Search results for: genetic algorithms

Authors: Bedilu Tafesse

Abstract:

Conservation of crop genetic resources presents a challenge of identifying specific determinants driving maintenance of diversity at farm and agroecosystems. The objectives of this study were to identify socioeconomic, market and agroecological determinants of farmers’ maintenance of wheat diversity at the household level and derive implications for policies in designing on-farm conservation programs. We assess wheat diversity at farm level using household survey data. A household decision making model is conceptualized using microeconomic theory to assess and identify factors influencing on-farm rice diversity. The model is then tested econometrically by using various factors affecting farmers’ variety choice and diversity decisions. The findings show that household-specific socioeconomic, agroecological and market factors are important in determining on-farm wheat diversity. The significant variables in explaining richness and evenness of wheat diversity include distance to the nearest market, subsistence ratio, modern variety sold, land types and adult labour working in agriculture. The statistical signs of the factors determining wheat diversity are consistent in explaining the richness, dominance and evenness among rice varieties. Finally, the study implies that the cost-effective means of promoting and sustaining on-farm conservation programmes is to target them in market isolated geographic locations of high crop diversity where farm households have more heterogeneity of agroecological conditions and more active family adult labour working on-farm.

Keywords: diversity indices, dominance, evenness, on-farm conservation, wheat diversity, richness

Procedia PDF Downloads 303

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

Abstract:

Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

Procedia PDF Downloads 136

Authors: Hans Frenk, Sonya Javadi, Semih Onur Sezer

Abstract:

We consider the so-called end of life inventory problem for the supplier of a product in its final phase of the service life cycle. This phase starts when the production of the items stops and continues until the warranty of the last sold item expires. At the beginning of this phase, the supplier places a final order for spare parts to serve customers coming with defective items. At any time during the final phase, the supplier may also decide to switch to an alternative and more cost-effective policy. This alternative policy may be in the form of replacing a defective item with a substitutable product or offering discounts / rebates on new generation products. In this setup, the objective is to find a final order quantity and also a switching time which will minimize the total expected discounted cost. We study this problem under a general cost structure in a continuous-time framework where arrivals of defective items are given by a non-homogeneous Poisson process. We consider four formulations which differ by the nature of the switching time. These formulations are studied in detail and properties of the objective function are derived in each case. Using these properties, we provide exact algorithms for efficient numerical implementations. Numerical examples are provided illustrating the application of these algorithms. In these examples, we also compare the costs associated with these different formulations.

Keywords: End-of-life inventory control, martingales, optimization, service parts

Procedia PDF Downloads 328

Authors: Haohao Wang

Abstract:

The purpose of this paper is to find elimination ideals via Grobner basis. We first introduce the concept of Grobner bases, and then, we provide computational algorithms to applications for curves and surfaces.

Keywords: curves, surfaces, Grobner basis, elimination

Procedia PDF Downloads 295

Authors: Aneal Khan, Elleine Allapitan, Desmond Koo, Katherine-Ann Piedalue, Shaneel Pathak, Utkarsh Subnis

Abstract:

Background: Disease management of metabolic, genetic disorders is long-term and can be cumbersome to patients and caregivers. Patient-Reported Outcome Measures (PROMs) have been a useful tool in capturing patient perspectives to help enhance treatment compliance and engagement with health care providers, reduce utilization of emergency services, and increase satisfaction with their treatment choices. Currently, however, PROMs are collected during infrequent and decontextualized clinic visits, which makes translation of patient experiences challenging over time. The GRIT study aims to evaluate a digital health journal application called Zamplo that provides a personalized health diary to record self-reported health outcomes accurately and efficiently in patients with metabolic, genetic disorders. Methods: This is a randomized controlled trial (RCT) (1:1) that assesses the efficacy of Zamplo to increase patient activation (primary outcome), improve healthcare satisfaction and confidence to manage medications (secondary outcomes), and reduce costs to the healthcare system (exploratory). Using standardized online surveys, assessments will be collected at baseline, 1 month, 3 months, 6 months, and 12 months. Outcomes will be compared between patients who were given access to the application versus those with no access. Results: Seventy-seven patients were recruited as of November 30, 2021. Recruitment for the study commenced in November 2020 with a target of n=150 patients. The accrual rate was 50% from those eligible and invited for the study, with the majority of patients having Fabry disease (n=48) and the remaining having Pompe disease and mitochondrial disease. Real-time clinical responses, such as pain, are being measured and correlated to disease-modifying therapies, supportive treatments like pain medications, and lifestyle interventions. Engagement with the application, along with compliance metrics of surveys and journal entries, are being analyzed. An interim analysis of the engagement data along with preliminary findings from this pilot RCT, and qualitative patient feedback will be presented. Conclusions: The digital self-care journal provides a unique approach to disease management, allowing patients direct access to their progress and actively participating in their care. Findings from the study can help serve the virtual care needs of patients with metabolic, genetic disorders in North America and the world over.

Keywords: eHealth, mobile health, rare disease, patient outcomes, quality of life (QoL), pain, Fabry disease, Pompe disease

Procedia PDF Downloads 149

Authors: Thobela Conco, Sheena Kumari, Chika Nnadozie, Mahmoud Nasr, Thor A. Stenström, Mushal Ali, Arshad Ismail, Faizal Bux

Abstract:

The discharge of antibiotics and its residues into the wastewater treatment plants (WWTP’s) create a conducive environment for the development of antibiotic resistant pathogens. This presents a risk of potential dissemination of antibiotic resistant pathogens and antibiotic resistance genes into the environment. It is, therefore, necessary to study the level of antibiotic resistance genes (ARG’s) among bacterial pathogens that proliferate in biological wastewater treatment systems. In the current study, metagenomic and meta-transcriptomic sequences of samples collected from the influents, secondary effluents and post chlorinated effluents of three wastewater treatment plants treating domestic and hospital effluents in Durban, South Africa, were analyzed for profiling of ARG’s among bacterial pathogens. Results show that a variety of ARG’s, mostly, aminoglycoside, β-lactamases, tetracycline and sulfonamide resistance genes were harbored by diverse bacterial genera found at different stages of treatment. A significant variation in diversity of pathogen and ARGs between the treatment plant was observed; however, treated final effluent samples from all three plants showed a significant reduction in bacterial pathogens and detected ARG’s. Both pre- and post-chlorinated samples showed the presence of mobile genetic elements (MGE’s), indicating the inefficiency of chlorination to remove of ARG’s integrated with MGE’s. In conclusion, the study showed the wastewater treatment plant efficiently caused the reduction and removal of certain ARG’s, even though the initial focus was the removal of biological nutrients.

Keywords: antibiotic resistance, mobile genetic elements, wastewater, wastewater treatment plants

Procedia PDF Downloads 213

Authors: Rafal Michalski, Jakub Zygadlo

Abstract:

We present the concept and scientific methods and algorithms of our computation system called ATOMIC MATTERS. This is the first presentation of the new computer package, that allows its user to describe physical properties of atomic localized electron systems subject to electromagnetic interactions. Our solution applies to situations where an unclosed electron 2p/3p/3d/4d/5d/4f/5f subshell interacts with an electrostatic potential of definable symmetry and external magnetic field. Our methods are based on Crystal Electric Field (CEF) approach, which takes into consideration the electrostatic ligands field as well as the magnetic Zeeman effect. The application allowed us to predict macroscopic properties of materials such as: Magnetic, spectral and calorimetric as a result of physical properties of their fine electronic structure. We emphasize the importance of symmetry of charge surroundings of atom/ion, spin-orbit interactions (spin-orbit coupling) and the use of complex number matrices in the definition of the Hamiltonian. Calculation methods, algorithms and convention recalculation tools collected in ATOMIC MATTERS were chosen to permit the prediction of magnetic and spectral properties of materials in isostructural series.

Keywords: atomic matters, crystal electric field (CEF) spin-orbit coupling, localized states, electron subshell, fine electronic structure

Procedia PDF Downloads 317

Authors: Shidrokh Goudarzi, Wan Haslina Hassan

Abstract:

Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers or vertical handoffs are necessary for seamless mobility. In this paper, we conduct a review of existing vertical handover decision-making mechanisms that aim to provide ubiquitous connectivity to mobile users. To offer a systematic comparison, we categorize these vertical handover measurement and decision structures based on their respective methodology and parameters. Subsequently, we analyze several vertical handover approaches in the literature and compare them according to their advantages and weaknesses. The paper compares the algorithms based on the network selection methods, complexity of the technologies used and efficiency in order to introduce our vertical handover decision framework. We find that vertical handovers on heterogeneous wireless networks suffer from the lack of a standard and efficient method to satisfy both user and network quality of service requirements at different levels including architectural, decision-making and protocols. Also, the consolidation of network terminal, cross-layer information, multi packet casting and intelligent network selection algorithm appears to be an optimum solution for achieving seamless service continuity in order to facilitate seamless connectivity.

Keywords: heterogeneous wireless networks, vertical handovers, vertical handover metric, decision-making algorithms

Procedia PDF Downloads 391

Authors: Xinhuang Wu, Yousef Sardahi

Abstract:

A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective.

Keywords: optimal control, many-objective optimization, sliding mode control, linear control, cascade controllers, UAV, drones

Procedia PDF Downloads 68

Authors: Dace Grauda, Gunta Cekstere, Inta Belogrudova, Andis Karlsons, Isaak Rashal

Abstract:

Trifolium repens L. (white or Dutch clover) is a perennial herb, belongs to legume family (Leguminosae Juss.), spread extensively by stolons and seeds. The species is cultivated worldwide and was naturalized in many countries in meadows, yards, gardens, along roads and streets etc., especially in temperate regions. It is widespread also in grasslands throughout Riga, the capital of Latvia. The goal of this study was to investigate genetic structure of white clover population in Riga and to evaluate influence of different salt concentration on plants. For this purpose universal retrotranspozone based IRAP (Inter-Retrotransposon Amplified Polymorphism) method was used. The plant material was collected in different regions of Riga and in several urban areas of Latvia. Plant DNA was isolated from in silicogel dried leaves of using 1% CTAB (cetyltrimet-ammonium bromide) buffer DNA extraction procedure. Genetic structure of city population and wild populations were compared. Soil salinization is an important issue associated with low water resources and highly urbanized areas in aride and semi-aride climate conditions, as well as de-icing salt application to prevent ice formation on roads in winter. The T. repens variety ‘Daile’ (form giganteum), one of the often used component of urban greeneries, was studied in this investigation. Plants were grown from seeds and cultivated in the light conditions (18-25 C, 16h/8h of day/night, light intensity 3000 lx) in plastic pots (200 ml), filled with commercial neutralized (pH 5.9 ± 0.3) peat substrate with mineral nutrients. To analyse the impact of increased soil salinity treatments with gradually rising NaCl (0; 20; 40; 60; 80; 100 mM) levels were arranged. Plants were watered when necessary with deionised water to provide optimum substrate moisture 60-70%. The experiment was terminated six weeks after establishment. For analysis of mineral nutrients, dry plant material (above ground part and roots) was used. Decrease of Na content can be significant under elevated salinity till 20 mM NaCl. High NaCl concentrations in the substrate increase Na, Cl, Cu, Fe, and Mn accumulation, but reduce S, Mg, K content in the plant above ground parts. Abiotic stresses generally changes the levels of DNA metilation. Several candidate gene for salt tolerance will be analysed for DNA metilation level using Pyromark-Q24 advanced.

Keywords: DNA metilation, IRAP, soil salinization, white clover

Procedia PDF Downloads 361

Authors: Simona Maria Ignat

Abstract:

Bullying online has been an increasing studied topic during the last years. Different approaches, psychological, linguistic, or computational, have been applied. To our best knowledge, a definition and a set of characteristics of phenomenon agreed internationally as a common framework are still waiting for answers. Thus, the objectives of this paper are the identification of bullying utterances on Twitter and their algorithms. This research paper is focused on the identification of words or groups of words, categorized as “utterances”, with bullying effect, from Twitter platform, extracted on a set of legislative criteria. This set is the result of analysis followed by synthesis of law documents on bullying(online) from United States of America, European Union, and Ireland. The outcome is a linguistic corpus with approximatively 10,000 entries. The methods applied to the first objective have been the following. The discourse analysis has been applied in identification of keywords with bullying effect in texts from Google search engine, Images link. Transcription and anonymization have been applied on texts grouped in CL1 (Corpus linguistics 1). The keywords search method and the legislative criteria have been used for identifying bullying utterances from Twitter. The texts with at least 30 representations on Twitter have been grouped. They form the second corpus linguistics, Bullying utterances from Twitter (CL2). The entries have been identified by using the legislative criteria on the the BoW method principle. The BoW is a method of extracting words or group of words with same meaning in any context. The methods applied for reaching the second objective is the conversion of parts of speech to alphabetical and numerical symbols and writing the bullying utterances as algorithms. The converted form of parts of speech has been chosen on the criterion of relevance within bullying message. The inductive reasoning approach has been applied in sampling and identifying the algorithms. The results are groups with interchangeable elements. The outcomes convey two aspects of bullying: the form and the content or meaning. The form conveys the intentional intimidation against somebody, expressed at the level of texts by grammatical and lexical marks. This outcome has applicability in the forensic linguistics for establishing the intentionality of an action. Another outcome of form is a complex of graphemic variations essential in detecting harmful texts online. This research enriches the lexicon already known on the topic. The second aspect, the content, revealed the topics like threat, harassment, assault, or suicide. They are subcategories of a broader harmful content which is a constant concern for task forces and legislators at national and international levels. These topic – outcomes of the dataset are a valuable source of detection. The analysis of content revealed algorithms and lexicons which could be applied to other harmful contents. A third outcome of content are the conveyances of Stylistics, which is a rich source of discourse analysis of social media platforms. In conclusion, this corpus linguistics is structured on legislative criteria and could be used in various fields.

Keywords: corpus linguistics, cyberbullying, legislation, natural language processing, twitter

Procedia PDF Downloads 83

Authors: Bo-Bin Xiaoa, Chia-Yi Liua

Abstract:

In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

Procedia PDF Downloads 105

Authors: Nemanja Kajtez, Yue Zhan, Basel Halak

Abstract:

The significant rise in the cost of manufacturing of nanoscale integrated circuits (IC) has led the majority of IC design companies to outsource the fabrication of their products to other companies, often located in different countries. This multinational nature of the hardware supply chain has led to a host of security threats, including IP piracy, IC overproduction, and Trojan insertion. To combat that, researchers have proposed logic locking techniques to protect the intellectual properties of the design and increase the difficulty of malicious modification of its functionality. However, the adoption of logic locking approaches is rather slow due to the lack of the integration with IC production process and the lack of efficacy of existing algorithms. This work automates the logic locking process by developing software using Python that performs the locking on a gate-level netlist and can be integrated with the existing digital synthesis tools. Analysis of the latest logic locking algorithms has demonstrated that the SFLL-HD algorithm is one of the most secure and versatile in trading-off levels of protection against different types of attacks and was thus selected for implementation. The presented tool can also be expanded to incorporate the latest locking mechanisms to keep up with the fast-paced development in this field. The paper also presents a case study to demonstrate the functionality of the tool and how it could be used to explore the design space and compare different locking solutions. The source code of this tool is available freely from (https://www.researchgate.net/publication/353195333_Source_Code_for_The_Lockit_Tool).

Keywords: design automation, hardware security, IP piracy, logic locking

Procedia PDF Downloads 173

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 186

Authors: Leila Noori, Rosario Barone, Francesca Rappa, Antonella Marino Gammazza, Alessandra Maria Vitale, Giuseppe Donato Mangano, Giusy Sentiero, Filippo Macaluso, Kathryn H. Myburgh, Francesco Cappello, Federica Scalia

Abstract:

The neuromuscular system controls, directs, and allows movement of the body through the action of neural circuits, which include motor neurons, sensory neurons, and skeletal muscle fibers. Protein homeostasis of the involved cytotypes appears crucial to maintain the correct and prolonged functions of the neuromuscular system, and both neuronal cells and skeletal muscle fibers express significant quantities of protein chaperones, the molecular machinery responsible to maintain the protein turnover. Genetic mutations or defective post-translational modifications of molecular chaperones (i.e., genetic or acquired chaperonopathies) may lead to neuromuscular disorders called as neurochaperonopathies. The limited knowledge of the effects of the defective chaperones on skeletal muscle fibers and neurons impedes the progression of therapeutic approaches. A distinct genetic variation of CCT5 gene encoding for the subunit 5 of the chaperonin CCT (Chaperonin Containing TCP1; also known as TRiC, TCP1 Ring Complex) was recently described associated with severe distal motor neuropathy by our team. In this study, we investigated the histopathological abnormalities of the skeletal muscle biopsy of the pediatric patient affected by the mutation Leu224Val in the CCT5 subunit. We provide molecular and structural features of the diseased skeletal muscle tissue that we believe may be useful to identify undiagnosed cases of this rare genetic disorder. We investigated the histological abnormalities of the affected tissue via hematoxylin and eosin staining. Then we used immunofluorescence and qPCR techniques to explore the expression and distribution of CCT5 in diseased and healthy skeletal muscle tissue. Immunofluorescence and immunohistochemistry assays were performed to study the sarcomeric and structural proteins of skeletal muscle, including actin, myosin, tubulin, troponin-T, telethonin, and titin. We performed Western blot to examine the protein expression of CCT5 and some heat shock proteins, Hsp90, Hsp60, Hsp27, and α-B crystallin, along with the main client proteins of the CCT5, actin, and tubulin. Our findings revealed muscular atrophy, abnormal morphology, and different sizes of muscle fibers in affected tissue. The swollen nuclei and wide interfiber spaces were seen. Expression of CCT5 had been decreased and showed a different distribution pattern in the affected tissue. Altered expression, distribution, and bandage pattern were detected by confocal microscopy for the interested muscular proteins in tissue from the patient compared to the healthy control. Protein levels of the studied Hsps normally located at the Z-disk were reduced. Western blot results showed increased levels of the actin and tubulin proteins in the diseased skeletal muscle biopsy compared to healthy tissue. Chaperones must be expressed at high levels in skeletal muscle to counteract various stressors such as mechanical, oxidative, and thermal crises; therefore, it seems relevant that defects of molecular chaperones may result in damaged skeletal muscle fibers. So far, several chaperones or cochaperones involved in neuromuscular disorders have been defined. Our study shows that alteration of the CCT5 subunit is associated with the damaged structure of skeletal muscle fibers and alterations of chaperone system components and paves the way to explore possible alternative substrates of chaperonin CCT. However, further studies are underway to investigate the CCT mechanisms of action to design applicable therapeutic strategies.

Keywords: molecular chaperones, neurochaperonopathy, neuromuscular system, protein homeostasis

Procedia PDF Downloads 67

Authors: Marcela de Oliveira, Guilherme Giacomini, Allan Felipe Fattori Alves, Ana Luiza Menegatti Pavan, Maria Eugenia Dela Rosa, Fernando Antonio Bacchim Neto, Diana Rodrigues de Pina

Abstract:

It is estimated that each year one death every 10 seconds (about 2 million deaths) in the world is attributed to tuberculosis (TB). Even after effective treatment, TB leaves sequelae such as, for example, pulmonary fibrosis, compromising the quality of life of patients. Evaluations of the aforementioned sequel are usually performed subjectively by radiology specialists. Subjective evaluation may indicate variations inter and intra observers. The examination of x-rays is the diagnostic imaging method most accomplished in the monitoring of patients diagnosed with TB and of least cost to the institution. The application of computational algorithms is of utmost importance to make a more objective quantification of pulmonary impairment in individuals with tuberculosis. The purpose of this research is the use of computer algorithms to quantify the pulmonary impairment pre and post-treatment of patients with pulmonary TB. The x-ray images of 10 patients with TB diagnosis confirmed by examination of sputum smears were studied. Initially the segmentation of the total lung area was performed (posteroanterior and lateral views) then targeted to the compromised region by pulmonary sequel. Through morphological operators and the application of signal noise tool, it was possible to determine the compromised lung volume. The largest difference found pre- and post-treatment was 85.85% and the smallest was 54.08%.

Keywords: algorithm, radiology, tuberculosis, x-rays exam

Procedia PDF Downloads 415

Authors: Abera Kotu, Girma Tesema, Mitiku Tadesse

Abstract:

This study investigated ACE supported instruction and pre-service mathematics teachers’ mental construction in solving equations and inequalities. A mixed approach with concurrent parallel design was employed. It was conducted on two intact groups of regular first-year pre-service mathematics teachers at Fiche College of Teachers’ Education in which one group was assigned as an intervention group and the other group as a comparison group using the lottery method. There were 33 participants in the intervention and 32 participants in the comparison. Six pre-service mathematics teachers were selected for interview using purposive sampling based on pre-test results. An instruction supported with ACE cycle was given to the intervention group for two weeks duration of time. Written tasks, interviews, and observations were used to collect data. Data collected from written tasks were analyzed quantitatively using independent samples t-test and effect size. Data collected from interviews and observations were analyzed narratively. The findings of the study uncovered that ACE-supported instruction has a moderate effect on Pre-service Mathematics Teachers’ levels of conceptualizations of action, process, object, ad schema. Moreover, the ACE supported group out scored and performed better than the usual traditional method supported groups across the levels of conceptualization. The majority of pre-service mathematics teachers’ levels of conceptualizations were at action and process levels and their levels of conceptualization were linked with genetic decomposition more at action and object levels than object and schema. The use of ACE supported instruction is recommended to improve pre-service mathematics teachers’ mental construction.

Keywords: ACE teaching cycle, APOS theory, mental construction, genetic composition

Procedia PDF Downloads 6

Authors: Bastien Batardière, Joon Kwon

Abstract:

For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

Procedia PDF Downloads 66

Authors: Zainab S. Ahmed, Mohammed S. Ali, Nadia A. Elshiekh, Sami Adam Ibrahim, Ghada M. El-Tayeb, Ahmed H. Elsadig, Rihab A. Omer, Sofia B. Mohamed

Abstract:

Maple syrup urine (MSUD) is an autosomal recessive disease that causes a deficiency in the enzyme branched-chain alpha-keto acid (BCKA) dehydrogenase. The development of disease has been associated with SNPs in the DBT gene. Despite that, the computational analysis of SNPs in coding and noncoding and their functional impacts on protein level still remains unknown. Hence, in this study, we carried out a comprehensive in silico analysis of missense that was predicted to have a harmful influence on DBT structure and function. In this study, eight different in silico prediction algorithms; SIFT, PROVEAN, MutPred, SNP&GO, PhD-SNP, PANTHER, I-Mutant 2.0 and MUpo were used for screening nsSNPs in DBT including. Additionally, to understand the effect of mutations in the strength of the interactions that bind protein together the ELASPIC servers were used. Finally, the 3D structure of DBT was formed using Mutation3D and Chimera servers respectively. Our result showed that a total of 15 nsSNPs confirmed by 4 software (R301C, R376H, W84R, S268F, W84C, F276C, H452R, R178H, I355T, V191G, M444T, T174A, I200T, R113H, and R178C) were found damaging and can lead to a shift in DBT gene structure. Moreover, we found 7 nsSNPs located on the 2-oxoacid_dh catalytic domain, 5 nsSNPs on the E_3 binding domain and 3 nsSNPs on the Biotin Domain. So these nsSNPs may alter the putative structure of DBT’s domain. Furthermore, we detected all these nsSNPs are on the core residues of the protein and have the ability to change the stability of the protein. Additionally, we found W84R, S268F, and M444T have high significance, and they affected Leucine, Isoleucine, and Valine, which reduces or disrupt the function of BCKD complex, E2-subunit which the DBT gene encodes. In conclusion, based on our extensive in-silico analysis, we report 15 nsSNPs that have possible association with protein deteriorating and disease-causing abilities. These candidate SNPs can aid in future studies on Maple Syrup Urine Disease type II base in the genetic level.

Keywords: DBT gene, ELASPIC, in silico analysis, UCSF chimer

Procedia PDF Downloads 200

Authors: Nour Elhouda Abed, Bedj Mimi, Wahid Slimani, Mourad Atif, Abdelhakim Ouzzane, Hocine Irekti, Abdelkader Bekki

Abstract:

The most frequent system of cereal production in Algeria is fallow-wheat. This is an extensive system that meets only the half needs some cereals and fodder demand. Resorption of fallow has become a strategic necessity to ensure food security in response to the instability of supply and the persistence of higher food prices on the world market. Despite several attempts to replace the fallow by crop cultures, choosing the best crop remains. Today, the agronomic and economic interests of legumes are demonstrated. However, their crop culture remains marginalized because of the weakness and instability of their performance. In the context of improving legumes and cereals crops as well as fallow resorption, we undertook to test, in the field, the effect of rhizobial inoculation of Phaseolus vulgaris in association with Zea Mays. We firstly studied the genetic diversity of rhizobial strains that nodulate P.vulgaris isolated from fifteen (15) different regions. ARDRA had shown 18 different genetic profiles. Symbiotic characterization highlighted a strain that highly significantly improved the fresh and dry weight of the host plant, in comparison to the negative control (un-inoculated) and the positive control (inoculated with the reference strain CIAT 899). In the field, the selected strain increased significantly the growth and yield of P.vulgaris and Zea Mays comparing to the non-inoculated control. However, the mix inoculation (selected strain+ Ciat 899) had not given the best parameters showing, thus, no synergy between the strains. These results indicate the replacing fallow by a crop legume in intercropping with cereals crops.

Keywords: fallow, intercropping, inoculation, legumes-cereals

Procedia PDF Downloads 363

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

Abstract:

Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 75

Authors: Taysir Soliman

Abstract:

One of the current popular clustering techniques is Spectral Clustering (SC) because of its advantages over conventional approaches such as hierarchical clustering, k-means, etc. and other techniques as well. However, one of the disadvantages of SC is the time consuming process because it requires computing the eigenvectors. In the past to overcome this disadvantage, a number of attempts have been proposed such as the Power Iteration Clustering (PIC) technique, which is one of versions from SC; some of PIC advantages are: 1) its scalability and efficiency, 2) finding one pseudo-eigenvectors instead of computing eigenvectors, and 3) linear combination of the eigenvectors in linear time. However, its worst disadvantage is an inter-class collision problem because it used only one pseudo-eigenvectors which is not enough. Previous researchers developed Deflation-based Power Iteration Clustering (DPIC) to overcome problems of PIC technique on inter-class collision with the same efficiency of PIC. In this paper, we developed Parallel DPIC (PDPIC) to improve the time and memory complexity which is run on apache spark framework using sparse matrix. To test the performance of PDPIC, we compared it to SC, ESCG, ESCALG algorithms on four small graph benchmark datasets and nine large graph benchmark datasets, where PDPIC proved higher accuracy and better time consuming than other compared algorithms.

Keywords: spectral clustering, power iteration clustering, deflation-based power iteration clustering, Apache spark, large graph

Procedia PDF Downloads 185

Authors: Alireza Gholami, Amir H. D. Markazi

Abstract:

In this paper, an observer-based direct adaptive fuzzy sliding mode (OAFSM) algorithm is proposed. In the proposed algorithm, the zero-input dynamics of the plant could be unknown. The input connection matrix is used to combine the sliding surfaces of individual subsystems, and an adaptive fuzzy algorithm is used to estimate an equivalent sliding mode control input directly. The fuzzy membership functions, which were determined by time consuming try and error processes in previous works, are adjusted by adaptive algorithms. The other advantage of the proposed controller is that the input gain matrix is not limited to be diagonal, i.e. the plant could be over/under actuated provided that controllability and observability are preserved. An observer is constructed to directly estimate the state tracking error, and the nonlinear part of the observer is constructed by an adaptive fuzzy algorithm. The main advantage of the proposed observer is that, the measured outputs is not limited to the first entry of a canonical-form state vector. The closed-loop stability of the proposed method is proved using a Lyapunov-based approach. The proposed method is applied numerically on a multi-link robot manipulator, which verifies the performance of the closed-loop control. Moreover, the performance of the proposed algorithm is compared with some conventional control algorithms.

Keywords: adaptive algorithm, fuzzy systems, membership functions, observer

Procedia PDF Downloads 200

Authors: Rachael Bentley, Callahan Gergen, Brodie Thiede

Abstract:

Roadways have a significant impact on the environment in so far as they function as barriers to wildlife movement, both through road mortality and through resultant road avoidance. Roads have an im-mense presence worldwide, and it is predicted to increase substantially in the next thirty years. As roadways become even more common, it is important to consider their environmental impact, and to mitigate the negative effects which they have on wildlife and wildlife mobility. In a thorough analysis of several related studies, a common conclusion was that roads cause habitat fragmentation, which can lead split populations to evolve differently, for better or for worse. Though some populations adapted positively to roadways, becoming more resistant to road mortality, and more tolerant to noise and chemical contamination, many others experienced maladaptation, either due to chemical contamination in and around their environment, or because of genetic mutations from inbreeding when their population was fragmented too substantially to support a large enough group for healthy genetic exchange. Large mammals were especially susceptible to maladaptation from inbreed-ing, as they require larger areas to roam and therefore require even more space to sustain a healthy population. Regardless of whether a species evolved positively or negatively as a result of their proximity to a road, animals tended to avoid roads, making the genetic diversity from habitat fragmentation an exceedingly prevalent issue in the larger discussion of road ecology. Additionally, the consideration of solu-tions, such as overpasses and underpasses, is crucial to ensuring the long term survival of many wildlife populations. In studies addressing the effectiveness of overpasses and underpasses, it seemed as though animals adjusted well to these sorts of solutions, but strategic place-ment, as well as proper sizing, proper height, shelter from road noise, and other considerations were important in construction. When an underpass or overpass was well-built and well-shielded from human activity, animals’ usage of the structure increased significantly throughout its first five years, thus reconnecting previously divided populations. Still, these structures are costly and they are often unable to fully address certain issues such as light, noise, and contaminants from vehicles. Therefore, the need for further discussion of new, crea-tive solutions remains paramount. Roads are one of the most consistent and prominent features of today’s landscape, but their environmental impacts are largely overlooked. While roads are useful for connecting people, they divide landscapes and animal habitats. Therefore, further research and investment in possible solutions is necessary to mitigate the negative effects which roads have on wildlife mobility and to pre-vent issues from resultant habitat fragmentation.

Keywords: fences, habitat fragmentation, roadways, wildlife mobility

Procedia PDF Downloads 170

Authors: Bajraktarevic Adnan, Djukic Branka, Sporisevic Lutvo, Krdzalic Zecevic Belma, Uzicanin Sajra, Hadzimuratovic Admir, Hadzimuratovic Hadzipasic Emina, Abduzaimovic Alisa, Kustric Amer, Suljevic Ismet, Serafi Ismail, Tahmiscija Indira, Khatib Hakam, Semic Jusufagic Aida, Haas Helmut, Vladicic Aleksandra, Aplenc Richard, Kadic Deovic Aida

Abstract:

Introduction: Joubert syndrome has an autosomal recessive pattern of inheritance. It is referred as the brain malfunctioning and caused due to the underdevelopment of the cerebellar vermis. Associated conditions involving the eye, the kidney, and ocular disease are well described. Aims: Research helps us better understand this diseases, Joubert syndrome and can lead to advances in diagnosis and treatment. Methods: Different several conditions have been described in which the molar tooth sign and characteristics of Joubert syndrome in ten different places in the world. Carrier testing and diagnosis are available if one of these gene mutations has been identified in an affected family member. Results: Authors have described eleven cases during twenty years of Joubert syndrome. It is a clinically and genetically heterogeneous group of disorders characterized by hypoplasia of the cerebellar vermis with the characteristic neuroradiologic molar tooth sign, and accompanying neurologic symptoms, including dysregulation of breathing pattern and developmental delay. We made confirmation of diagnosis in twin sisters with Joubert syndrome with renal anomalies. Ocular symptoms have existed in seven cases (63.64%) from total eleven. Eleven cases were different sex, five boys (45.45%) and six girls (54.44%). Conclusions: Joubert syndrome is inherited as an autosomal recessive genetic disorder with several features of the disease.

Keywords: Joubert syndrome, cerebellooculorenal syndrome, autosomal recessive genetic disorder (ARGD), children

Procedia PDF Downloads 276

Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

Abstract:

Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

Procedia PDF Downloads 143

Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

Abstract:

This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

Procedia PDF Downloads 320

Authors: Samuel Knox, Verdon Crann, Peyman Amiri, William Crowther

Abstract:

There exists a shortage of open-source firmware for allowing researchers to focus on implementing high-level planning and control strategies for multi aerial robotic systems in simulation and experiment. Within this body of work, practical firmware is presented, which performs all supplementary tasks, including communications, pre and post-experiment procedures, and emergency safety measures. This allows researchers to implement high-level planning and control algorithms for path planning, traffic management, flight formation and swarming of aerial robots. The framework is built in Python using the MAVSDK library, which is compatible with flight controllers running PX4 firmware and onboard computers based on Linux. Communication is performed using Wi-Fi and the MQTT protocol, currently implemented using a centralized broker. Finally, a graphical user interface (GUI) has been developed to send general commands and monitor the agents. This framework enables researchers to prepare customized planning and control algorithms in a modular manner. Studies can be performed experimentally and in simulation using PX4 software in the loop (SITL) and the Gazebo simulator. An example experimental use case of the framework is presented using novel distributed planning and control strategies. The demonstration is performed using off-the-shelf components and minimal setup.

Keywords: aerial robotics, distributed framework, experimental, planning and control

Procedia PDF Downloads 109

Authors: Feng Tao, Han Ye, Shaoyi Liao

Abstract:

City constructions collapse after earthquake and people will be buried under ruins. Search and rescue should be conducted as soon as possible to save them. Therefore, according to the complicated environment, irregular aftershocks and rescue allow of no delay, a kind of target localization method based on RSSI (Received Signal Strength Indication) is proposed in this article. The target localization technology based on RSSI with the features of low cost and low complexity has been widely applied to nodes localization in WSN (Wireless Sensor Networks). Based on the theory of RSSI transmission and the environment impact to RSSI, this article conducts the experiments in five scenes, and multiple filtering algorithms are applied to original RSSI value in order to establish the signal propagation model with minimum test error respectively. Target location can be calculated from the distance, which can be estimated from signal propagation model, through improved centroid algorithm. Result shows that the localization technology based on RSSI is suitable for large-scale nodes localization. Among filtering algorithms, mixed filtering algorithm (average of average, median and Gaussian filtering) performs better than any other single filtering algorithm, and by using the signal propagation model, the minimum error of distance between known nodes and target node in the five scene is about 3.06m.

Keywords: signal propagation model, centroid algorithm, localization, mixed filtering, RSSI

Procedia PDF Downloads 296

Authors: Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das

Abstract:

Tea (Camellia sinensis) has received substantial attention from the scientific world time to time, not only for its commercial importance, but also for its demand to the health-conscious people across the world for its extensive use as potential sources of antioxidant supplement. These health-benefit traits primarily rely on some regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions is being worthwhile for studying the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea the trait-specific Simple Sequence Repeats (SSRs) are yet to be identified, which can be used for marker assisted breeding technique. MicroRNAs are endogenous, noncoding, short RNAs directly involved in regulating gene expressions at the post-transcriptional level. It has been found that diversity in miRNA gene interferes the formation of its characteristic hair pin structure and the subsequent function. In the present study, the precursors of small regulatory RNAs (microRNAs) has been fished out from tea Expressed Sequence Tag (EST) database. Furthermore, the simple sequence repeat motifs within the putative miRNA precursor genes are also identified in order to experimentally validate their existence and function. It is already known that genic-SSR markers are very adept and breeder-friendly source for genetic diversity analysis. So, the potential outcome of this in-silico study would provide some novel clues in understanding the miRNA-triggered polymorphic genic expression controlling specific metabolic pathways, accountable for tea quality.

Keywords: micro RNA, simple sequence repeats, tea quality, trait specific marker

Procedia PDF Downloads 309