Search results for: tabu search algorithm

Authors: Raphaël Weuts, Mykyta Petik, Anton Vedder

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Artificial Intelligence has the potential to create and already creates enormous value in healthcare. Prescriptive systems might be able to make the use of healthcare capacity more efficient. Such systems might entail interpretations that exclude the effect of confounders that brings risks with it. Those risks might be mitigated by regulation that prevents systems entailing such risks to come to market. One modality of regulation is that of legislation, and the European AI Act is an example of such a regulatory instrument that might mitigate these risks. To assess the risk mitigation potential of the AI Act for those risks, this research focusses on a case study of a hypothetical application of medical device software that entails the aforementioned risks. The AI Act refers to the harmonised norms for already existing legislation, here being the European medical device regulation. The issue at hand is a causal link between a confounder and the value the algorithm optimises for by proxy. The research identifies where the AI Act already looks at confounders (i.a. feedback loops in systems that continue to learn after being placed on the market). The research identifies where the current proposal by parliament leaves legal uncertainty on the necessity to check for confounders that do not influence the input of the system, when the system does not continue to learn after being placed on the market. The authors propose an amendment to article 15 of the AI Act that would require high-risk systems to be developed in such a way as to mitigate risks from those aforementioned confounders.

Keywords: AI Act, healthcare, confounders, risks

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Andrea Parisi, Samantha Vilkins, Luis Furuya-Kanamori, John A. Crump, Benjamin P. Howden, Darren Gray, Kathryn Glass, Martyn Kirk

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Objectives: Salmonella is a leading cause of foodborne enterocolitis worldwide. Nontyphoidal Salmonella (NTS) infections that are Multi-Drug Resistant (MDR) (non-susceptible to ≥1 agent in ≥3 antimicrobial categories) may result in more severe outcomes, although these effects have not been systematically examined. We conducted a systematic review and meta-analysis to examine impacts of MDR NTS on health in high-income settings. Methods: We systematically reviewed the literature from scientific databases, including PubMed, Scopus and grey literature sources, using PRISMA guidelines. We searched for data from case-control studies, cohorts, outbreaks, reports and theses, imposing no language restriction. We included only publications from January 1990 to September 2016 from high income countries as classified by World Bank. We extracted data from papers on duration of illness, hospitalisation rates, morbidity and mortality for MDR and non-MDR NTS strains. Results: After removing duplicates, the initial search revealed 4258 articles. After further screening, we identified 16 eligible studies for the systematic review, and 9 of these were included in meta-analysis. NTS serotypes differed among the reported studies but serotype Typhimurium, Enteritidis, Newport and Heidelberg were among the most often reported as MDR pathogens. Salmonella infections that were MDR were associated with excess bloodstream infections (OR 1.63; 95%CI 1.18-2.26), excess hospitalisations (OR 2.77; 95%CI 1.47-5.21) and higher mortality (OR 3.54; 95%CI 1.10-11.40). Conclusions: MDR NTS infections are a serious public health concern. With the emergence of MDR Salmonella strains in the high-income countries, it is crucial to restrict the use of antimicrobials both in animals and humans, and intervene to prevent foodborne infections.

Keywords: Antimicrobial Resistance, Bloodstream Infection, Health Outcomes, Hospitalisation, Invasive Disease, Multi-Drug Resistance (MDR), Mortality, Nontyphoidal Salmonella

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Authors: Salah Alrabeei, Mohammad Yousuf

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The goal of option pricing theory is to help the investors to manage their money, enhance returns and control their financial future by theoretically valuing their options. Modeling option pricing by Black-School models with jumps guarantees to consider the market movement. However, only numerical methods can solve this model. Furthermore, not all the numerical methods are efficient to solve these models because they have nonsmoothing payoffs or discontinuous derivatives at the exercise price. In this paper, the exponential time differencing (ETD) method is applied for solving partial integrodifferential equations arising in pricing European options under Merton’s and Kou’s jump-diffusion models. Fast Fourier Transform (FFT) algorithm is used as a matrix-vector multiplication solver, which reduces the complexity from O(M2) into O(M logM). A partial fraction form of Pad`e schemes is used to overcome the complexity of inverting polynomial of matrices. These two tools guarantee to get efficient and accurate numerical solutions. We construct a parallel and easy to implement a version of the numerical scheme. Numerical experiments are given to show how fast and accurate is our scheme.

Keywords: Integral differential equations, , L-stable methods, pricing European options, Jump–diffusion model

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Authors: Khadiza Akter, Tammy Greer, Raegan Bishop

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An in-depth study of the psycho-social effects of gynecological health disparities among immigrant women in the USA is the goal of this integrative review paper. Immigrant women frequently encounter unique obstacles that have severe psycho-social repercussions when it comes to receiving high-quality gynecological treatment. The review investigates the interaction of psychological, sociological, and health factors that affect the psycho-social effects that immigrant women experience in recognition of the significance of addressing these imbalances. The difficulties that immigrant women face in providing high-quality gynecological treatment in the USA are examined in this study. These difficulties are caused by a variety of psychological issues, including acculturation stress and stigma, as well as by social problems like prejudice, language hurdles, and cultural norms. Additionally, variations in healthcare access and affordability have a role. This study highlights the particular challenges that immigrant women have in receiving high-quality gynecological treatment in the United States. These difficulties are caused by both social problems like language obstacles, cultural norms, and biases, as well as psychological ones like acculturation stress and stigma. Additionally, variations in gynecological care for immigrant women are greatly influenced by variances in healthcare availability and price. To find pertinent research looking at the psychological effects of gynecological health disparities among immigrant women in the USA, a thorough search of numerous databases was done. Numerous approaches, including mixed, quantitative, and qualitative ones, were used in the studies. The important findings from various investigations were extracted and synthesized after they underwent a careful evaluation. In order to lessen these discrepancies and enhance the overall well-being of immigrant women, healthcare professionals, legislators, and researchers must collaborate to create specialized treatments, regulations, and health system reforms.

Keywords: cultural barriers, gynecological health disparities, health care access, immigrant women, mental health, psycho-social consequences, social stigma

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis

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Authors: Jinkoo Kim, Minsung Kim

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This study develops a hybrid seismic energy dissipation device composed of a viscoelastic damper and a steel slit damper connected in parallel. A cyclic loading test is conducted on a test specimen to validate the seismic performance of the hybrid damper. Then a moment-framed model structure is designed without seismic load so that it is retrofitted with the hybrid dampers. The model structure is transformed into an equivalent simplified system to find out optimum story-wise damper distribution pattern using genetic algorithm. The effectiveness of the hybrid damper is investigated by fragility analysis and the life cycle cost evaluation of the structure with and without the dampers. The analysis results show that the model structure has reduced probability of reaching damage states, especially the complete damage state, after seismic retrofit. The expected damage cost and consequently the life cycle cost of the retrofitted structure turn out to be significantly small compared with those of the original structure. Acknowledgement: This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R & D program (N043100016).

Keywords: seismic retrofit, slit dampers, friction dampers, hybrid dampers

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Authors: Mavis Yaa Konadu Agyemang

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Despite provisions to uphold and safeguard the rights of persons with disability in Ghana, there is evidence that they still encounter several challenges which limit their full and effective involvement in mainstream society, including the gold mining sector. The study sought to explore how persons with physical disability (PWPDs) experience gold mining in the Obuasi Municipal Area. A qualitative research design was used to discover and understand the experiences of PWPDs regarding mining. The purposive sampling technique was used to select five key informants for the study with the age range of (24-52 years) while snowball sampling aided the selection of 16 persons with various forms of physical disability with the age range of (24-60 years). In-depth interviews were used to gather data. The interviews lasted from forty-five minutes to an hour. In relation to the setting, the interviews of thirteen (13) of the participants with disability were done in their houses, two (2) were done on the phone, and one (1) was done in the office. Whereas the interviews of the five (5) key informants were all done in their offices. Data were analyzed using Creswell’s (2009) concept of thematic analysis. The findings suggest that even though land degradation affected everyone in the area, persons with mobility and visual impairment experienced many difficulties trekking the undulating land for long distances in search of arable land. Also, although mining activities are mostly labour-intensive, PWPDs were not employed even in areas where they could work. Further, the cost of items, in general, was high, affecting PWPDs more due to their economic immobility and paying for other sources of water due to land degradation and water pollution. The study also discovered that the peculiar conditions of PWPDs were not factored into compensation payments, and neither were females with physical disability engaged in compensation negotiations. Also, although some of the infrastructure provided by the gold mining companies in the area was physically accessible to some extent, it was not accessible in terms of information delivery. There is a need to educate the public on the effects of mining on PWPDs, their needs as well as disability issues in general. The Minerals and Mining Act (703) should be amended to include provisions that would consider the peculiar needs of PWPDs in compensation payment.

Keywords: mining, resettlement, compensation, environmental, social, disability

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte, V. Grincas

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This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.

Keywords: biomass concentration, extended Kalman filter, particle filter, state estimation, specific growth rate

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Authors: E. T. Carvalho Filho, J. T. N. Medeiros, L. G. Martinez

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Residual stresses are self equilibrating in a rigid body that acts on the microstructure of the material without application of an external load. They are elastic stresses and can be induced by mechanical, thermal and chemical processes causing a deformation gradient in the crystal lattice favoring premature failure in mechanicals components. The search for measurements with good reliability has been of great importance for the manufacturing industries. Several methods are able to quantify these stresses according to physical principles and the response of the mechanical behavior of the material. The diffraction X-ray technique is one of the most sensitive techniques for small variations of the crystalline lattice since the X-ray beam interacts with the interplanar distance. Being very sensitive technique is also susceptible to variations in measurements requiring a study of the factors that influence the final result of the measurement. Instrumental, operational factors, form deviations of the samples and geometry of analyzes are some variables that need to be considered and analyzed in order for the true measurement. The aim of this work is to analyze the sources of errors inherent to the residual stress measurement process by X-ray diffraction technique making an interlaboratory comparison to verify the reproducibility of the measurements. In this work, two specimens were machined, differing from each other by the surface finishing: grinding and polishing. Additionally, iron powder with particle size less than 45 µm was selected in order to be a reference (as recommended by ASTM E915 standard) for the tests. To verify the deviations caused by the equipment, those specimens were positioned and with the same analysis condition, seven measurements were carried out at 11Ψ tilts. To verify sample positioning errors, seven measurements were performed by positioning the sample at each measurement. To check geometry errors, measurements were repeated for the geometry and Bragg Brentano parallel beams. In order to verify the reproducibility of the method, the measurements were performed in two different laboratories and equipments. The results were statistically worked out and the quantification of the errors.

Keywords: residual stress, x-ray diffraction, repeatability, reproducibility, error analysis

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Authors: Puntarikorn Rungrattanakasin

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Objectives: To develop the trigger tool to warn about the risk of bleeding as an adverse event from warfarin drug usage during admission in Medical Wards of Chumphae Hospital. Methods: A retrospective study was performed by reviewing the medical records for the patients admitted between June 1st,2020- May 31st, 2021. ADEs were evaluated by Naranjo’s algorithm. The international normalized ratio (INR) and events of bleeding during admissions were collected. Statistical analyses, including Chi-square test and Reciever Operating Characteristic (ROC) curve for optimal INR threshold, were used for the study. Results: Among the 139 admissions, the INR range was found to vary between 0.86-14.91, there was a total of 15 bleeding events, out of which 9 were mild, and 6 were severe. The occurrence of bleeding started whenever the INR was greater than 2.5 and reached the statistical significance (p <0.05), which was in concordance with the ROC curve and yielded 100 % sensitivity and 60% specificity in the detection of a bleeding event. In this regard, the INR greater than 2.5 was considered to be an optimal threshold to alert promptly for bleeding tendency. Conclusions: The INR value of greater than 2.5 (>2.5) would be an appropriate trigger tool to warn of the risk of bleeding for patients taking warfarin in Chumphae Hospital.

Keywords: trigger tool, warfarin, risk of bleeding, medical wards

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Hussein Lujuo Mohamed

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Introduction: Public health has always been influenced by climate and weather. Changes in climate and climate variability, particularly changes in weather extremes affect the environment that provides people with clean air, food, water, shelter, and security. Tanzania is not an exception to the threats of climate change. The health sector is mostly affected due to emergence and proliferation of infectious diseases, thereby affecting health of the population and thus impacting achievement of sustainable development goals. Methodology: A desk review on documented issues pertaining to climate change and health in Tanzania was done using Google search engine. Keywords included climate change, link, health, climate initiatives. In cases where information was not available, documents from Ministry of Health, Vice Presidents Office-Environment, Local Government Authority, Ministry of Water, WHO, research, and training institutions were reviewed. Some of the reviewed documents from these institutions include policy brief papers, fieldwork activity reports, training manuals, and guidelines. Results: Six main climate resilience activities were identified in Tanzania. These were development and implementation of climate resilient water safety plans guidelines both for rural and urban water authorities, capacity building of rural and urban water authorities on implementation of climate-resilient water safety plans, and capacity strengthening of local environmental health practitioners on mainstreaming climate change and health into comprehensive council health plans. Others were vulnerability and adaptation assessment for the health sector, mainstreaming climate change in the National Health Policy, and development of risk communication strategy on climate. In addition information, education, and communication materials on climate change and to create awareness were developed aiming to sensitize and create awareness among communities on climate change issues and its effect on public health. Conclusion: Proper implementation of these interventions will help the country become resilient to many impacts of climate change in the health sector and become a good example for other least developed countries.

Keywords: climate, change, Tanzania, health

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Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

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In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: parameterization, response surface, structure optimization, pressure hull

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Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: inversion, limitations, optimization, resistivity

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Authors: Fredrick Olatunji Ajegbomogun, Bello Modinat Morenikeji, Okorie Nancy Chituru

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Electronic information resources are highly relevant to students' academic and research needs but are grossly underutilized, despite the institutional commitment to making them available. The under-utilisation of these resources could be attributed to a low level of study behaviour coupled with a low level of computer self-efficacy. This study assessed computer self-efficacy, study behaviour, and the use of electronic information resources by students in selected polytechnics in Ogun State. A simple random sampling technique using Krejcie and Morgan's (1970) Table was used to select 370 respondents for the study. A structured questionnaire was used to collect data on respondents. Data were analysed using frequency counts, percentages, mean, standard deviation, Pearson Product Moment Correlation (PPMC) and multiple regression analysis. Results reveal that the internet (= 1.94), YouTube (= 1.74), and search engines (= 1.72) were the common information resources available to the students, while the Internet (= 4.22) is the most utilized resource. Major reasons for using electronic information resources were to source materials and information (= 3.30), for research (= 3.25), and to augment class notes (= 2.90). The majority (91.0%) of the respondents have a high level of computer self-efficacy in the use of electronic information resources through selecting from screen menus (= 3.12), using data files ( = 3.10), and efficient use of computers (= 3.06). Good preparation for tests (= 3.27), examinations (= 3.26), and organization of tutorials (= 3.11) are the common study behaviours of the respondents. Overall, 93.8% have good study behaviour. Inadequate computer facilities to access information (= 3.23), and poor internet access (= 2.87) were the major challenges confronting students’ use of electronic information resources. According to the PPMC results, study behavior (r = 0.280) and computer self-efficacy (r = 0.304) have significant (p 0.05) relationships with the use of electronic information resources. Regression results reveal that self-efficacy (=0.214) and study behavior (=0.122) positively (p 0.05) influenced students' use of electronic information resources. The study concluded that students' use of electronic information resources depends on the purpose, their computer self-efficacy, and their study behaviour. Therefore, the study recommended that the management should encourage the students to improve their study habits and computer skills, as this will enhance their continuous and more effective utilization of electronic information resources.

Keywords: computer self-efficacy, study behaviour, electronic information resources, polytechnics, Nigeria

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Authors: Yu-Mei Tseng, Chih-Chun Wu

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As information and communication technology (ICT) has become a part of people’s lives, most teenagers born after the 1980s and grew up in internet generation are called digital natives. Meanwhile, those teenagers’ parents are called digital immigrants. They need to keep learning new skills of ICT. This study investigated that high school students helped their parents set up social network services (SNS) and taught them how to use ICT. This study applied paper and pencil anonymous questionnaires that asked the ICT learning and ICT products using in high school students’ parents. The sample size was 2,621 high school students, including 1,360 (51.9%) males and 1,261 (48.1%) females. The sample was from 12 high school and vocational high school in central Taiwan. Results from paired sample t-tests demonstrated regardless genders, both male and female high school students help mothers set up Facebook and LINE more often than fathers. In addition, both male and female high school students taught mothers to use ICT more often than fathers. Meanwhile, both male and female high school students teach mothers to use SNS more often than fathers. The results showed that intergenerational ICT teaching occurred more often between mothers and her children than fathers. It could imply that mothers play a more important role in family ICT learning than fathers, or it could be that mothers need more help regarding ICT than fathers. As for gender differences, results from the independent t-tests showed that female high school students were more likely than male ones to help their parents setup Facebook and LINE. In addition, compared to male high school students, female ones were more likely to teach their parents to use smartphone, Facebook and LINE. However, no gender differences were detected in teaching mothers. The gender differences results suggested that female teenagers offer more helps to their parents regarding ICT learning than their male counterparts. As for area differences, results from the independent t-tests showed that the high school in remote area students were more likely than metropolitan ones to teach parents to use computer, search engine and download files of audio and video. The area differences results might indicate that remote area students were more likely to teach their parents how to use ICT. The results from this study encourage children to help and teach their parents with ICT products.

Keywords: adult ICT learning, family ICT learning, ICT learning, urban-rural gap

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Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

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Authors: Wesley Too, Christine Chesiror

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Hypertension is a public health challenge that globally, with the World Health Organization estimating that by 2025, more than 1.5 billion people would have been diagnosed with it. Kenya’s prevalence of hypertension is estimated at 24.6 percent; however, 55% of the affected have uncontrolled blood pressure, which is worst in some parts of the country with different lifestyle: nomads and migratory communities. Kenyan pastoralists comprise 20% of the nation's population and are constantly on the move for search of water, pasture for their herd, and desertification have driven nomadic populations to the brink, given their unique and dynamic challenges. Nomads face myriad of challenges and barriers towards the management of their health care problems. Nomadic area is predominantly rural, with a low population density and a nomadic population. Health care access and quality are further hampered by poor telecommunications, infrastructure, and security. In Kenya, nomadic communities experience the worst health outcomes, disproportionate health disparities, and inequalities due to unresponsive, culturally sensitive health care system to nomad’s lifestyle and their health care needs. Marsabit covering a surface area of 66,923.1 km2, is the second largest county in Kenya, constituting about 2.3 million people of North-Eastern region, with only 2.3 percent and 1.9 percent of Kenya's total number of doctors and nurses in the country. In Kenya, there are scanty research on hypertension managementin this region and, at best, non-existent study on hypertension among nomads-migratory communities of Northern Kenya. Therefore, the purpose seeks to determine the prevalence of hypertension among nomads and document nomads' practices regarding early detections, management, and levels of control of hypertension in one of the Counties in Kenya with high- hypertensive case load per year. Methods: A cross-sectional study design was used to collect data from multiple sites and health facilities. A total of 260 participants were enrolled into the study. The study is currently ongoing. It is anticipated that by September, we will have initial findings & recommendations to share for conference

Keywords: pastoralists, hypertension, health, kenya

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Authors: Antonio Gioiosa, Simone Doanti, Eric Flumerfelt, Luca Morescalchi, Elena Pedreschi, Gianantonio Pezzullo, Ryan A. Rivera, Franco Spinella

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The Mu2e experiment at Fermilab aims to measure the charged-lepton flavour violating neutrino-less conversion of a negative muon into an electron in the field of an aluminum nucleus. With the expected experimental sensitivity, Mu2e will improve the previous limit of four orders of magnitude. The Mu2e data acquisition (DAQ) system provides hardware and software to collect digitized data from the tracker, calorimeter, cosmic ray veto, and beam monitoring systems. Mu2e’s trigger and data acquisition system (TDAQ) uses otsdaq as its solution. developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under-the-hood, for event transfer, filtering, and processing. Otsdaq is an online DAQ software suite with a focus on flexibility and scalability while providing a multi-user, web-based interface accessible through the Chrome or Firefox web browser. The detector read out controller (ROC) from the tracker and calorimeter stream out zero-suppressed data continuously to the data transfer controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes from a cosmic ray veto system (CRV).

Keywords: trigger, daq, mu2e, Fermilab

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Authors: Mayowa Philips Ibitola, Abe Oluwaseun Banji, Olorunfemi Akinade-Solomon

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A total of 20 bottom sediment samples were collected from the Lekki Lagoon during the wet and dry season. The study was carried out to determine the textural characteristics, sediment distribution pattern and energy of transportation within the lagoon system. The sediment grain sizes and depth profiling was analyzed using dry sieving method and MATLAB algorithm for processing. The granulometric reveals fine grained sand both for the wet and dry season with an average mean value of 2.03 ϕ and -2.88 ϕ, respectively. Sediments were moderately sorted with an average inclusive standard deviation of 0.77 ϕ and -0.82 ϕ. Skewness varied from strongly coarse and near symmetrical 0.34- ϕ and 0.09 ϕ. The kurtosis average value was 0.87 ϕ and -1.4 ϕ (platykurtic and leptokurtic). Entirely, the bathymetry shows an average depth of 4.0 m. The deepest and shallowest area has a depth of 11.2 m and 0.5 m, respectively. High concentration of fine sand was observed at deep areas compared to the shallow areas during wet and dry season. Statistical parameter results show that the overall sediments are sorted, and deposited under low energy condition over a long distance. However, sediment distribution and sediment transport pattern of Lekki Lagoon is controlled by a low energy current and the down slope configuration of the bathymetry enhances the sorting and the deposition rate in the Lekki Lagoon.

Keywords: Lekki Lagoon, Marine sediment, bathymetry, grain size distribution

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Authors: Abir Touati, Syed Jawad Hussain, Farid Touati, Ammar Bouallegue

Abstract:

Although there has been a growing interest in the hybrid free-space optical link and radio frequency FSO/RF communication system, the current literature is limited to results obtained in moderate or cold environment. In this paper, using a soft switching approach, we investigate the effect of weather inhomogeneities on the strength of turbulence hence the channel refractive index under Qatar harsh environment and their influence on the hybrid FSO/RF availability. In this approach, either FSO/RF or simultaneous or none of them can be active. Based on soft switching approach and a finite state Markov Chain (FSMC) process, we model the channel fading for the two links and derive a mathematical expression for the outage probability of the hybrid system. Then, we evaluate the behavior of the hybrid FSO/RF under hazy and harsh weather. Results show that the FSO/RF soft switching renders the system outage probability less than that of each link individually. A soft switching algorithm is being implemented on FPGAs using Raptor code interfaced to the two terminals of a 1Gbps/100 Mbps FSO/RF hybrid system, the first being implemented in the region. Experimental results are compared to the above simulation results.

Keywords: atmospheric turbulence, haze, hybrid FSO/RF, outage probability, refractive index

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Authors: Bahar Ayhan

Abstract:

The present paper is concerned with the numerical modeling of the inelastic behavior of the anisotropically damaged ductile materials, which are based on a generalized macroscopic theory within the framework of continuum damage mechanics. Kinematic decomposition of the strain rates into elastic, plastic and damage parts is basis for accomplishing the structure of continuum theory. The evolution of the damage strain rate tensor is detailed with the consideration of anisotropic effects. Helmholtz free energy functions are constructed separately for the elastic and inelastic behaviors in order to be able to address the plastic and damage process. Additionally, the constitutive structure, which is based on the standard dissipative material approach, is elaborated with stress tensor, a yield criterion for plasticity and a fracture criterion for damage besides the potential functions of each inelastic phenomenon. The finite element method is used to approximate the linearized variational problem. Stress and strain outcomes are solved by using the numerical integration algorithm based on operator split methodology with a plastic and damage (multiplicator) variable separately. Numerical simulations are proposed in order to demonstrate the efficiency of the formulation by comparing the examples in the literature.

Keywords: anisotropic damage, finite element method, plasticity, coupling

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Authors: Obiora E. Anisiji, Jeremiah L. Chukwuneke, Chinonso H. Achebe, Paul C. Okolie

Abstract:

This work developed a mathematical model of a biogas plant from a mechanistic point of view, for urban area clean energy requirement. It aimed at integrating thermodynamics; which deals with the direction in which a process occurs and Biochemical kinetics; which gives the understanding of the rates of biochemical reaction. The mathematical formulation of the proposed gas plant follows the fundamental principles of thermodynamics, and further analysis were accomplished to develop an algorithm for evaluating the plant performance preferably in terms of daily production capacity. In addition, the capacity of the plant is equally estimated for a given cycle of operation and presented in time histories. A nominal 1500m3 biogas plant was studied characteristically and its performance efficiency evaluated. It was observed that the rate of biogas production is essentially a function of enthalpy ratio, the reactor temperature, pH, substrate concentration, rate of degradation of the biomass, and the accumulation of matter in the system due to bacteria growth. The results of this study conform to a very large extent with reported empirical data of some existing plant and further model validations were conducted in line with classical records found in literature.

Keywords: anaerobic digestion, biogas plant, biogas production, bio-reactor, energy, fermentation, rate of production, temperature, therm

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Authors: Muhammad Adeel Arshad, Shaukat Ali Bhatti, Faiz-ul Hassan

Abstract:

Manipulating microbial ecosystem in the rumen is considered as an important strategy to optimize production efficiency in ruminants. In the past, antibiotics and synthetic chemical compounds have been used for the manipulation of rumen fermentation. However, since the non-therapeutic use of antibiotics has been banned, efforts are being focused to search out safe alternative products. In tropics, crop residues and forage grazing are major dietary sources for ruminants. Poor digestibility and utilization of these feedstuffs by animals is a limiting factor to exploit the full potential of ruminants in this area. Hence, there is a need to enhance the utilization of these available feeding resources. One of the potential strategies in this regard is the use of direct-fed microbes. Bacteria and fungi are mostly used as direct-fed microbes to improve animal health and productivity. Commonly used bacterial species include lactic acid-producing and utilizing bacteria (Lactobacillus, Streptococcus, Enterococcus, Bifidobacterium, and Bacillus) and fungal species of yeast are Saccharomyces and Aspergillus. Direct-fed microbes modulate microbial balance in the gastrointestinal tract through the competitive exclusion of pathogenic species and favoring beneficial microbes. Improvement in weight gain and feed efficiency has been observed as a result of feeding direct-fed bacteria. The use of fungi as a direct-fed microbe may prevent excessive production of lactate and harmful oxygen in the rumen leading to better feed digestibility. However, the mechanistic mode of action for bacterial or fungal direct-fed microbes has not been established yet. Various reports have confirmed an increase in dry matter intake, milk yield, and milk contents in response to the administration of direct-fed microbes. However, the application of a direct-fed microbe has shown variable responses mainly attributed to dosages and strains of microbes. Nonetheless, it is concluded that the inclusion of direct-fed microbes may mediate the rumen ecosystem to manage lactic acid production and utilization in both clinical and sub-acute rumen acidosis.

Keywords: microbes, roughages, rumen, feed efficiency, production, fermentation

Procedia PDF Downloads 125

Authors: Mohammed Cherifi, Abderrahmane Benbrik, Siham Laouar-Meftah, Denis Lemonnier

Abstract:

The present study was conducted to numerically investigate the interaction of non-gray-gas radiation with opposed mixed convection in a vertical two-sided lid-driven square cavity. The opposing flows are simultaneously generated by the vertical boundary walls which slide at a constant speed and the natural convection due to the gradient temperature of differentially heated cavity. The horizontal walls are thermally insulated and perfectly reflective. The enclosure is filled with air-H2O-CO2 gas mixture, which is considered as a non-gray, absorbing, emitting and not scattering medium. The governing differential equations are solved by a finite-volume method, by adopting the SIMPLER algorithm for pressure–velocity coupling. The radiative transfer equation (RTE) is solved by the discrete ordinates method (DOM). The spectral line weighted sum of gray gases model (SLW) is used to account for non-gray radiation properties. Three cases of the effects of radiation (transparent, gray and non-gray medium) are studied. Comparison is also made with the parametric studies of the effect of the mixed convection parameter, Ri (0.1, 1, 10), on the fluid flow and heat transfer have been performed.

Keywords: opposed mixed convection, non-gray-gas radiation, two-sided lid-driven cavity, discrete ordinate method, SLW model

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Authors: Karel Šebesta, Jiří Žáček, Matuš Salva, Mohammad Housam

Abstract:

Drivers of agricultural vehicles are exposed to continuous vibration caused by driving over rough terrain. The long-term effects of these vibrations could start with a decreased level of vigilance at work and could reach the level of several health problems. Therefore, eliminating the vibration to maximize the comfort of the driver is essential for better/longer performance. One of the modern damping systems, which can deal with this problem is the Semi-active (S/A) suspension system featuring a Magnetorheological (MR) damper. With this damper, the damping level can be adjusted using varying currents through the coil. Adjustments of the damping force can be carried out continuously based on the evaluated data (position and acceleration of seat) by the control algorithm. The advantage of this system is the wide dynamic range and the high speed of force response time. Compared to other S/A or active systems, the MR damper does not need as much electrical power, and the system is much simpler. This paper aims to prove the effectiveness of this damping system used in the tractor seat. The vibration testing stand was designed and manufactured specifically for this type of research, which is used to simulate vibrations with constant amplitude at variable frequency.

Keywords: magnetorheological damper, semi-active suspension, seat scissor mechanism, sky-hook

Procedia PDF Downloads 89

Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

Abstract:

Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

Procedia PDF Downloads 99