Search results for: Signal Processing

Authors: Archana G. Lamdande, Shyam R. Garud, K. S. M. S. Raghavarao

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Coconut has two edible parts, that is, a white kernel (solid endosperm) and coconut water (liquid endosperm). The white kernel is generally used in fresh or dried form for culinary purposes. Coconut testa, is the brown skin, covering the coconut kernel. It is removed by paring of wet coconut and obtained as a by-product in coconut processing industries during the production of products such as desiccated coconut, coconut milk, whole coconut milk powder and virgin coconut oil. At present, it is used as animal feed component after drying and recovering the residual oil (by expelling). Experiments were carried out on expelling of coconut milk for shredded coconut with and without testa removal, in order to explore the possibility of increasing the milk yield and value addition in terms of increased polyphenol content. The color characteristics of coconut milk obtained from the grating without removal of testa were observed to be L* 82.79, a* 0.0125, b* 6.245, while that obtained from grating with removal of testa were L* 83.24, a* -0.7925, b* 3.1. A significant increase was observed in total phenol content of coconut milk obtained from the grating with testa (833.8 µl/ml) when compared to that from without testa (521.3 µl/ml). However, significant difference was not observed in protein content of coconut milk obtained from the grating with and without testa (4.9 and 5.0% w/w, respectively). Coconut milk obtained from grating without removal of testa showed higher milk yield (62% w/w) when compared to that obtained from grating with removal of testa (60% w/w). The fat content in coconut milk was observed to be 32% (w/w), and it is unstable due to such a high fat content. Therefore, several experiments were carried out for examining its stability by adjusting the fat content at different levels (32, 28, 24, and 20% w/w). It was found that the coconut milk was more stable with a fat content of 24 % (w/w). Homogenization and ultrasonication and their combinations were used for exploring the possibility of increasing the stability of coconut milk. The microscopic study was carried out for analyzing the size of fat globules and the degree of their uniform distribution.

Keywords: coconut milk, homogenization, stability, testa, ultrasonication

Procedia PDF Downloads 314

Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

Procedia PDF Downloads 112

Authors: Bouayachi Ali, Alikouss Saida, Baroudi Zouhir, Zerhouni Youssef, Zouhair Mohammed, El Idrissi Assia, Essalhi Mourad

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The world-class Imiter silver deposit is located on the northern flank of the Precambrian Imiter buttonhole. This deposit is formed by epithermal veins hosted in the sandstone-pelite formations of the lower complex and in the basic conglomerates of the upper complex, these veins are controlled by a regional scale fault cluster, oriented N70°E to N90°E. The present work on the contribution of remote sensing on the geological mapping of the Imiter buttonhole and application to the mineralized structures of the Principal Corps B3. Mapping on satellite images is a very important tool in mineral prospecting. It allows the localization of the zones of interest in order to orientate the field missions by helping the localization of the major structures which facilitates the interpretation, the programming and the orientation of the mining works. The predictive map also allows for the correction of field mapping work, especially the direction and dimensions of structures such as dykes, corridors or scrapings. The use of a series of processing such as SAM, PCA, MNF and unsupervised and supervised classification on a Landsat 8 satellite image of the study area allowed us to highlight the main facies of the Imite area. To improve the exploration research, we used another processing that allows to realize a spatial distribution of the alteration mineral indices, and the application of several filters on the different bands to have lineament maps.

Keywords: principal corps B3, teledetection, Landsat 8, Imiter II, silver mineralization, lineaments

Procedia PDF Downloads 95

Authors: Tai Chen, Caihuan Tian, Xiuxia Ren, Jingqi Xue, Xiuxin Zhang

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The herbaceous peony has become increasingly popular worldwide in recent years, especially as a cut flower with great economic value. However, peony has a very short vase life, only 3-5 d usually, which seriously affects its commodity value. In this study, we used the cut peony (Paeonia lactiflora cv. Sarah) as a material and found that melatonin treatment significantly improved its postharvest performance. In the control group, its vase life was 4.8 d, accompanied by petal dropping at last; melatonin treatment (40 μM) increased this time to 6.9 d without petal dropping at the end. Further study showed that melatonin treatment significantly increased the activity of antioxidant enzymes as well as reduced sugar content in petals, whereas the starch content in petals decreased. These results indicated that melatonin treatment may delay the oxidation reaction caused by aging, which also provides extra energy for maintaining flowering. Through full-length transcriptome sequencing, a total of 2819 differentially expressed genes (DEGs) between control and melatonin treatment groups were identified. KEGG enrichment analysis showed that these DEGs were mainly involved in three pathways, including melatonin synthesis, starch and sucrose conversion, and plant disease resistance. After the RT-qPCR verification, we identified three DEGs, named PlBAM3, PlWRKY22 and PlTIP1, and they should play major roles in melatonin-improved postharvest performance. One possible reason is that PlBAM3 caused maltose production (by starch degradation), maintained the proline biosynthesis, and then alleviated oxidative stress. Another reason is that both PlBAM3 and PlWRKY22 are key drought resistance regulators, which have the ability to alleviate osmotic stress and improve water absorption, which may also help to improve the postharvest quality of cut peony. In addition, PlTIP1 is involved in the sugar signal pathway, indicating sugar may also as a signal substance during this process. Our work may give new ideas for developing new ways to prolong the vase life of cut peony and improve its commodity value eventually.

Keywords: cut peony, melatonin, vase life, oxidation reaction, energy supply, differentially expressed genes

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Authors: Jiraporn Rojtinnakorn

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ICAM-2 (intercellular adhesion molecule 2) or CD102 (Cluster of Differentiation 102) is type I trans-membrane glycoproteins, composing 2-9 immunoglobulin-like C2-type domains. ICAM-2 plays the particular role in immune response and cell surveillance. It is concerned in innate and specific immunity, cell survival signal, apoptosis, and anticancer. EST clone of ICAM-2, from P. gigas blood cell EST libraries, showed high identity to human ICAM-2 (92%) with conserve region of ICAM N-terminal domain and part of Ig superfamily. Gene and protein of ICAM-2 has been founded in mammals. This is the first report of ICAM-2 in fish.

Keywords: ICAM-2, CD102, Pangasianodon gigas, antitumor

Procedia PDF Downloads 226

Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

Procedia PDF Downloads 137

Authors: M. Habibi

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The time-resolved studies of soft and hard X-ray were carried out over a wide range of argon pressures by employing an array of eight filtered photo PIN diodes and a scintillation detector, simultaneously. In 50% of the discharges, the soft X-ray is seen to be emitted in short multiple pulses corresponding to different compression, whereas it is a single pulse for hard X-rays corresponding to only the first strong compression. It should be stated that multiple compressions dominantly occur at low pressures and high pressures are mostly in the single compression regime. In 43% of the discharges, at all pressures except for optimum pressure, the first period is characterized by two or more sharp peaks.The X–ray signal intensity during the second and subsequent compressions is much smaller than the first compression.

Keywords: plasma focus device, SXR, HXR, Pin-diode, argon plasma

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Shutchapol Chopvitayakun

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Nowadays, especially for the last 5 years, mobile devices, mobile applications and mobile users, through the deployment of wireless communication and mobile phone cellular network, all these components are growing significantly bigger and stronger. They are being integrated into each other to create multiple purposes and pervasive deployments into every business and non-business sector such as education, medicine, traveling, finance, real estate and many more. Objective of this study was to develop a mobile application for seniors or last-year students who enroll the internship program at each tertiary school (undergraduate school) and do onsite practice at real field sties, real organizations and real workspaces. During the internship session, all students as the interns are required to exercise, drilling and training onsite with specific locations and specific tasks or may be some assignments from their supervisor. Their work spaces are both private and government corporates and enterprises. This mobile application is developed under schema of a transactional processing system that enables users to keep daily work or practice log, monitor true working locations and ability to follow daily tasks of each trainee. Moreover, it provides useful guidance from each intern’s advisor, in case of emergency. Finally, it can summarize all transactional data then calculate each internship cumulated hours from the field practice session for each individual intern.

Keywords: internship, mobile application, Android OS, smart phone devices, mobile transactional processing system, guidance and monitoring, tertiary education, senior students, model view controller (MVC)

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Abdulaziz Alakeel, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Methotrexate (MTX) is an antimetabolite used to treat multiple conditions, including neoplastic diseases, severe psoriasis, and rheumatoid arthritis. Skin cancer is the out-of-control growth of abnormal cells in the epidermis, the outermost skin layer, caused by unrepaired DNA damage that triggers mutations. These mutations lead the skin cells to multiply rapidly and form malignant tumors. The aim of this review is to evaluate the risk of skin cancer associated with the use of methotrexate and to suggest regulatory recommendations if required. Methodology: Signal Detection team at Saudi Food and Drug Authority (SFDA) performed a safety review using National Pharmacovigilance Center (NPC) database as well as the World Health Organization (WHO) VigiBase, alongside with literature screening to retrieve related information for assessing the causality between skin cancer and methotrexate. The search conducted in July 2020. Results: Four published articles support the association seen while searching in literature, a recent randomized control trial published in 2020 revealed a statistically significant increase in skin cancer among MTX users. Another study mentioned methotrexate increases the risk of non-melanoma skin cancer when used in combination with immunosuppressant and biologic agents. In addition, the incidence of melanoma for methotrexate users was 3-fold more than the general population in a cohort study of rheumatoid arthritis patients. The last article estimated the risk of cutaneous malignant melanoma (CMM) in a cohort study shows a statistically significant risk increase for CMM was observed in MTX exposed patients. The WHO database (VigiBase) searched for individual case safety reports (ICSRs) reported for “Skin Cancer” and 'Methotrexate' use, which yielded 121 ICSRs. The initial review revealed that 106 cases are insufficiently documented for proper medical assessment. However, the remaining fifteen cases have extensively evaluated by applying the WHO criteria of causality assessment. As a result, 30 percent of the cases showed that MTX could possibly cause skin cancer; five cases provide unlikely association and five un-assessable cases due to lack of information. The Saudi NPC database searched to retrieve any reported cases for the combined terms methotrexate/skin cancer; however, no local cases reported up to date. The data mining of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by the WHO Uppsala Monitoring Centre to measure the reporting ratio. Positive IC reflects higher statistical association, while negative values translated as a less statistical association, considering the null value equal to zero. Results showed that a combination of 'Methotrexate' and 'Skin cancer' observed more than expected when compared to other medications in the WHO database (IC value is 1.2). Conclusion: The weighted cumulative pieces of evidence identified from global cases, data mining, and published literature are sufficient to support a causal association between the risk of skin cancer and methotrexate. Therefore, health care professionals should be aware of this possible risk and may consider monitoring any signs or symptoms of skin cancer in patients treated with methotrexate.

Keywords: methotrexate, skin cancer, signal detection, pharmacovigilance

Procedia PDF Downloads 114

Authors: Abdul Muqsit Abbasi, Ibrahim Chhipa, Asad Anwer, Saad Farooq, Hassan Berry, Sonu Kumar, Sundar Ali, Muhammad Owais Mahmood, Areeb Ur Rehman, Bahram Baloch

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Accurate translation between structurally diverse languages, such as Arabic and English, presents a critical challenge in natural language processing due to significant linguistic and cultural differences. This paper investigates the effectiveness of Facebook’s mBART model, fine-tuned specifically for sequence-tosequence (seq2seq) translation tasks between Arabic and English, and enhanced through advanced refinement techniques. Our approach leverages the Alef Dataset, a meticulously curated parallel corpus spanning various domains to capture the linguistic richness, nuances, and contextual accuracy essential for high-quality translation. We further refine the model’s output using advanced language models such as GPT-3.5 and GPT-4, which improve fluency, coherence, and correct grammatical errors in translated texts. The fine-tuned model demonstrates substantial improvements, achieving a BLEU score of 38.97, METEOR score of 58.11, and TER score of 56.33, surpassing widely used systems such as Google Translate. These results underscore the potential of mBART, combined with refinement strategies, to bridge the translation gap between Arabic and English, providing a reliable, context-aware machine translation solution that is robust across diverse linguistic contexts.

Keywords: natural language processing, machine translation, fine-tuning, Arabic-English translation, transformer models, seq2seq translation, translation evaluation metrics, cross-linguistic communication

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Authors: N. Boudinar, A. Djekoun, A. Otmani, B. Bouzabata, J. M. Greneche

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High-energy ball milling is a solid-state powder processing technique that allows synthesizing a variety of equilibrium and non-equilibrium alloy phases starting from elemental powders. The advantage of this process technology is that the powder can be produced in large quantities and the processing parameters can be easily controlled, thus it is a suitable method for commercial applications. It can also be used to produce amorphous and nanocrystalline materials in commercially relevant amounts and is also amenable to the production of a variety of alloy compositions. Mechanical alloying (high-energy ball milling) provides an inter-dispersion of elements through a repeated cold welding and fracture of free powder particles; the grain size decreases to nano metric scale and the element mix together. Progressively, the concentration gradients disappear and eventually the elements are mixed at the atomic scale. The end products depend on many parameters such as the milling conditions and the thermodynamic properties of the milled system. Here, the mechanical alloying technique has been used to prepare nano crystalline Fe_50 and Fe_64 wt.% Ni alloys from powder mixtures. Scanning electron microscopy (SEM) with energy-dispersive, X-ray analyses and Mössbauer spectroscopy were used to study the mixing at nanometric scale. The Mössbauer Spectroscopy confirmed the ferromagnetic ordering and was use to calculate the distribution of hyperfin field. The Mössbauer spectrum for both alloys shows the existence of a ferromagnetic phase attributed to γ-Fe-Ni solid solution.

Keywords: nanocrystalline, mechanical alloying, X-ray diffraction, Mössbauer spectroscopy, phase transformations

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Authors: Vahid Tabrizi, Reza GHasemi, Ahmadreza Vali

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This paper presents robust nonlinear control law for a quadrotor UAV using fast terminal sliding mode control. Fast terminal sliding mode idea is used for introducing a nonlinear sliding variable that guarantees the finite time convergence in sliding phase. Then, in reaching phase for removing chattering and producing smooth control signal, continuous approximation idea is used. Simulation results show that the proposed algorithm is robust against parameter uncertainty and has better performance than conventional sliding mode for controlling a quadrotor UAV.

Keywords: quadrotor UAV, fast terminal sliding mode, second order sliding mode t

Procedia PDF Downloads 547

Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: A. Zh. Aupova, A. Ulankyzy, A. Sarsenova, A. Kussayin, Sh. Turarbek, N. Moldagulova, A. Kurmanbayev

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One of the methods of organic waste bioconversion into environmentally-friendly fertilizer is composting. Microorganisms that produce hydrolytic enzymes play a significant role in accelerating the process of organic waste composting. We studied the enzymatic potential (amylase, protease, cellulase, lipase, urease activity) of bacteria isolated from the sewage sludge of Nur-Sultan, Rudny, and Fort-Shevchenko cities, the dacha soil of Nur-Sultan city, and freshly cut grass from the dacha for processing organic waste and identifying active strains. Microorganism isolation was carried out by the cultures enrichment method on liquid nutrient media, followed by inoculating on different solid media to isolate individual colonies. As a result, sixty-one microorganisms were isolated, three of which were thermophiles (DS1, DS2, and DS3). The highest number of isolates, twenty-one and eighteen, were isolated from sewage sludge of Nur-Sultan and Rudny cities, respectively. Ten isolates were isolated from the wastewater of the sewage treatment plant in Fort-Shevchenko. From the dacha soil of Nur-Sultan city and freshly cut grass - 9 and 5 isolates were revealed, respectively. The lipolytic, proteolytic, amylolytic, cellulolytic, ureolytic, and oil-oxidizing activities of isolates were studied. According to the results of experiments, starch hydrolysis (amylolytic activity) was found in 2 isolates - CB2/2, and CB2/1. Three isolates - CB2, CB2/1, and CB1/1 were selected for the highest ability to break down casein. Among isolated 61 bacterial cultures, three isolates could break down fats - CB3, CBG1/1, and IL3. Seven strains had cellulolytic activity - DS1, DS2, IL3, IL5, P2, P5, and P3. Six isolates rapidly decomposed urea. Isolate P1 could break down casein and cellulose. Isolate DS3 was a thermophile and had cellulolytic activity. Thus, based on the conducted studies, 15 isolates were selected as a potential for sewage sludge composting - CB2, CB3, CB1/1, CB2/2, CBG1/1, CB2/1, DS1, DS2, DS3, IL3, IL5, P1, P2, P5, P3. Selected strains were identified on a mass spectrometer (Maldi-TOF). The isolate - CB 3 was referred to the genus Rhodococcus rhodochrous; two isolates CB2 and CB1 / 1 - to Bacillus cereus, CB 2/2 - to Cryseobacterium arachidis, CBG 1/1 - to Pseudoxanthomonas sp., CB2/1 - to Bacillus megaterium, DS1 - to Pediococcus acidilactici, DS2 - to Paenibacillus residui, DS3 - to Brevibacillus invocatus, three strains IL3, P5, P3 - to Enterobacter cloacae, two strains IL5, P2 - to Ochrobactrum intermedium, and P1 - Bacillus lichenoformis. Hence, 60 isolates were isolated from the wastewater of the cities of Nur-Sultan, Rudny, Fort-Shevchenko, the dacha soil of Nur-Sultan city, and freshly cut grass from the dacha. Based on the highest enzymatic activity, 15 active isolates were selected and identified. These strains may become the candidates for bio preparation for sewage sludge processing.

Keywords: sewage sludge, composting, bacteria, enzymatic activity

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Authors: Teles de Sales Bezerra, Saulo Aislan da Silva Eleuterio, José Anderson Rodrigues de Souza, Ítalo de Pontes Oliveira

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This paper presents a analysis of performance the Received Strength Signal Indicator (RSSI) to Wireless Sensor Networks, with a finality of investigate a behavior of ZigBee devices operating into real environments. The test of performance was realize using two Series 1 ZigBee Module and two modules of development Arduino Uno R3, evaluating in this form a measurements of RSSI into environments like places of sports, preservation forests and water reservoir.

Keywords: wireless sensor networks, RSSI, Arduino, environments

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Authors: Vedansh Gupta, Allyson Lutz, Ameen Razavi, Fatemeh Shirazi

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The agriculture industry is especially vulnerable to forecasted water shortages. In the fresh and fresh-cut produce sector, conventional flume-based washing with recirculation exhibits high water demand. This leads to a large water footprint and possible cross-contamination of pathogens. These can be alleviated through advanced water reuse processes, such as membrane technologies including reverse osmosis (RO). Water reuse technologies effectively remove dissolved constituents but can easily foul without pre-treatment. Biological treatment is effective for the removal of organic compounds responsible for fouling, but not at the low temperatures encountered at most produce processing facilities. This study showed that the Microvi MicroNiche Engineering (MNE) technology effectively removes organic compounds (> 80%) at low temperatures (6-8 °C) from wash water. The MNE technology uses synthetic microorganism-material composites with negligible solids production, making it advantageously situated as an effective bio-pretreatment for RO. A preliminary technoeconomic analysis showed 60-80% savings in operation and maintenance costs (OPEX) when using the Microvi MNE technology for organics removal. This study and the accompanying economic analysis indicated that the proposed technology process will substantially reduce the cost barrier for adopting water reuse practices, thereby contributing to increased food safety and furthering sustainable water reuse processes across the agricultural industry.

Keywords: biological pre-treatment, innovative technology, vegetable processing, water reuse, agriculture, reverse osmosis, MNE biocatalysts

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Authors: James Banda, Jupiter Simbeye, Essau Chisale, Geoffrey Kanyerere, Kings Kamtambe

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Improved solar tent dryers for processing small fish species were designed to reduce post-harvest fish losses and improve supply of quality fish products in the southern part of Lake Malawi under CultiAF project. A comparative analysis of the quality of Diplotaxodon (Ndunduma) from Lake Malawi processed in solar tent dryer and open sun drying was conducted using proximate analysis, microbial analysis and sensory evaluation. Proximates for solar tent dried fish and open sun dried fish in terms of proteins, fats, moisture and ash were 63.3±0.15% and 63.3±0.34%, 19.6±0.09% and 19.9±0.25%, 8.3±0.12% and 17.0±0.01%, and 15.6±0.61% and 21.9±0.91% respectively. Crude protein and crude fat showed non-significant differences (p = 0.05), while moisture and ash content were significantly different (p = 001). Open sun dried fish had significantly higher numbers of viable bacteria counts (5.2×10⁶ CFU) than solar tent dried fish (3.9×10² CFU). Most isolated bacteria from solar tent dried and open sun dried fish were 1.0×10¹ and 7.2×10³ for Total coliform, 0 and 4.5 × 10³ for Escherishia coli, 0 and 7.5 × 10³ for Salmonella, 0 and 5.7×10² for shigella, 4.0×10¹ and 6.1×10³ for Staphylococcus, 1.0×10¹ and 7.0×10² for vibrio. Qualitative evaluation of sensory properties showed higher acceptability of 3.8 for solar tent dried fish than 1.7 for open sun dried fish. It is concluded that promotion of solar tent drying in processing small fish species in Malawi would support small-scale fish processors to produce quality fish in terms of nutritive value, reduced microbial contamination, sensory acceptability and reduced moisture content.

Keywords: diplotaxodon, Malawi, open sun drying, solar tent drying

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Authors: Zhang Songning, Guan Zheng, Ci Yan, Ding Gangyi

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The application of physical simulation engines in collaborative editing systems has an important background and role. Firstly, physical simulation engines can provide real-world physical simulations, enabling users to interact and collaborate in real time in virtual environments. This provides a more intuitive and immersive experience for collaborative editing systems, allowing users to more accurately perceive and understand various elements and operations in collaborative editing. Secondly, through physical simulation engines, different users can share virtual space and perform real-time collaborative editing within it. This real-time sharing and collaborative editing method helps to synchronize information among team members and improve the efficiency of collaborative work. Through experiments, the average model transmission speed of a single person in the collaborative editing system has increased by 141.91%; the average model processing speed of a single person has increased by 134.2%; the average processing flow rate of a single person has increased by 175.19%; the overall efficiency improvement rate of a single person has increased by 150.43%. With the increase in the number of users, the overall efficiency remains stable, and the physical simulation engine running status collaborative editing system also has horizontal scalability. It is not difficult to see that the design and implementation of a collaborative editing system based on physical simulation engines not only enriches the user experience but also optimizes the effectiveness of team collaboration, providing new possibilities for collaborative work.

Keywords: physics engine, simulation technology, collaborative editing, system design, data transmission

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Authors: A. Manallah, C. Meier

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Confocal spectral interferometry (CSI) is an innovative optical method for determining microtopography of surfaces and thickness of transparent layers, based on the combination of two optical principles: confocal imaging, and spectral interferometry. Confocal optical system images at each instant a single point of the sample. The whole surface is reconstructed by plan scanning. The interference signal generated by mixing two white-light beams is analyzed using a spectrometer. In this work, five ‘rugotests’ of known standard roughnesses are investigated. The topography is then measured and illustrated, and the equivalent roughness is determined and compared with the standard values.

Keywords: confocal spectral interferometry, nondestructive testing, optical metrology, surface topography, roughness

Procedia PDF Downloads 276

Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Tae Jin Choi, Jong Ok Kim, Sang Min Jin, Gilwon Yoon

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Analysis of EEG brainwave provides information on mental or emotional states. One of the particular states that can have various applications in human machine interface (HMI) is concentration. 8-channel EEG signals were measured and analyzed. The concentration index was compared during resting and concentrating periods. Among eight channels, locations the frontal lobe (Fp1 and Fp2) showed a clear increase of the concentration index during concentration regardless of subjects. The rest six channels produced conflicting observations depending on subjects. At this time, it is not clear whether individual difference or how to concentrate made these results for the rest six channels. Nevertheless, it is expected that Fp1 and Fp2 are promising locations for extracting control signal for HMI applications.

Keywords: concentration, EEG, human machine interface, biophysical

Procedia PDF Downloads 482

Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha

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This paper deals with a stabilization problem for multi-agent systems, when all agents in a multi-agent system receive the same broadcasting control signal and the controller can measure not each agent output but the sum of all agent outputs. It is analytically shown that when the sum of all agent outputs is bounded with a certain broadcasting controller for a given reference, each agent output is separately bounded:stabilization of the sum of agent outputs always results in the stability of every agent output. A numerical example is presented to illustrate our theoretic findings in this paper.

Keywords: broadcasting control, multi-agent system, transfer function, stabilization

Procedia PDF Downloads 380

Authors: Martin H¨ausl, Maximilian Auch, Johannes Forster, Peter Mandl, Alexander Schill

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In order to survive on the market, companies must constantly develop improved and new products. These products are designed to serve the needs of their customers in the best possible way. The creation of new products is also called innovation and is primarily driven by a company’s internal research and development department. However, a new approach has been taking place for some years now, involving external knowledge in the innovation process. This approach is called open innovation and identifies customer knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its initial phase, the Ideation phase. For this purpose, the social media posts are semantically structured with the help of an ontology and the authors are evaluated using graph-theoretical metrics such as density. For the structuring and evaluation of relevant social media posts, we also use the findings of Natural Language Processing, e. g. Named Entity Recognition, specific dictionaries, Triple Tagger and Part-of-Speech-Tagger. The selection and evaluation of the tools used are discussed in this paper. Using our ontology and metrics to structure social media posts enables users to semantically search these posts for new product ideas and thus gain an improved insight into the external sources such as customer needs.

Keywords: idea ontology, innovation management, semantic search, open information extraction

Procedia PDF Downloads 188

Authors: Nir Nissim, Ran Yahalom, Tomer Lancewiki, Yuval Elovici, Boaz Lerner

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Background: Attackers increasingly take advantage of innocent users who tend to use USB devices casually, assuming these devices benign when in fact they may carry an embedded malicious behavior or hidden malware. USB devices have many properties and capabilities that have become the subject of malicious operations. Many of the recent attacks targeting individuals, and especially organizations, utilize popular and widely used USB devices, such as mice, keyboards, flash drives, printers, and smartphones. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched via USB devices. Significance: We propose USBWARE, a project that focuses on the vulnerabilities of USB devices and centers on the development of a comprehensive detection framework that relies upon a crucial attack repository. USBWARE will allow researchers and companies to better understand the vulnerabilities and attacks associated with USB devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The framework of USBWARE is aimed at accurate detection of both known and unknown USB-based attacks by a process that efficiently enhances the framework's detection capabilities over time. The framework will integrate two main security approaches in order to enhance the detection of USB-based attacks associated with a variety of USB devices. The first approach is aimed at the detection of known attacks and their variants, whereas the second approach focuses on the detection of unknown attacks. USBWARE will consist of six independent but complimentary detection modules, each detecting attacks based on a different approach or discipline. These modules include novel ideas and algorithms inspired from or already developed within our team's domains of expertise, including cyber security, electrical and signal processing, machine learning, and computational biology. The establishment and maintenance of the USBWARE’s dynamic and up-to-date attack repository will strengthen the capabilities of the USBWARE detection framework. The attack repository’s infrastructure will enable researchers to record, document, create, and simulate existing and new USB-based attacks. This data will be used to maintain the detection framework’s updatability by incorporating knowledge regarding new attacks. Based on our experience in the cyber security domain, we aim to design the USBWARE framework so that it will have several characteristics that are crucial for this type of cyber-security detection solution. Specifically, the USBWARE framework should be: Novel, Multidisciplinary, Trusted, Lightweight, Extendable, Modular and Updatable and Adaptable. Major Findings: Based on our initial survey, we have already found more than 23 types of USB-based attacks, divided into six major categories. Our preliminary evaluation and proof of concepts showed that our detection modules can be used for efficient detection of several basic known USB attacks. Further research, development, and enhancements are required so that USBWARE will be capable to cover all of the major known USB attacks and to detect unknown attacks. Conclusion: USBWARE is a crucial detection framework that must be further enhanced and developed.

Keywords: USB, device, cyber security, attack, detection

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Authors: Sudeshna Chandra, Christian Gäbler, Christian Schliebe, Heinrich Lang

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Highly branched dendrimers provide structural homogeneity, controlled composition, comparable size to biomolecules, internal porosity and multiple functional groups for conjugating reactions. Electro-active dendrimers containing multiple redox units have generated great interest in their use as electrode modifiers for development of biosensors. The electron transfer between the redox-active dendrimers and the biomolecules play a key role in developing a biosensor. Ferrocenes have multiple and electrochemically equivalent redox units that can act as electron “pool” in a system. The ferrocenyl-terminated polyamidoamine dendrimer is capable of transferring multiple numbers of electrons under the same applied potential. Therefore, they can be used for dual purposes: one in building a film over the electrode for immunosensors and the other for immobilizing biomolecules for sensing. Electrochemical immunosensor, thus developed, exhibit fast and sensitive analysis, inexpensive and involve no prior sample pre-treatment. Electrochemical amperometric immunosensors are even more promising because they can achieve a very low detection limit with high sensitivity. Detection of the cancer biomarkers at an early stage can provide crucial information for foundational research of life science, clinical diagnosis and prevention of disease. Elevated concentration of biomarkers in body fluid is an early indication of some type of cancerous disease and among all the biomarkers, IgG is the most common and extensively used clinical cancer biomarkers. We present an IgG (=immunoglobulin) electrochemical immunosensor using a newly synthesized redox-active ferrocenyl dendrimer of generation 2 (G2Fc) as glassy carbon electrode material for immobilizing the antibody. The electrochemical performance of the modified electrodes was assessed in both aqueous and non-aqueous media using varying scan rates to elucidate the reaction mechanism. The potential shift was found to be higher in an aqueous electrolyte due to presence of more H-bond which reduced the electrostatic attraction within the amido groups of the dendrimers. The cyclic voltammetric studies of the G2Fc-modified GCE in 0.1 M PBS solution of pH 7.2 showed a pair of well-defined redox peaks. The peak current decreased significantly with the immobilization of the anti-goat IgG. After the immunosensor is blocked with BSA, a further decrease in the peak current was observed due to the attachment of the protein BSA to the immunosensor. A significant decrease in the current signal of the BSA/anti-IgG/G2Fc/GCE was observed upon immobilizing IgG which may be due to the formation of immune-conjugates that blocks the tunneling of mass and electron transfer. The current signal was found to be directly related to the amount of IgG captured on the electrode surface. With increase in the concentration of IgG, there is a formation of an increasing amount of immune-conjugates that decreased the peak current. The incubation time and concentration of the antibody was optimized for better analytical performance of the immunosensor. The developed amperometric immunosensor is sensitive to IgG concentration as low as 2 ng/mL. Tailoring of redox-active dendrimers provides enhanced electroactivity to the system and enlarges the sensor surface for binding the antibodies. It may be assumed that both electron transfer and diffusion contribute to the signal transformation between the dendrimers and the antibody.

Keywords: ferrocenyl dendrimers, electrochemical immunosensors, immunoglobulin, amperometry

Procedia PDF Downloads 337

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Sung-Bok Choi, Hyun-Jun Shin, Hyoung-Kyu Song

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This paper proposes a single relay selection scheme in cooperative communication. Decode and forward scheme is considered when a source node wants to cooperate with a single relay for data transmission. To use the proposed single relay selection scheme, the source node make a little different pattern signal which is not complex pattern and broadcasts it. The proposed scheme does not require the channel state information between the source node and candidates of the relay during the relay selection. Therefore, it is able to be used in many fields.

Keywords: relay selection, cooperative communication, df, channel codes

Procedia PDF Downloads 670

Authors: Seongsoo Lee

Abstract:

IP reuse is an effective design methodology for modern SoC design to reduce effort and time. However, description and modeling methods of IP interfaces are different due to different IP designers. In this paper, standardized description and modeling methods of IP interfaces are proposed. It consists of 11 items such as IP information, model provision, data type, description level, interface information, port information, signal information, protocol information, modeling level, modeling information, and source file. The proposed description and modeling methods enables easy understanding, simulation, verification, and modification in IP reuse.

Keywords: interface, standardization, description, modeling, semiconductor IP

Procedia PDF Downloads 502