Search results for: microwave-assisted air drying processing

Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

Procedia PDF Downloads 98

Authors: Marianna Maiaru, Gregory M. Odegard

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During the processing of high-performance thermoset polymer matrix composites, chemical reactions occur during elevated pressure and temperature cycles, causing the constituent monomers to crosslink and form a molecular network that gradually can sustain stress. As the crosslinking process progresses, the material naturally experiences a gradual shrinkage due to the increase in covalent bonds in the network. Once the cured composite completes the cure cycle and is brought to room temperature, the thermal expansion mismatch of the fibers and matrix cause additional residual stresses to form. These compounded residual stresses can compromise the reliability of the composite material and affect the composite strength. Composite process modeling is greatly complicated by the multiscale nature of the composite architecture. At the molecular level, the degree of cure controls the local shrinkage and thermal-mechanical properties of the thermoset. At the microscopic level, the local fiber architecture and packing affect the magnitudes and locations of residual stress concentrations. At the macroscopic level, the layup sequence controls the nature of crack initiation and propagation due to residual stresses. The goal of this research is use molecular dynamics (MD) and finite element analysis (FEA) to predict the residual stresses in composite laminates and the corresponding effect on composite failure. MD is used to predict the polymer shrinkage and thermomechanical properties as a function of degree of cure. This information is used as input into FEA to predict the residual stresses on the microscopic level resulting from the complete cure process. Virtual testing is subsequently conducted to predict strength allowables. Experimental characterization is used to validate the modeling.

Keywords: molecular dynamics, finite element analysis, processing modeling, multiscale modeling

Procedia PDF Downloads 89

Authors: Radovan Čobanović, Milica Rankov Šicar

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The meat is in the group of perishable food which can be spoiled very rapidly if stored at room temperature. Salting in combination with smoke is intended to extend shelf life, and also to form the specific taste, odor and color. The smoke do not affect only on taste and flavor of the product, it has a bactericidal and oxidative effect and that is the reason because smoked products are less susceptible to oxidation and decay processes. According to mentioned the goal of this study was to evaluate shelf life of smoked ham, which is stored in conditions of high temperature (+15 °C). For the purposes of this study analyzes were conducted on eight samples of smoked ham every 7th day from the day of reception until 21st day. During this period, smoked ham is subjected to sensory analysis (appearance, odor, taste, color, aroma) and bacteriological analyzes (Listeria monocytogenes, Salmonella spp. and yeasts and molds) according to Serbian state regulation. All analyses were tested according to ISO methodology: sensory analysis ISO 6658, Listeria monocytogenes ISO 11 290-1, Salmonella spp ISO 6579 and yeasts and molds ISO 21527-2. Results of sensory analysis of smoked ham indicating that the samples after the first seven days of storage showed visual changes at the surface in the form of allocations of salt, most likely due to the process of drying out the internal parts of the product. The sample, after fifteen days of storage had intensive exterior changes, but the taste was still acceptable. Between the fifteenth and twenty-first day of storage, there is an unacceptable change on the surface and inside of the product and the occurrence of molds and yeasts but neither one analyzed pathogen was found. Based on the obtained results it can be concluded that this type of product cannot be stored for more than seven days at an elevated temperature of +15°C because there are a visual changes that would certainly have influence on decision of customers when purchase of this product is concerned.

Keywords: sustainability, smoked meat products, food engineering, agricultural process engineering

Procedia PDF Downloads 359

Authors: M. Shahin Alam, Satoru Takahashi, Mariko Itoh, Miyuki Komura, Mayuko Suzuki, Natthanan Sangsriratanakul, Kazuaki Takehara

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Cleaning and disinfection are key components of routine biosecurity in livestock farming and food processing industry. The usage of suitable disinfectants and their proper concentration are important factors for a successful biosecurity program. Disinfectants have optimum bactericidal and virucidal efficacies at temperatures above 20°C, but very few studies on application and effectiveness of disinfectants at low temperatures have been done. In the present study, the bactericidal efficacies of food additive grade calcium hydroxide (FdCa(OH)), quaternary ammonium compound (QAC) and their mixture, were investigated under different conditions, including time, organic materials (fetal bovine serum: FBS) and temperature, either in suspension or in carrier test. Salmonella Infantis and Escherichia coli, which are the most prevalent gram negative bacteria in commercial poultry housing and food processing industry, were used in this study. Initially, we evaluated these disinfectants at two different temperatures (4°C and room temperature (RT) (25°C ± 2°C)) and 7 contact times (0, 5 and 30 sec, 1, 3, 20 and 30 min), with suspension tests either in the presence or absence of 5% FBS. Secondly, we investigated the bactericidal efficacies of these disinfectants by carrier tests (rubber, stainless steel and plastic) at same temperatures and 4 contact times (30 sec, 1, 3, and 5 min). Then, we compared the bactericidal efficacies of each disinfectant within their mixtures, as follows. When QAC was diluted with redistilled water (dW2) at 1: 500 (QACx500) to obtain the final concentration of didecyl-dimethylammonium chloride (DDAC) of 200 ppm, it could inactivate Salmonella Infantis within 5 sec at RT either with or without 5% FBS in suspension test; however, at 4°C it required 30 min in presence of 5% FBS. FdCa(OH)2 solution alone could inactivate bacteria within 1 min both at RT and 4°C even with 5% FBS. While FdCa(OH)2 powder was added at final concentration 0.2% to QACx500 (Mix500), the mixture could inactivate bacteria within 30 sec and 5 sec, respectively, with or without 5% FBS at 4°C. The findings from the suspension test indicated that low temperature inhibited the bactericidal efficacy of QAC, whereas Mix500 was effective, regardless of short contact time and low temperature, even with 5% FBS. In the carrier test, single disinfectant required bit more time to inactivate bacteria on rubber and plastic surfaces than on stainless steel. However, Mix500 could inactivate S. Infantis on rubber, stainless steel and plastic surfaces within 30 sec and 1 min, respectively, at RT and 4°C; but, for E. coli, it required only 30 sec at both temperatures. So, synergistic effects were observed on different carriers at both temperatures. For a successful enhancement of biosecurity during winter, the disinfectants should be selected that could have short contact times with optimum efficacy against the target pathogen. The present study findings help farmers to make proper strategies for application of disinfectants in their livestock farming and food processing industry.

Keywords: carrier, food additive grade calcium hydroxide (FdCa(OH)₂), quaternary ammonium compound, synergistic effects

Procedia PDF Downloads 293

Authors: Hassan Sher

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Agriculture has a pivotal role in Pakistan’s economy, accounting for about one-fourth of the GDP and employing almost half the population. However, the competitiveness, productivity, growth, employment potential, export opportunity, and contribution to GDP of the sector is significantly hampered by agriculture marketing laws/regulations at the provincial level that reward rent seeking behavior, promote monopoly power, artificially reduce farmer incomes while inflating prices to consumers, and act as disincentives to investment. Although of more recent vintage than some other provincial agricultural marketing laws, the NWFP Agricultural and Livestock Produce Markets Act, 2007 is a throwback to a colonial paradigm, where restrictions on agricultural produce marketing and Government control of distribution channels is the norm. The Swat Valley (in which we include its tributary valleys) is an area of Pakistan in which there is poverty is both extreme and pervasive. For many, a significant portion of the family’s income comes from selling plants that are used as herbs, medicines, and perfumes. Earlier studies have shown that the benefit they derive from this work is less than they might because of: Lack of knowledge concerning which plants and which plant parts are valuable, Lack of knowledge concerning optimal preservation and storage of material, illiteracy. Another concern that much of the plant material sold from the valley is collected in the wild, without an appreciation of the negative impact continued collecting has on wild populations. We propose: Creating colored cards to help inhabitants recognize the 25 most valuable plants in their area; Developing and sharing protocols for growing the 25 most valuable plants in a home garden; Developing and sharing efficient mechanisms for drying plants so they do not lose value; Encouraging increased literacy by incorporating numbers and a few words in the handouts.

Keywords: food security, medicinal plants, industrial plants, economic development

Procedia PDF Downloads 325

Authors: Xiaochen Mu

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Data rights confirmation, as a key legal issue in the development of the digital economy, is undergoing a transition from a traditional rights paradigm to a more complex private-economic paradigm. In this process, data rights confirmation has evolved from a simple claim of rights to a complex structure encompassing multiple dimensions of personality rights and property rights. Current data rights confirmation practices are primarily reflected in two models: holistic rights confirmation and process rights confirmation. The holistic rights confirmation model continues the traditional "one object, one right" theory, while the process rights confirmation model, through contractual relationships in the data processing process, recognizes rights that are more adaptable to the needs of data circulation and value release. In the design of the data property rights system, there is a hierarchical characteristic aimed at decoupling from raw data to data applications through horizontal stratification and vertical staging. This design not only respects the ownership rights of data originators but also, based on the usufructuary rights of enterprises, constructs a corresponding rights system for different stages of data processing activities. The subjects of data property rights include both data originators, such as users, and data producers, such as enterprises, who enjoy different rights at different stages of data processing. The intellectual property rights system, with the mission of incentivizing innovation and promoting the advancement of science, culture, and the arts, provides a complete set of mechanisms for protecting innovative results. However, unlike traditional private property rights, the granting of intellectual property rights is not an end in itself; the purpose of the intellectual property system is to balance the exclusive rights of the rights holders with the prosperity and long-term development of society's public learning and the entire field of science, culture, and the arts. Therefore, the intellectual property granting mechanism provides both protection and limitations for the rights holder. This perfectly aligns with the dual attributes of data. In terms of achieving the protection of data property rights, the granting of intellectual property rights is an important institutional choice that can enhance the effectiveness of the data property exchange mechanism. Although this is not the only path, the granting of data property rights within the framework of the intellectual property rights system helps to establish fundamental legal relationships and rights confirmation mechanisms and is more compatible with the classification and grading system of data. The modernity of the intellectual property rights system allows it to adapt to the needs of big data technology development through special clauses or industry guidelines, thus promoting the comprehensive advancement of data intellectual property rights legislation. This paper analyzes data protection under the virtual property layer theory and two-fold virtual property rights system. Based on the “bundle of right” theory, this paper establishes specific three-level data rights. This paper analyzes the cases: Google v. Vidal-Hall, Halliday v Creation Consumer Finance, Douglas v Hello Limited, Campbell v MGN and Imerman v Tchenquiz. This paper concluded that recognizing property rights over personal data and protecting data under the framework of intellectual property will be beneficial to establish the tort of misuse of personal information.

Keywords: data protection, property rights, intellectual property, Big data

Procedia PDF Downloads 39

Authors: Paula Lopez-Arce, Hector Altamirano, Dimitrios Rovas, James Berry, Bryan Hindle, Steven Hodgson

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Efficient mould remediation and accurate moisture diagnostics leading to condensation and mould growth in dwellings are largely untapped. Number of factors are contributing to the rising trend of excessive moisture in homes mainly linked with modern living, increased levels of occupation and rising fuel costs, as well as making homes more energy efficient. Environmental monitoring by means of data collection though loggers sensors and survey forms has been performed in a range of buildings from different UK regions. Air and surface temperature and relative humidity values of residential areas affected by condensation and/or mould issues were recorded. Additional measurements were taken through different trials changing type, location, and position of loggers. In some instances, IR thermal images and ventilation rates have also been acquired. Results have been interpreted together with environmental key parameters by processing and connecting data from loggers and survey questionnaires, both in buildings with and without moisture issues. Monitoring exercises carried out during Winter and Spring time show the importance of developing and following accurate protocols for guidance to obtain consistent, repeatable and comparable results and to improve the performance of environmental monitoring. A model and a protocol are being developed to build a diagnostic tool with the goal of performing a simple but precise residential atmospheric moisture diagnostics to distinguish the cause entailing condensation and mould generation, i.e., ventilation, insulation or heating systems issue. This research shows the relevance of monitoring and processing environmental data to assign moisture risk levels and determine the origin of condensation or mould when dealing with a building atmospheric moisture excess.

Keywords: environmental monitoring, atmospheric moisture, protocols, mould

Procedia PDF Downloads 137

Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

Procedia PDF Downloads 115

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

Procedia PDF Downloads 146

Authors: Ozan Kahraman, Hao Feng

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Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

Procedia PDF Downloads 361

Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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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 353

Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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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 78

Authors: Roopchand Tandon, Zaheer Khan Yusufzai, R. Manna, R. K. Mandal

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Present work describes microstructures, mechanical properties, and texture characteristics of the friction stir processed AA7075T7352 aluminum alloy. Phases were analyzed with the help of x-ray diffractometre (XRD), transmission electron microscope (TEM) along with the differential scanning calorimeter (DSC). Depth-wise microstructures and dislocation characteristics from the nugget-zone of the friction stir processed specimens were studied using the bright field (BF) and weak beam dark-field (WBDF) TEM micrographs, and variation in the microstructures as well as dislocation characteristics were the noteworthy features found. XRD analysis display changes in the chemistry as well as size of the phases in the nugget and heat affected zones (Nugget and HAZ). Whereas the base metal (BM) microstructures remain un-affected. High density dislocations were noticed in the nugget regions of the processed specimen, along with the formation of dislocation contours and tangles. .The ɳ’ and ɳ phases, along with the GP-Zones were completely dissolved and trapped by the dislocations. Such an observations got corroborated to the improved mechanical as well as stress corrosion cracking (SCC) performances. Bulk texture and residual stress measurements were done by the Panalytical Empyrean MRD system with Co- kα radiation. Nugget zone (NZ) display compressive residual stress as compared to thermo-mechanically(TM) and heat affected zones (HAZ). Typical f.c.c. deformation texture components (e.g. Copper, Brass, and Goss) were seen. Such a phenomenon is attributed to the enhanced hardening as well as other mechanical performance of the alloy. Mechanical characterizations were done using the tensile test and Anton Paar Instrumented Micro Hardness tester. Enhancement in the yield strength value is reported from the 89MPa to the 170MPa; on the other hand, highest hardness value was reported in the nugget-zone of the processed specimens.

Keywords: aluminum alloy, mechanical characterization, texture characterstics, friction stir processing

Procedia PDF Downloads 106

Authors: Obafemi Akinwotu, Aylin Tas, Tony Taylor, Bukola Onarinde

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The study was conducted to explore the methods and tools to improve texture and preserve the total phenolic and antioxidant compounds of dysphagia foods produced from carrot-based puree with decolourised Chlorella algae. Textural properties (Texture profile analysis [TPA]; the International Dysphagia Diet Standardization Initiative, particle size test [PST]) and rheological properties (viscosity and viscoelastic properties) of carrot puree defrosted by different treatments (microwave, steamer, oven), were characterised using hydrocolloids (guar gum, k. carrageenan, and xanthan gum), and the results were compared to a level 4 commercial sample. DPPH (2,2-diphenyl-1-picrylhydrazyl) antiradical scavenging radicals and total phenolic contents were employed to evaluate the total phenolics, and radical scavenging properties of defrosted carrot puree sonicated carrot puree (20 Hz, 30 min, 60 oC), and vacuum-dried carrot powder with the addition of algae. Results show that the viscosity, viscoelasticity test, TPA, and PST of the commercial sample were comparable to those of guar gum and xanthan gum containing puree, suggesting that they could be used as dysphagia diets. There was no noticeable decolourisation of the Chlorella pigment. Additionally, the use of the microwave, stemmer, and oven for defrosting treatment had an impact on the textural characteristics of the moulded samples upon cooling and also contributed to the reduction in the total phenolic and antioxidant properties of the samples. Sonication treatments of algae exposure reduced the cloudiness of the green pigment and lightened the colour of the samples containing algae, and they also reduced the drying time from 2.5 to 1.5 hours during the preliminary work. The low-temperature vacuum- and freeze-dried samples increased the concentration of the powder and resulted in an increase in the total phenolic content of the dry samples. The dried products may therefore have the potential to become more nutrient-dense to benefit the health of individuals with dysphagia.

Keywords: dysphagia, elderly, hydrocolloids, carrot puree

Procedia PDF Downloads 60

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

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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 227

Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

Procedia PDF Downloads 386

Authors: David Lloyd, David Owen, Martin Jardine

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Introduction: European weight regulations with respect to food products require a full understanding of regulation guidelines to assure regulatory compliance. It is suggested that the complexity of regulation leads to practices which result to over filling of food packages by food processors. Purpose: To establish current practices by food processors and the financial, sustainable and societal impacts on the food supply chain of ineffective food production practices. Methods: An analysis of food packing controls with 10 companies of varying food categories and quantitative based research of a further 15 food processes on the confidence in weight control analysis of finished food packs within their organisation. Results: A process floor analysis of manufacturing operations focussing on 10 products found over fill of packages ranging from 4.8% to 20.2%. Standard deviation figures for all products showed a potential for reducing average weight of the pack whilst still retain the legal status of the product. In 20% of cases, an automatic weight analysis machine was in situ however weight packs were still significantly overweight. Collateral impacts noted included the effect of overfill on raw material purchase and added food miles often on a global basis with one raw material alone creating 10,000 extra food miles due to the poor weight control of the processing unit. A case study of a meat and bakery product will be discussed with the impact of poor controls resulting from complex legislation. The case studies will highlight extra energy costs in production and the impact of the extra weight on fuel usage. If successful a risk assessment model used primarily on food safety but adapted to identify waste /sustainability risks will be discussed within the presentation.

Keywords: legislation, overfill, profile, waste

Procedia PDF Downloads 404

Authors: Rashida Rahmat Zohra, Afsheen Aman, Shah Ali Ul Qader

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Commercially important biopolymer, dextran, is enzymatically degraded into lower molecular weight fractions of vast industrial potential. Various organisms are associated with dextranase production, among which fungal, yeast and bacterial origins are used for commercial production. Dextranases are used to remove contaminating dextran in sugar processing industry and also used in oral care products for efficient removal of dental plaque. Among the hydrolytic products of dextran, isomaltooligosaccharides have prebiotic effect in humans and reduces the cariogenic effect of sucrose in oral cavity. Dextran derivatives produced by hydrolysis of high molecular polymer are also conjugated with other chemical and metallic compounds for usage in pharmaceutical, fine chemical industry, cosmetics, and food industry. Owing to the vast application of dextran and dextranases, current study focused on purification and analysis of kinetic parameters of dextranase from a newly isolated strain of Bacillus licheniformis KIBGE-IB25. Dextranase was purified up to 35.75 folds with specific activity of 1405 U/mg and molecular weight of 158 kDa. Analysis of kinetic parameters revealed that dextranase performs optimum cleavage of low molecular weight dextran (5000 Da, 0.5%) at 35ºC in 15 min at pH 4.5 with a Km and Vmax of 0.3738 mg/ml and 182.0 µmol/min, respectively. Thermal stability profiling of dextranase showed that it retained 80% activity up to 6 hours at 30-35ºC and remains 90% active at pH 4.5. In short, the dextranase reported here performs rapid cleavage of substrate at mild operational conditions which makes it an ideal candidate for dextran removal in sugar processing industry and for commercial production of low molecular weight oligosaccharides.

Keywords: Bacillus licheniformis, dextranase, gel permeation chromatograpy, enzyme purification, enzyme kinetics

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Authors: Thomas Heffernan, Nick Neave, Colin Hamilton, Gill Case

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Hoarding is the acquisition of and failure to discard possessions, leading to excessive clutter and significant psychological/emotional distress. From a cognitive-behavioural approach, excessive hoarding arises from information-processing deficits, as well as from problems with emotional attachment to possessions and beliefs about the nature of possessions. In terms of information processing, hoarders have shown deficits in executive functions, including working memory, planning, inhibitory control, and cognitive flexibility. However, this previous research is often confounded by co-morbid factors such as anxiety, depression, or obsessive-compulsive disorder. The current study adopted a cognitive-behavioural approach, specifically assessing executive deficits and working memory in a non-clinical sample of hoarders, compared with non-hoarders. In this study, a non-clinical sample of 40 hoarders and 73 non-hoarders (defined by The Savings Inventory-Revised) completed the Adult Executive Functioning Inventory, which measures working memory and inhibition, Dysexecutive Questionnaire-Revised, which measures general executive function and the Hospital Anxiety and Depression Scale, which measures mood. The participant sample was made up of unpaid young adult volunteers who were undergraduate students and who completed the questionnaires on a university campus. The results revealed that, after observing no differences between hoarders and non-hoarders on age, sex, and mood, hoarders reported significantly more deficits in inhibitory control and general executive function when compared with non-hoarders. There was no between-group difference on general working memory. This suggests that non-clinical hoarders have a specific difficulty with inhibition-control, which enables you to resist repeated, unwanted urges. This might explain the hoarder’s inability to resist urges to buy and keep items that are no longer of any practical use. These deficits may be underpinned by general executive function deficiencies.

Keywords: hoarding, memory, executive, deficits

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Authors: Xun Sun, Le Li, Ce Mo, Lei Mo, Ruiming Wang, Guosheng Ding

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Cognitive control plays a central role in information processing, which is comprised of various components including response suppression and inhibitory control. Response suppression is considered to inhibit the irrelevant response during the cognitive process; while inhibitory control to inhibit the irrelevant stimulus in the process of cognition. Both of them undertake distinct functions for the cognitive control, so as to enhance the performances in behavior. Among numerous factors on cognitive control, bilingual experience is a substantial and indispensible factor. It has been reported that bilingual experience can influence the neural activity of cognitive control as whole. However, it still remains unknown how the neural influences specifically present on the components of cognitive control imposed by bilingualism. In order to explore the further issue, the study applied fMRI, used anti-saccade paradigm and compared the cerebral activations between high and low proficient Chinese-English bilinguals. Meanwhile, the study provided experimental evidence for the brain plasticity of language, and offered necessary bases on the interplay between language and cognitive control. The results showed that response suppression recruited the middle frontal gyrus (MFG) in low proficient Chinese-English bilinguals, but the inferior patrietal lobe in high proficient Chinese-English bilinguals. Inhibitory control engaged the superior temporal gyrus (STG) and middle temporal gyrus (MTG) in low proficient Chinese-English bilinguals, yet the right insula cortex was more active in high proficient Chinese-English bilinguals during the process. These findings illustrate insights that bilingual experience has neural influences on different components of cognitive control. Compared with low proficient bilinguals, high proficient bilinguals turn to activate advanced neural areas for the processing of cognitive control. In addition, with the acquisition and accumulation of language, language experience takes effect on the brain plasticity and changes the neural basis of cognitive control.

Keywords: bilingual experience, cognitive control, inhibition control, response suppression

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Authors: Sunjay

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Plate tectonics is of very great significance as it represents the spatial relationships of volcanic rock suites at plate margins, the distribution in space and time of the conditions of different metamorphic facies, the scheme of deformation in mountain belts, or orogens, and the association of different types of economic deposit. Orogenic belts are characterized by extensive thrust faulting, movements along large strike-slip fault zones, and extensional deformation that occur deep within continental interiors. Within oceanic areas there also are regions of crustal extension and accretion in the backarc basins that are located on the landward sides of many destructive plate margins.Collisional orogens develop where a continent or island arc collides with a continental margin as a result of subduction. collisional and noncollisional orogens can be explained by differences in the strength and rheology of the continental lithosphere and by processes that influence these properties during orogenesis.Seismic Imaging Difficulties-In triangle zones, several factors reduce the effectiveness of seismic methods. The topography in the central part of the triangle zone is usually rugged and is associated with near-surface velocity inversions which degrade the quality of the seismic image. These characteristics lead to low signal-to-noise ratio, inadequate penetration of energy through overburden, poor geophone coupling with the surface and wave scattering. Depth Seismic Imaging Techniques-Seismic processing relates to the process of altering the seismic data to suppress noise, enhancing the desired signal (higher signal-to-noise ratio) and migrating seismic events to their appropriate location in space and depth. Processing steps generally include analysis of velocities, static corrections, moveout corrections, stacking and migration. Exploration seismology Bow-tie effect -Shadow Zones-areas with no reflections (dead areas). These are called shadow zones and are common in the vicinity of faults and other discontinuous areas in the subsurface. Shadow zones result when energy from a reflector is focused on receivers that produce other traces. As a result, reflectors are not shown in their true positions. Subsurface Discontinuities-Diffractions occur at discontinuities in the subsurface such as faults and velocity discontinuities (as at “bright spot” terminations). Bow-tie effect caused by the two deep-seated synclines. Seismic imaging of thrust faults and structural damage-deepwater thrust belts, Imaging deformation in submarine thrust belts using seismic attributes,Imaging thrust and fault zones using 3D seismic image processing techniques, Balanced structural cross sections seismic interpretation pitfalls checking, The seismic pitfalls can originate due to any or all of the limitations of data acquisition, processing, interpretation of the subsurface geology,Pitfalls and limitations in seismic attribute interpretation of tectonic features, Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Carbon capture and geological storage leakage surveillance because fault behave as a seal or a conduit for hydrocarbon transportation to a trap,etc.

Keywords: tectonics, seismic imaging, fold and thrust belts, seismic interpretation

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Authors: T. Kuchukhidze, N. Jalagonia, Z. Phachulia, R. Chedia

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Ceramic obtained on the base of aluminum oxide has wide application range, because it has unique properties, for example, wear-resistance, dielectric characteristics, exploitation ability at high temperatures and in corrosive atmosphere. Low temperature synthesis of α-Al2O3 is energo-economical process and it is actual for developing technologies of corundum ceramics fabrication. In the present work possibilities of low temperature transformation of oxyhydroxides in α-Al2O3, during a presence of small amount of rare–earth elements compounds (also Th, Re), have been discussed. Aluminium unstable oxyhydroxides have been obtained by hydrolysis of aluminium isopropoxide, nitrates, sulphate, chloride in alkaline environment at 80-90ºC tempertures. β-Al(OH)3 has been received from aluminium powder by ultrasonic development. Drying of oxyhydroxide sol has been conducted with presence of various types seeds, which amount reaches 0,1-0,2% (mas). Neodymium, holmium, thorium, lanthanum, cerium, gadolinium, disprosium nitrates and rhenium carbonyls have been used as seeds and they have been added to the sol specimens in amount of 0.1-0.2% (mas) calculated on metals. Annealing of obtained gels is carried out at 70 – 1100ºC for 2 hrs. The same specimen transforms in α-Al2O3 at 1100ºC. At this temperature in case of presence of lanthanum and gadolinium transformation takes place by 70-85%. In case of presence of thorium stabilization of γ-and θ-phases takes place. It is established, that thorium causes inhibition of α-phase generation at 1100ºC, at the time in all other doped specimens α-phase is generated at lower temperatures (1000-1050ºC). During the work the following devices have been used: X-ray difractometer DRON-3M (Cu-Kα, Ni filter, 2º/min), High temperature vacuum furnace OXY-GON, electronic scanning microscopes Nikon ECLIPSE LV 150, NMM-800TRF, planetary mill Pulverisette 7 premium line, SHIMADZU Dynamic Ultra Micro Hardness Tester, DUH-211S, Analysette 12 Dyna sizer.

Keywords: α-Alumina, combustion, phase transformation, seeding

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Weihao Wang, Zhulin Zong

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Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

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Authors: Hubert Feiglstorfer

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The technique of making terrazzo has been known since ancient times. During the Roman Empire, known as opus signinum, at the time of the Renaissance, known as composto terrazzo marmorino or at the turn of the 19th and 20th centuries, the use of terrazzo experienced a common use in Europe. In Asia, especially in the Himalayas and the Tibetan highlands, a particular floor and roof manufacturing technique is commonly used for about 1500 years, known as arga. The research question in this contribution asks for technical and cultural-historical synergies of these floor-making techniques. The making process of an arga floor shows constructive parallels to the European terrazzo. Surface processing by grinding, burnishing and sealing, in particular, reveals technological similarities. The floor structure itself, on the other hand, shows differences, for example in the use of hydraulic aggregate in the terrazzo, while the arga floor is used without hydraulic material, but the result of both techniques is a tight, water-repellent and shiny surface. As part of this comparative study, the materials, processing techniques and quality features of the two techniques are compared and parallels and differences are analysed. In addition to text and archive research, the methods used are results of material analyses and ethnographic research such as participant observation. Major findings of the study are the investigation of the mineralogical composition of arga floors and its comparison with terrazzo floors. The study of the cultural-historical context in which both techniques are embedded will give insight into technical developments in Europe and Asia, parallels and differences. Synergies from this comparison let possible technological developments in the production, conservation and renovation of European terrazzo floors appear in a new light. By making arga floors without cement-based aggregates, the renovation of historical floors from purely natural products and without using energy by means of a burning process can be considered.

Keywords: European and Asian crafts, material culture, floor making technology, terrazzo, arga, Tibetan building traditions

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Authors: Zahra Abdi, Roghayeh Alipour, Babak Farahi Ghasraboonasr

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Introduction: Diabetes is a serious medical condition that requires constant care. People with type 2 diabetes may also be likely to experience dry, itchy skin and poor wound healing. Some people with diabetes will have a skin problems at some time in their lives and for those not yet diagnosed with diabetes, a skin problem can be an indication of the disease. our purpose was to assess the capability and knowledge of students of Shiraz University of Medical Sciences about prevent from skin complications in diabetes type 2. Methods: In this descriptive cross-sectional study, knowledge of 360 students of Shiraz University of Medical Sciences was evaluated about different ways to avoid skin complications in diabetes type 2. Data were analyzed by spss19.(P<0.05) was considered significant. Results: 360 students of Shiraz University of Medical Sciences participated in this study. 45% of students agree with the effect of Moisturize skin daily, If Diabetics have sensitive skin, choose a fragrance-free, dye-free moisturizer that won’t irritate skin. 52% believe that Protect skin from sun can be so useful, Sun exposure is drying and aging. Use sunscreen with SPF 30 or higher whenever you’re outside. Wear gloves when doing yardwork to protect the skin on your hands. 62% of students strongly agree with Carefully clean any cuts and scrapes, If diabetics notice any sign of infection skin that’s red, swollen, or warm to the touch, or has a foul-smelling drainage or pus should consulting with a doctor immediately. Diabetics should be careful about any injury that takes longer than normal to heal and they should consulting with doctor about them too. 72% of students believe that diabetics should be diligent about daily foot care. Clean and moisturize feet each day and check each foot closely, top and bottom, for wounds even a tiny cut, blisters, or cracked skin. Conclusions: The risk of getting these diabetes complications can be lessened by controlling blood sugar. Skin complications can cause serious consequences. Taking care of skin is so important and using these tips are remarkable effective and help diabetics to look after their skin easier.

Keywords: skin complications, diabetes type 2, Shiraz University of Medical Sciences, diabetics

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Authors: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Netanel Stern

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Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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Authors: Khalid Alshallash

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Small fragments of the roots of some Rumex species including R. obtusifolius and R. crispus have been found to regenerate readily, contributing to the severity of infestations by these very common, widespread and difficult to control perennial weeds of agricultural crops and grasslands. Their root fragments are usually created during routine agricultural practices. We found that fresh root fragments of both species containing 65-70 % of moisture, progressively lose their moisture content when desiccated under controlled growth room conditions matching summer weather of southeast England, with the greatest reduction occurring in the first 48 hours. Probability of shoot emergence and the time taken for emergence in glasshouse conditions were also reduced significantly by desiccation, with R. obtusifolius least affected up to 48-hour. However, the effects converged after 120 hours. In contrast, R. obtusifolius was significantly slower to emerge after up to 48 hours desiccation, again effects converging after longer periods, R. crispus entirely failed to emerge at 120 hours. The dry weight of emerged shoots was not significantly different between the species, until desiccated for 96 hours when R. obtusifolius was significantly reduced. At 120 hours, R. obtusifolius did not emerge. In outdoor trials, desiccation for 24 or 48 hours had less effect on emergence when planted at the soil surface or up to 10 cm of depth, compared to deeper plantings. In both species, emergence was significantly lower when desiccated fragments were planted at 15 or 20 cm. Time taken for emergence was not significantly different between the species until planted at 15 or 20 cm when R. obtusifolius was slower than R. crispus and reduced further by increasing desiccation. Similar variation in effects of increasing soil depth interacting with increasing desiccation was found in reductions in dry weight, the number of tillers and leaf area, with R obtusifolius generally but not exclusively better able to withstand more extreme trial conditions. Our findings suggest that infestations of these highly troublesome weeds may be partly controlled by appropriate agricultural practices, notably exposing cut fragments to drying environmental conditions followed by deep burial.

Keywords: regeneration, root fragment, rumex crispus, rumex obtusifolius

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