Search results for: search algorithms

Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models, and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. It is important to design systems based on structural analysis, research, and evaluation of efficiency indicators. One of the important efficiency criteria is the reliability of the system, which depends on the components of the structure. Quantifying the reliability of large-scale systems is a computationally complex process, and it is advisable to perform it with the help of a computer. Logical-probabilistic modeling is one of the effective means of describing the structure of a complex system and quantitatively evaluating its reliability, which was the basis of our application. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of “weights” of elements of system. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research, and designing of optimal structure systems are carried out.

Keywords: complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability of systems, “weights” of elements

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Authors: Matthew Lee Turnbough

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In 2007 Spain began to experience the effects of a deep economic crisis, which would generate a situation characterised by instability and uncertainty. This has been an obstacle, especially acute for the youth of this country seeking to enter the workforce. As a result of the impact of COVID-19, the youth in Spain are now suffering the effects of a new crisis that has deepened an already fragile labour environment. In this paper, we analyse the discourses that have emerged from a precarious labour market, specifically from two companies dedicated to operating job portals and job listings in Spain, Job Today, and CornerJob. These two start-up businesses have developed mobile applications geared towards young adults in search of employment in the service sector, two of the companies with the highest user rates in Spain. Utilizing a discourse analysis approach, we explore the impact of individualization and how the process of psychologization may contribute to an increasing reliance on individual solutions to social problems. As such, we seek to highlight the expectations and demands that are placed upon young workers and the type of subjectivity that this dynamic could foster, all this within an unstable framework seemingly marked by chance, a context which is key for the emergence of individualization. Furthermore, we consider the extent to which young adults incorporate these discourses and the strategies they employ basing our analysis on the VULSOCU (New Forms of Socio-Existential Vulnerability, Supports, and Care in Spain) research project, specifically the results of nineteen in-depth interviews and three discussion groups with young adults in this country. Consequently, we seek to elucidate the argumentative threads rooted in the process of individualization and underline the implications of this dynamic for the young worker and his/her labour insertion while also identifying manifestations of the goddess of fortune as a representation of chance in this context. Finally, we approach this panorama of social change in Spain from the perspective of the individuals or young adults who find themselves immersed in this transition from one crisis to another.

Keywords: chance, crisis, discourses, individualization, work, youth

Procedia PDF Downloads 117

Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: Duygu Çelgin

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Until the new Occupational Health and Safety Law of Turkey, most of the workers were excluded from the mandatory occupational health and safety services. This new law, made the OHS services mandatory for all workers from all sectors including both public and private. However, in the application some problems and disadvantageous cases are occurred and the government also considered these cases. In this study, the new OHS law of Turkey and the regulations prepared according to the law are studied with the literature search.

Keywords: occupational health and safety in Turkey, OHS servicing in Turkey, safety experts, OHS support

Procedia PDF Downloads 470

Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Faeze omid, Morteza Banakar

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Background: The COVID-19 pandemic has had a significant impact on global health and healthcare systems, including oral health. The lockdown measures implemented in many countries have led to changes in oral health behaviors, access to dental care, and the delivery of dental services. However, the extent of these changes and their effects on oral health outcomes remains unclear. This systematic review aims to synthesize the available evidence on the state of oral health after the COVID-19 lockdown. Methods: We conducted a systematic search of electronic databases (PubMed, Embase, Scopus, and Web of Science) and grey literature sources for studies reporting on oral health outcomes after the COVID-19 lockdown. We included studies published in English between January 2020 and March 2023. Two reviewers independently screened the titles, abstracts, and full texts of potentially relevant articles and extracted data from included studies. We used a narrative synthesis approach to summarize the findings. Results: Our search identified 23 studies from 12 countries, including cross-sectional surveys, cohort studies, and case reports. The studies reported on changes in oral health behaviors, access to dental care, and the prevalence and severity of dental conditions after the COVID-19 lockdown. Overall, the evidence suggests that the lockdown measures had a negative impact on oral health outcomes, particularly among vulnerable populations. There were decreases in dental attendance, increases in dental anxiety and fear, and changes in oral hygiene practices. Furthermore, there were increases in the incidence and severity of dental conditions, such as dental caries and periodontal disease, and delays in the diagnosis and treatment of oral cancers. Conclusion: The COVID-19 pandemic and associated lockdown measures have had significant effects on oral health outcomes, with negative impacts on oral health behaviors, access to care, and the prevalence and severity of dental conditions. These findings highlight the need for continued monitoring and interventions to address the long-term effects of the pandemic on oral health.

Keywords: COVID-19, oral health, systematic review, dental public health

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Authors: Mostafa Mjahed

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This paper concerns the control of a nonlinear system using two different methods, reference model and genetic algorithm. The quadcopter is a nonlinear unstable system, which is a part of aerial robots. It is constituted by four rotors placed at the end of a cross. The center of this cross is occupied by the control circuit. Its motions are governed by six degrees of freedom: three rotations around 3 axes (roll, pitch and yaw) and the three spatial translations. The control of such system is complex, because of nonlinearity of its dynamic representation and the number of parameters, which it involves. Numerous studies have been developed to model and stabilize such systems. The classical PID and LQ correction methods are widely used. If the latter represent the advantage to be simple because they are linear, they reveal the drawback to require the presence of a linear model to synthesize. It also implies the complexity of the established laws of command because the latter must be widened on all the domain of flight of these quadcopter. Note that, if the classical design methods are widely used to control aeronautical systems, the Artificial Intelligence methods as genetic algorithms technique receives little attention. In this paper, we suggest comparing two PID design methods. Firstly, the parameters of the PID are calculated according to the reference model. In a second phase, these parameters are established using genetic algorithms. By reference model, we mean that the corrected system behaves according to a reference system, imposed by some specifications: settling time, zero overshoot etc. Inspired from the natural evolution of Darwin's theory advocating the survival of the best, John Holland developed this evolutionary algorithm. Genetic algorithm (GA) possesses three basic operators: selection, crossover and mutation. We start iterations with an initial population. Each member of this population is evaluated through a fitness function. Our purpose is to correct the behavior of the quadcopter around three axes (roll, pitch and yaw) with 3 PD controllers. For the altitude, we adopt a PID controller.

Keywords: quadcopter, genetic algorithm, PID, fitness, model, control, nonlinear system

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Authors: Francesca Croce

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Governmental development strategies aimed at entrepreneurship as a major resource for economic development and poverty reduction of indigenous people. As initiatives and programs are local based, there is a need to better understand the contextual factors of indigenous entrepreneurial models. The purpose of this paper is, therefore, to analyze and integrated the indigenous entrepreneurship literature in order to identify the main models of indigenous entrepreneurship. To answer this need, a systematic literature review was conducted. Relevant articles were identified in selected electronic databases (ABI/Inform Global, Business Source Premier, Web of Science; International Bibliography of the Social Sciences, Academic Search, Sociological Abstract, Entrepreneurial Studies Sources and Bibliography of Native North America) and in selected electronic review. Beginning to 1st January 1995 (first International Day of the World’s Indigenous People), 59 academic articles were selected from 1411. Through systematic analysis of the cultural, social and organizational variables, the paper highlights that a typology of indigenous entrepreneurial models is possible thought the concept of entrepreneurial ecosystem, which includes the geographical position and the environment of the indigenous communities. The results show three models of indigenous entrepreneurship: the urban indigenous entrepreneurship, the semi-urban indigenous entrepreneurship, and rural indigenous entrepreneurship. After the introduction, the paper is organized as follows. In the first part theoretical and practical needs of a systematic literature review on indigenous entrepreneurship are provided. In the second part, the methodology, the selection process and evaluation of the articles are explained. In the third part, findings are presented and each indigenous entrepreneurial model characteristics are discussed. The results of this study bring a new theorization about indigenous entrepreneurship and may be useful for scientists in the field in search of overcoming the cognitive border of Indigenous business models still too little known. Also, the study is addressed to policy makers in charge of indigenous entrepreneurial development strategies more focused on contextual factors studies.

Keywords: community development, entrepreneurial ecosystem, indigenous entrepreneurship model, indigenous people, systematic literature review

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Jin-Siang Shaw, Patricia Moya Caceres, Sheng-Xiang Xu

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This paper aims to integrate the particle swarm optimization (PSO) method with the adaptive fuzzy sliding mode controller (AFSMC) to achieve vibration attenuation in a piezoelectric actuator subject to base excitation. The piezoelectric actuator is a complicated system made of ferroelectric materials and its performance can be affected by nonlinear hysteresis loop and unknown system parameters and external disturbances. In this study, an adaptive fuzzy sliding mode controller is proposed for the vibration control of the system, because the fuzzy sliding mode controller is designed to tackle the unknown parameters and external disturbance of the system, and the adaptive algorithm is aimed for fine-tuning this controller for error converging purpose. Particle swarm optimization method is used in order to find the optimal controller parameters for the piezoelectric actuator. PSO starts with a population of random possible solutions, called particles. The particles move through the search space with dynamically adjusted speed and direction that change according to their historical behavior, allowing the values of the particles to quickly converge towards the best solutions for the proposed problem. In this paper, an initial set of controller parameters is applied to the piezoelectric actuator which is subject to resonant base excitation with large amplitude vibration. The resulting vibration suppression is about 50%. Then PSO is applied to search for an optimal controller in the neighborhood of this initial controller. The performance of the optimal fuzzy sliding mode controller found by PSO indeed improves up to 97.8% vibration attenuation. Finally, adaptive version of fuzzy sliding mode controller is adopted for further improving vibration suppression. Simulation result verifies the performance of the adaptive controller with 99.98% vibration reduction. Namely the vibration of the piezoelectric actuator subject to resonant base excitation can be completely annihilated using this PSO based adaptive fuzzy sliding mode controller.

Keywords: adaptive fuzzy sliding mode controller, particle swarm optimization, piezoelectric actuator, vibration suppression

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Authors: Syeda Sahra, Abdullah Jahangir, Rachelle Hamadi, Ahmad Jahangir, Allison Glaser

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Background: Antimicrobial resistance is on the rise. The use of redundant and inappropriate antibiotics is contributing to recurrent infections and resistance. Newer antibiotics with more robust coverage for gram-negative bacteria are in great demand for complicated urinary tract infections (cUTIs), complicated intra-abdominal infections (cIAIs), hospital-acquired bacterial pneumonia (H.A.B.P.), and ventilator-associated bacterial pneumonia (V.A.B.P.). Objective: We performed this meta-analysis to evaluate the efficacy and safety profile of a new antibiotic, Imipenem/cilastatin/relebactam, compared to other broad-spectrum antibiotics for complicated infections. Search Strategy: We conducted a systemic review search on PubMed, Embase, and Central Cochrane Registry. Selection Criteria: We included randomized clinical trials (R.C.T.s) with the standard of care as comparator arm with Imipenem/cilastatin/relebactam as intervention arm. Analysis: For continuous variables, the mean difference was used. For discrete variables, we used the odds ratio. For effect sizes, we used a confidence interval of 95%. A p-value of less than 0.05 was used for statistical significance. Analysis was done using a random-effects model irrespective of heterogeneity. Heterogeneity was evaluated using the I2 statistic. Results: The authors observed similar efficacy at clinical and microbiologic response levels on early follow-up and late follow-up compared to the established standard of care. The incidence of drug-related adverse events, serious adverse events, and drug discontinuation due to adverse events were comparable across both groups. Conclusion: Imipenem/cilastatin/relebactam has a non-inferior safety and efficacy profile compared to peer antibiotics to treat severe bacterial infections (cUTIs, cIAIs, H.A.B.P., V.A.B.P.).

Keywords: bacterial pneumonia, complicated intra-abdominal infections, complicated urinary tract infection, Imipenem, cilastatin, relebactam

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Authors: Meenu S. Babu, K. Jayasankara Reddy

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Depression is a serious mental health concern that leads to significant distress and dysfunction in an individual. Due to the high physical, psychological, social, and economic burden it causes, it is important to study various bio-psycho-social factors that influence the onset, course, duration, intensity of depressive symptoms. The study aims to explore relationship between autobiographical memory (AM) functions, perceived control over stressful events and depressive symptoms. AM functions and perceived control were both found to be protective factors for individuals against depression and were both modifiable to predict better behavioral and affective outcomes. An extensive review of literatur, with a systematic search on Google Scholar, JSTOR, Science Direct and Springer Journals database, was conducted for the purpose of this review paper. These were used for all the aforementioned databases. The time frame used for the search was 2010-2021. An additional search was conducted with no time bar to map the development of the theoretical concepts. The relevant studies with quantitative, qualitative, experimental, and quasi- experimental research designs were included for the review. Studies including a sample with a DSM- 5 or ICD-10 diagnosis of depressive disorders were excluded from the study to focus on the behavioral patterns in a non-clinical population. The synthesis of the findings that were obtained from the review indicates there is a significant relationship between cognitive variables of AM functions and perceived control and depressive symptoms. AM functions were found to be have significant effects on once sense of self, interpersonal relationships, decision making, self- continuity and were related to better emotion regulation and lower depressive symptoms. Not all the components of AM function were equally significant in their relationships with various depressive symptoms. While self and directive functions were more related to emotion regulation, anhedonia, motivation and hence mood and affect, the social function was related to perceived social support and social engagement. Perceived control was found to be another protective cognitive factor that provides individuals a sense of agency and control over one’s life outcomes which was found to be low in individuals with depression. This was also associated to the locus of control, competency beliefs, contingency beliefs and subjective well being in individuals and acted as protective factors against depressive symptoms. AM and perceived control over stressful events serve adaptive functions, hence it is imperative to study these variables more extensively. They can be imperative in planning and implementing therapeutic interventions to foster these cognitive protective factors to mitigate or alleviate depressive symptoms. Exploring AM as a determining factor in depressive symptoms along with perceived control over stress creates a bridge between biological and cognitive factors underlying depression and increases the scope of developing a more eclectic and effective treatment plan for individuals. As culture plays a crucial role in AM functions as well as certain aspects of control such as locus of control, it is necessary to study these variables keeping in mind the cultural context to tailor culture/community specific interventions for depression.

Keywords: autobiographical memories, autobiographical memory functions, perceived control, depressive symptoms, depression, young adults

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Authors: Thomas Doutre, Emmanuel De Meric De Bellefon

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This paper describes the application of local-search metaheuristic quantum annealing to portfolio opti- mization. Heuristic technics are particularly handy when Markowitz’ classical Mean-Variance problem is enriched with additional realistic constraints. Once tailored to the problem, computational experiments on real collected data have shown the superiority of quantum annealing over simulated annealing for this constrained optimization problem, taking advantages of quantum effects such as tunnelling.

Keywords: optimization, portfolio risk management, quantum annealing, metaheuristic

Procedia PDF Downloads 383

Authors: Lima Antony

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In G. M. Hopkins’s poetics and aesthetics there is scope for a comparative study with Indian discourses on aesthetics, an area not adequately explored so far. This exploration will enrich the field of comparative study of diverse cultural expressions and their areas of similarity. A comparative study of aesthetic and religious experiences in diverse cultures will open up avenues for the discovery of similarities in self-experiences and their transcendence. Such explorations will reveal similar patterns in aesthetic and religious experiences. The present paper intends to prove this in the theories of Hopkins and Indian aesthetics. From the time of the Vedas Indian sages have believed that aesthetic enjoyment could develop into a spiritual realm. From the Natyasastra of Bharata, Indian aesthetics develops and reaches its culmination in later centuries into a consciousness of union with the mystery of the Ultimate Being, especially in Dhvanaāloka of Anandavardhana and Locana of Abhinavagupta. Dhvanyaloka elaborates the original ideas of rasa (mood or flavor) and dhvani (power of suggestion) in Indian literary theory and aesthetics. Hopkins was successful, like the ancient Indian alankarikas, in creating aesthetically superb patterns at various levels of sound and sense for which he coined the term ‘inscape’. So Hopkins’s aesthetic theory becomes suitable for transcultural comparative study with Indian aesthetics especially the dhvani theories of Anandavardhana and Abhinavagupta. Hopkins’s innovative approach to poetics and his selection of themes are quite suitable for analysis in the light of Indian literary theories. Indian philosophy views the ultimate reality called Brahman, as the 'soul,' or inner essence, of all reality. We see in Hopkins also a search for the essence of things and the chiming of their individuality with the Ultimate Being in multidimensional patterns of sound, sense and ecstatic experience. This search culminates in the realization of a synthesis of the individual self with the Ultimate Being. This is achieved through an act of surrender of the individuality of the self before the Supreme Being. Attempts to reconcile the immanent and transcendent aspects of the Ultimate Being can be traced in the Indian as well as Hopkins’s aesthetics which can contribute to greater understanding and harmony between cultures.

Keywords: Dhvani, Indian aesthetics, transcultural studies, Rasa

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Authors: Tolulope Aremu

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The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

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Authors: Semire Yekta

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Many online retailers set up manual review teams to overcome the limitations of automated online fraud detection systems. This study critically examines the strategies they adapt in their decision-making process to set apart fraudulent individuals from non-fraudulent online shoppers. The study uses a mix method research approach. 32 in-depth interviews have been conducted alongside with participant observation and auto-ethnography. The study found out that all steps of the decision-making process are significantly affected by a level of subjectivity, personal understandings of online fraud, preferences and judgments and not necessarily by objectively identifiable facts. Rather clearly knowing who the fraudulent individuals are, the team members have to predict whether they think the customer might be a fraudster. Common strategies used are relying on the classification and fraud scorings in the automated fraud detection systems, weighing up arguments for and against the customer and making a decision, using cancellation to test customers’ reaction and making use of personal experiences and “the sixth sense”. The interaction in the team also plays a significant role given that some decisions turn into a group discussion. While customer data represent the basis for the decision-making, fraud management teams frequently make use of Google search and Google Maps to find out additional information about the customer and verify whether the customer is the person they claim to be. While this, on the one hand, raises ethical concerns, on the other hand, Google Street View on the address and area of the customer puts customers living in less privileged housing and areas at a higher risk of being classified as fraudsters. Phone validation is used as a final measurement to make decisions for or against the customer when previous strategies and Google Search do not suffice. However, phone validation is also characterized by individuals’ subjectivity, personal views and judgment on customer’s reaction on the phone that results in a final classification as genuine or fraudulent.

Keywords: online fraud, data mining, manual review, social construction

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Authors: Vadim Vagin, Marina Fomina, Oleg Morosin

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This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.

Keywords: argumentation, justification degrees, inductive concept formation, noise, generalization

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Authors: Eimar Yadir Quintero Tapias

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Background: Science is not generally self-correcting; many clinical studies end with the same conclusion "more research is needed." This study hypothesizes that first, we need a better appraisal of the available (and unavailable) evidence instead of creating more of the same false inquiries. Methods: Systematic review of ClinicalTrials.gov study records using the following Boolean operators: (food OR nut OR milk OR egg OR shellfish OR wheat OR peanuts) AND (allergy OR allergies OR hypersensitivity OR hypersensitivities). Variables included the status of the study (e g., active and completed), availability of results, sponsor type, sample size, among others. To determine the rates of non-publication in journals indexed by PubMed, an advanced search query using the specific Number of Clinical Trials (e.g., NCT000001 OR NCT000002 OR...) was performed. As a prophylactic measure to prevent P-hacking, data analyses only included descriptive statistics and not inferential approaches. Results: A total of 2092 study records matched the search query described above (date: September 13, 2019). Most studies were interventional (n = 1770; 84.6%) and the remainder observational (n = 322; 15.4%). Universities, hospitals, and research centers sponsored over half of these investigations (n = 1208; 57.7%), 308 studies (14.7%) were industry-funded, and 147 received NIH grants; the remaining studies got mixed sponsorship. Regarding completed studies (n = 1156; 55.2%), 248 (21.5%) have results available at the registry site, and 417 (36.1%) matched NCT numbers of journal papers indexed by PubMed. Conclusions: The internal and external validity of human research is critical for the appraisal of medical evidence. It is imperative to analyze the entire dataset of clinical studies, preferably at a patient-level anonymized raw data, before rushing to conclusions with insufficient and inadequate information. Publication bias and non-registration of clinical trials limit the evaluation of the evidence concerning therapeutic interventions for food allergy, such as oral and sublingual immunotherapy, as well as any other medical condition. Over half of the food allergy human research remains unpublished.

Keywords: allergy, clinical trials, immunology, systematic reviews

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Authors: Mohammad Javaherian, Siamak Bashardoust Tajali, Monavvar Hadizadeh

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Objective: This systematic review study was conducted to evaluate the effects of Pulsed Electromagnetic (PEMF) and Static Magnetic Fields (SMG) on pain relief and functional improvement in patients with musculoskeletal Low Back Pain (LBP). Methods: Seven electronic databases were searched by two researchers independently to identify the published Randomized Controlled Trials (RCTs) on the efficacy of pulsed electromagnetic, static magnetic, and therapeutic nuclear magnetic fields. The identified databases for systematic search were Ovid Medline®, Ovid Cochrane RCTs and Reviews, PubMed, Web of Science, Cochrane Library, CINAHL, and EMBASE from 1968 to February 2016. The relevant keywords were selected by Mesh. After initial search and finding relevant manuscripts, all references in selected studies were searched to identify second hand possible manuscripts. The published RCTs in English would be included to the study if they reported changes on pain and/or functional disability following application of magnetic fields on chronic musculoskeletal low back pain. All studies with surgical patients, patients with pelvic pain, and combination of other treatment techniques such as acupuncture or diathermy were excluded. The identified studies were critically appraised and the data were extracted independently by two raters (M.J and S.B.T). Probable disagreements were resolved through discussion between raters. Results: In total, 1505 abstracts were found following the initial electronic search. The abstracts were reviewed to identify potentially relevant manuscripts. Seventeen possibly appropriate studies were retrieved in full-text of which 48 were excluded after reviewing their full-texts. Ten selected articles were categorized into three subgroups: PEMF (6 articles), SMF (3 articles), and therapeutic nuclear magnetic fields (tNMF) (1 article). Since one study evaluated tNMF, we had to exclude it. In the PEMF group, one study of acute LBP did not show significant positive results and the majority of the other five studies on Chronic Low Back Pain (CLBP) indicated its efficacy on pain relief and functional improvement, but one study with the lowest sessions (6 sessions during 2 weeks) did not report a significant difference between treatment and control groups. In the SMF subgroup, two articles reported near significant pain reduction without any functional improvement although more studies are needed. Conclusion: The PEMFs with a strength of 5 to 150 G or 0.1 to 0.3 G and a frequency of 5 to 64 Hz or sweep 7 to 7KHz can be considered as an effective modality in pain relief and functional improvement in patients with chronic low back pain, but there is not enough evidence to confirm their effectiveness in acute low back pain. To achieve the appropriate effectiveness, it is suggested to perform this treatment modality 20 minutes per day for at least 9 sessions. SMFs have not been reported to be substantially effective in decreasing pain or improving the function in chronic low back pain. More studies are necessary to achieve more reliable results.

Keywords: pulsed electromagnetic field, static magnetic field, magnetotherapy, low back pain

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Authors: Carie Cunningham, Krsiten Lynch

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Despite a viewer’s thought that they may be paying attention, many times they are missing out on their surrounds-- a phenomenon referred to as inattentional blindness. Inattention blindness refers to the failure of an individual to orient their attention to a particular item in their visual field. This well-defined in the psychology literature. Similarly, this phenomenon has been evaluated in media types in advertising. In advertising, not comprehending/remembering items in one’s field of vision is known as banner blindness. On the other hand, banner blindness is a phenomenon that occurs when individuals habitually see a banner in a specific area on a webpage, and thus condition themselves to ignore those habitual areas. Another reason that individuals avoid these habitual areas (usually on the top or sides of a webpage) is due to the lack of personal relevance or pertinent information to the viewer. Banner blindness, while a web-based concept, may also relate this inattention blindness. This paper is proposing an analysis of the true similarities and differences between these concepts bridging the two dimensions of thinking together. Forty participants participated in an eye-tracking and post-survey experiment to test attention and memory measures in both a banner blindness and inattention blindness condition. The two conditions were conducted between subjects semi-randomized order. Half of participants were told to search through the content ignoring the advertising banners; the other half of participants were first told to search through the content ignoring the distractor icon. These groups were switched after 5 trials and then 5 more trials were completed. In review of the literature, sustainability communication was found to have many inconsistencies with message production and viewer awareness. For the purpose of this study, we used advertising materials as stimuli. Results suggest that there are gaps between the two concepts and that more research should be done testing these effects in a real world setting versus an online environment. This contributes to theory by exploring the overlapping concepts—inattention blindness and banner blindness and providing the advertising industry with support that viewers can still fall victim to ignoring items in their field of view even if not consciously, which will impact message development.

Keywords: attention, banner blindness, eye movement, inattention blindness

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Authors: Rainner Roweder

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Theme: The present paper aims to analyze the specificity of the judicial evidence linked to the subjects of dignity and personality rights, otherwise known as moral rights, in the determination of the truth and formation of the judicial reasoning in cases concerning these areas. This research is about the way courts in Brazilian domestic law search for truth and handles evidence in cases involving moral rights that are abundant and important in Brazil. The main object of the paper is to analyze the effectiveness of the evidence in the formation of judicial conviction in matters related to morally controverted rights, based on the Brazilian, and as a comparison, the Latin American legal systems. In short, the rights of dignity and personality are moral. However, the evidential legal system expects a rational demonstration of moral rights that generate judicial conviction or persuasion. Moral, in turn, tends to be difficult or impossible to demonstrate in court, generating the problem considered in this paper, that is, the study of the moral demonstration problem as proof in court. In this sense, the more linked to moral, the more difficult to be demonstrated in court that right is, expanding the field of judicial discretion, generating legal uncertainty. More specifically, the new personality rights, such as gender, and their possibility of alteration, further amplify the problem being essentially an intimate manner, which does not exist in the objective, rational evidential system, as normally occurs in other categories, such as contracts. Therefore, evidencing this legal category in court, with the level of security required by the law, is a herculean task. It becomes virtually impossible to use the same evidentiary system when judging the rights researched here; therefore, it generates the need for a new design of the evidential task regarding the rights of the personality, a central effort of the present paper. Methodology: Concerning the methodology, the Method used in the Investigation phase was Inductive, with the use of the comparative law method; in the data treatment phase, the Inductive Method was also used. Doctrine, Legislative, and jurisprudential comparison was the technique research used. Results: In addition to the peculiar characteristics of personality rights that are not found in other rights, part of them are essentially linked to morale and are not objectively verifiable by design, and it is necessary to use specific argumentative theories for their secure confirmation, such as interdisciplinary support. The traditional pragmatic theory of proof, for having an obvious objective character, when applied in the rights linked to the morale, aggravates decisionism and generates legal insecurity, being necessary its reconstruction for morally charged cases, with the possible use of the “predictive theory” ( and predictive facts) through algorithms in data collection and treatment.

Keywords: moral rights, proof, pragmatic proof theory, insufficiency, Brazil

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Authors: Sakshi Singh, Shampa Chakraverty

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One of the essential characteristics of Blockchain is the ability to validate the integrity of new transactions added to the Blockchain. Moreover, one of the essential consensus algorithms, Proof of Work, performs this job. In this work, we implemented the Proof of Work consensus method on the block formed by performing the transaction using Ganache. The primary goal of this implementation is to understand the process and record how Proof of Work works in reality on newly created blocks.

Keywords: proof of work, blockchain, ganache, smart contract

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Authors: Sergey Sorokin, Irina Sorokina, Alexander Yazenin

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We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation.

Keywords: possibility distribution, parameters estimation, Maxmin u\E estimator, fuzzy model identification

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Authors: Zinhle Cindi, Collet Dandara, Mpiko Ntsekhe, Edson Makambwa, Miguel Larceda

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Background: Warfarin is the most commonly used drug in the management of thromboembolic disease. However, there is a huge variability in the time, number of doses or starting doses for patients to achieve the required international normalised ratio (INR) which is compounded by a narrow therapeutic index. Many genetic-association studies have reported on European and Asian populations which have led to the designing of specific algorithms that are now being used to assist in warfarin dosing. However, very few or no studies have looked at the pharmacogenetics of warfarin in African populations, yet, huge differences in dosage requirements to reach the same INR have been observed. Objective: We set out to investigate the distribution of 3 SNPs CYP4F2 c.1347C > T, VKORC1 g.-1639G > A and VKORC1 c.1173C > T among South African Mixed Ancestry (MA) and Black African patients. Methods: DNA was extracted from 383 participants and subsequently genotyped using PCR/RFLP for the CYP4F2 c.1347 (V433M) (rs2108622), VKORC1 g.-1639 (rs9923231) and VKORC1 c.1173 (rs9934438) SNPs. Results: Comparing the Black and MA groups, significant differences were observed in the distribution of the following genotypes; CYP4F2 c.1347C/T (23% vs. 39% p=0.03). All VKORC1 g.-1639G > A genotypes (p < 0.006) and all VKORC1 c.1173C > T genotypes (p < 0.007). Conclusion: CYP4F2 c.1347T (V433M) reduces CYP4F2 protein levels and therefore expected to affect the amount of warfarin needed to block vitamin k recycling. The VKORC1 g-1639A variant alters transcriptional regulation therefore affecting the function of vitamin k epoxide reductase in vitamin k production. The VKORC1 c.1173T variant reduces the enzyme activity of VKORC1 consequently enhancing the effectiveness of warfarin. These are preliminary results; more genetic characterization is required to understand all the genetic determinants affecting how patients respond to warfarin.

Keywords: algorithms, pharmacogenetics, thromboembolic disease, warfarin

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Authors: Javad Sadidi, Ehsan Babaei, Hani Rezayan

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The advent of web 2.0 gives a possibility to scale down the costs of data collection and mapping, specifically if the process is done by volunteers. Every volunteer can be thought of as a free and ubiquitous sensor to collect spatial, descriptive as well as multimedia data for tourist services. The lack of large-scale information, such as real-time climate and weather conditions, population density, and other related data, can be considered one of the important challenges in developing countries for tourists to make the best decision in terms of time and place of travel. The current research aims to design and implement a spatiotemporal web map service using volunteer-submitted data. The service acts as a tourist-guide service in which tourists can search interested places based on their requested time for travel. To design the service, three tiers of architecture, including data, logical processing, and presentation tiers, have been utilized. For implementing the service, open-source software programs, client and server-side programming languages (such as OpenLayers2, AJAX, and PHP), Geoserver as a map server, and Web Feature Service (WFS) standards have been used. The result is two distinct browser-based services, one for sending spatial, descriptive, and multimedia volunteer data and another one for tourists and local officials. Local official confirms the veracity of the volunteer-submitted information. In the tourist interface, a spatiotemporal search engine has been designed to enable tourists to find a tourist place based on province, city, and location at a specific time of interest. Implementing the tourist-guide service by this methodology causes the following: the current tourists participate in a free data collection and sharing process for future tourists, a real-time data sharing and accessing for all, avoiding a blind selection of travel destination and significantly, decreases the cost of providing such services.

Keywords: VGI, tourism, spatiotemporal, browser-based, web mapping

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Authors: Saadia Elamin

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The quasi-universal use of smart phones with internet connection available all the time makes it a reflex action for translation undergraduates, once they encounter the least translation problem, to turn to the freely available web resource: Google Translate. Like for other translator resources and aids, the use of Google Translate needs to be moderated in such a way that it contributes to developing translation competence. Here, instead of interfering with students’ learning by providing ready-made solutions which might not always fit into the contexts of use, it can help to consolidate the skills of analysis and transfer which students have already acquired. One way to do so is by training students to adhere to the basic principles of translation work. The most important of these is that analyzing the source text for comprehension comes first and foremost before jumping into the search for target language equivalents. Another basic principle is that certain translator aids and tools can be used for comprehension, while others are to be confined to the phase of re-expressing the meaning into the target language. The present paper reports on the experience of making a measured and reasonable use of Google Translate in translation teaching at Prince Sultan University (PSU), Riyadh. First, it traces the development that has taken place in the field of translation in this age of information technology, be it in translation teaching and translator training, or in the real-world practice of the profession. Second, it describes how, with the aim of reflecting this development onto the way translation is taught, senior students, after being trained on post-editing machine translation output, are authorized to use Google Translate in classwork and assignments. Third, the paper elaborates on the findings of this case study which has demonstrated that Google Translate, if used at the appropriate levels of training, can help to enhance students’ ability to perform different translation tasks. This help extends from the search for terms and expressions, to the tasks of drafting the target text, revising its content and finally editing it. In addition, using Google Translate in this way fosters a reflexive and critical attitude towards web resources in general, maximizing thus the benefit gained from them in preparing students to meet the requirements of the modern translation job market.

Keywords: Google Translate, post-editing machine translation output, principles of translation work, translation competence, translation teaching, translator aids and tools

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Ahmad Alhawarat, Mustafa Mamat, Mohd Rivaie, Ismail Mohd

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Conjugate gradient method has been enormously used to solve large scale unconstrained optimization problems due to the number of iteration, memory, CPU time, and convergence property, in this paper we find a new class of nonlinear conjugate gradient coefficient with global convergence properties proved by exact line search. The numerical results for our new βK give a good result when it compared with well-known formulas.

Keywords: conjugate gradient method, conjugate gradient coefficient, global convergence

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Authors: Mehmet Hakan Karaata

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In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring.

Keywords: algorithm, cycle, graph algorithm, graph theory, network structuring

Procedia PDF Downloads 386

Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

Procedia PDF Downloads 214