Search results for: gamma neural oscillation

Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

Procedia PDF Downloads 61

Authors: Jiangang Shen

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Olfactory dysfunction is a common symptom companied by anxiety- and depressive-like behaviors in depressive patients. Chronic stress triggers hormone responses and inhibits the proliferation and differentiation of neural stem cells (NSCs) in the hippocampus and subventricular zone (SVZ)-olfactory bulb (OB), contributing to depressive behaviors and olfactory dysfunction. However, the cellular signaling molecules to regulate chronic stress mediated olfactory dysfunction are largely unclear. Adaptor proteins containing the pleckstrin homology domain, phosphotyrosine binding domain, and leucine zipper motif (APPLs) are multifunctional adaptor proteins. Herein, we tested the hypothesis that APPL2 could inhibit hippocampal neurogenesis by affecting glucocorticoid receptor (GR) signaling, subsequently contributing to depressive and anxiety behaviors as well as olfactory dysfunctions. The major discoveries are included: (1) APPL2 Tg mice had enhanced GR phosphorylation under basic conditions but had no different plasma corticosterone (CORT) level and GR phosphorylation under stress stimulation. (2) APPL2 Tg mice had impaired hippocampal neurogenesis and revealed depressive and anxiety behaviors. (3) GR antagonist RU486 reversed the impaired hippocampal neurogenesis in the APPL2 Tg mice. (4) APPL2 Tg mice displayed higher GR activity and less capacity for neurogenesis at the olfactory system with lesser olfactory sensitivity than WT mice. (5) APPL2 negatively regulates olfactory functions by switching fate commitments of NSCs in adult olfactory bulbs via interaction with Notch1 signaling. Furthermore, baicalin, a natural medicinal compound, was found to be a promising agent targeting APPL2/GR signaling and promoting adult neurogenesis in APPL2 Tg mice and chronic corticosterone-induced depression mouse models. Behavioral tests revealed that baicalin had antidepressant and olfactory-improving effects. Taken together, APPL2 is a critical therapeutic target for antidepressant treatment.

Keywords: APPL2, hippocampal neurogenesis, depressive behaviors and olfactory dysfunction, stress

Procedia PDF Downloads 61

Authors: Jason Clarke, Michaela Porubanova, Angela Mazzoli, Gulsah Kut

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What accounts for our perception that time inexorably passes in one direction, from the past to the future, the so-called arrow of time, given that the laws of physics permit motion in one temporal direction to also happen in the reverse temporal direction? Modern physics says that the reason for time’s unidirectional physical arrow is the relationship between time and entropy, the degree of disorder in the universe, which is evolving from low entropy (high order; thermal disequilibrium) toward high entropy (high disorder; thermal equilibrium), the second law of thermodynamics. Accordingly, our perception of the direction of time, from past to future, is believed to emanate as a result of the natural evolution of entropy from low to high, with low entropy defining our notion of ‘before’ and high entropy defining our notion of ‘after’. Here we explored this proposed relationship between entropy and the perception of time’s arrow. We predicted that if the brain has some mechanism for detecting entropy, whose output feeds into processes involved in constructing our perception of the direction of time, presentation of violations to the expectation that low entropy defines ‘before’ and high entropy defines ‘after’ would alert this mechanism, leading to measurable behavioral effects, namely a disruption in duration perception. To test this hypothesis, participants were shown briefly-presented (1000 ms or 500 ms) computer-generated visual dynamic events: novel 3D shapes that were seen either to evolve from whole figures into parts (low to high entropy condition) or were seen in the reverse direction: parts that coalesced into whole figures (high to low entropy condition). On each trial, participants were instructed to reproduce the duration of their visual experience of the stimulus by pressing and releasing the space bar. To ensure that attention was being deployed to the stimuli, a secondary task was to report the direction of the visual event (forward or reverse motion). Participants completed 60 trials. As predicted, we found that duration reproduction was significantly longer for the high to low entropy condition compared to the low to high entropy condition (p=.03). This preliminary data suggests the presence of a neural mechanism that detects entropy, which is used by other processes to construct our perception of the direction of time or time’s arrow.

Keywords: time perception, entropy, temporal illusions, duration perception

Procedia PDF Downloads 150

Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

Procedia PDF Downloads 88

Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

Procedia PDF Downloads 72

Authors: Michael R. Shurin, Galina Shurin, Vladimir A. Kirichenko

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Background. Visibility of hepatic malignancies is poor on non-contrast imaging for daily verification of liver malignancies prior to radiation therapy on MRI-guided Linear Accelerators (MR-Linac). Ferumoxytol® (Feraheme, AMAG Pharmaceuticals, Waltham, MA) is a SPION agent that is increasingly utilized off-label as hepatic MRI contrast. This agent has the advantage of providing a functional assessment of the liver based upon its uptake by hepatic Kupffer cells proportionate to vascular perfusion, resulting in strong T1, T2 and T2* relaxation effects and enhanced contrast of malignant tumors, which lack Kupffer cells. The latter characteristic has been recently utilized for MRI-guided radiotherapy planning with precision targeting of liver malignancies. However potential radiotoxicity of SPION has never been addressed for its safe use as an MRI-contrast agent during liver radiotherapy on MRI-Linac. This study defines the radiomodulating properties of SPIONs in vitro on human monocyte and macrophage cell lines exposed to 60Go gamma-rays within clinical radiotherapy dose range. Methods. Human monocyte and macrophages cell line in cultures were loaded with a clinically relevant concentration of Ferumoxytol (30µg/ml) for 2 and 24 h and irradiated to 3Gy, 5Gy and 10Gy. Cells were washed and cultured for additional 24 and 48 h prior to assessing their phenotypic activation by flow cytometry and function, including viability (Annexin V/PI assay), proliferation (MTT assay) and cytokine expression (Luminex assay). Results. Our results reveled that SPION affected both human monocytes and macrophages in vitro. Specifically, iron oxide nanoparticles decreased radiation-induced apoptosis and prevented radiation-induced inhibition of human monocyte proliferative activity. Furthermore, Ferumoxytol protected monocytes from radiation-induced modulation of phenotype. For instance, while irradiation decreased polarization of monocytes to CD11b+CD14+ and CD11bnegCD14neg phenotype, Ferumoxytol prevented these effects. In macrophages, Ferumoxytol counteracted the ability of radiation to up-regulate cell polarization to CD11b+CD14+ phenotype and prevented radiation-induced down-regulation of expression of HLA-DR and CD86 molecules. Finally, Ferumoxytol uptake by human monocytes down-regulated expression of pro-inflammatory chemokines MIP-1α (Macrophage inflammatory protein 1α), MIP-1β (CCL4) and RANTES (CCL5). In macrophages, Ferumoxytol reversed the expression of IL-1RA, IL-8, IP-10 (CXCL10) and TNF-α, and up-regulates expression of MCP-1 (CCL2) and MIP-1α in irradiated macrophages. Conclusion. SPION agent Ferumoxytol increases resistance of human monocytes to radiation-induced cell death in vitro and supports anti-inflammatory phenotype of human macrophages under radiation. The effect is radiation dose-dependent and depends on the duration of Feraheme uptake. This study also finds strong evidence that SPIONs reversed the effect of radiation on the expression of pro-inflammatory cytokines involved in initiation and development of radiation-induced liver damage. Correlative translational work at our institution will directly assess the cyto-protective effects of Ferumoxytol on human Kupfer cells in vitro and ex vivo analysis of explanted liver specimens in a subset of patients receiving Feraheme-enhanced MRI-guided radiotherapy to the primary liver tumors as a bridge to liver transplant.

Keywords: superparamagnetic iron oxide nanoparticles, radioprotection, magnetic resonance imaging, liver

Procedia PDF Downloads 59

Authors: Maftunakhon Latipova, Feruza Khaydarova

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Obesity is considered a chronic metabolic disease that occurs at any age. Regulation of body weight in the body is carried out through complex interaction of a complex of interrelated systems that control the body's energy system. Energy imbalance is the cause of obesity and overweight, in which the supply of energy from food exceeds the energy needs of the body. Obesity is closely related to impaired appetite regulation, and a hypothalamus is a key place for neural regulation of food consumption. The nucleus of the hypothalamus is connected and interdependent on receiving, integrating and sending hunger signals to regulate appetite. Purpose of the study: to identify markers of food behavior. Materials and methods: The screening was carried out to identify eating disorders in 200 men and women aged 18 to 35 years with overweight and obesity and to check the effects of Orexin A and Neuropeptide Y markers. A questionnaire and questionnaires were conducted with over 200 people aged 18 to 35 years. Questionnaires were for eating disorders and hidden depression (on the Zang scale). Anthropometry is measured by OT, OB, BMI, Weight, and Height. Based on the results of the collected data, 3 groups were divided: People with obesity, People with overweight, Control Group of Healthy People. Results: Of the 200 analysed persons, 86% had eating disorders. Of these, 60% of eating disorders were associated with childhood. According to the Zang test result: Normal condition was about 37%, mild depressive disorder 20%, moderate depressive disorder 25% and 18% of people suffered from severe depressive disorder without knowing it. One group of people with obesity had eating disorders and moderate and severe depressive disorder, and group 2 was overweight with mild depressive disorder. According to laboratory data, the first group had the lowest concentration of Orexin A and Neuropeptide U in blood serum. Conclusions: Being overweight and obese are the first signal of many diseases, and prevention and detection of these disorders will prevent various diseases, including type 2 diabetes. Obesity etiology is associated with eating disorders and signal transmission of the orexinorghetic system of the hypothalamus.

Keywords: obesity, endocrinology, hypothalamus, overweight

Procedia PDF Downloads 58

Authors: Noreen Noreen, Aamir Ijaz, Hamza Akhtar

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Background: Screening plays a major role to detect chromosomal abnormalities, Down syndrome, neural tube defects and other inborn diseases of the newborn. Serum biomarkers in the second trimester are useful in determining risk of most common chromosomal anomalies; these test include Alpha-fetoprotein (AFP), Human chorionic gonadotropin (hCG), Unconjugated Oestriol (UEȝ)and inhibin-A. Quadruple biomarkers are worth test in diagnosing the congenital pathology during pregnancy, these procedures does not form a part of routine health care of pregnant women in Pakistan, so the median value is lacking for population in Pakistan. Objective: To determine median values of biochemical maternal serum markers in local population during second trimester maternal screening. Study settings: Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology (AFIP) Rawalpindi. Methods: Cross-Sectional study for estimation of reference values. Non-probability consecutive sampling, 155 healthy pregnant women, of 30-40 years of age, will be included. As non-parametric statistics will be used, the minimum sample size is 120. Result: Total 155 women were enrolled into this study. The age of all women enrolled ranged from 30 to39 yrs. Among them, 39 per cent of women were less than 34 years. Mean maternal age 33.46±2.35 SD and maternal body weight were 54.98±2.88. Median value of quadruple markers calculated from 15-18th week of gestation that will be used for calculation of MOM for screening of trisomy21 in this gestational age. Median value at 15 week of gestation were observed hCG 36650 mIU/ml, AFP 23.3 IU/ml, UEȝ 3.5 nmol/L, InhibinA 198 ng/L, at 16 week of gestation hCG 29050 mIU/ml, AFP 35.4 IU/ml, UEȝ 4.1 nmol/L, InhibinA 179 ng/L, at 17 week of gestation hCG 28450 mIU/ml, AFP 36.0 IU/ml, UEȝ 6.7 nmol/L, InhibinA 176 ng/L and at 18 week of gestation hCG 25200 mIU/ml, AFP 38.2 IU/ml, UEȝ 8.2 nmol/L, InhibinA 190 ng/L respectively.All the comparisons were significant (p-Value <0.005) with 95% confidence Interval (CI) and level of significance of study set by going through literature and set at 5%. Conclusion: The median values for these four biomarkers in Pakistani pregnant women can be used to calculate MoM.

Keywords: screening, down syndrome, quadruple test, second trimester, serum biomarkers

Procedia PDF Downloads 161

Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

Procedia PDF Downloads 122

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

Procedia PDF Downloads 161

Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

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Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

Procedia PDF Downloads 172

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 67

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 54

Authors: Godwin Dennison, C. E. Boulind, O. Gould, B. de Lacy Costello, J. Allison, P. White, P. Ewings, A. Wicaksono, N. J. Curtis, A. Pullyblank, D. Jayne, J. A. Covington, N. Ratcliffe, N. K. Francis

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Background: Colorectal symptoms are common but only infrequently represent serious pathology, including colorectal cancer (CRC). A large number of invasive tests are presently performed for reassurance. We investigated the feasibility of urinary volatile organic compound (VOC) testing as a potential triage tool in patients fast-tracked for assessment for possible CRC. Methods: A prospective, multi-centre, observational feasibility study was performed across three sites. Patients referred on NHS fast-track pathways for potential CRC provided a urine sample which underwent Gas Chromatography Mass Spectrometry (GC-MS), Field Asymmetric Ion Mobility Spectrometry (FAIMS) and Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) analysis. Patients underwent colonoscopy and/or CT colonography and were grouped as either CRC, adenomatous polyp(s), or controls to explore the diagnostic accuracy of VOC output data supported by an artificial neural network (ANN) model. Results: 558 patients participated with 23 (4.1%) CRC diagnosed. 59% of colonoscopies and 86% of CT colonographies showed no abnormalities. Urinary VOC testing was feasible, acceptable to patients, and applicable within the clinical fast track pathway. GC-MS showed the highest clinical utility for CRC and polyp detection vs. controls (sensitivity=0.878, specificity=0.882, AUROC=0.884). Conclusion: Urinary VOC testing and analysis are feasible within NHS fast-track CRC pathways. Clinically meaningful differences between patients with cancer, polyps, or no pathology were identified therefore suggesting VOC analysis may have future utility as a triage tool. Acknowledgment: Funding: NIHR Research for Patient Benefit grant (ref: PB-PG-0416-20022).

Keywords: colorectal cancer, volatile organic compound, gas chromatography mass spectrometry, field asymmetric ion mobility spectrometry, selected ion flow tube mass spectrometry

Procedia PDF Downloads 75

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

Procedia PDF Downloads 200

Authors: Samaneh Nazeri, Bagher Mojazi Amiri, Hamid Farahmand

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Production of high quality fish eggs with reasonable hatching rate makes a success in aquaculture industries. It is influenced by the environmental stimulators and inhibitors. Diazinon is a widely-used pesticide in Golestan province (Southern Caspian Sea, North of Iran) which is washed to the aquatic environment (3 mg/L in the river). It is little known about the effect of this pesticide on the embryogenesis of sturgeon fish, the valuable species of the Caspian Sea. Hormonal content of the egg is an important factor to guaranty the successful passes of embryonic stages. In this study, the fate of Persian sturgeon embryo to 24, 48, 72, and 96-hours exposure of diazinon (LC₅₀ dose) was tested. Also, the effect of thyroid hormones (T3 and T4) on these embryos was tested concurrently or separately with diazinon LC ₅₀ dose. Fertilized eggs are exposed to T3 (low dose: 1 ng/ml, high dose: 10 ng/ml), T4 (low dose: 1 ng/ml, high dose: 10 ng/ml). Six eggs were randomly selected from each treatment (with three replicates) in five developmental stages (two cell- division, neural, heart present, heart beaten, and hatched larvae). The possibility of changing T3, T4, and cortisol contents of the embryos were determined in all treated groups and in every mentioned embryonic stage. The hatching rate in treated groups was assayed at the end of the embryogenesis to clarify the effect of thyroid hormones and diazinon. The results indicated significant differences in thyroid hormone contents, but no significant differences were recognized in cortisol levels at various early life stages of embryos. There was also significant difference in thyroid hormones in (T3, T4) + diazinon treated embryos (P˂0.05), while no significant difference between control and treatments in cortisol levels was observed. The highest hatching rate was recorded in HT3 treatment, while the lowest hatching rate was recorded for diazinon LC₅₀ treatment. The result confirmed that Persian sturgeon embryo is less sensitive to diazinon compared to teleost embryos, and thyroid hormones may increase hatching rate even in the presence of diazinon.

Keywords: Persian sturgeon, diazinon, thyroid hormones, cortisol, embryo

Procedia PDF Downloads 289

Authors: James Britton, Vijaya Krishna, Manus Biggs, Abhay Pandit

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Regeneration of the spinal cord after injury remains a great challenge due to the complexity of this organ. Inflammation and gliosis at the injury site hinder the outgrowth of axons and hence prevent synaptic reconnection and reinnervation. Hyaluronic acid (HA) is the main component of the spinal cord extracellular matrix and plays a vital role in cell proliferation and axonal guidance. In this study, we have synthesized and characterized a photo-cross-linkable HA-tyramine (tyr) hydrogel from a chemical, mechanical, electrical, biological and structural perspective. From our experimentation, we have found that HA-tyr can be synthesized with controllable degrees of tyramine substitution using click chemistry. The complex modulus (G*) of HA-tyr can be tuned to mimic the mechanical properties of the native spinal cord via optimization of the photo-initiator concentration and UV exposure. We have examined the degree of tyramine-tyramine covalent bonding (polymerization) as a function of UV exposure and photo-initiator use via Photo and Nuclear magnetic resonance spectroscopy. Both swelling and enzymatic degradation assays were conducted to examine the resilience of our 3D printed hydrogel constructs in-vitro. Using a femtosecond 780nm laser, the two-photon polymerization of HA-tyr hydrogel in the presence of riboflavin photoinitiator was optimized. A laser power of 50mW and scan speed of 30,000 μm/s produced high-resolution spatial patterning within the hydrogel with sustained mechanical integrity. Using dorsal root ganglion explants, the cytocompatibility of photo-crosslinked HA-tyr was assessed. Using potentiometry, the electrical conductivity of photo-crosslinked HA-tyr was assessed and compared to that of native spinal cord tissue as a function of frequency. In conclusion, we have developed a biocompatible hydrogel that can be used for photolithographic 3D printing to fabricate tissue engineered constructs for neural tissue regeneration applications.

Keywords: 3D printing, hyaluronic acid, photolithography, spinal cord injury

Procedia PDF Downloads 140

Authors: Yasar Samet Gokceoglu, Ayse Nur Incesu, Furkan Okatar, Berk Nimetoglu, Serkan Bayram, Turgut Akgul

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Ankle syndesmosis injuries pose a significant challenge in orthopedic practice due to their potential for prolonged recovery and chronic ankle dysfunction. Accurate diagnosis and management of these injuries are essential for achieving optimal patient outcomes. The use of radiological methods, such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a vital role in the accurate diagnosis of syndesmosis injuries in the context of ankle fractures. Treatment options for ankle syndesmosis injuries vary, with surgical interventions such as screw fixation and suture-button implantation being commonly employed. The choice of treatment is influenced by the severity of the injury and the presence of associated fractures. Additionally, the mechanism of injury, such as pure syndesmosis injury or specific fracture types, can impact the stability and management of syndesmosis injuries. Ankle fractures with syndesmosis injury present a complex clinical scenario, requiring accurate diagnosis, appropriate reduction, and tailored management strategies. The interplay between the mechanism of injury, associated fractures, and treatment modalities significantly influences the outcomes of these challenging injuries. The long-term outcomes and patient satisfaction following ankle fractures with syndesmosis injury are crucial considerations in the field of orthopedics. Patient-reported outcome measures, such as the Foot and Ankle Outcome Score (FAOS), provide essential information about functional recovery and quality of life after these injuries. When diagnosing syndesmosis injuries, standard measurements, such as the medial clear space, tibiofibular overlap, tibiofibular clear space, anterior tibiofibular ratio (ATFR), and the anterior-posterior tibiofibular ratio (APTF), are assessed through radiographs and computed tomography (CT) scans. These parameters are critical in evaluating the presence and severity of syndesmosis injuries, enabling clinicians to choose the most appropriate treatment approach. Despite advancements in diagnostic imaging, challenges remain in accurately diagnosing and treating ankle syndesmosis injuries. Traditional diagnostic parameters, while beneficial, may not capture the full extent of the injury or provide sufficient information to guide therapeutic decisions. This gap highlights the need for exploring additional diagnostic parameters that could enhance the accuracy of syndesmosis injury diagnoses and inform treatment strategies more effectively. The primary goal of this research is to evaluate the usefulness of traditional radiographic measurements in comparison to new CT-based measurements for diagnosing ankle syndesmosis injuries. Specifically, this study aims to assess the accuracy of conventional parameters, including medial clear space, tibiofibular overlap, tibiofibular clear space, ATFR, and APTF, in contrast with the recently proposed CT-based measurements such as the delta and gamma angles. Moreover, the study intends to explore the relationship between these diagnostic parameters and functional outcomes, as measured by the Foot and Ankle Outcome Score (FAOS). Establishing a correlation between specific diagnostic measurements and FAOS scores will enable us to identify the most reliable predictors of functional recovery following syndesmosis injuries. This comparative analysis will provide valuable insights into the accuracy and dependability of CT-based measurements in diagnosing ankle syndesmosis injuries and their potential impact on predicting patient outcomes. The results of this study could greatly influence clinical practices by refining diagnostic criteria and optimizing treatment planning for patients with ankle syndesmosis injuries.

Keywords: ankle syndesmosis injury, diagnostic accuracy, computed tomography, radiographic measurements, Tibiofibular syndesmosis distance

Procedia PDF Downloads 50

Authors: Surbhi Surbhi, Andrea Erni, Gunter Meister, Harold Cremer, Christophe Beclin

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MicroRNAs (miRNAs) are short 20-23 nucleotide long non-coding RNAs; there are 2605 miRNA in humans and 1936 miRNA in mouse in total (miRBase). The nervous system expresses the most abundant miRNA and most diverse. MiRNAs play a role in many steps during neurogenesis, like cell proliferation, differentiation, neural patterning, axon pathfinding, etc. Moreover, in vitro studies suggested a role in the regulation of local translation at the synapse, thus controlling neuronal plasticity. However, due to the specific structure of miRNA molecules, an in-vivo confirmation of the general role of miRNAs in the control of neuronal plasticity is still pending. For example, their small size and their high level of sequence homology make difficult the analysis of their cellular and sub-cellular localization in-vivo by in-situ hybridization. Moreover, it was found that only 40% of the expressed miRNA molecules in a cell are included in RNA-Induced Silencing Complexes (RISC) and, therefore, involved in inhibitory interactions while the rest is silent. Definitively, the development of new tools is needed to have a better understanding of the cellular function of miRNAs, in particular their role in neuronal plasticity. Here we describe a new technique called in-vivo AGO-APP designed to investigate miRNA expression and function in-vivo. This technique is based on the expression of a small peptide derived from the human RISC-complex protein TNRC6B, called T6B, which binds all known Argonaute (Ago) proteins with high affinity allowing the efficient immunoprecipitation of AGO-bound miRNAs. We have generated two transgenic mouse lines conditionally expressing T6B either ubiquitously in the cell or targeted at the synapse. A comparison of the repertoire of miRNAs immuno-precipitated from mature neurons of both mouse lines will provide us with a list of miRNAs showing a specific activity at the synapse. The physiological role of these miRNAs will be subsequently addressed through gain and loss of function experiments.

Keywords: RNA-induced silencing complexes, TNRC6B, miRNA, argonaute, synapse, neuronal plasticity, neurogenesis

Procedia PDF Downloads 113

Authors: Wedad Albalawi

Abstract:

The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is defined as a closed subset contains real numbers. Then the inequalities of time scales version have received a lot of attention and has had a major field in both pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on double integrals to obtain new time-scale inequalities of Copson driven by Steklov operator. They will be applied in the solution of the Cauchy problem for the wave equation. The proof can be done by introducing restriction on the operator in several cases. In addition, the obtained inequalities done by using some concepts in time scale version such as time scales calculus, theorem of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of Hardy, inequality of Coposon, Steklov operator

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Authors: Samuel J. Ahmad, Michael Zhu, Andrew J. Kobets

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Segmentation tools continue to advance, evolving from manual methods to automated contouring technologies utilizing convolutional neural networks. These techniques have evaluated ventricular and hemorrhagic volumes in the past but may be applied in novel ways to assess posterior fossa-associated pathologies such as Chiari malformations. Herein, we summarize literature pertaining to segmentation in the context of this and other posterior fossa-based diseases such as trigeminal neuralgia, hemifacial spasm, and posterior fossa syndrome. A literature search for volumetric analysis of the posterior fossa identified 27 papers where semi-automated, automated, manual segmentation, linear measurement-based formulas, and the Cavalieri estimator were utilized. These studies produced superior data than older methods utilizing formulas for rough volumetric estimations. The most commonly used segmentation technique was semi-automated segmentation (12 studies). Manual segmentation was the second most common technique (7 studies). Automated segmentation techniques (4 studies) and the Cavalieri estimator (3 studies), a point-counting method that uses a grid of points to estimate the volume of a region, were the next most commonly used techniques. The least commonly utilized segmentation technique was linear measurement-based formulas (1 study). Semi-automated segmentation produced accurate, reproducible results. However, it is apparent that there does not exist a single semi-automated software, open source or otherwise, that has been widely applied to the posterior fossa. Fully-automated segmentation via such open source software as FSL and Freesurfer produced highly accurate posterior fossa segmentations. Various forms of segmentation have been used to assess posterior fossa pathologies and each has its advantages and disadvantages. According to our results, semi-automated segmentation is the predominant method. However, atlas-based automated segmentation is an extremely promising method that produces accurate results. Future evolution of segmentation technologies will undoubtedly yield superior results, which may be applied to posterior fossa related pathologies. Medical professionals will save time and effort analyzing large sets of data due to these advances.

Keywords: chiari, posterior fossa, segmentation, volumetric

Procedia PDF Downloads 94

Authors: Breanna Macumber, Victor A. Huayamave, Emir A. Vela, Wangdo Kim, Tamara T. Chamber, Esteban Centeno

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Spina bifida is a type of neural tube defect that affects the nervous system and can lead to problems such as total leg paralysis. Treatment requires physical therapy and rehabilitation. Robotic exoskeletons have been used for rehabilitation to train muscle movement and assist in injury recovery; however, current models focus on the adult populations and not on the infant population. The proposed framework aims to couple a musculoskeletal infant model with a robotic exoskeleton using vacuum-powered artificial muscles to provide rehabilitation to infants affected by spina bifida. The study that drove the input values for the robotic exoskeleton used motion capture technology to collect data from the spontaneous kicking movement of a 2.4-month-old infant lying supine. OpenSim was used to develop the musculoskeletal model, and Inverse kinematics was used to estimate hip joint angles. A total of 4 kicks (A, B, C, D) were selected, and the selection was based on range, transient response, and stable response. Kicks had at least 5° of range of motion with a smooth transient response and a stable period. The robotic exoskeleton used a Vacuum-Powered Artificial Muscle (VPAM) the structure comprised of cells that were clipped in a collapsed state and unclipped when desired to simulate infant’s age. The artificial muscle works with vacuum pressure. When air is removed, the muscle contracts and when air is added, the muscle relaxes. Bench testing was performed using a 6-month-old infant mannequin. The previously developed exoskeleton worked really well with controlled ranges of motion and frequencies, which are typical of rehabilitation protocols for infants suffering with spina bifida. However, the random kicking motion in this study contained high frequency kicks and was not able to accurately replicate all the investigated kicks. Kick 'A' had a greater error when compared to the other kicks. This study has the potential to advance the infant rehabilitation field.

Keywords: musculoskeletal modeling, soft robotics, rehabilitation, pediatrics

Procedia PDF Downloads 95

Authors: Breanna Macumber, Victor A. Huayamave, Emir A. Vela, Wangdo Kim, Tamara T. Chamber, Esteban Centeno

Abstract:

Spina bifida is a type of neural tube defect that affects the nervous system and can lead to problems such as total leg paralysis. Treatment requires physical therapy and rehabilitation. Robotic exoskeletons have been used for rehabilitation to train muscle movement and assist in injury recovery; however, current models focus on the adult populations and not on the infant population. The proposed framework aims to couple a musculoskeletal infant model with a robotic exoskeleton using vacuum-powered artificial muscles to provide rehabilitation to infants affected by spina bifida. The study that drove the input values for the robotic exoskeleton used motion capture technology to collect data from the spontaneous kicking movement of a 2.4-month-old infant lying supine. OpenSim was used to develop the musculoskeletal model, and Inverse kinematics was used to estimate hip joint angles. A total of 4 kicks (A, B, C, D) were selected, and the selection was based on range, transient response, and stable response. Kicks had at least 5° of range of motion with a smooth transient response and a stable period. The robotic exoskeleton used a Vacuum-Powered Artificial Muscle (VPAM) the structure comprised of cells that were clipped in a collapsed state and unclipped when desired to simulate infant’s age. The artificial muscle works with vacuum pressure. When air is removed, the muscle contracts and when air is added, the muscle relaxes. Bench testing was performed using a 6-month-old infant mannequin. The previously developed exoskeleton worked really well with controlled ranges of motion and frequencies, which are typical of rehabilitation protocols for infants suffering with spina bifida. However, the random kicking motion in this study contained high frequency kicks and was not able to accurately replicate all the investigated kicks. Kick 'A' had a greater error when compared to the other kicks. This study has the potential to advance the infant rehabilitation field.

Keywords: musculoskeletal modeling, soft robotics, rehabilitation, pediatrics

Procedia PDF Downloads 61

Authors: Farid Risheq, M. Zaid Alrisheq, Shuaa Al-Sadoon, Karim Al-Faqih, Mays Abdulazeez

Abstract:

Breast cancer is the most frequently diagnosed cancer worldwide, and a common cause of death among women. Various conventional anatomical imaging tools are utilized for diagnosis, histological assessment and TNM (Tumor, Node, Metastases) staging of breast cancer. Biopsy of sentinel lymph node is becoming an alternative to the axillary lymph node dissection. Advances in 18-Fluoro-Deoxi-Glucose Positron Emission Tomography/Computed Tomography (18FDG-PET/CT) imaging have facilitated breast cancer diagnosis utilizing biological trapping of 18FDG inside lesion cells, expressed as Standardized Uptake Value (SUVmax). Objective: To present the utility of 18FDG uptake PET/CT scans in detecting active extra lymph nodes and distant occult metastases for breast cancer staging. Subjects and Methods: Four female patients were presented with initially classified TNM stages of breast cancer based on conventional anatomical diagnostic techniques. 18FDG-PET/CT scans were performed one hour post 18FDG intra-venous injection of (300-370) MBq, and (7-8) bed/130sec. Transverse, sagittal, and coronal views; fused PET/CT and MIP modality were reconstructed for each patient. Results: A total of twenty four lesions in breast, extended lesions to lung, liver, bone and active extra lymph nodes were detected among patients. The initial TNM stage was significantly changed post 18FDG-PET/CT scan for each patient, as follows: Patient-1: Initial TNM-stage: T1N1M0-(stage I). Finding: Two lesions in right breast (3.2cm2, SUVmax=10.2), (1.8cm2, SUVmax=6.7), associated with metastases to two right axillary lymph nodes. Final TNM-stage: T1N2M0-(stage II). Patient-2: Initial TNM-stage: T2N2M0-(stage III). Finding: Right breast lesion (6.1cm2, SUVmax=15.2), associated with metastases to right internal mammary lymph node, two right axillary lymph nodes, and sclerotic lesions in right scapula. Final TNM-stage: T2N3M1-(stage IV). Patient-3: Initial TNM-stage: T2N0M1-(stage III). Finding: Left breast lesion (11.1cm2, SUVmax=18.8), associated with metastases to two lymph nodes in left hilum, and three lesions in both lungs. Final TNM-stage: T2N2M1-(stage IV). Patient-4: Initial TNM-stage: T4N1M1-(stage III). Finding: Four lesions in upper outer quadrant area of right breast (largest: 12.7cm2, SUVmax=18.6), in addition to one lesion in left breast (4.8cm2, SUVmax=7.1), associated with metastases to multiple lesions in liver (largest: 11.4cm2, SUV=8.0), and two bony-lytic lesions in left scapula and cervicle-1. No evidence of regional or distant lymph node involvement. Final TNM-stage: T4N0M2-(stage IV). Conclusions: Our results demonstrated that 18FDG-PET/CT scans had significantly changed the TNM stages of breast cancer patients. While the T factor was unchanged, N and M factors showed significant variations. A single session of PET/CT scan was effective in detecting active extra lymph nodes and distant occult metastases, which were not identified by conventional diagnostic techniques, and might advantageously replace bone scan, and contrast enhanced CT of chest, abdomen and pelvis. Applying 18FDG-PET/CT scan early in the investigation, might shorten diagnosis time, helps deciding adequate treatment protocol, and could improve patients’ quality of life and survival. Trapping of 18FDG in malignant lesion cells, after a PET/CT scan, increases the retention index (RI%) for a considerable time, which might help localize sentinel lymph node for biopsy using a hand held gamma probe detector. Future work is required to demonstrate its utility.

Keywords: axillary lymph nodes, breast cancer staging, fluorodeoxyglucose positron emission tomography/computed tomography, lymph nodes

Procedia PDF Downloads 297

Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

Abstract:

Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

Procedia PDF Downloads 63

Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

Abstract:

The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 338

Authors: Aastha Arora, Vikas Bhuria, Saurabh Singh, Uma Pathak, Shweta Mathur, Puja P. Hazari, Rajat Sandhir, Ravi Soni, Anant N. Bhatt, Bilikere S. Dwarakanath

Abstract:

Radiotherapy is an effective curative and palliative option for patients with thoracic malignancies. However, lung injury, comprising of pneumonitis and fibrosis, remains a significant clin¬ical complication of thoracic radiation, thus making it a dose-limiting factor. Also, injury to the lung is often reported as part of multi-organ failure in victims of accidental radiation exposures. Radiation induced inflammatory response in the lung, characterized by leukocyte infiltration and vascular changes, is an important contributing factor for the injury. Therefore, countermeasure agents to attenuate radiation induced inflammatory response are considered as an important approach to prevent chronic lung damage. Although Amifostine, the widely used, FDA approved radio-protector, has been found to reduce the radiation induced pneumonitis during radiation therapy of non-small cell lung carcinoma, its application during mass and field exposure is limited due to associated toxicity and ineffectiveness with the oral administration. The amifostine analogue (DRDE-30) overcomes this limitation as it is orally effective in reducing the mortality of whole body irradiated mice. The current study was undertaken to investigate the potential of DRDE-30 to ameliorate radiation induced lung damage. DRDE-30 was administered intra-peritoneally, 30 minutes prior to 13.5 Gy thoracic (60Co-gamma) radiation in C57BL/6 mice. Broncheo- alveolar lavage fluid (BALF) and lung tissues were harvested at 12 and 24 weeks post irradiation for studying inflammatory and fibrotic markers. Lactate dehydrogenase (LDH) leakage, leukocyte count and protein content in BALF were used as parameters to evaluate lung vascular permeability. Inflammatory cell signaling (p38 phosphorylation) and anti-oxidant status (MnSOD and Catalase level) was assessed by Western blot, while X-ray CT scan, H & E staining and trichrome staining were done to study the lung architecture and collagen deposition. Irradiation of the lung increased the total protein content, LDH leakage and total leukocyte count in the BALF, reflecting endothelial barrier dysfunction. These disruptive effects were significantly abolished by DRDE-30, which appear to be linked to the DRDE-30 mediated abrogation of activation of the redox-sensitive pro- inflammatory signaling cascade, the MAPK pathway. Concurrent administration of DRDE-30 with radiation inhibited radiation-induced oxidative stress by strengthening the anti-oxidant defense system and abrogated p38 mitogen-activated protein kinase activation, which was associated with reduced vascular leak and macrophage recruitment to the lungs. Histopathological examination (by H & E staining) of the lung showed radiation-induced inflammation of the lungs, characterized by cellular infiltration, interstitial oedema, alveolar wall thickening, perivascular fibrosis and obstruction of alveolar spaces, which were all reduced by pre-administration of DRDE-30. Structural analysis with X-ray CT indicated lung architecture (linked to the degree of opacity) comparable to un-irradiated mice that correlated well with the lung morphology and reduced collagen deposition. Reduction in the radiation-induced inflammation and fibrosis brought about by DRDE-30 resulted in a profound increase in animal survival (72 % in the combination vs 24% with radiation) observed at the end of 24 weeks following irradiation. These findings establish the potential of the Amifostine analogue, DRDE-30, in reducing radiation induced pulmonary injury by attenuating the inflammatory and fibrotic responses.

Keywords: amifostine, fibrosis, inflammation, lung injury radiation

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

Abstract:

With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: M. Cabrera, W. Tizani, J. Ninic, F. Wang

Abstract:

Open section beam to concrete-filled tubular column structures has been increasingly utilized in construction over the past few decades due to their enhanced structural performance, as well as economic and architectural advantages. However, the use of this configuration in construction is limited due to the difficulties in connecting the structural members as there is no access to the inner part of the tube to install standard bolts. Blind-bolted systems are a relatively new approach to overcome this limitation as they only require access to one side of the tubular section to tighten the bolt. The performance of these connections in concrete-filled steel tubular sections remains uncharacterized due to the complex interactions between concrete, bolt, and steel section. Over the last years, research in structural performance has moved to a more sophisticated and efficient approach consisting of machine learning algorithms to generate metamodels. This method reduces the need for developing complex, and computationally expensive finite element models, optimizing the search for desirable design variables. Metamodels generated by a data fusion approach use numerical and experimental results by combining multiple models to capture the dependency between the simulation design variables and connection performance, learning the relations between different design parameters and predicting a given output. Fully characterizing this connection will transform high-rise and multistorey construction by means of the introduction of design guidance for moment-resisting blind-bolted connections, which is currently unavailable. This paper presents a review of the steps taken to develop metamodels generated by means of artificial neural network algorithms which predict the connection stress and stiffness based on the design parameters when using Extended Hollo-Bolt blind bolts. It also provides consideration of the failure modes and mechanisms that contribute to the deformability as well as the feasibility of achieving blind-bolted rigid connections when using the blind fastener.

Keywords: blind-bolted connections, concrete-filled tubular structures, finite element analysis, metamodeling

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

Abstract:

Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

Procedia PDF Downloads 28