Search results for: mortality prediction

Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Andy H. Clark

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This research looks into the relationship between language comprehension and production with a specific focus on the role of salience in shaping these processes. Salience, our most immediate perception of what is most probable out of all possible situations and outcomes strongly affects our perception and action in language production and comprehension. This study investigates the impact of geographic and emotional attachments to the target language on the differences in the learners’ comprehension and production abilities. Using quantitative research methods (Qualtrics, SPSS), this study examines preferential choices of two groups of Japanese English language learners: those residing in the United States and those in Japan. By comparing and contrasting these two groups, we hope to gain a better understanding of how salience of linguistics cues influences language processing.

Keywords: intercultural pragmatics, salience, production, comprehension, pragmatics, action, perception, cognition

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Authors: Teodora Mocan, Matea Cristian, Cornel Iancu, Flaviu A. Tabaran, Florin Zaharie, Bartos Dana, Lucian Mocan

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Colon cancer (CC) a lethal human malignancy, is one of the most commonly diagnosed cancer. With its high increased mortality rate, as well as low survival rate combined with high resistance to chemotherapy CC, represents one of the most important global health issues. In the presented research, we have developed a distinct nanostructured colon carcinoma vaccine model based on a nano-biosystem composed of 39 nm gold nanoparticles conjugated to colon cancer epitopes. We prove by means of proteomic analysis, immunocytochemistry, flow cytometry and hyperspectral microscopy that our developed nanobioconjugate was able to contribute to an optimal prophylactic effect against CC by promoting major histocompatibility complex mediated (MHC) antigen presentation by dendritic cells. We may conclude that the proposed immunoprophylactic approach could be more effective than the current treatments of CC because it promotes recognition of the tumoral antigens by the immune system.

Keywords: anticancer vaccine, colon cancer, gold nanoparticles, tumor antigen

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Authors: Ali Ashraf Hamedi Oghul Beyk

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The main objective of the study is to determine the factors which influence the acceptance or rejection of rural livestock insurance in Garmsar. The research method is descriptive one. There are two groups of research populations: 1467 cases in acceptance group and 7000 cases in rejection group. The sample population is 320 cases among 8467 ones. Data collection instrument is questionnaire. The validity of the questionnaire was measured by faculty members and other agriculture experts and also reliability of it determined through Cronbach alpha which was %83. Correlation between acceptance and rejection of investigated population. According to the findings of the research, between educational level, basic income from farm-related communication channels, contacts of experts and acceptance and rejection of livestock insurance at %5 & the mortality rate, loan awareness of the objectives of the livestock insurance benefits %1 there is a meaningful relationship. Mann-Whitney test shows the different educational levels, different awareness and interest to livestock insurance between the two groups. Besides, the T-test shows the livestock losses rate in two groups.

Keywords: insurance, livestock, Garmsar, Semnan

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: B. Gepts, E. Meex, E. Nuyts, E. Knaepen, G. Verbeeck

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The importance of resource efficiency and environmental impact assessment has raised the interest in knowing the amount of materials used in buildings. If no BIM model or energy performance certificate is available, material quantities can be obtained through an estimation or time-consuming calculation. For the interior wall area, no validated estimation method exists. However, in the case of environmental impact assessment or evaluating the existing building stock as future material banks, knowledge of the material quantities used in interior walls is indispensable. This paper presents a validated method for the estimation of the interior wall area for dwellings based on easy-to-collect building characteristics. A database of 4963 residential buildings spread all over Belgium is used. The data are collected through onsite measurements of the buildings during the construction phase (between mid-2010 and mid-2017). The interior wall area refers to the area of all interior walls in the building, including the inner leaf of exterior (party) walls, minus the area of windows and doors, unless mentioned otherwise. The two predictive modelling techniques used are 1) a (stepwise) linear regression and 2) a decision tree. The best estimation method is selected based on the best R² k-fold (5) fit. The research shows that the building volume is by far the most important variable to estimate the interior wall area. A stepwise regression based on building volume per building, building typology, and type of house provides the best fit, with R² k-fold (5) = 0.88. Although the best R² k-fold value is obtained when the other parameters ‘building typology’ and ‘type of house’ are included, the contribution of these variables can be seen as statistically significant but practically irrelevant. Thus, if these parameters are not available, a simplified estimation method based on only the volume of the building can also be applied (R² k-fold = 0.87). The robustness and precision of the method (output) are validated three times. Firstly, the prediction of the interior wall area is checked by means of alternative calculations of the building volume and of the interior wall area; thus, other definitions are applied to the same data. Secondly, the output is tested on an extension of the database, so it has the same definitions but on other data. Thirdly, the output is checked on an unrelated database with other definitions and other data. The validation of the estimation methods demonstrates that the methods remain accurate when underlying data are changed. The method can support environmental as well as economic dimensions of impact assessment, as it can be used in early design. As it allows the prediction of the amount of interior wall materials to be produced in the future or that might become available after demolition, the presented estimation method can be part of material flow analyses on input and on output.

Keywords: buildings as material banks, building stock, estimation method, interior wall area

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Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

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This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: M. A. Ghebouli, A. Bouhemadou, H. Choutri, L. Louaila

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Some physical properties of the cubic perovskites CsXF3 (X = Sr, Ca, and Hg) have been investigated using pseudopotential plane–wave (PP-PW) method based on the density functional theory (DFT). The calculated lattice constants within GGA (PBE) and LDA (CA-PZ) agree reasonably with the available experiment data. The elastic constants and their pressure derivatives are predicted using the static finite strain technique. We derived the bulk and shear moduli, Young’s modulus, Poisson’s ratio and Lamé’s constants for ideal polycrystalline aggregates. The analysis of B/G ratio indicates that CsXF3 (X = Ca, Sr, and Hg) are ductile materials. The thermal effect on the volume, bulk modulus, heat capacities CV, CP, and Debye temperature was predicted.

Keywords: perovskite, PP-PW method, elastic constants, electronic band structure

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Saira Baloch, Mohsin Ali Baloch

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Background: Malaria is a potentially life-threatening disease and also a major public health problem in Pakistan. Renal failure is an emerging problem correlated with morbidity and mortality, however can be diagnosed and treated in the early stages. Objectives: To elucidate the biochemical renal parameters in patients with falciparum malaria and comparison with healthy control subjects. Method: 80 patients, who were diagnosed to be affected by falciparum malaria. Detailed history, general physical and systemic examination and necessary pathological, biochemical renal laboratory parameters and investigations were done. Results: Among the 80 patients, 43 were males and 37 were females. All patients were infected with P. falciparum. All patients had increased serum creatinine and urea levels and urine output of less than 400 ml/day were categorized as suffering from renal failure. Conclusion: Patients infected with P. falciparum are at an increased risk of developing renal failure when compared to patients infected with other complications. P. vivax has massive potential to cause life threatening complications and even death. Further research is required to understand the exact pathogenesis of various complications encountered in vivax malaria.

Keywords: falciparum malaria, renal failure, biochemical parameters, pathogenesis

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Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

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Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

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Authors: Mohammad Asadpour, Maryam Sadeghi, Mahmoud Jafari

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The transformation of carbon dioxide into fuels and commodity chemicals is considered one of the most attractive methods to meet energy demands and reduce atmospheric CO₂ levels. Cobalt complexes have previously shown high faradaic efficiency in the reduction of CO₂ to CO. In this study, a nanostructure, referred to as a porphyrin-like buckyball, is simulated and analyzed for its electrical properties. The investigation aims to understand the unique characteristics of this material and its potential applications in electronic devices. Through computational simulations and analysis, the electrocatalytic reduction of CO₂ to CO by Cobalt-porphyrin-like buckyball is explored. The findings of this study offer valuable insights into the electrocatalytic properties of this predicted structure, paving the way for further research and development in the field of nanotechnology.

Keywords: porphyrin-like buckyball, DFT, nanomaterials, CO₂ to CO

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Authors: Sharifah Insyirah, Noraliza A.

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Eucalyptus pellita is a popular species of plantation tree in many nations and regions because of its fast growth and excellent timber qualities. Moreover, Eucalyptus leaves are known as forest harvesting waste with the potential to generate essential oils. Eucalyptus is one of the plants utilized in the pulp and paper industry. This study aims to investigate the impact of two parameters, which are types of fertilizer and polybags (black polybags and transparent polybags), on Eucalyptus growth performance in the nursery. The present investigation was carried out at Main Nursery, Forestry Research Institute Malaysia under agro-climatic and irrigation conditions of the nursery. Twenty seedlings were prepared for this study consisting of two treatments of eco-friendly soil conditioner and NPK (ratio of NPK 8:8:8). Survival and height measurements were collected accordingly. Seedlings without any treatment showed better growth than treatment with soil conditioner or NPK. Seedlings as in C1, shows consistently fastest growth compared to T1 (B) and T2 (SC), and the mortality rates were 0%, 15% and 5%, respectively. The results demonstrated that fertilizer and soil conditioner applied at a younger age of seedlings had less effect on growth performance.

Keywords: eucalyptus pellita, potting media, high quality planting materials, nursery

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Authors: Pacharaporn Lekyan

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The purpose of this study is to explore the conflict management affecting the workplace and analyze the ability of the prediction of leadership of the headman and the methods to handle the conflict in an organization. The quantitative research and developed the questionnaire in order to collect information from the respondents from 200 samples from leader or manager who worked in frozen food factories in Thailand. The result analysis shows about the problem of the relationship between conflict management factors, leadership, and the confliction in organization. The emotion of the leader in the organization is not the only factor that can affect conflict management but also the emotion of surrounding people which this factor can happen all the time and shows that four out of five factors of interpersonal conflict management have affected on emotion intelligence and also shows that the behaviors of leadership have an influence on conflict management.

Keywords: conflict management, emotional intelligence, leadership, factories' employee

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Authors: Maeah Stephanie A. Macapaz Abadejos

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Covid-19 shook the whole world. Every person on all sides of the world was affected by the pandemic. All the nations and world leaders were searching for a variety of cures and solutions to stop the spread of the virus. In connection with this, this investigative case study aims to show a relationship between COVID-19 vaccinations to the immune system of the seniors and elderly. The seniors and elderly are one of the most vulnerable populations that show high morbidity and mortality in any illnesses and diseases. This study will show lived experiences of the senior’s immune system and health status, how it is being affected by the COVID-19 virus and its vaccines, Risk for COVID-19 Infection, Hospitalization, and Death by Age Group. Participants of this study are from 3 Cebu City Barangays and 2 from Barangays of the Cities of the Cebu Province. To conclude, this study shall serve its purpose of providing clear and concise results in strengthening the evidence of the effects of the COVID-19 virus infection, especially the free vaccinations, on the health and overall well-being of the elderly and seniors.

Keywords: gerontology nursing, vaccination, protection and immunity, seniors and the elderly

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Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Ruixia Liu

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Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Vincent Oghenekevbe Olughor, Olusoji Mayowa Ige

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Except for a preexisting liver disease and malnutrition, there are no predilections for low serum albumin (SA) levels in humans. At normal reference levels (4.0-6.0g/dl) SA is a universal marker for mortality and morbidity risks assessments where depletion by 1.0g/dl increases mortality risk by 137% and morbidity by 89%.It has 40 known functions contributing significantly to the sustenance of human life. A depletion in SA to <2.2g/dl, in most clinical settings worldwide, leads to loss of oncotic pressure of blood causing clinical manifestations of bipedal Oedema, in which the patients remain conscious. SA also contributes significantly to buffering of blood to a life-sustaining pH of 7.35-7.45. A drop in blood pH to <6.9 will lead to instant coma and death, which can occur after SA continues to deplete after manifestations of bipedal Oedema. In an intervention study conducted in 2014 following the discovery that “SA is depleted during malaria fever”, a Nutraceutical formulated for use as treatment adjunct to prevent SA depletions during malaria to <2.4g/dl after Efficacy testing was found to be satisfactory. There are five known types of Malaria caused by Apicomplexan parasites, Plasmodium: the most lethal being that caused by Plasmodium falciparum causing malignant tertian malaria, in which the fever was occurring every 48 hours coincides with the dumping of malaria-toxins (Hemozoin) into blood, causing contamination: blood must remain sterile. Other Apicomplexan parasites, Toxoplasma and Cryptosporidium, are opportunistic infections of HIV. Separate studies showed SA depletions in MDRTB (multidrug resistant TB), and MDRTB-HIV patients by the same mechanism discovered with malaria and such depletions will be further complicated whenever Apicomplexan parasitic infections co-exist. Both Apicomplexan parasites and the TB parasite belong to the Obligate-group of Parasites, which are parasites that replicate only inside its host; and most of them have capacities to over-consume host nutrients during parasitaemia. In MDRTB patients the body attempts repeatedly to prevent depletions in SA to critical levels in the presence of adequate nutrients and only for a while in MDRTB-HIV patients. These groups of patients will, therefore, benefit from the already tested Nutraceutical in malaria patients. The Nutraceutical bio-Powder was formulated (to BP 1988 specification) from twelve nature-based food-grade nutrients containing all dedicated nutrients for ensuring improved synthesis of Albumin by the liver. The Nutraceutical was administered daily for 38±2days in 23 children, in a prospective phase-2 clinical trial, and its impact on body weight and core blood parameters were documented at the start and end of efficacy testing period. Sixteen children who did not experience malaria-induced depletions of SA had significant SA increase; seven children who experienced malaria-induced depletions of SA had insignificant SA decrease. The Packed Cell Volume Percentage (PCV %), a measure of the Oxygen carrying capacity of blood and the amount of nutrients the body can absorb, increased in both groups. The total serum proteins (SA+ Globulins) increased or decreased within the continuum of normal. In conclusion, MDRTB and MDRTB-HIV patients will benefit from a variant of this Nutraceutical when used as treatment adjunct.

Keywords: antitrypsin-free Nutraceutical, apicomplexan parasites, no predilections for low serum albumin, toxoplasmosis

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Bhargavi Santebennur Dwarakanath, Praveen Vadakke Kamath, Savitha Janakiraman

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Cervical cancer is one of the leading causes of mortality in women and is the second most common malignancy worldwide. Lovastatin, a non polar, anticholesterol drug which also exerts antitumour activity in vitro. In the present study, lovastatin from Aspergillus terreus (KM017963) was purified by adsoprtion chromatography and evaluated for its anticancer and anti-oxidant properties in human cervical cancer cell lines (HeLa). The growth inhibitory and proapoptotic effects of purified lovastatin on HeLa cell lines were investigated by determining its influence on cytotoxicity, Mitochondrial Membrane Potential (MMP), DNA fragmentation and antioxidant property (Hydroxy radical scavenging effect and the levels of total reduced glutathione). Flow cytometry analysis by propidium iodide staining confirmed the induction of apoptotic cell death and revealed cell cycle arrest at G0/G1 phase. Results of the study give leads for anticancer effects of lovastatin and its potential efficacy in the chemotherapy of cervical cancer.

Keywords: apoptosis, Aspergillus terreus, cervical cancer, lovastatin

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Authors: Luciano Tarantino

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Gas gangrene is a rare, severe infection with a very high mortality rate caused by Clostridium species. The infection causes a non-suppurative localized producing gas lesion from which harmful toxins that impair the inflammatory response cause vessel damage and multiple organ failure. Gas gangrene of the liver is very rare and develops suddenly, often as a complication of abdominal surgery and liver transplantation. The present paper deals with a case of gas gangrene of the liver that occurred after percutaneous MW ablation of hepatocellular carcinoma, resulting in progressive liver necrosis and multi-organ failure in spite of specific antibiotics administration. The patient was successfully treated with percutaneous Radiofrequency ablation. Case report: Female, 76 years old, Child A class cirrhosis, treated with synchronous insertion of 3 MW antennae for large HCC (5.5 cm) in the VIII segment. 24 hours after treatment, the patient was asymptomatic and left the hospital . 2 days later, she complained of fever, weakness, abdominal swelling, and pain. Abdominal US detected a 2.3 cm in size gas-containing area, eccentric within the large (7 cm) ablated area. The patient was promptly hospitalized with the diagnosis of anaerobic liver abscess and started antibiotic therapy with Imipenem/cilastatine+metronidazole+teicoplanine. On the fourth day, the patient was moved to the ICU because of dyspnea, congestive heart failure, atrial fibrillation, right pleural effusion, ascites, and renal failure. Blood tests demonstrated severe leukopenia and neutropenia, anemia, increased creatinine and blood nitrogen, high-level FDP, and high INR. Blood cultures were negative. At US, unenhanced CT, and CEUS, a progressive enlargement of the infected liver lesion was observed. Percutaneous drainage was attempted, but only drops of non-suppurative brownish material could be obtained. Pleural and peritoneal drainages gave serosanguineous muddy fluid. The Surgeon and the Anesthesiologist excluded any indication of surgical resection because of the high perioperative mortality risk. Therefore, we asked for the informed consent of the patient and her relatives to treat the gangrenous liver lesion by percutaneous Ablation. Under conscious sedation, percutaneous RFA of GG was performed by double insertion of 3 cool-tip needles (Covidien LDT, USA ) into the infected area. The procedure was well tolerated by the patient. A dramatic improvement in the patient's condition was observed in the subsequent 24 hours and thereafter. Fever and dyspnea disappeared. Normalization of blood tests, including creatinine, was observed within 4 days. Heart performance improved, 10 days after the RFA the patient left the hospital and was followed-up with weekly as an outpatient for 2 months and every two months thereafter. At 18 months follow-up, the patient is well compensated (Child-Pugh class B7), without any peritoneal or pleural effusion and without any HCC recurrence at imaging (US every 3 months, CT every 6 months). Percutaneous RFA could be a valuable therapy of focal GG of the liver in patients non-responder to antibiotics and when surgery and liver transplantation are not feasible. A fast and early indication is needed in case of rapid worsening of patient's conditions.

Keywords: liver tumor ablation, interventional ultrasound, liver infection, gas gangrene, radiofrequency ablation

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Authors: A. M. U. P. Kumari, Vidanapathirana. J., Amarasekara J., Karunanayaka L.

Abstract:

Leptospirosis is a zoonotic infection with significant morbidity and mortality. As an occupational disease, it has become a global concern due to its disease burden in endemic countries and rural areas. The aim of this study was to assess disease burden in terms of DALYs of leptospirosis. A hospital-based descriptive cross-sectional study was conducted using 450 clinically diagnosed leptospirosis patients admitted to base and above hospitals in Monaragala district, Sri Lanka, using a pretested interviewer administered questionnaire. The patients were followed up till normal day today life after discharge. Estimation of DALYs was done using laboratory confirmed leptospirosis patients. Leptospirosis disease burden in the Monaragala district was 44.9 DALYs per 100,000 population which includes 33.18 YLLs and 10.9 YLDs. The incidence of leptospirosis in the Monaragala district during the study period was 59.8 per 100,000 population, and the case fatality rate (CFR) was 1.5% due to delay in health seeking behaviour; 75% of deaths were among males due to multi organ failure. The disease burden of leptospirosis in the Moneragala district was significantly high, and urgent efforts to control and prevent leptospirosis should be a priority.

Keywords: human leptospirosis, disease burden, disability adjusted life Years, Sri Lanka

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Authors: Martin Kendra, Jaroslav Mašek, Juraj Čamaj, Matej Babin

Abstract:

The railway transport is considered as a one of the most environmentally friendly mode of transport. With future prediction of increasing of freight transport there are lines facing problems with demanded capacity. Increase of the track capacity could be achieved by infrastructure constructive adjustments. The contribution shows how the travel time can be minimized and the track capacity increased by changing some of the basic infrastructure and operation parameters, for example, the minimal curve radius of the track, the number of tracks, or the usable track length at stations. Calculation of the necessary parameter changes is based on the fundamental physical laws applied to the train movement, and calculation of the occupation time is dependent on the changes of controlling the traffic between the stations.

Keywords: curve radius, maximum curve speed, track mass capacity, reconstruction

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Authors: Prakash Dhakal

Abstract:

Hepatic portal venous gas (HPVG) is diagnosed via computed tomography due to unusual imaging features. HPVG, when linked with pneumatosis intestinalis, has a high mortality rate and requires urgent intervention. We present a case of a 26-year-old young adult with superior mesenteric artery thrombosis who presented with severe abdominal pain. He had a history of COVID vaccination (First dose of COVISHILED) 15 days back. On imaging, HPVG and pneumatosis intestinalis were seen owing to the urgent intervention of the patient. The reliable interpretation of the imaging findings along with quick intervention led to a favorable outcome in our case. Herein we present a thorough review of the patient with a history of COVID-19 vaccination with superior mesenteric artery thrombosis leading to bowel ischemia and hepatic portal venous gas. The patient underwent subtotal small bowel resection.

Keywords: COVID-19 vaccination, SMA thrombosis, portal venoius gas, pneumatosis intestinalis

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Authors: Iñaki Iturria, Pasquale Russo, Montserrat Nacher-Vázquez, Giuseppe Spano, Paloma López, Miguel Angel Pardo

Abstract:

Introduction: It is known that some Lactic Acid Bacteria (LAB) present an interesting probiotic effect. Probiotic bacteria stimulate host resistance to microbial pathogens and thereby aid in immune response, and modulate the host's immune responses to antigens with a potential to down-regulate hypersensitivity reactions. Therefore, probiotic therapy is valuable against intestinal infections and may be beneficial in the treatment of Inflammatory Bowel Disease (IBD). Several in vitro tests are available to evaluate the probiotic potential of a LAB strain. However, an in vivo model is required to understand the interaction between the host immune system and the bacteria. During the last few years, zebrafish (Danio rerio) has gained interest as a promising vertebrate model in this field. This organism has been extensively used to study the interaction between the host and the microbiota, as well as the host immune response under several microbial infections. In this work, we report on the use of the zebrafish model to investigate in vivo the colonizing ability and the immunomodulatory effect of probiotic LAB. Methods: Lactobacillus strains belonging to different LAB species were fluorescently tagged and used to colonize germ-free zebrafish larvae gastrointestinal tract (GIT). Some of the strains had a well-documented probiotic effect (L. acidophilus LA5); while others presented an exopolysaccharide (EPS) producing phenotype, thus allowing evaluating the influence of EPS in the colonization and immunomodulatory effect. Bacteria colonization was monitored for 72 h by direct observation in real time using fluorescent microscopy. CFU count per larva was also evaluated at different times. The immunomodulatory effect was assessed analysing the differential expression of several innate immune system genes (MyD88, NF-κB, Tlr4, Il1β and Il10) by qRT- PCR. The anti-inflammatory effect was evaluated using a chemical enterocolitis zebrafish model. The protective effect against a pathogen was also studied. To that end, a challenge test was developed using a fluorescently tagged pathogen (Vibrio anguillarum-GFP+). The progression of the infection was monitored up to 3 days using a fluorescent stereomicroscope. Mortality rates and CFU counts were also registered. Results and conclusions: Larvae exposed to EPS-producing bacteria showed a higher fluorescence and CFU count than those colonized with no-EPS phenotype LAB. In the same way, qRT-PCR results revealed an immunomodulatory effect on the host after the administration of the strains with probiotic activity. A downregulation of proinflammatory cytoquines as well as other cellular mediators of inflammation was observed. The anti-inflammatory effect was found to be particularly marked following exposure to LA% strain, as well as EPS producing strains. Furthermore, the challenge test revealed a protective effect of probiotic administration. As a matter of fact, larvae fed with probiotics showed a decrease in the mortality rate ranging from 20 to 35%. Discussion: In this work, we developed a promising model, based on the use of gnotobiotic zebrafish coupled with a bacterial fluorescent tagging in order to evaluate the probiotic potential of different LAB strains. We have successfully used this system to monitor in real time the colonization and persistence of exogenous LAB within the gut of zebrafish larvae, to evaluate their immunomodulatory effect and for in vivo competition assays. This approach could bring further insights into the complex microbial-host interactions at intestinal level.

Keywords: gnotobiotic, immune system, lactic acid bacteria, probiotics, zebrafish

Procedia PDF Downloads 315

Authors: Zulaika Mohd Khasiran

Abstract:

The capability of ionic liquids in various applications makes them attracted by many researchers. They have potential to be developed as “green” solvents for gas separation, especially H2S gas. In this work, it is attempted to predict the solubility of hydrogen sulfide (H2S) in ILs by COSMO-RS method. Since H2S is a toxic pollutant, it is difficult to work on it in the laboratory, therefore an appropriate model will be necessary in prior work. The COSMO-RS method is implemented to predict the Henry’s law constants and activity coefficient of H2S in 140 ILs with various combinations of cations and anions. It is found by the screening that more H2S can be absorbed in ILs with [Cl] and [Ac] anion. The solubility of H2S in ILs with different alkyl chain at the cations not much affected and with different type of cations are slightly influence H2S capture capacities. Even though the cations do not affect much in solubility of H2S, we still need to consider the effectiveness of cation in different way. The prediction results only show their physical absorption ability, but the absorption of H2S need to be consider chemically to get high capacity of absorption of H2S.

Keywords: H2S, hydrogen sulfide, ionic liquids, COSMO-RS

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Authors: S. Belhour, A. K. Ferouani, C. Azizi

Abstract:

In recent years, a considerable effort (experience, numerical simulation, and theoretical prediction models) has characterised by high efficiency and low cost. Then an improved physics analytical model for simulating is proposed. The performance of GaAs MESFETs has been developed for use in device design for high frequency. This model is based on mathematical analysis, and a new approach for the standard model is proposed, this approach allowed to conceive applicable model for MESFET’s operating in the turn-one or pinch-off region and valid for the short-channel and the long channel MESFET’s in which the two dimensional potential distribution contributed by the depletion layer under the gate is obtained by conventional approximation. More ever, comparisons between the analytical models with different values of mobility are proposed, and a good agreement is obtained.

Keywords: analytical, gallium arsenide, MESFET, mobility, models

Procedia PDF Downloads 62