Search results for: laser processing

Authors: Wei-Na Li, Mei-Lan Piao, Nam Kim

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We proposed a novel computer-generated hologram (CGH) achieved scheme, wherein the CGH is generated from a point cloud which is transformed by a mapping relationship of a series of elemental images captured from a real three-dimensional (3D) object by using a lens array. This scheme is composed of three procedures: mapping from elemental images to point cloud, hologram generation, and hologram display. A mapping method is figured out to achieve a virtual volume date (point cloud) from a series of elemental images. This mapping method consists of two steps. Firstly, the coordinate (x, y) pairs and its appearing number are calculated from the series of sub-images, which are generated from the elemental images. Secondly, a series of corresponding coordinates (x, y, z) are calculated from the elemental images. Then a hologram is generated from the volume data that is calculated by the previous two steps. Eventually, a spatial light modulator (SLM) and a green laser beam are utilized to display this hologram and reconstruct the original 3D object. In this paper, in order to show a more auto stereoscopic display of a real 3D object, we successfully obtained the actual depth data of every discrete point of the real 3D object, and overcame the inherent drawbacks of the depth camera by obtaining point cloud from the elemental images.

Keywords: elemental image, point cloud, computer-generated hologram (CGH), autostereoscopic display

Procedia PDF Downloads 589

Authors: Sachi Nandan Mohanty, Shreya Sinha, Sweeti Sah, Shweta Sharma

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The advancement of natural language processing has majorly influenced the field of medical transcript classification, providing a robust framework for enhancing the accuracy of clinical data processing. It has enormous potential to transform healthcare and improve people's livelihoods. This research focuses on improving the accuracy of medical transcript categorization using Bidirectional Encoder Representations from Transformers (BERT) and its specialized variants, including BioBERT, ClinicalBERT, SciBERT, and BlueBERT. The experimental work employs Optuna, an optimization framework, for hyperparameter tuning to identify the most effective variant, concluding that BioBERT yields the best performance. Furthermore, various optimizers, including Adam, RMSprop, and Layerwise adaptive large batch optimization (LAMB), were evaluated alongside BERT's default AdamW optimizer. The findings show that the LAMB optimizer achieves a performance that is equally good as AdamW's. Synthetic data generation techniques from Gretel were utilized to augment the dataset, expanding the original dataset from 5,000 to 10,000 rows. Subsequent evaluations demonstrated that the model maintained its performance with synthetic data, with the LAMB optimizer showing marginally better results. The enhanced dataset and optimized model configurations improved classification accuracy, showcasing the efficacy of the BioBERT variant and the LAMB optimizer. It resulted in an accuracy of up to 98.2% and 90.8% for the original and combined datasets.

Keywords: BioBERT, clinical data, healthcare AI, transformer models

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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

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Authors: Nawel Ouennoughi, Kamel Daoud

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During recent years, new pharmaceutical dosage forms were developed in the research laboratories and which consists of encapsulating one or more active molecules in a polymeric envelope. Several techniques of encapsulation allow obtaining the microparticles or the nanoparticles containing one or several polymers. In the industry, microencapsulation is implemented to fill the following objectives: to ensure protection, the compatibility and the stabilization of an active matter in a formulation, to carry out an adapted working, to improve the presentation of a product, to mask a taste or an odor, to modify and control the profile of release of an active matter to obtain, for example, prolonged or started effect. To this end, we focus ourselves on the encapsulation of the antidiabetic. It is an oral hypoglycemic agent belonging to the second generation of sulfonylurea’s commonly employed in the treatment of type II non-insulin-dependent diabetes in order to improve profile them dissolution. Our choice was made on the technique of encapsulation by complex coacervation with two types of polymers (gelatin and the gum Arabic) which is a physicochemical process. Several parameters were studied at the time of the formulation of the microparticles and the nanoparticles: temperature, pH, ratio of polymers etc. The microparticles and the nanoparticles obtained were characterized by microscopy, laser granulometry, FTIR and UV-visible spectrophotometry. The profile of dissolution obtained for the microparticles showed an improvement of the kinetics of dissolution compared to that obtained for the active ingredient.

Keywords: coacervation, gum Arabic, microencapsulation, gelatin

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Authors: F. Boukazouha, G. Poulin-Vittrant, M. Rguiti, M. Lethiecq

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Since its invention, by virtue of its remarkable features, the piezoelectric transformer (PT) has drawn the attention of the scientific community. In past years, it has been extensively studied and its performances have been continuously improved. Nowadays, such devices are designed in more and more sophisticated architectures with associated models describing their behavior quite accurately. However, the different studies usually carried out on such devices mainly focus on their electrical characteristics induced by direct piezoelectric effects such as voltage gain, efficiency or supplied power. In this work, we are particularly interested in the characterization of mechanical displacements induced by the inverse piezoelectric effect in a PT in vibration. For this purpose, a detailed three-dimensional finite element analysis is proposed to examine the mechanical behavior of a Rosen-type transformer made of a single bar of soft PZT (P191) and with dimensions 22mm×2.35mm×2.5mm. At the first three modes of vibration, output voltage and mechanical displacements ux, uy and uz along the length, the width and the thickness, respectively, are calculated. The amplitude of displacements varies in a range from a few nanometers to a few hundred nanometers. The validity of the simulations was successfully confirmed by experiments carried out on a prototype using a laser interferometer. A good match was observed between simulation and experimental results, especially for us at the second mode. Such 3D simulations thus appear as a helpful tool for a better understanding of mechanical phenomena in Rosen-type PT.

Keywords: piezoelectricity, gain, dispalcement, simulations

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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)

Procedia PDF Downloads 319

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: Georgii Emelianov, Mikhail Polikarpov, Fabian Hübner, Jochen Deuse, Jochen Schiemann

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Recycling refrigerating appliances plays a major role in protecting the Earth's atmosphere from ozone depletion and emissions of greenhouse gases. The performance of refrigerator recycling plants in terms of material retention is the subject of strict environmental certifications and is reviewed periodically through specialized audits. The continuous collection of Refrigerator data required for the input-output analysis is still mostly manual, error-prone, and not digitalized. In this paper, we propose an automated data collection system for recycling plants in order to deduce expected material contents in individual end-of-life refrigerating appliances. The system utilizes laser scanner measurements and optical data to extract attributes of individual refrigerators by applying transfer learning with pre-trained vision models and optical character recognition. Based on Recognized features, the system automatically provides material categories and target values of contained material masses, especially foaming and cooling agents. The presented data collection system paves the way for continuous performance monitoring and efficient control of refrigerator recycling plants.

Keywords: automation, data collection, performance monitoring, recycling, refrigerators

Procedia PDF Downloads 169

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

Procedia PDF Downloads 88

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: Aria Vitkova, Hanna Sykulska-Lawrence

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In recent years, Europa has been one of the major focus points in astrobiology due to its high potential of harbouring life in the vast ocean underneath its icy crust. However, the detection of life on Europa faces many challenges due to the harsh environmental conditions and mission constraints. Raman spectroscopy is a highly capable and versatile in-situ characterisation technique that does not require any sample preparation. It has only been used on Earth to date; however, recent advances in optical and laser technology have also allowed it to be considered for extraterrestrial exploration. So far, most efforts have been focused on the exploration of Mars, the most imminent planetary target. However, as an emerging technology with high miniaturization potential, Raman spectroscopy also represents a promising tool for the exploration of Europa. In this study, the capabilities of Raman technology in terms of life detection on Europa are explored and assessed. Spectra of biosignatures identified as high priority molecular targets for life detection on Europa were acquired at various excitation wavelengths and conditions analogous to Europa. The effects of extremely low temperatures and low concentrations in water ice were explored and evaluated in terms of the effectiveness of various configurations of Raman instruments. Based on the findings, a design of a miniaturized Raman instrument optimized for in-situ detection of life on Europa is proposed.

Keywords: astrobiology, biosignatures, Europa, life detection, Raman Spectroscopy

Procedia PDF Downloads 222

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

Procedia PDF Downloads 133

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

Procedia PDF Downloads 91

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: Monica Trifitriana, Rido Mulawarman

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Introduction: Androgenetic alopecia (AGA) is a genetically predetermined disorder due to an excessive response to dihydrotestosterone (DHT). Currently, non-surgical treatment of androgenetic alopecia is more in demand by the patient. There are many non-surgical treatments, ranging from topical treatments oral medications, and procedure treatments. Objective: We aim to assess the latest evidence of the efficacy of non-surgical treatments of androgenetic alopecia in men in comparison to placebo for improving hair density, thickness, and growth. Method: We performed a comprehensive search on topics that assess non-surgical treatments of androgenetic alopecia in men from inception up until November 2021. Result: There were 24 studies out of a total of 2438 patients divided into five non-surgical treatment groups to assess the effectiveness of hair growth, namely: minoxidil 2% (MD: 8.11 hairs/cm²), minoxidil 5% (MD: 12.02 hairs/cm²), low-level laser light therapy/LLLT (MD: 12.35 hairs/cm²), finasteride 1mg (MD: 20.43 hairs/cm²), and Platelete-Rich Plasma/PRP with microneedling (MD: 26.33 hairs/cm²). All treatments had significant results for increasing hair growth, particularly in cases of androgenetic alopecia in men (P<0.00001). Conclusion: From the results, it was found that the five non-surgical treatment groups proved to be effective and significant for hair growth, particularly in cases of androgenetic alopecia in men. In order of the best non-surgical treatment for hair growth is starting from PRP with microneedling, Finasteride 1mg, LLLT, minoxidil 5%, to minoxidil 2%.

Keywords: androgenetic alopecia, non-surgical, men, meta-analysis, systematic review

Procedia PDF Downloads 164

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 192

Authors: Amina Farooq, Nauman Zafar Butt

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This paper is about methods and tools for counting particles of interest, such as cells. A microfluidic system with interconnected electronics on a flexible substrate, inlet-outlet ports and interface schemes, sensitive and selective detection of cells specificity, and processing of cell counting at polymer interfaces in a microscale biosensor for use in the detection of target biological and non-biological cells. The development of fluidic channels, planar fluidic contact ports, integrated metal electrodes on a flexible substrate for impedance measurements, and a surface modification plasma treatment as an intermediate bonding layer are all part of the fabrication process. Magnetron DC sputtering is used to deposit a double metal layer (Ti/Pt) over the polypropylene film. Using a photoresist layer, specified and etched zones are established. Small fluid volumes, a reduced detection region, and electrical impedance measurements over a range of frequencies for cell counts improve detection sensitivity and specificity. The procedure involves continuous flow of fluid samples that contain particles of interest through the microfluidic channels, counting all types of particles in a portion of the sample using the electrical differential counter to generate a bipolar pulse for each passing cell—calculating the total number of particles of interest originally in the fluid sample by using MATLAB program and signal processing. It's indeed potential to develop a robust and economical kit for cell counting in whole-blood samples using these methods and similar devices.

Keywords: impedance, biochip, cell counting, microfluidics

Procedia PDF Downloads 166

Authors: Maria Nejjari, Michel Cloutier, Guylaine Talbot, Martin Lanthier

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The fluorescence in situ hybridization (FISH) is a staining technique that allows the identification, detection and quantification of microorganisms without prior cultivation by means of epifluorescence and confocal laser scanning microscopy (CLSM). Oligonucleotide probes have been used to detect bacteria and archaea that colonize the cattle and swine digestive systems. These bacterial strains have been obtained from fecal samples issued from cattle manure and swine slurry. The collection of these samples has been done at 3 different pit’s levels A, B and C with same height. Two collection depth levels have been taken in consideration, one collection level just under the pit’s surface and the second one at the bottom of the pit. Cells were fixed and FISH was performed using oligonucleotides of 15 to 25 nucleotides of length associated with a fluorescent molecule Cy3 or Cy5. The double hybridization using Cy3 probe targeting bacteria (Cy3-EUB338-I) along with a Cy5 probe targeting Archaea (Gy5-ARCH915) gave a better signal. The CLSM images show that there are more bacteria than archaea in swine slurry. However, the choice of fluorescent probes is critical for getting the double hybridization and a unique signature for each microorganism. FISH technique is an easy way to detect pathogens like E. coli O157, Listeria, Salmonella that easily contaminate water streams, agricultural soils and, consequently, food products and endanger human health.

Keywords: archaea, bacteria, detection, FISH, fluorescence

Procedia PDF Downloads 392

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

Procedia PDF Downloads 214

Authors: Néstor Iván Rojas, Wilson Medina Sierra

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Managing spatial information is described through a Geographic Information System (GIS), for some neighborhoods in the city of Tunja, in relation to the structural typology of the buildings. The use of GIS provides tools that facilitate the capture, processing, analysis and dissemination of cartographic information, product quality evaluation of the classification of buildings. Allows the development of a method that unifies and standardizes processes information. The project aims to generate a geographic database that is useful to the entities responsible for planning and disaster prevention and care for vulnerable populations, also seeks to be a basis for seismic vulnerability studies that can contribute in a study of urban seismic microzonation. The methodology consists in capturing the plat including road naming, neighborhoods, blocks and buildings, to which were added as attributes, the product of the evaluation of each of the housing data such as the number of inhabitants and classification, year of construction, the predominant structural systems, the type of mezzanine board and state of favorability, the presence of geo-technical problems, the type of cover, the use of each building, damage to structural and non-structural elements . The above data are tabulated in a spreadsheet that includes cadastral number, through which are systematically included in the respective building that also has that attribute. Geo-referenced data base is obtained, from which graphical outputs are generated, producing thematic maps for each evaluated data, which clearly show the spatial distribution of the information obtained. Using GIS offers important advantages for spatial information management and facilitates consultation and update. Usefulness of the project is recognized as a basis for studies on issues of planning and prevention.

Keywords: microzonation, buildings, geo-processing, cadastral number

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Authors: Claudia Ehrentraut, Osama Ibrahim, Hercules Dalianis

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Policy making situations are real-world problems that exhibit complexity in that they are composed of many interrelated problems and issues. To be effective, policies must holistically address the complexity of the situation rather than propose solutions to single problems. Formulating and understanding the situation and its complex dynamics, therefore, is a key to finding holistic solutions. Analysis of text based information on the policy problem, using Natural Language Processing (NLP) and Text analysis techniques, can support modelling of public policy problem situations in a more objective way based on domain experts knowledge and scientific evidence. The objective behind this study is to support modelling of public policy problem situations, using text analysis of verbal descriptions of the problem. We propose a formal methodology for analysis of qualitative data from multiple information sources on a policy problem to construct a causal diagram of the problem. The analysis process aims at identifying key variables, linking them by cause-effect relationships and mapping that structure into a graphical representation that is adequate for designing action alternatives, i.e., policy options. This study describes the outline of an algorithm used to automate the initial step of a larger methodological approach, which is so far done manually. In this initial step, inferences about key variables and their interrelationships are extracted from textual data to support a better problem structuring. A small prototype for this step is also presented.

Keywords: public policy, problem structuring, qualitative analysis, natural language processing, algorithm, inference extraction

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Authors: V. K. Gupta, Tejeet Singh

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In many whisker reinforced composites, anisotropy may result due to material flow during processing operations such as forging, extrusion etc. The consequence of anisotropy, introduced during processing of disc material, has been investigated on the steady state creep deformations of the rotating disc. The disc material is assumed to undergo plastic deformations according to Hill’s anisotropic criterion. Steady state creep has been analyzed in a constant thickness rotating disc made of functionally graded 6061Al-SiCw (where the subscript ‘w’ stands for whisker) using Hill’s The content of reinforcement (SiCw) in the disc is assumed to decrease linearly from the inner to outer radius. The stresses and strain rates in the disc are estimated by solving the force equilibrium equation along with the constitutive equations describing multi-axial creep. The results obtained for anisotropic FGM disc have been compared with those estimated for isotropic FGM disc having the same average whisker content. The anisotropic constants, appearing in Hill’s yield criterion, have been obtained from the available experimental results. The results show that the presence of anisotropy reduces the tangential stress in the middle of the disc but near the inner and outer radii the tangential stress is higher when compared to isotropic disc. On the other hand, the steady state creep rates in the anisotropic disc are reduced significantly over the entire disc radius, with the maximum reduction observed at the inner radius. Further, in the presence of anisotropy the distribution of strain rate becomes relatively uniform over the entire disc, which may be responsible for reducing the extent of distortion in the disc.

Keywords: anisotropy, creep, functionally graded composite, rotating disc

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Anne Giles

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Humans naturally wish they could forget traumatic experiences. To help prevent future harm, however, the human brain has evolved to retain data about experiences of threat, alarm, or violation. When given compassionate support and assistance with thinking helpfully and realistically about traumatic events, most people can adjust to experiencing hardships, albeit with residual sad, unfortunate memories. Persistent, recurrent, intrusive memories, difficulty sleeping, emotion dysregulation, and avoidance of reminders, however, may be symptoms of Post-traumatic Stress Disorder (PTSD). Brain scans show that PTSD affects brain functioning. We currently have no physical means of restoring the system of brain structures and functions involved with PTSD. Medications may ease some symptoms but not others. However, forms of "talk therapy" with cognitive components have been found by researchers to reduce, even resolve, a broad spectrum of trauma symptoms. Many cultures have taboos against talking about hardships. Individuals may present themselves to mental health care professionals with severe, disabling trauma symptoms but, because of cultural norms, be unable to speak about them. In China, for example, relationship expectations may include the belief, "Bad things happening in the family should stay in the family (jiāchǒu bùkě wàiyán 家丑不可外扬)." The concept of "family (jiā 家)" may include partnerships, close and extended families, communities, companies, and the nation itself. In contrast to many trauma therapies, Cognitive Processing Therapy (CPT) for Post-traumatic Stress Disorder asks its participants to focus not on "what" happened but on "why" they think the trauma(s) occurred. The question "why" activates and exercises cognitive functioning. Brain scans of individuals with PTSD reveal executive functioning portions of the brain inadequately active, with emotion centers overly active. CPT conceptualizes PTSD as a network of cognitive distortions that keep an individual "stuck" in this under-functioning and over-functioning dynamic. Through asking participants forms of the question "why," plus offering a protocol for examining answers and relinquishing unhelpful beliefs, CPT assists individuals in consciously reactivating the cognitive, executive functions of their brains, thus restoring normal functioning and reducing distressing trauma symptoms. The culturally sensitive components of CPT that allow people to "talk about it without talking about it" may offer the possibility for worldwide relief from symptoms of trauma.

Keywords: cognitive processing therapy (CPT), cultural norms, post-traumatic stress disorder (PTSD), trauma recovery

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Authors: A. Bensaid, T. Mostephaoui, R. Nedjai

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A considerable area of Algerian lands is threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mecheria department generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of LANDSAT images (5, 7, and 8) of three scenes 197/37, 198/36 and 198/37 for the year 2020. As a second step, we prospect the use of geospatial techniques to monitor the progression of sand dunes on developed (urban) lands as well as on the formation of sandy accumulations (dune, dunes fields, nebkha, barkhane, etc.). For this purpose, this study made use of the semi-automatic processing method for the dynamic segmentation of images with very high spatial resolution (SENTINEL-2 and Google Earth). This study was able to demonstrate that urban lands under current conditions are located in sand transit zones that are mobilized by the winds from the northwest and southwest directions.

Keywords: land development, GIS, segmentation, remote sensing

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Authors: Yu Zhang, Il Kim

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Polypeptides with defined peptide sequences illustrate the power of remarkable applications in drug delivery, tissue engineering, sensing and catalysis. Especially the cyclic polypeptides, the distinctive topological architecture imparts many characteristic properties comparing to linear polypeptides. Here, a facile and highly efficient strategy for the synthesis of linear and cyclic polypeptides is reported using N-heterocyclic carbenes (NHCs)-mediated ring-opening polymerization (ROP) of α-amino acid N-carboxyanhydrides (NCA) in the presence or absence of primary amine initiator. The polymerization proceeds rapidly in a quasi-living manner, allowing access to linear and cyclic polypeptides of well-defined chain length and narrow polydispersity, as evidenced by nuclear magnetic resonance spectrum (1H NMR and 13C NMR spectra) and size exclusion chromatography (SEC) analysis. The cyclic architecture of the polypeptides was further verified by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectra (MALDI-TOF MS) and electrospray ionization (ESI) mass spectra, as well as viscosity studies. This approach can also simplify workup procedures and make bulk scale synthesis possible, which thereby opens avenues for practical uses in diverse areas, opening up the new generation of polypeptide synthesis.

Keywords: α-amino acid N-carboxyanhydrides, living polymerization, polypeptides, N-heterocyclic carbenes, ring-opening polymerization

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Authors: Guijie Sang, Geo Davis, Momchil Terziev

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Geological CO2 storage has been regarded as a promising strategy to mitigate the emission of greenhouse gas generated from traditional power stations and energy-intensive industry. Caprocks with very low permeability and ultra-fine pores create viscous and capillary barriers to guarantee CO2 sealing efficiency. However, caprock fractures, either naturally existing or artificially induced due to injection, could provide preferential paths for CO₂ escaping. Seeking an efficient technique to seal and strengthen caprock fractures is crucial. We apply microbial-induced-calcite-precipitation (MICP) technique for sealing and strengthening caprock fractures in the laboratory scale. The MICP bio-grouting technique has several advantages over conventional cement grouting methods, including its low viscosity, micron-size microbes (accessible to fine apertures), and low carbon footprint, among others. Different injection strategies are tested to achieve relatively homogenous calcite precipitation along the fractures, which is monitored dynamically based on laser ultrasonic technique. The MICP process in caprock fractures, which integrates the coupled flow and bio-chemical precipitation, is also modeled and validated through the experiment. The study could provide an effective bio-mediated grouting strategy for caprock sealing and thus ensuring a long-term safe geological CO2 storage.

Keywords: caprock sealing, geological CO2 storage, grouting strategy, microbial induced calcite precipitation

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Authors: Andrei Pomana, Graham Brewer

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Autism is a lifelong developmental disorder that affects verbal and non-verbal communication, behaviour and sensory processing. As a result, people on the autism spectrum have a difficult time when confronted with environments that have high levels of sensory stimulation. This is often compounded by the inability to properly communicate their wants and needs to caregivers. The capacity for people with autism to integrate depends on their ability to at least tolerate highly stimulating public environments for short periods of time. The overall challenges that people on the spectrum and their caregivers face need to be established in order to properly create and assess methods to mitigate the effects of high stimulus public spaces. The paper aims to identify the challenges that people on the autism spectrum and their caregivers face in typical public environments. Nine experienced autism therapists have participated in a semi-structured interview regarding the challenges that people with autism and their caregivers face in public environments. The qualitative data shows that the unpredictability of events and the high sensory stimulation present in public environments, especially auditory, are the two biggest contributors to the difficulties that people on the spectrum face. If the stimuli are not removed in a short period of time, uncontrollable behaviours or 'meltdowns' can occur, which leave the person incapacitated and unable to respond to any outside input. Possible solutions to increase integration in public spaces for people with autism revolve around removing unwanted sensory stimulus, creating personalized barriers for certain stimuli, equipping people with autism with better tools to communicate their needs or to orient themselves to a safe location and providing a predictable pattern of events that would prepare individuals for tasks ahead of time.

Keywords: autism, built environment, meltdown, public environment, sensory processing disorders

Procedia PDF Downloads 169