Search results for: light detection and ranging

Authors: Sara Segura, Diego Nuñez, Miryam Villamil

Abstract:

In this work, we studied the microscale interaction of foreign substances with blood inside an artificial transparent artery system that represents medium and small muscular arteries. This artery system had channels ranging from 75 μm to 930 μm and was fabricated using glass and transparent polymer blends like Phenylbis(2,4,6-trimethylbenzoyl) phosphine oxide, Poly(ethylene glycol) and PDMS in order to be monitored in real time. The setup was performed using a computer controlled precision micropump and a high resolution optical microscope capable of tracking fluids at fast capture. Observation and analysis were performed using a real time software that reconstructs the fluid dynamics determining the flux velocity, injection dependency, turbulence and rheology. All experiments were carried out with fully computer controlled equipment. Interactions between substances like water, serum (0.9% sodium chloride and electrolyte with a ratio of 4 ppm) and blood cells were studied at microscale as high as 400nm of resolution and the analysis was performed using a frame-by-frame observation and HD-video capture. These observations lead us to understand the fluid and mixing behavior of the interest substance in the blood stream and to shed a light on the use of implantable devices for drug delivery at arteries with different Endothelial dysfunction. Several substances were tested using the artificial artery system. Initially, Milli-Q water was used as a control substance for the study of the basic fluid dynamics of the artificial artery system. However, serum and other low viscous substances were pumped into the system with the presence of other liquids to study the mixing profiles and behaviors. Finally, mammal blood was used for the final test while serum was injected. Different flow conditions, pumping rates, and time rates were evaluated for the determination of the optimal mixing conditions. Our results suggested the use of a very fine controlled microinjection for better mixing profiles with and approximately rate of 135.000 μm3/s for the administration of drugs inside arteries.

Keywords: artificial artery, drug delivery, microfluidics dynamics, arteriosclerosis

Procedia PDF Downloads 298

Authors: I. Filikhin, B. Vlahovic

Abstract:

We focus on a novel type of detection based on electron tunneling properties of double nanoscale structures in semiconductor materials. Semiconductor heterostructures as quantum wells (QWs), quantum dots (QDs), and quantum rings (QRs) may have energy level structure of several hundred of electron confinement states. The single electron spectra of the double quantum objects (DQW, DQD, and DQR) were studied in our previous works with relation to the electron localization and tunneling between the objects. The wave function of electron may be localized in one of the QDs or be delocalized when it is spread over the whole system. The localizing-delocalizing tunneling occurs when an electron transition between both states is possible. The tunneling properties of spectra differ strongly for “regular” and “chaotic” systems. We have shown that a small violation of the geometry drastically affects localization of electron. In particular, such violations lead to the elimination of the delocalized states of the system. The same symmetry violation effect happens if electrical or magnetic fields are applied. These phenomena could be used to propose a new type of detection based on the high sensitivity of charge transport between double nanostructures and small violations of the shapes. It may have significant technological implications.

Keywords: double quantum dots, single electron levels, tunneling, electron localizations

Procedia PDF Downloads 509

Authors: Alexander Machikhin, Olga Polschikova, Vitold Pozhar, Alina Ramazanova

Abstract:

White-light quantitative phase imaging is an effective technique for achieving sub-nanometer phase sensitivity. Highly stable interferometers based on common-path geometry have been developed in recent years to solve this task. Some of these methods also apply multispectral approach. The purpose of this research is to suggest a simple and effective interferometer for such systems. We developed a three-lens common-path interferometer, which can be used for quantitative phase imaging with or without multispectral modality. The lens system consists of two components, the first one of which is a compound lens, consisting of two lenses. A pinhole is placed between the components. The lens-in-lens approach enables effective light transmission and high stability of the interferometer. The multispectrality is easily implemented by placing a tunable filter in front of the interferometer. In our work, we used an acousto-optical tunable filter. Some design considerations are discussed and multispectral quantitative phase retrieval is demonstrated.

Keywords: acousto-optical tunable filter, common-path interferometry, digital holography, multispectral quantitative phase imaging

Procedia PDF Downloads 311

Authors: Totsaporn Suwannaruang, Kitirote Wantala

Abstract:

The purposes of this research were to synthesize titanium dioxide photocatalyst doped with nitrogen (N-doped TiO₂) by hydrothermal method and to test the photocatalytic degradation of paraquat under UV and visible light illumination. The effect of calcination treatment temperature on their physical and chemical properties and photocatalytic efficiencies were also investigated. The characterizations of calcined N-doped TiO₂ photocatalysts such as specific surface area, textural properties, bandgap energy, surface morphology, crystallinity, phase structure, elements and state of charges were investigated by Brunauer, Emmett, Teller (BET) and Barrett, Joyner, Halenda (BJH) equations, UV-Visible diffuse reflectance spectroscopy (UV-Vis-DRS) by using the Kubelka-Munk theory, Wide-angle X-ray scattering (WAXS), Focussed ion beam scanning electron microscopy (FIB-SEM), X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS), respectively. The results showed that the effect of calcination temperature was significant on surface morphology, crystallinity, specific surface area, pore size diameter, bandgap energy and nitrogen content level, but insignificant on phase structure and oxidation state of titanium (Ti) atom. The N-doped TiO₂ samples illustrated only anatase crystalline phase due to nitrogen dopant in TiO₂ restrained the phase transformation from anatase to rutile. The samples presented the nanorice-like morphology. The expansion on the particle was found at 650 and 700°C of calcination temperature, resulting in increased pore size diameter. The bandgap energy was determined by Kubelka-Munk theory to be in the range 3.07-3.18 eV, which appeared slightly lower than anatase standard (3.20 eV), resulting in the nitrogen dopant could modify the optical absorption edge of TiO₂ from UV to visible light region. The nitrogen content was observed at 100, 300 and 400°C only. Also, the nitrogen element disappeared at 500°C onwards. The nitrogen (N) atom can be incorporated in TiO₂ structure with the interstitial site. The uncalcined (100°C) sample displayed the highest percent paraquat degradation under UV and visible light irradiation due to this sample revealed both the highest specific surface area and nitrogen content level. Moreover, percent paraquat removal significantly decreased with increasing calcination treatment temperature. The nitrogen content level in TiO₂ accelerated the rate of reaction with combining the effect of the specific surface area that generated the electrons and holes during illuminated with light. Therefore, the specific surface area and nitrogen content level demonstrated the important roles in the photocatalytic activity of paraquat under UV and visible light illumination.

Keywords: restraining phase transformation, interstitial site, chemical charge state, photocatalysis, paraquat degradation

Procedia PDF Downloads 159

Authors: Akila Murali, Prithika Manivel

Abstract:

Disasters are defined as a serious disruption of the functioning of a community or a society, which involves widespread human, material, economic or environmental impacts. The number of people suffering food crisis as a result of natural disasters has tripled in the last thirty years. The economic losses due to natural disasters have shown an increase with a factor of eight over the past four decades, caused by the increased vulnerability of the global society, and also due to an increase in the number of weather-related disasters. Efficient disaster detection and alerting systems could reduce the loss of life and properties. In the event of a disaster, another important issue is a good search and rescue system with high levels of precision, timeliness and safety for both the victims and the rescuers. Wireless Sensor Networks technology has the capability of quick capturing, processing, and transmission of critical data in real-time with high resolution. This paper studies the capacity of sensors and a Wireless Sensor Network to collect, collate and analyze valuable and worthwhile data, in an ordered manner to help with disaster management.

Keywords: alerting systems, disaster detection, Ad Hoc network, WSN technology

Procedia PDF Downloads 406

Authors: Julia Ive, Lucia Specia

Abstract:

Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

Procedia PDF Downloads 172

Authors: Abrahão S. Fontes, Carlos A. V. Cardoso, Levi P. B. Oliveira

Abstract:

The induction motors are used worldwide in various industries. Several maintenance techniques are applied to increase the operating time and the lifespan of these motors. Among these, the predictive maintenance techniques such as Motor Current Signature Analysis (MCSA), Motor Square Current Signature Analysis (MSCSA), Park's Vector Approach (PVA) and Park's Vector Square Modulus (PVSM) are used to detect and diagnose faults in electric motors, characterized by patterns in the stator current frequency spectrum. In this article, these techniques are applied and compared on a real motor, which has the fault of eccentricity in the air-gap. It was used as a theoretical model of an electric induction motor without fault in order to assist comparison between the stator current frequency spectrum patterns with and without faults. Metrics were purposed and applied to evaluate the sensitivity of each technique fault detection. The results presented here show that the above techniques are suitable for the fault of eccentricity in the air gap, whose comparison between these showed the suitability of each one.

Keywords: eccentricity in the air-gap, fault diagnosis, induction motors, predictive maintenance

Procedia PDF Downloads 352

Authors: Leydi Noemi Peraza Gómez, Jose Álvarez Nemegyei, Damaris Francis Estrella Castillo

Abstract:

In order to have an epidemiological tool to detect biomechanical stress (ERGO-Mex), which impose physical labor or recreational activities, a questionnaire is constructed in Spanish, validated and culturally adapted to the Mayan indigenous population of Yucatan. Through the seven steps proposed by Guillemin and Beaton the procedure was: initial translation, synthesis of the translations, feed back of the translation. After that review by a committee of experts, pre-test of the preliminary version, and presentation of the results to the committee of experts and members of the community. Finally the evaluation of its internal validity (Cronbach's α coefficient) and external (intraclass correlation coefficient). The results for the validation in Spanish indicated that 45% of the participants have biomechanical stress. The ERGO-Mex correlation was 0.69 (p <0.0001). Subjects with high biomechanical stress had a higher score than subjects with low biomechanical stress (17.4 ± 8.9 vs.9.8 ± 2.8, p = 0.003). The Cronbach's α coefficient was 0.92; and for validation in Cronbach's α maya it was 0.82 and CCI = 0.70 (95% CI: 0.58-0.79; p˂0.0001); ERGO-Mex is suitable for performing early detection of musculoskeletal diseases and helping to prevent disability.

Keywords: biomechanical stress, disability, musculoskeletal disorders, prevention

Procedia PDF Downloads 184

Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

Abstract:

Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

Procedia PDF Downloads 191

Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

Abstract:

The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

Procedia PDF Downloads 107

Authors: Seonaid Hume, Gordon Dobie, Graeme West

Abstract:

Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

Procedia PDF Downloads 202

Authors: Hongyu Chen, Li Jiang

Abstract:

Ubiquitous anomalies endanger the security of our system con- stantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case, the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.

Keywords: GAN, discrete feature, Wasserstein distance, multiple intermediate layers

Procedia PDF Downloads 130

Authors: Zhongde Wang, Fanwei Meng, Ling Yang

Abstract:

Today, social media deeply engages in urban spatial production in a ‘Disembedding’ form, allowing the public to ‘foresight’ physical spaces through online platforms and subsequently engage in corresponding ‘sight’ and visits, thus leading to the emergence of ‘Internet Celebrity Spots’. This paper delves into the laws of action of online media, focusing on experiences. From the perspectives of the public, space, and media, it thoroughly analyzes the experiential design strategies of Chongqing's Liziba Light Rail Station, including the construction of the experiential mainline capturing the matrix of public behavior, the creation of experiential sidelines leveraging spatial advantages, and the deepening of experiential touchpoints to promote media resonance. This analysis aims to provide insights and references for similar urban spaces to transition from ‘internet-famous’ to ‘real-famous’ attractions.

Keywords: online media, urban space, disembedding, internet celebrity spots, experience design

Procedia PDF Downloads 71

Authors: Rita Chattopadhyay, Vivek Anand Thoutam

Abstract:

Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

Procedia PDF Downloads 81

Authors: Xiaodi Su, Xin Ting Zheng, Laura Sutarlie, Nur Asinah binte Mohamed Salleh, Yong Yu

Abstract:

Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We have developed paper-like battery-free multiplexed sensors for holistic wound assessment via quantitative detection of multiple inflammation and infection markers. In one of the designs, the sensor patch consists of a wax-printed paper panel with five colorimetric sensor channels arranged in a pattern resembling a five-petaled flower (denoted as a ‘Petal’ sensor). The five sensors are for temperature, pH, trimethylamine, uric acid, and moisture. The sensor patch is sandwiched between a top transparent silicone layer and a bottom adhesive wound contact layer. In the second design, a palm-like-shaped paper strip is fabricated by a paper-cutter printer (denoted as ‘Palm’ sensor). This sensor strip carries five sensor regions connected by a stem sampling entrance that enables rapid colorimetric detection of multiple bacteria metabolites (aldehyde, lactate, moisture, trimethylamine, tryptophan) from wound exudate. For both the “\’ Petal’ and ‘Palm’ sensors, color images can be captured by a mobile phone. According to the color changes, one can quantify the concentration of the biomarkers and then determine wound healing status and identify/quantify bacterial species in infected wounds. The ‘Petal’ and ‘Palm’ sensors are validated with in-situ animal and ex-situ skin wound models, respectively. These sensors have the potential for integration with wound dressing to allow early warning of adverse events without frequent removal of the plasters. Such in-situ and early detection of non-healing condition can trigger immediate clinical intervention to facilitate wound care management.

Keywords: wound infection, colorimetric sensor, paper fluidic sensor, wound care

Procedia PDF Downloads 83

Authors: Bengi Hakguder Taze, Sevcan Unluturk

Abstract:

Şalak apricot (Prunus armeniaca L., cv. Şalak) is a specific variety grown in Igdir, Turkey. The fruit has distinctive properties distinguish it from other cultivars, such as its unique size, color, taste and higher water content. Drying is the widely used method for preservation of apricots. However, fresh consumption is preferred for Şalak apricot instead of drying due to its low dry matter content. Higher amounts of water in the structure and climacteric nature make the fruit sensitive against rapid quality loss during storage. Hence, alternative processing methods need to be introduced to extend the shelf life of the fresh produce. Mild heat (MH) treatment is of great interest as it can reduce the microbial load and inhibit enzymatic activities. Therefore, the aim of this study was to evaluate the impact of mild heat treatment on the natural microflora found on Şalak apricot surfaces and some physical quality parameters of the fruit, such as color and firmness. For this purpose, apricot samples were treated at different temperatures between 40 and 60 ℃ for different periods ranging between 10 to 60 min using a temperature controlled water bath. Natural flora on the fruit surfaces was examined using standard plating technique both before and after the treatment. Moreover, any changes in color and firmness of the fruit samples were also monitored. It was found that control samples were initially containing 7.5 ± 0.32 log CFU/g of total aerobic plate count (TAPC), 5.8±0.31 log CFU/g of yeast and mold count (YMC), and 5.17 ± 0.22 log CFU/g of coliforms. The highest log reductions in TAPC and YMC were observed as 3.87-log and 5.8-log after the treatments at 60 ℃ and 50 ℃, respectively. Nevertheless, the fruit lost its characteristic aroma at temperatures above 50 ℃. Furthermore, great color changes (ΔE ˃ 6) were observed and firmness of the apricot samples was reduced at these conditions. On the other hand, MH treatment at 41 ℃ for 10 min resulted in 1.6-log and 0.91-log reductions in TAPC and YMC, respectively, with slightly noticeable changes in color (ΔE ˂ 3). In conclusion, application of temperatures higher than 50 ℃ caused undesirable changes in physical quality of Şalak apricots. Although higher microbial reductions were achieved at those temperatures, temperatures between 40 and 50°C should be further investigated considering the fruit quality parameters. Another strategy may be the use of high temperatures for short time periods not exceeding 1-5 min. Besides all, MH treatment with UV-C light irradiation can be also considered as a hurdle strategy for better inactivation results.

Keywords: color, firmness, mild heat, natural flora, physical quality, şalak apricot

Procedia PDF Downloads 138

Authors: Samiul Islam Hazarika, Domngam Boje, Ananta Kumar Atta

Abstract:

It is well familiar that solvents play a significant role in modern chemistry. Solvents can change the reactivity and physicochemical properties of molecules in a solution. Keeping this in mind, we have designed and synthesized a mono-triazolyl-linked pyrenyl-appended xylofuranose derivative for the detection of metal ions with changing solvent systems. The incorporation of a sugar backbone in the sensor increases the water solubility and biocompatibility. The experimental study revealed that the xylofuranose-based fluorescence probe did not exhibit any specific selectivity towards metal ions in acetonitrile (CH₃CN) solvent. Whereas, we revealed that triazole-linked pyrenyl-appended xylofuranose-based fluorescent sensor would exhibit high selectivity and sensitivity towards Au³⁺ ions in CH₃CN-H₂O (1/1, v/v) system. This observation might be explained by the viscosity and polarity differences of CH₃CN and CH₃CN-H₂O solvent systems. The formation of the sensor-Au³⁺ complex was also established by high-resolution mass spectrometry (HRMS) data of the complex.

Keywords: triazole, furanose, fluorometric, solvent dependent

Procedia PDF Downloads 118

Authors: S. A. M. Sabrina, Gouget Lammel, Anne Chantal, Chazalviel, Jean Noël, Ozanam François, Etcheberry Arnaud, Tighlit Fatma Zohra, B. Samia, Gabouze Noureddine

Abstract:

The elaboration and characterization of hybrid nano materials give rise to considerable interest due to the new properties that arising. They are considered as an important category of new materials having innovative characteristics by combining the specific intrinsic properties of inorganic compounds (semiconductors) with the grafted organic species. This open the way to improved properties and spectacular applications in various and important fields, especially in the environment. In this work, nano materials based-semiconductors were elaborated by chemical route. The obtained surfaces were grafted with organic functional groups. The functionalization process was optimized in order to confer to the hybrid nano material a good stability as well as the right properties required for the subsequent applications. Different characterization techniques were used to investigate the resulting nano structures, such as SEM, UV-Visible, FTIR, Contact angle and electro chemical measurements. Finally, applications were envisaged in environmental area. The elaborated nano structures were tested for the detection and the elimination of pollutants.

Keywords: hybrid materials, porous silicon, peptide, metal detection

Procedia PDF Downloads 503

Authors: Lessly Borja, Anthony Gualli, Kelly Baño, Fabricio Santacruz

Abstract:

In this article, a prototype of underwater communication using a long-range laser (50mW) has been carried out in two aquatic scenarios (fish tank and swimming pool) with the aim of recreating Aqua-Fi technology (the future of underwater communications) using a Bluetooth connection to the transmitter to send data in ASCII code by means of light. Initially, the transmitter and receiver circuits were programmed in Arduino so that the data would travel by light pulses in the aforementioned code. To obtain the results of the underwater communication, two scenarios were chosen (fish tank and swimming pool), where the power value of the received signal was calculated from its peak-to-peak voltage using the Oscilloscope equipment (ESPOCH). Finally, it was concluded that the maximum communication range of this prototype is 12m underwater, and it was observed that the power decreases as the distance increases. However, this prototype still needs to improve communication so that the information is not distorted or lost when there is movement and dispersion of the water. It is hoped that it will form the basis for future research.

Keywords: prototype, underwater, communication, power, voltage, distance

Procedia PDF Downloads 94

Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

Abstract:

Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

Procedia PDF Downloads 357

Authors: V. S. Bessa, I. S. Moreira, S. Murgolo, C. Piccirillo, G. Mascolo, P. M. L. Castro

Abstract:

The occurrence of pharmaceuticals in the environment has been a topic of increasing concern. Pharmaceuticals are not completely mineralized in the human body and are released on the sewage systems as the pharmaceutical itself and as their “biologically active” metabolites through excretion, as well as by improper elimination and disposal. Conventional wastewater treatment plants (WWTPs) are not designed to remove these emerging pollutants and they are thus released into the environment. The antiepileptic drug carbamazepine (CBZ) and the non-steroidal anti-inflammatory diclofenac (DCF) are two widely used pharmaceuticals, frequently detected in water bodies, including rivers and groundwater, in concentrations ranging from ng L 1 to mg L 1. These two compounds were classified as medium to high-risk pollutants in WWTP effluents and surface waters. Also, CBZ has been suggested as a molecular marker of wastewater contamination in surface water and groundwater and the European Union included DCF in the watch list of substances Directive to be monitored. In the present study, biodegradation of CBZ and DCF by the bacterial strain Labrys portucalensis F11, a strain able to degrade other pharmaceutical compounds, was assessed; tests were performed with F11 as single carbon and energy source, as well as in presence of 5.9mM of sodium acetate. In assays supplemented with 2.0 and 4.0 µM of CBZ, the compound was no longer detected in the bulk medium after 24hr and 5days, respectively. Complete degradation was achieved in 21 days for 11.0 µM and in 23 days for 21.0 µM. For the highest concentration tested (43.0 µM), 95% of degradation was achieved in 30days. Supplementation with acetate increased the degradation rate of CBZ, for all tested concentrations. In the case of DCF, when supplemented as a single carbon source, approximately 70% of DCF (1.7, 3.3, 8.4, 17.5 and 34.0 µM) was degraded in 30days. Complete degradation was achieved in the presence of acetate for all tested concentrations, at higher degradation rates. The detection of intermediates produced during DCF biodegradation was performed by UPLC-QTOF/MS/MS, which allowed the identification of a range of metabolites. Stoichiometric liberation of chorine occurred and no metabolites were detected at the end of the biodegradation assays suggesting a complete mineralization of DCF. Strain Labrys portucalensis F11 proved to be able to degrade these two top priority environmental contaminants and may be potentially useful for biotechnological applications/environment remediation.

Keywords: biodegradation, carbamazepine, diclofenac, pharmaceuticals

Procedia PDF Downloads 275

Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

Abstract:

Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

Procedia PDF Downloads 337

Authors: Francesca Giliberto

Abstract:

Heritage and culture, in general, plays a central role in addressing the complexity and broad variety of global development challenges, ranging from environmental degradation and refugee and humanitarian crisis to extreme poverty, food insecurity, persisting inequalities, and unsustainable urbanisation, just to mention some examples. Nevertheless, the potential of harnessing heritage to address global challenges has remained largely under-represented and underestimated in the most recent international development agenda adopted by the United Nations in 2015 (2030 Agenda). Among the 17 sustainable development goals (SDGs) and 169 associated targets established, only target 11.4 explicitly mentions heritage, stating that efforts should be strengthened “to protect and safeguard the world’s cultural and natural heritage in order to make our cities safe, resilient, and sustainable”. However, this global target continues to reflect a rather limited approach to heritage for development. This paper will provide a critical reflection on the contribution that using (tangible and intangible) heritage in international research can make to tackling global challenges and supporting the achievement of all the SDGs. It will present key findings and insights from the heritage strand of PRAXIS, a research project from the University of Leeds, which focuses on Arts and Humanities research across 300+ projects funded through the Global Challenges Research Fund and Newton Fund. In particular, this paper will shed light on successful practices and lessons learned from 87 research projects funded through the Global Challenges Research Fund and Newton Fund portfolios in 49 countries eligible for Official Development Assistance (ODA) between 2014 and 2021. Research data were collected through a desk assessment of project data available on UKRI Gateway to Research, online surveys, and qualitative interviews with research principal investigators and partners. The findings of this research provide evidence of how heritage and heritage research can foster innovative, interdisciplinary, inclusive, and transformative sustainable development and the achievement of the SDGs in ODA countries and beyond. This paper also highlights current challenges and research gaps that still need to be overcome to rethink current approaches and transform our development models to be more integrated, human-centred, and sustainable.

Keywords: global challenges, heritage, international research, sustainable development

Procedia PDF Downloads 75

Authors: Saud Hamdan Alshammari

Abstract:

Photocatalytic water splitting to produce hydrogen (H₂) has obtained significant attention as an environmentally friendly technology. This process, which produces hydrogen from water and sunlight, represents a renewable energy source. Titanium dioxide (TiO₂) plays a critical role in photocatalytic hydrogen production due to its chemical stability, availability, and low cost. Nevertheless, TiO₂'s wide band gap (3.2 eV) limits its visible light absorption and might affect the effectiveness of the photocatalytic. Coupling TiO₂ with other semiconductors is a strategy that can enhance TiO₂ by narrowing its band gap and improving visible light absorption. This paper studies the modification of TiO₂ by coupling it with another semiconductor such as CdS nanoparticles using a reflux reactor and autoclave reactor that helps form a core-shell structure. Characterization techniques, including TEM and UV-Vis spectroscopy, confirmed successful coating of TiO₂ on CdS core, reduction of the band gap from 3.28 eV to 3.1 eV, and enhanced light absorption in the visible region. These modifications are attributed to the heterojunction structure between TiO₂ and CdS.The essential goal of this study is to improve TiO₂ for use in photocatalytic water splitting to enhance hydrogen production. The core-shell TiO₂@CdS nanoparticles exhibited promising results, due to band gap narrowing and improved light absorption. Future work will involve adding Pt as a co-catalyst, which is known to increase surface reaction activity by enhancing proton adsorption. Evaluation of the TiO₂@CdS@Pt catalyst will include performance assessments and hydrogen productivity tests, considering factors such as effective shapes and material ratios. Moreover, the study could be enhanced by studying further modifications to the catalyst and displaying additional performance evaluations. For instance, doping TiO₂ with metals such as nickel (Ni), iron (Fe), and cobalt (Co) and non-metals such as nitrogen (N), carbon (C), and sulfur (S) could positively influence the catalyst by reducing the band gap, enhancing the separation of photogenerated electron-hole pairs, and increasing the surface area, respectively. Additionally, to further improve catalytic performance, examining different catalyst morphologies, such as nanorods, nanowires, and nanosheets, in hydrogen production could be highly beneficial. Optimizing photoreactor design for efficient photon delivery and illumination will further enhance the photocatalytic process. These strategies collectively aim to overcome current challenges and improve the efficiency of hydrogen production via photocatalysis.

Keywords: hydrogen production, photocatalytic, water spliiting, semiconductor, nanoparticles

Procedia PDF Downloads 26

Authors: Andrei Bejan, Luminita Marin, Dalila Belei

Abstract:

Phenothiazine with electron-rich nitrogen and sulfur heteroatoms has a high electron-donating ability which promotes a good conjugation and therefore low band-gap with consequences upon charge carrier mobility improving and shifting of light emission in visible domain. Moreover, its non-planar butterfly conformation inhibits molecular aggregation and thus preserves quite well the fluorescence quantum yield in solid state compared to solution. Therefore phenothiazine and its derivatives are promising hole transport materials for use in organic electronic and optoelectronic devices as light emitting diodes, photovoltaic cells, integrated circuit sensors or driving circuits for large area display devices. The objective of this paper was to obtain a series of new phenothiazine derivatives by introduction of different electron withdrawing substituents as formyl, carboxyl and cyanoacryl units in order to create a push pull system which has potential to improve the electronic and optical properties. Bromine atom was used as electrono-donor moiety to extend furthermore the existing conjugation. The understudy compounds were structural characterized by FTIR and 1H-NMR spectroscopy and single crystal X-ray diffraction. Besides, the single crystal X-ray diffraction brought information regarding the supramolecular architecture of the compounds. Photophysical properties were monitored by UV-vis and photoluminescence spectroscopy, while the electrochemical behavior was established by cyclic voltammetry. The absorption maxima of the studied compounds vary in a large range (322-455 nm), reflecting the different electronic delocalization degree, depending by the substituent nature. In a similar manner, the emission spectra reveal different color of emitted light, a red shift being evident for the groups with higher electron withdrawing ability. The emitted light is pure and saturated for the compounds containing strong withdrawing formyl or cyanoacryl units and reach the highest quantum yield of 71% for the compound containing bromine and cyanoacrilic units. Electrochemical study show reversible oxidative and reduction processes for all the compounds and a close correlation of the HOMO-LUMO band gap with substituent nature. All these findings suggest the obtained compounds as promising materials for optoelectronic devices.

Keywords: electrochemical properties, phenothiazine derivatives, photoluminescence, quantum yield

Procedia PDF Downloads 332

Authors: P. Senthil Kumari

Abstract:

Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

Procedia PDF Downloads 510

Authors: Rasha Salameh

Abstract:

In light of the tense relationship between Saudi Arabia and Iran, this research paper sheds some light on Al-Arabiya’s reporting of Coronavirus in the Gulf. Particularly because most of the cases, in the beginning, were coming from Iran, some programs of this Saudi channel embraced a conspiracy theory. Hate speech has been used in talking about the topic and discussing it. The results of these discussions will be detailed in this paper in percentages with regard to the research sample, which includes five programs on Al-Arabiya channel: ‘DNA’, ‘Marraya’ (Mirrors), ‘Panorama’, ‘Tafaolcom’ (Your Interaction) and the ‘Diplomatic Street’, in the period between January 19, that is, the date of the first case in Iran, and April 10, 2020. The research shows the use of a conspiracy theory in the programs, in addition to some professional violations. The surveyed sample also shows that the matter receded due to the Arab Gulf states' preoccupation with the successively increasing cases that have appeared there since the start of the pandemic. The results indicate that hate speech was present in the sample at a rate of 98.1% and that most of the programs that dealt with the Iranian issue under the Corona pandemic on Al Arabiya used the conspiracy theory at a rate of 75.5%.

Keywords: Al-Arabiya, Iran, Corona, hate speech, conspiracy theory, politicization of the pandemic

Procedia PDF Downloads 139

Authors: Pedro Gómez-del-Hoyo, Jose-Luis Bárcena-Humanes, Nerea del-Rey-Maestre, María-Pilar Jarabo-Amores, David Mata-Moya

Abstract:

A study of achievable coverages using passive radar systems in terrestrial traffic monitoring applications is presented. The study includes the estimation of the bistatic radar cross section of different commercial vehicle models that provide challenging low values which make detection really difficult. A semi-urban scenario is selected to evaluate the impact of excess propagation losses generated by an irregular relief. A bistatic passive radar exploiting UHF frequencies radiated by digital video broadcasting transmitters is assumed. A general method of coverage estimation using electromagnetic simulators in combination with estimated car average bistatic radar cross section is applied. In order to reduce the computational cost, hybrid solution is implemented, assuming free space for the target-receiver path but estimating the excess propagation losses for the transmitter-target one.

Keywords: bistatic radar cross section, passive radar, propagation losses, radar coverage

Procedia PDF Downloads 339

Authors: Nádia Pereira, Paula Ferreira, Sofia Francisco, Sofia Oliveira, Sidclay Souza, Paula Paulino, Ana Margarida Veiga Simão

Abstract:

Social media has progressed into a platform for hate speech among its users, and thus, there is an increasing need to develop automatic detection classifiers of offense and conflicts to help decrease the prevalence of such incidents. Online communication can be used to intentionally harm someone, which is why such classifiers could be essential in social networks. A possible application of these classifiers is the automatic detection of cyberbullying. Even though identifying the aggressive language used in online interactions could be important to build cyberbullying datasets, there are other criteria that must be considered. Being able to capture the language, which is indicative of the intent to harm others in a specific context of online interaction is fundamental. Offense and hate speech may be the foundation of online conflicts, which have become commonly used in social media and are an emergent research focus in machine learning and natural language processing. This study presents two Portuguese language offense-related datasets which serve as examples for future research and extend the study of the topic. The first is similar to other offense detection related datasets and is entitled Aggressiveness dataset. The second is a novelty because of the use of the history of the interaction between users and is entitled the Conflicts/Attacks dataset. Both datasets were developed in different phases. Firstly, we performed a content analysis of verbal aggression witnessed by adolescents in situations of cyberbullying. Secondly, we computed frequency analyses from the previous phase to gather lexical and linguistic cues used to identify potentially aggressive conflicts and attacks which were posted on Twitter. Thirdly, thorough annotation of real tweets was performed byindependent postgraduate educational psychologists with experience in cyberbullying research. Lastly, we benchmarked these datasets with other machine learning classifiers.

Keywords: aggression, classifiers, cyberbullying, datasets, hate speech, machine learning

Procedia PDF Downloads 230

Authors: Jonathan Heng, Yoong Cheah Huei

Abstract:

A strategic model that does not trigger any false alarms to detect anomalies in Secure Water Treatment (SWaT) test bed is presented. This model uses machine learning invariants formulated from streamlining the general form of Auto-Regressive models with eXogenous input. A creative generalized CUSUM algorithm to integrate the invariants and the detection strategy technique is successfully developed and tested in the SWaT Programmable Logic Controllers (PLCs). Three steps to fine-tune parameters, b and τ in the generalized algorithm are stated and an example used to demonstrate the tuning process is discussed. This approach can swiftly and effectively detect various scopes of cyber-attacks such as multiple points single stage and multiple points multiple stages in SWaT. This technique can be applied in water treatment plants and other cyber physical systems like power and gas plants too.

Keywords: machine learning invariants, generalized CUSUM algorithm with invariants and detection strategy, scope of cyber attacks, strategic model, tuning parameters

Procedia PDF Downloads 184