow-through assay, FTA, farmer level, immunodot, pond site, shrimp

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Authors: Isaiah Ifeanyichukwu Agbo

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Human speech is capable of serving many purposes. Power and control are not always exercised overtly by linguistic acts, but maybe enacted and exercised in the myriad of taken-for-granted actions of everyday life. Domination, power control, discrimination and mind control exist in human speech and may lead to asymmetrical power relations. In discourse, there are persuasive and manipulative linguistic acts that serve to establish solidarity and identification with the 'we group' and polarize with the 'they group'. Political discourse is crafted to defend and promote the problematic narrative of outright controversial events in a nation’s history thereby sustaining domination, marginalization, manipulation, inequalities and injustices, often without the dominated and marginalized group being aware of them. They are designed and positioned to serve the political and social needs of the producers. Political crisis speeches in Nigeria, just like in other countries concentrate on positive self-image, de-legitimization of political opponents, reframing accusation to one’s advantage, redefining problematic terms and adopting reversal strategy. In most cases, the people are ignorant of the hidden ideological positions encoded in the text. Few researches have been conducted adopting the frameworks of critical discourse analysis and systemic functional linguistics to investigate this situation in the political crisis speeches in Nigeria. In this paper, we focus attention on the analyses of the linguistic, semantic, and ideological elements in selected political crisis speeches in Nigeria to investigate if they create and sustain unequal power relations and manipulative tendencies from the perspectives of Critical Discourse Analysis (CDA) and Systemic Functional Linguistics (SFL). Critical Discourse Analysis unpacks both opaque and transparent structural relationships of power dominance, power relations and control as manifested in language. Critical discourse analysis emerged from a critical theory of language study which sees the use of language as a form of social practice where social relations are reproduced or contested and different interests are served. Systemic function linguistics relates the structure of texts to their function. Fairclough’s model of CDA and Halliday’s systemic functional approach to language study are adopted in this paper. This paper probes into language use that perpetuates inequalities. This study demystifies the hidden implicature of the selected political crisis speeches and reveals the existence of information that is not made explicit in what the political actors actually say. The analysis further reveals the ideological configurations present in the texts. These ideological standpoints are the basis for naturalizing implicit ideologies and hegemonic influence in the texts. The analyses of the texts further uncovered the linguistic and discursive strategies deployed by text producers to manipulate the unsuspecting members of the public both mentally and conceptually in order to enact, sustain and maintain unhealthy power relations at crisis times in the Nigerian political history.

Keywords: critical discourse analysis, language, political crisis, power relations, systemic functional linguistics

Procedia PDF Downloads 341

Authors: A. K. M. Kafi, S. N. Nina, Mashitah M. Yusoff

Abstract:

In this work, we have described a new 3-dimensional (3D) network of crosslinked Horseradish Peroxidase/Carbon Nanotube (HRP/CNT) on a thiol-modified Au surface in order to build up the effective electrical wiring of the enzyme units with the electrode. This was achieved by the electropolymerization of aniline-functionalized carbon nanotubes (CNTs) and 4-aminothiophenol -modified-HRP on a 4-aminothiophenol monolayer-modified Au electrode. The synthesized 3D HRP/CNT networks were characterized with cyclic voltammetry and amperometry, resulting the establishment direct electron transfer between the redox active unit of HRP and the Au surface. Electrochemical measurements reveal that the immobilized HRP exhibits high biological activity and stability and a quasi-reversible redox peak of the redox center of HRP was observed at about −0.355 and −0.275 V vs. Ag/AgCl. The electron transfer rate constant, KS and electron transfer co-efficient were found to be 0.57 s-1 and 0.42, respectively. Based on the electrocatalytic process by direct electrochemistry of HRP, a biosensor for detecting H2O2 was developed. The developed biosensor exhibits excellent electrocatalytic activity for the reduction of H2O2. The proposed biosensor modified with HRP/CNT 3D network displays a broader linear range and a lower detection limit for H2O2 determination. The linear range is from 1.0×10−7 to 1.2×10−4M with a detection limit of 2.2.0×10−8M at 3σ. Moreover, this biosensor exhibits very high sensitivity, good reproducibility and long-time stability. In summary, ease of fabrication, a low cost, fast response and high sensitivity are the main advantages of the new biosensor proposed in this study. These obvious advantages would really help for the real analytical applicability of the proposed biosensor.

Keywords: biosensor, nanomaterials, redox enzyme, thiol-modified Au surface

Procedia PDF Downloads 329

Authors: Javier Romera, Alberto Justo, Ignacio Fidalgo, Joshue Perez, Javier Araluce

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Nowadays, individuals with mobility needs face a significant challenge when docking vehicles. In many cases, after parking, they encounter insufficient space to exit, leading to two undesired outcomes: either avoiding parking in that spot or settling for improperly placed vehicles. To address this issue, the following paper presents a parking control system employing gestural teleoperation. The system comprises three main phases: capturing body markers, interpreting gestures, and transmitting orders to the vehicle. The initial phase is centered around the MediaPipe framework, a versatile tool optimized for real-time gesture recognition. MediaPipe excels at detecting and tracing body markers, with a special emphasis on hand gestures. Hands detection is done by generating 21 reference points for each hand. Subsequently, after data capture, the project employs the MultiPerceptron Layer (MPL) for indepth gesture classification. This tandem of MediaPipe's extraction prowess and MPL's analytical capability ensures that human gestures are translated into actionable commands with high precision. Furthermore, the system has been trained and validated within a built-in dataset. To prove the domain adaptation, a framework based on the Robot Operating System (ROS), as a communication backbone, alongside CARLA Simulator, is used. Following successful simulations, the system is transitioned to a real-world platform, marking a significant milestone in the project. This real vehicle implementation verifies the practicality and efficiency of the system beyond theoretical constructs.

Keywords: gesture detection, mediapipe, multiperceptron layer, robot operating system

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: Shyamani Hettiarachchi, Thilini Lokubalasuriya, Shakeela Saleem, Dinusha Nonis, Isuru Dharmaratne, Lakshika Udugama

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Introduction: Late identification of language difficulties in children could result in long-term negative consequences for communication, literacy and self-esteem. This highlights the need for early identification and intervention for speech, language and communication difficulties. Speech and language therapy is a relatively new profession in Sri Lanka and at present, there are no formal standardized screening tools to assess language skills in Sinhala-speaking children. The development and validation of a short, accurate screening tool to enable the identification of children with syntactic difficulties in Sinhala is a current need. Aims: 1) To develop test items for a Sinhala Syntactic Structures (S3 Short Form) test on children aged between 3;0 to 5;0 years 2) To validate the test of Sinhala Syntactic Structures (S3 Short Form) on children aged between 3; 0 to 5; 0 years Methods: The Sinhala Syntactic Structures (S3 Short Form) was devised based on the Renfrew Action Picture Test. As Sinhala contains post-positions in contrast to English, the principles of the Renfrew Action Picture Test were followed to gain an information score and a grammar score but the test devised reflected the linguistic-specificity and complexity of Sinhala and the pictures were in keeping with the culture of the country. This included the dative case marker ‘to give something to her’ (/ejɑ:ʈə/ meaning ‘to her’), the instrumental case marker ‘to get something from’ (/ejɑ:gən/ meaning ‘from him’ or /gɑhən/ meaning ‘from the tree’), possessive noun (/ɑmmɑge:/ meaning ‘mother’s’ or /gɑhe:/ meaning ‘of the tree’ or /male:/ meaning ‘of the flower’) and plural markers (/bɑllɑ:/ bɑllo:/ meaning ‘dog/dogs’, /mɑlə/mɑl/ meaning ‘flower/flowers’, /gɑsə/gɑs/ meaning ‘tree/trees’ and /wɑlɑ:kulə/wɑlɑ:kulu/ meaning ‘cloud/clouds’). The picture targets included socio-culturally appropriate scenes of the Sri Lankan New Year celebration, elephant procession and the Buddhist ‘Wesak’ ceremony. The test was piloted with a group of 60 participants and necessary changes made. In phase 1, the test was administered to 100 Sinhala-speaking children aged between 3; 0 and 5; 0 years in one district. In this presentation on phase 2, the test was administered to another 100 Sinhala-speaking children aged between 3; 0 to 5; 0 in three districts. In phase 2, the selection of the test items was assessed via measures of content validity, test-retest reliability and inter-rater reliability. The age of acquisition of each syntactic structure was determined using content and grammar scores which were statistically analysed using t-tests and one-way ANOVAs. Results: High percentage agreement was found on test-retest reliability on content validity and Pearson correlation measures and on inter-rater reliability. As predicted, there was a statistically significant influence of age on the production of syntactic structures at p<0.05. Conclusions: As the target test items included generated the information and the syntactic structures expected, the test could be used as a quick syntactic screening tool with preschool children.

Keywords: Sinhala, screening, syntax, language

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: A. Ghanbari Mardasi, N. Wu, C. Wu

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In this study, a spatial wavelet-based crack localization technique for a thick beam is presented. Wavelet scale in spatial wavelet transformation is optimized to enhance crack detection sensitivity. A windowing function is also employed to erase the edge effect of the wavelet transformation, which enables the method to detect and localize cracks near the beam/measurement boundaries. Theoretical model and vibration analysis considering the crack effect are first proposed and performed in MATLAB based on the Timoshenko beam model. Gabor wavelet family is applied to the beam vibration mode shapes derived from the theoretical beam model to magnify the crack effect so as to locate the crack. Relative wavelet coefficient is obtained for sensitivity analysis by comparing the coefficient values at different positions of the beam with the lowest value in the intact area of the beam. Afterward, the optimal wavelet scale corresponding to the highest relative wavelet coefficient at the crack position is obtained for each vibration mode, through numerical simulations. The same procedure is performed for cracks with different sizes and positions in order to find the optimal scale range for the Gabor wavelet family. Finally, Hanning window is applied to different vibration mode shapes in order to overcome the edge effect problem of wavelet transformation and its effect on the localization of crack close to the measurement boundaries. Comparison of the wavelet coefficients distribution of windowed and initial mode shapes demonstrates that window function eases the identification of the cracks close to the boundaries.

Keywords: edge effect, scale optimization, small crack locating, spatial wavelet

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Authors: Radka Obořilová, Hana Šimečková, Matěj Pastucha, Jan Přibyl, Petr Skládal, Ivana Mašlaňová, Zdeněk Farka

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Due to the spreading of antimicrobial resistance, alternative approaches to combat superinfections are being sought, both in the field of lysing agents and methods for studying bacterial lysis. A suitable alternative to antibiotics is phage therapy and enzybiotics, for which it is also necessary to study the mechanism of their action. Biosensor-based techniques allow rapid detection of pathogens in real time, verification of sensitivity to commonly used antimicrobial agents, and selection of suitable lysis agents. The detection of lysis takes place on the surface of the biosensor with immobilized bacteria, which has the potential to be used to study biofilms. An example of such a biosensor is surface plasmon resonance (SPR), which records the kinetics of bacterial lysis based on a change in the resonance angle. The bacteria are immobilized on the surface of the SPR chip, and the action of phage as the mass loss is monitored after a typical lytic cycle delay. Atomic force microscopy (AFM) is a technique for imaging of samples on the surface. In contrast to electron microscopy, it has the advantage of real-time imaging in the native conditions of the nutrient medium. In our case, Staphylococcus aureus was lysed using the enzyme lysostaphin and phage P68 from the familyPodoviridae at 37 ° C. In addition to visualization, AFM was used to study changes in mechanical properties during lysis, which resulted in a reduction of Young’s modulus (E) after disruption of the bacterial wall. Changes in E reflect the stiffness of the bacterium. These advanced methods provide deeper insight into bacterial lysis and can help to fight against bacterial diseases.

Keywords: biosensors, atomic force microscopy, surface plasmon resonance, bacterial lysis, staphylococcus aureus, phage P68

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Authors: Munenari Inoguchi, Keiko Tamura

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In Japan, we were suffered by earthquake, typhoon, and flood disaster in 2019. Especially, 38 of 47 prefectures were affected by typhoon #1919 occurred in October 2019. By this disaster, 99 people were dead, three people were missing, and 484 people were injured as human damage. Furthermore, 3,081 buildings were totally collapsed, 24,998 buildings were half-collapsed. Once disaster occurs, local responders have to inspect damage level of each building by themselves in order to certificate building damage for survivors for starting their life reconstruction process. At that disaster, the total number to be inspected was so high. Based on this situation, Cabinet Office of Japan approved the way to detect building damage level efficiently, that is collectively detection. However, they proposed a just guideline, and local responders had to establish the concrete and infallible method by themselves. Against this issue, we decided to establish the effective and efficient methodology to detect building damage level collectively with flood depth. Besides, we thought that the flood depth was relied on the land height, and we decided to utilize GIS (Geographic Information System) for analyzing the elevation spatially. We focused on the analyzing tool of spatial interpolation, which is utilized to survey the ground water level usually. In establishing the methodology, we considered 4 key-points: 1) how to satisfy the condition defined in the guideline approved by Cabinet Office for detecting building damage level, 2) how to satisfy survivors for the result of building damage level, 3) how to keep equitability and fairness because the detection of building damage level was executed by public institution, 4) how to reduce cost of time and human-resource because they do not have enough time and human-resource for disaster response. Then, we proposed a methodology for detecting building damage level collectively with flood depth utilizing GIS with five steps. First is to obtain the boundary of flooded area. Second is to collect the actual flood depth as sampling over flooded area. Third is to execute spatial analysis of interpolation with sampled flood depth to detect two-dimensional flood depth extent. Fourth is to divide to blocks by four categories of flood depth (non-flooded, over the floor to 100 cm, 100 cm to 180 cm and over 180 cm) following lines of roads for getting satisfaction from survivors. Fifth is to put flood depth level to each building. In Koriyama city of Fukushima prefecture, we proposed the methodology of collectively detection for building damage level as described above, and local responders decided to adopt our methodology at typhoon #1919 in 2019. Then, we and local responders detect building damage level collectively to over 1,000 buildings. We have received good feedback that the methodology was so simple, and it reduced cost of time and human-resources.

Keywords: building damage inspection, flood, geographic information system, spatial interpolation

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Authors: Okike Benjamin

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Terrorism is the use of violence and threat to intimidate or coerce a person, group, society or even government especially for political purposes. Terrorism may be a way of resisting government by some group who may feel marginalized. It could also be a way of expressing displeasure over the activities of government. On 26th December, 2009, US placed Nigeria as a terrorist nation. Recently, the occurrences of terrorism in Nigeria have increased considerably. In Jos, Plateau state, Nigeria, there was a bomb blast which claimed many lives on the eve of 2010 Christmas. Similarly, there was another bomb blast in Mugadishi (Sani Abacha) Barracks Mammy market on the eve of 2011 New Year. For some time now, it is no longer news that bomb exploded in some Northern part of Nigeria. About 25 years ago, stopping terrorism in America by the Americans relied on old-fashioned tools such as strict physical security at vulnerable places, intelligence gathering by government agents, or individuals, vigilance on the part of all citizens, and a sense of community in which citizens do what could be done to protect each other. Just as technology has virtually been used to better the way many other things are done, so also this powerful new weapon called computer technology can be used to detect and prevent terrorism not only in Nigeria, but all over the world. This paper will x-ray the possible causes and effects of bomb blast, which is an act of terrorism and suggest ways in which Explosive Detection Devices (EDDs) and computer software technology could be deployed to reduce the occurrences of terrorism in Nigeria. This become necessary with the abduction of over 200 schoolgirls in Chibok, Borno State from their hostel by members of Boko Haram sect members on 14th April, 2014. Presently, Barrack Obama and other world leaders have sent some of their military personnel to help rescue those innocent schoolgirls whose offence is simply seeking to acquire western education which the sect strongly believe is forbidden.

Keywords: terrorism, bomb blast, computer technology, explosive detection devices, Nigeria

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Authors: Ogbuagu Josephat Okechukwu, Okeke Abuchi Princewill, Arinze Rosemary Uche, Tabugbo Ifeyinwa Blessing, Ogbuagu Adaora Stellamaris

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Phthalates or Phthalate esters (PAEs), categorized as an endocrine disruptor and persistent organic pollutants, are known for their environmental contamination and toxicological effects. In this study, the concentration of selected phthalates was determined across the sampling site to investigate their occurrence and the ecological and health risk assessment they pose to the environment. Water and sediment samples were collected following standard procedures. Solid phase and ultrasonic methods were used to extract seven different PAEs, which were analyzed by Gas Chromatography with Mass Detector (GCMS). The analytical average recovery was found to be within the range of 83.4% ± 2.3%. The results showed that PAEs were detected in six out of seven samples with a high percentage of detection rate in water. Di-n-butyl phthalate (DPB) and disobutyl phthalates (DiBP) showed a greater detection rate compared to other PAE monomers. The concentration of PEs was found to be higher in sediment samples compared to water samples due to the fact that sediments serve as a sink for most persistent organic pollutants. The concentrations of PAEs in water samples and sediments ranged from 0.00 to 0.23 mg/kg and 0.00 to 0.028 mg/l, respectively. Ecological risk assessment using the risk quotient method (RQ) reveals that the estimated environmental risk caused by phthalates lies within the moderate level as RQ ranges from 0.1 to 1.0, whereas the health risk assessment caused by phthalates on estimating the average daily dose reveals that the ingestion of phthalates was found to be approaching permissible limit which can cause serious carcinogenic occurrence in the human system with time due to excess accumulation.

Keywords: phthalates, endocrine disruptor, risk assessment, ecological risk, health risk

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Authors: R. K. Yadav, S. Baral, H. R. Paudel, R. Basnet

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The Public-Private Mix (PPM) approach is a strategic initiative that involves engaging all private and public healthcare providers in the fight against tuberculosis using international healthcare standards. For tuberculosis control in Nepal, the PPM approach could be a milestone. This study aimed to explore the barriers to a public-private mix approach in the management of tuberculosis cases in Nepal. A total of 20 respondents participated in the study. Barriers to PPM were identified in the following three themes: 1) Obstacles related to TB case detection, 2) Obstacles related to patients, and 3) Obstacles related to the healthcare system. PPM implementation was challenged by following subthemes that included staff turnover, low private sector participation in workshops, a lack of training, poor recording and reporting, insufficient joint monitoring and supervision, poor financial benefit, lack of coordination and collaboration, and non-supportive TB-related policies and strategies. The study concludes that numerous barriers exist in the way of effective implementation of the PPM approach, including TB cases detection barriers such as knowledge of TB diagnosis and treatment, HW attitude, workload, patient-related barriers such as knowledge of TB, self-medication practice, stigma and discrimination, financial status, and health system-related barriers such as staff turnover and poor engagement of the private sector in workshops, training, recording, and re-evaluation. Government stakeholders must work together with private sector stakeholders to perform joint monitoring and supervision. Private practitioners should receive training and orientation, and presumptive TB patients should be given adequate time and counseling as well as motivation to visit a government health facility.

Keywords: barrier, tuberculosis, case finding, PPM, nepal

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Aanh Eduardo Dimate-Garcia, Diana Carolina Rodriguez-Romero, Edna Yuliana Gonzalez Rincon, Diana Marcela Pardo Lopez, Yessica Garibello Cubillos

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Musculoskeletal disorders (MD) are the new epidemic of chronic diseases, are multifactorial and affect the different productive sectors. Although there are multiple instruments to evaluate the static and dynamic load, the method of repetitive occupational action (OCRA) seems to be an attractive option. Objective: It is aimed to analyze the use of the OCRA method and the prevalence of MD in workers of various productive sectors according to the literature (2007-2018). Materials and Methods: A literature review (following the PRISMA statement) of studies aimed at assessing the level of biomechanical risk (OCRA) and the prevalence of MD in the databases Scielo, Science Direct, Scopus, ProQuest, Gale, PubMed, Lilacs and Ebsco was realized; 7 studies met the selection criteria; the majority are quantitative (cross section). Results: it was evidenced (gardening and flower-growers) in this review that 79% of the conditions related to the task require physical requirements and involve repetitive movements. In addition, of the high appearance of DM in the high-low back, upper and lower extremities that are produced by the frequency of the activities carried out (footwear production). Likewise, there was evidence of 'very high risks' of developing MD (salmon industry) and a medium index (OCRA) for repetitive movements that require special care (U-Assembly line). Conclusions: the review showed the limited use of the OCRA method for the detection of MD in workers from different sectors, and this method can be used for the detection of biomechanical risk and the appearance of MD.

Keywords: checklist, cumulative trauma disorders, musculoskeletal diseases, repetitive movements

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Şeyma Özçirak Ergün, Ergün Şakalar, Emrah Yalazi̇, Nebahat Şahi̇n

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Food irradiation is a usage of exposing food to ionising radiation (IR) such as gamma rays. IR has been used to decrease the number of harmful microorganisms in the food such as spices. Excessive usage of IR can cause damage to both food and people who consuming food. And also it causes to damages on food DNA. Generally, IR detection techniques were utilized in literature for spices are Electron Spin Resonance (ESR), Thermos Luminescence (TL). Storage creates negative effect on IR detection method then analyses of samples have been performed without storage in general. In the experimental part, red pepper (Capsicum annuum) and mint (Mentha piperita) as spices were exposed to 0, 0.272, 0.497, 1.06, 3.64, 8.82, and 17.42 kGy ionize radiation. ESR was applied to samples irradiated. DNA isolation from irradiated samples was performed using GIDAGEN Multi Fast DNA isolation kit. The DNA concentration was measured using a microplate reader spectrophotometer (Infinite® 200 PRO-Life Science–Tecan). The concentration of each DNA was adjusted to 50 ng/µL. Genomic DNA was imaged by UV transilluminator (Gel Doc XR System, Bio-Rad) for the estimation of genomic DNA bp-fragment size after IR. Thus, agarose gel profiles of irradiated spices were obtained to determine the change of band profiles. Besides, samples were examined at three different time periods (0, 3, 6 months storage) to show the feasibility of developed method. Results of gel electrophoresis showed especially degradation of DNA of irradiated samples. In conclusion, this study with gel electrophoresis can be used as a basis for the identification of the dose of irradiation by looking at degradation profiles at specific amounts of irradiation. Agarose gel results of irradiated samples were confirmed with ESR analysis. This method can be applied widely to not only food products but also all biological materials containing DNA to predict radiation-induced damage of DNA.

Keywords: DNA, electrophoresis, gel electrophoresis, ionizeradiation

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: Nadia El Alami El Hassani, Soukaina Motia, Benachir Bouchikhi, Nezha El Bari

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Doxycycline (DXy) is a cycline antibiotic, most frequently prescribed to treat bacterial infections in veterinary medicine. However, its broad antimicrobial activity and low cost, lead to an intensive use, which can seriously affect human health. Therefore, its spread in the food products has to be monitored. The scope of this work was to synthetize a sensitive and very selective molecularly imprinted polymer (MIP) for DXy detection in honey samples. Firstly, the synthesis of this biosensor was performed by casting a layer of carboxylate polyvinyl chloride (PVC-COOH) on the working surface of a gold screen-printed electrode (Au-SPE) in order to bind covalently the analyte under mild conditions. Secondly, DXy as a template molecule was bounded to the activated carboxylic groups, and the formation of MIP was performed by a biocompatible polymer by the mean of polyacrylamide matrix. Then, DXy was detected by measurements of differential pulse voltammetry (DPV). A non-imprinted polymer (NIP) prepared in the same conditions and without the use of template molecule was also performed. We have noticed that the elaborated biosensor exhibits a high sensitivity and a linear behavior between the regenerated current and the logarithmic concentrations of DXy from 0.1 pg.mL⁻¹ to 1000 pg.mL⁻¹. This technic was successfully applied to determine DXy residues in honey samples with a limit of detection (LOD) of 0.1 pg.mL⁻¹ and an excellent selectivity when compared to the results of oxytetracycline (OXy) as analogous interfering compound. The proposed method is cheap, sensitive, selective, simple, and is applied successfully to detect DXy in honey with the recoveries of 87% and 95%. Considering these advantages, this system provides a further perspective for food quality control in industrial fields.

Keywords: doxycycline, electrochemical sensor, food control, gold nanoparticles, honey, molecular imprinted polymer

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Authors: Vadivel Ayyasamy

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The present approach deals with the identification of Emotions and classification of Emotional patterns at Phrase-level with respect to Positive and Negative Orientation. The proposed approach considers emotion triggered terms, its co-occurrence terms and also associated sentences for recognizing emotions. The proposed approach uses Part of Speech Tagging and Emotion Actifiers for classification. Here sentence patterns are broken into phrases and Neuro-Fuzzy model is used to classify which results in 16 patterns of emotional phrases. Suitable intensities are assigned for capturing the degree of emotion contents that exist in semantics of patterns. These emotional phrases are assigned weights which supports in deciding the Positive and Negative Orientation of emotions. The approach uses web documents for experimental purpose and the proposed classification approach performs well and achieves good F-Scores.

Keywords: emotions, sentences, phrases, classification, patterns, fuzzy, positive orientation, negative orientation

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Authors: Vaibhav Budhiraja, Chandra Mouli Pandey

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The ultrasonic synthesis of nanostructured conducting copolymer is an effective technique to synthesize polymer with desired chemical properties. This tailored nanostructure, shows tremendous improvement in sensitivity and stability to detect a variety of analytes. The present work reports ultrasonically synthesized nanostructured conducting poly(o-phenylenediamine)-co-poly(1-naphthylamine) (POPD-co-PNA). The synthesized material has been characterized using Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy, transmission electron microscopy, X-ray diffraction and cyclic voltammetry. FTIR spectroscopy confirmed random copolymerization, while UV-visible studies reveal the variation in polaronic states upon copolymerization. High crystallinity was achieved via ultrasonic synthesis which was confirmed by X-ray diffraction, and the controlled morphology of the nanostructures was confirmed by transmission electron microscopy analysis. Cyclic voltammetry shows that POPD-co-PNA has rather high electrochemical activity. This behavior was explained on the basis of variable orientations adopted by the conducting polymer chains. The synthesized material was electrophoretically deposited at onto indium tin oxide coated glass substrate which is used as cathode and parallel platinum plate as the counter electrode. The fabricated bioelectrode was further used for detection of glucose by crosslinking of glucose oxidase in the PODP-co-PNA film. The bioelectrode shows a surface-controlled electrode reaction with the electron transfer coefficient (α) of 0.72, charge transfer rate constant (ks) of 21.77 s⁻¹ and diffusion coefficient 7.354 × 10⁻¹⁵ cm²s⁻¹.

Keywords: conducting, electrophoretic, glucose, poly (o-phenylenediamine), poly (1-naphthylamine), ultrasonic

Procedia PDF Downloads 140