Search results for: artificial intelligence in medicine

Authors: Rizwan Ahmed Bhutto, Noor ul ain Hira Bhutto, Mingwei Wang, Shahid Iqbal, Jiang Yi

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Curcumin, a natural polyphenolic compound, boasts numerous health benefits; however, its industrial applications are hindered by instabilities and poor solubility. Encapsulating curcumin in Pickering emulsion presents a promising strategy to enhance its bioavailability. Yet, the development of an efficient and straightforward method to fabricate a natural emulsifier for Pickering emulsion poses a significant challenge. Chitosan has garnered attention due to its non-toxicity and excellent emulsifying properties. This study aimed to prepare four distinct types of self-aggregated chitosan particles using a pH-responsive self-assembling approach. The properties of the aggregated particles were adjusted by pH, degree of deacetylation (DDA), and molecular weight (MW), thereby controlling surface charge, size (ranging from nano to micro and floc), and contact angle. Pickering emulsions were then formulated using these various aggregated particles. As MW and pH increased and DDA decreased, the networked structures of the aggregated particles formed, resulting in highly elastic gels that were more resistant to the breakdown of Pickering emulsion at ambient temperature. With elevated temperatures, the kinetic energy of the aggregated particles increased, disrupting hydrogen bonds and potentially transforming the systems from fluids to gels. The Pickering emulsion based on aggregated particles served as a carrier for curcumin encapsulation. It was observed that DDA and MW played crucial roles in regulating drug loading, encapsulation efficiency, and release profile. This research sheds light on selecting suitable chitosan for controlling the release of bioactive compounds in Pickering emulsions, considering factors such as adjustable rheological properties, microstructure, and macrostructure. Furthermore, this study introduces an environmentally friendly and cost-effective synthesis of pH-responsive aggregate particles without the need for high-pressure homogenizers. It underscores the potential of aggregate particles with various MWs and DDAs for encapsulating other bioactive compounds, offering valuable applications in industries including food, flavor/fragrance, cosmetics, and medicine.

Keywords: chitosan, molecular weight, rheological properties, curcumin encapsulation

Procedia PDF Downloads 62

Authors: Ioannis N. Sergentanis, Stavroula Papadodima, Maria Tsellou, Dimitrios Vlachodimitropoulos, Sotirios Athanaselis, Chara Spiliopoulou

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Introduction: The aim of this study is the investigation of characteristics of suicide, as documented in autopsies during a five-year interval in the greater area of Attica, including the city of Athens. This could reveal possible protective or aggravating factors for suicide risk during a period strongly associated with the Greek debt crisis. Materials and Methods: Data was obtained following registration of suicide cases among autopsies performed in the Forensic Medicine and Toxicology Department, School of Medicine, National and Kapodistrian University of Athens, Greece, during the time interval from January 2011 to December 2015. Anonymity and medical secret were respected. A series of demographic and social factors in addition to special characteristics of suicide were entered into a specially established pre-coded database. These factors include social data as well as psychiatric background and certain autopsy characteristics. Data analysis was performed using descriptive statistics and Fisher’s exact test. The software used was STATA/SE 13 (Stata Corp., College Station, TX, USA). Results: A total of 162 cases were studied, 128 men and 34 women. Age ranged from 14 to 97 years old with an average of 53 years, presenting two peaks around 40 and 60 years. A 56% of cases were single/ divorced/ widowed. 25% of cases occurred during the weekend, and 66% of cases occurred in the house. A predominance of hanging as the leading method of suicide (41.4%) followed by jumping from a height (22.8%) and firearms (19.1%) was noted. Statistical analysis showed an association was found between suicide method and gender (P < 0.001, Fisher’s exact test); specifically, no woman used a firearm while only one man used medication overdose (against four women). Discussion: Greece has historically been one of the countries with the lowest suicide rates in Europe. Given a possible change in suicide trends during the financial crisis, further research seems necessary in order to establish risk factors. According to our study, suicide is more frequent in men who are not married, inside their house. Gender seems to be a factor affecting the method of suicide. These results seem in accordance with the international literature. Stronger than expected predominance in male suicide can be associated with failure to live up to social and family expectations for financial reasons.

Keywords: autopsy, Greece, risk factors, suicide

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Authors: Yosra M. Badiei, Evelyn Ortiz, Marisa Portenti, David Szalda

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Water-oxidation half-reaction is a critical reaction that can be driven by a sustainable energy source (e.g., solar or wind) and be coupled with a chemical fuel making reaction which stores the released electrons and protons from water (e.g., H₂ or methanol). The use of molecular water-oxidation catalysts (WOC) allow the rationale design of redox active metal centers and provides a better understanding of their structure-activity-relationship. Herein, the structure of a Ru(III) complex bearing a doubly deprotonated N,N'-bis(aryl)pyridine-2,6-dicarboxamide ligand which contains a water molecule in its primary coordination sphere was elucidated by single-crystal X-ray diffraction. Further spectroscopic experimental data and pH-dependent electrochemical studies reveal its water-oxidation reactivity. Emphasis on mechanistic details for O₂ formation of this complex will be addressed.

Keywords: water-oxidation, catalysis, ruthenium, artificial photosynthesis

Procedia PDF Downloads 200

Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

Procedia PDF Downloads 336

Authors: Matthew Ocheleka Itodo, Ogo Agbo Ogo, Agnes Ogbene Abutu, Bawa Inalegwu

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Hyperlipidemia is characterised by elevated serum total cholesterol and low density and very low-density lipoprotein cholesterol and decreased high-density lipoprotein are the risk factor for coronary heart diseases. There are claims by traditional medicine practitioners in Nigeria that Moringa oleifera plants are used for the treatment of cardiovascular diseases, but it appears there is no scientific research and, publication or documented work to verify these claims. This study aimed to determine the effect of methanol root extracts of Moringa oleifera on Lipid profile, Atherogenic indices and 3 hydroxyl 3 methylglutaryl Coenzyme A reductase activity of poloxamer 407-induced hyperlipidemic rats. The animals were grouped into 8; Group 1: Normal control, Group 2: Hyperlipidemic control. Groups 2 to 8 were induced with Poloxamer 407 1000 mg/Kg body weight. However, group 3 were treated with standard drugs (atorvastatin). Group 4 was treated with crude extract, and groups 5 to 8 were treated with purified fractions from column chromatography. The preliminary antihyperlipidemic study showed Methanol root extract at 200 mg/kg body weight significantly (p≤0.05) decreased total cholesterol, low-density lipoprotein, triacylglyceride, 3 hydroxyls 3 methylglutaryl Coenzyme A reductase, and increase high-density lipoprotein of hyperlipidemic treated groups. Screening the extracts for the most potent anti-hyperlipidemic activity reveals that fraction 1 of Total Cholesterol and Fraction 3 of Triacylglyceride have the highest percentage reduction of 56% and 51%, respectively. The atherogenic risk factor of all induced treated rats shows a significant (p<0.05) decrease in levels of Castelli’s risk index II, atherogenic index of plasma and a significant (p<0.05) higher level of Castelli’s risk index I ratio. The study shows that the methanol extract of root possesses antihyperlipidemic effects and may explain why it has been found to be useful in the management of cardiovascular diseases by traditional medicine practitioners.

Keywords: hyperlipidemia, moringa oleifera, poloxamer 407, lipid profile

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Authors: Ali Seddiki, Mohamed Djebbouri, Driss Guerchi

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Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. In some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to quality purpose compression in the region of interest of scintigraphic images based on dual tree complex wavelet transform (DT-CWT) using Run-Length coding (RLE) and Huffman coding (HC).

Keywords: DT-CWT, region of interest, run length coding, Scintigraphic images

Procedia PDF Downloads 280

Authors: Henrry Vicente Rojas Asuero, Holger Manuel Benavides Muñoz

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Approximately 80% of the energy consumed worldwide is generated from fossil sources, which are responsible for the emission of a large volume of greenhouse gases. For this reason, the global trend, at present, is the widespread use of energy produced from renewable sources. This seeks safety and diversification of energy supply, based on social cohesion, economic feasibility and environmental protection. In this scenario, small hydropower systems (P ≤ 10MW) stand out due to their high efficiency, economic competitiveness and low environmental impact. Small hydropower systems, along with wind and solar energy, are expected to represent a significant percentage of the world's energy matrix in the near term. Among the various technologies present in the state of the art, relating to small hydropower systems, is the Gravitational Water Vortex Power Plant, a recent technology that excels because of its versatility of operation, since it can operate with jumps in the range of 0.70 m-2.00 m and flow rates from 1 m3/s to 20 m3/s. Its operating system is based on the utilization of the energy of rotation contained within a large water vortex artificially induced. This paper presents the study and experimental design of an optimal hydraulic structure with the capacity to induce the artificial formation of a gravitational water vortex trough a system of easy application and high efficiency, able to operate in conditions of very low head and minimum flow. The proposed structure consists of a channel, with variable base, vortex inductor, tangential flow generator, coupled to a circular tank with a conical transition bottom hole. In the laboratory test, the angular velocity of the water vortex was related to the geometric characteristics of the inductor channel, as well as the influence of the conical transition bottom hole on the physical characteristics of the water vortex. The results show angular velocity values of greater magnitude as a function of depth, in addition the presence of the conical transition in the bottom hole of the circular tank improves the water vortex formation conditions while increasing the angular velocity values. Thus, the proposed system is a sustainable solution for the energy supply of rural areas near to watercourses.

Keywords: experimental model, gravitational water vortex power plant, renewable energy, small hydropower

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Authors: Melby Chacko, Jagannath Nayak

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The aging behaviour of 6061 Al-15 vol% SiC composite was investigated using Rockwell B hardness measurement. The composite was solutionized at 350°C and quenched in water. The composite was aged at room temperature (T4 treatment) and also at 140°C, 160°C, 180°C and 200°C (T6 treatment). The natural and artificial aging behaviour of composite was studied using aging curves determined at different temperatures. The aging period for peak aging for different temperatures was identified. The time required for attaining peak aging decreased with increase in the aging temperature. The peak hardness was found to increase with increase with aging temperature and the highest peak hardness was observed at 180ºC. Beyond 180ºC the peak hardness was found to be decreasing.

Keywords: 6061 Al-SiC composite, aging curve, Rockwell B hardness, T4, T6 treatments

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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: S. Hameed, S. Anwer, M. Saad, M. Saady

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For autonomic recovery with server virtualization, a cogent plan that includes recovery techniques and backups with virtualized servers can be developed instead of assigning an idle server to backup operations. In addition to hardware cost reduction and data center trail, the disaster recovery plan can ensure system uptime and to meet objectives of high availability, recovery time, recovery point, server provisioning, and quality of services. This autonomic solution would also support disaster management, testing, and development of the recovery site. In this research, a workflow plan is proposed for supporting disaster recovery with virtualization providing virtual monitoring, requirements engineering, solution decision making, quality testing, and disaster management. This recovery model would make disaster recovery a lot easier, faster, and less error prone.

Keywords: autonomous intelligence, disaster recovery, cloud computing, server virtualization

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Authors: Ogunrinde Roseline Bosede, Ogunrinde Rowland Rotimi

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Just as the development of ideas of early mathematics was essentially motivated by social needs, the invention of the computer was equally inspired by social needs. The early years of the twenty-first century have been remarkable in advances in mathematical and computer sciences. Mathematical and computer sciences work are fast becoming an increasingly integral and essential components of a growing catalogues of areas of interests in biology, business, military, medicine, social sciences, advanced design, advanced materials, climate, banking and finance, and many other fields of disciplines. This paper seeks to highlight the trend and impacts of the duo in the technological advancements being witnessed in our today's world.

Keywords: computer, impacts, mathematics, modern society

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Authors: Norimichi Nagano, Yoshio Hirano, Tsutomu Yasukawa

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Mesenchymal stem cells are thought to confer neuroprotection, facilitate tissue regeneration and exert their effects on retinal degenerative diseases, however, adverse events such as proliferative vitreoretinopathy and preretinal membrane disease associated with cell suspension transplantation have also been reported. We have recently developed human (allogeneic) adipose tissue-derived mesenchymal stem cell (adMSC) sheets through our proprietary sheet transformation technique, which could potentially mitigate these adverse events. To clarify the properties of our adMSC sheets named PAL-222, we performed in vitro studies such as viability testing, cytokine secretions by ELISA, immunohistochemical study, and migration assay. The viability of the cells exceeded 70%. Vascular Endothelial Growth Factor (VEGF) and Pigment Epithelium-Derived Factor (PEDF), which are quite important cytokines for the retinal area, were observed. PAL-222 expressed type I collagen, a strength marker, type IV collagen, a marker of the basement membrane, and elastin, an elasticity marker. Finally, the migration assay was performed and showed negative, which means that PAL-222 is stably kept in the topical area and does not come to pieces. Next, to evaluate the efficacy in vivo, we transplanted PAL-222 into the subretinal space of the eye of Royal College of Surgeons rats with congenital retinal degeneration and assessed it for three weeks after transplantation. We confirmed that PAL-222 suppressed the decrease in the thickness of the outer nuclear layer, which means that the photoreceptor protective effect treated with PAL-222 was significantly higher than that in the sham group. (p < 0.01). This finding demonstrates that PAL-222 showed their retinoprotective effect in a model of congenital retinal degeneration. As the study suggested the efficacy of PAL-222 in both in vitro and in vivo studies, we are presently engaged in clinical trials of PAL-222 for myopic chorioretinal atrophy, which is one of the retinal degenerative diseases, for the purpose of regenerative medicine.

Keywords: cell sheet, clinical trial, mesenchymal stem cell, myopic chorioretinal atrophy

Procedia PDF Downloads 89

Authors: T. Mcgraw, F. N. Morin, N. Desai

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Background: Antimicrobial resistance is a critical issue in global health care and a significant contributor to increased patient morbidity and mortality. Suspected urinary tract infection (UTI) is a key area of inappropriate antibiotic prescription in pediatrics. Management patterns of infectious diseases have been shown to vary by provider type within a single setting. The aim of this study was to assess compliance with national UTI management guidelines by nurse practitioners in a pediatric emergency department (ED). Methods: This was a post-hoc analysis of a retrospective cohort study to review and evaluate visits to a tertiary care freestanding pediatric emergency department. Patients were included if they were 60 days to 36 months old and discharged with a diagnosis of UTI or ‘rule-out UTI’ between July 2015 and July 2020. Primary outcome measure was proportion of visits seen by Nurse Practitioners (NP) which were associated with national guideline compliance in the diagnosis and treatment of suspected UTI. We performed descriptive statistics and comparative analyses to determine differences in practice patterns between NPs, and physicians. Results: A total of 636 charts were reviewed, of which 402 patients met inclusion criteria. 17 patients were treated by NPs, 385 were treated by either Pediatric Emergency Medicine physicians (PEM) or non-PEM physicians. Overall, the proportion of infants receiving guideline-compliant care was 25.9% (21.8-30.4%). Of those who were prescribed antibiotics, 79.6% (74.7-83.8%) received first line guideline recommended therapy and 58.9% (53.8-63.8%) received fully compliant therapy with respect to age, dose, duration, and frequency. In patients treated by NPs, 16/17 (94%(95% CI:73.0-99.0)) required antibiotics, 15/16 (93%(95% CI: 71.7-98.9)) were treated with first line agent (cephalexin), 8/16 (50%(95% CI:28-72)) were guideline compliant of dose and duration. 5/8 (63%(95% CI:30.6-86.3)) were noncompliant for dose being too high. There was no difference in receiving guideline compliant empiric antibiotic therapy between physicians and nurse practitioners (OR: 0.837 CI: 0.302-2.69). Conclusion: In this post-hoc analysis, guideline noncompliance by nurse practitioners is common in children tested and treated for UTIs in a pediatric emergency department. Care by a Nurse Practitioner was not associated with greater rate of noncompliance than care by a Pediatric Emergency Medicine physician. Future appropriately powered studies may focus on confirming these results.

Keywords: antibiotic stewardship, infectious disease, nurse practitioner, urinary tract infection

Procedia PDF Downloads 104

Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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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: Olcay Çam, Ayşegül Bilge, Merve Uğuryol, Hacer Demirkol

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Complementary and alternative medicine are important for human health. It has stood out that from past to present people have resorted to particularly Turkish bath houses, cupping therapy, mud bath, hirudotheraphy and healing waters for the purpose of recovering from diseases and refresh their souls. Now, methods such as herbal treatments, massage, aromatherapy, prayer, meditation, yoga and thermal springs have been recently observed to be the most frequently used complementary therapies in Turkey. These methods are not known by people exactly. As a result, complementary therapies are applied along with the modern therapies in Turkey, we are considered to be effective in maintaining and improving individuals’ health.

Keywords: complementary therapy, health, health services, modern therapies

Procedia PDF Downloads 276

Authors: Z. H. Safari, M. Anbia, G. H. Kouzegari, R. Amirkhani

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Toluene and Nitro derivatives are widely used in industry particularly in various defense applications. Tri-nitro-toluene derivative is a powerful basic explosive material that is a basis upon which to compare equivalent explosive power of similar materials. The aim of this paper is to measure the explosive power of these hazardous substances in fuels having different shelf-life and therefore optimizing their storage and maintenance. The methodology involves measuring the amounts of di- nitro- toluene and tri-nitro-toluene in the aged samples at 90 ° C by gas chromatography. Results show no significant difference in the concentration of the TNT compound over a given time while there was a significant difference in DNT compound over the same period. The underlying reason is attributed to the simultaneous production of the material with destruction of stabilizer.

Keywords: dinitrotoluene, trinitrotoluene, double-base solid propellants, artificial aging

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Authors: Selman Selim Akyüz, Mete Kazaz

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Since The First Gulf War, USA, “Leader of The Free World” has been in trouble with terror. The USA created a complexity in The Middle East and paid the price with terrorist attacks in homeland. USA has made serious mistakes in terms of antiterrorism and fight against its supporters. Democrats have repaired damages caused by the Republican Party's management. Old methods about antiterrorism have been slowly abandoned. Hollywood, too, has played an important part in this war. Sometimes, Hollywood became an unquestioned patriot, sometimes it cried for the death of American Soldiers far away. In this study, messages in The Homeland, broadcast in the USA and a lot of countries around the world, are analyzed in terms of Washington’s foreign policy and position of the CIA in the fight against antiterrorism. The series reflect an orientalist viewpoint and has been criticized for offensive policy against the government. Homeland wanted to offer a perspective for the USA to be the “Leader of The Free World” again but with a liberal-democrat approach, dialogue and rational intelligence methods.

Keywords: antiterrorism, CIA, homeland, USA

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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: 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: Yeimy L. Quintana, Juan G. Zuluaga, Sandra S. Arango

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The comet assay is a method based on electrophoresis that is used to measure DNA damage in cells and has shown important results in the identification of substances with a potential risk to the human population as innumerable physical, chemical and biological agents. With this technique is possible to obtain images like a comet, in which the tail of these refers to damaged fragments of the DNA. One of the main problems is that the image has unequal luminosity caused by the fluorescence microscope and requires different processing to condition it as well as to know how many optimal comets there are per sample and finally to perform the measurements and determine the percentage of DNA damage. In this paper, we propose the design and implementation of software using Image Processing Toolbox-MATLAB that allows the automation of image processing. The software chooses the optimum comets and measuring the necessary parameters to detect the damage.

Keywords: artificial vision, comet assay, DNA damage, image processing

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Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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Authors: Naser Zourikalatehsamad, Seyed Abdorreza Payambarpour, Ibrahim Alwashali, Zahra Abdolkarimi

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The paper aims to evaluate the effect of online advertising on consumer purchase behavior in Malaysian organizations. The paper has potential to extend and refine theory. A survey was distributed among Students of UTM university during the winter 2014 and 160 responses were collected. Regression analysis was used to test the hypothesized relationships of the model. Result shows that the predictors (cost saving factor, convenience factor and customized product or services) have positive impact on intention to continue seeking online advertising.

Keywords: consumer purchase, convenience, customized product, cost saving, customization, flow theory, mass communication, online advertising ads, online advertising measurement, online advertising mechanism, online intelligence system, self-confidence, willingness to purchase

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Authors: Sabri Serkan Gulluoglu

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It is possible with Geographic Information Systems (GIS) that the information about all natural and artificial resources on the earth is obtained taking advantage of satellite images are obtained by remote sensing techniques. However, determination of unknown sources, mapping of the distribution and efficient evaluation of resources are defined may not be possible with the original image. For this reasons, some process steps are needed like transformation, pre-processing, image enhancement and classification to provide the most accurate assessment numerically and visually. Many studies which present the phases of obtaining and processing of the satellite images have examined in the literature study. The research showed that the determination of the process steps may be followed at this subject with the existence of a common whole may provide to progress the process rapidly for the necessary and possible studies which will be.

Keywords: remote sensing, satellite imaging, gis, computer science, information

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Authors: Shalini TV, Gangadharan GG, Sriranjini S Jaideep, ASN Seshasayee, Awadhesh Pandit

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Introduction: Prakriti (body-mind constitution of an individual) is a conventional, customized and unique understanding of which is essential for the personalized medicine described in Ayurveda, Indian System of Medicine. Based on the Doshas( functional, bio humoral unit in the body), individuals are categorized into three major Prakriti- Vata, Pitta, and Kapha. The human gut microbiome hosts plenty of highly diverse and metabolically active microorganisms, mainly dominated by the bacteria, which are known to influence the physiology of an individual. Few researches have shown the correlation between the Prakriti and the biochemical parameters. In this study, an attempt was made to explore any correlation between the Prakriti (phenotype of an individual) with the Genetic makeup of the gut microbiome in healthy individuals. Materials and methods: 270 multi-ethnic, healthy volunteers of both sex with the age group between 18 to 40 years, with no history of antibiotics in the last 6 months were recruited into three groups of Vata, Pitta, and Kapha. The Prakriti of the individual was determined using Ayusoft, a software designed by CDAC, Pune, India. The volunteers were subjected to initial screening for the assessment of their height, weight, Body Mass Index, Vital signs and Blood investigations to ensure they are healthy. The stool and saliva samples of the recruited volunteers were collected as per the standard operating procedure developed, and the bacterial DNA was isolated using Qiagen kits. The extracted DNA was subjected to 16s rRNA sequencing using the Illumina kits. The sequencing libraries are targeting the variable V3 and V4 regions of the 16s rRNA gene. Paired sequencing was done on the MiSeq system and data were analyzed using the CLC Genomics workbench 11. Results: The 16s rRNA sequencing of the V3 and V4 regions showed a diverse pattern in both the oral and stool microbial DNA. The study did not reveal any specific pattern of bacterial flora amongst the Prakriti. All the p-values were more than the effective alpha values for all OTUs in both the buccal cavity and stool samples. Therefore, there was no observed significant enrichment of an OTU in the patient samples from either the buccal cavity or stool samples. Conclusion: In healthy volunteers of multi-ethnicity, due to the influence of the various factors, the correlation between the Prakriti and the gut microbiome was not seen.

Keywords: gut microbiome, ayurveda Prakriti, sequencing, multi-ethnic urban population

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Authors: Alexandra Papagianni, George Filis, Panagiotis Papadopoulos

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The liberalization of the energy market and the increasing penetration of fluctuating renewables (e.g., wind and solar power) have heightened the importance of the spot market for ensuring efficient electricity supply. This is further emphasized by the EU’s goal of achieving net-zero emissions by 2050. The day-ahead market (DAM) plays a key role in European energy trading, accounting for 80-90% of spot transactions and providing critical insights for next-day pricing. Therefore, short-term electricity price forecasting (EPF) within the DAM is crucial for market participants to make informed decisions and improve their market positioning. Existing literature highlights out-of-sample performance as a key factor in assessing EPF accuracy, with influencing factors such as predictors, forecast horizon, model selection, and strategy. Several studies indicate that electricity demand is a primary price determinant, while renewable energy sources (RES) like wind and solar significantly impact price dynamics, often lowering prices. Additionally, incorporating data from neighboring countries, due to market coupling, further improves forecast accuracy. Most studies predict up to 24 steps ahead using hourly data, while some extend forecasts using higher-frequency data (e.g., half-hourly or quarter-hourly). Short-term EPF methods fall into two main categories: statistical and computational intelligence (CI) methods, with hybrid models combining both. While many studies use advanced statistical methods, particularly through different versions of traditional AR-type models, others apply computational techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). Recent research combines multiple methods to enhance forecasting performance. Despite extensive research on EPF accuracy, a gap remains in understanding how forecasting strategy affects prediction outcomes. While iterated strategies are commonly used, they are often chosen without justification. This paper contributes by examining whether the choice of forecasting strategy impacts the quality of day-ahead price predictions, especially for multi-step forecasts. We evaluate both iterated and direct methods, exploring alternative ways of conducting iterated forecasts on benchmark and state-of-the-art forecasting frameworks. The goal is to assess whether these factors should be considered by end-users to improve forecast quality. We focus on the Greek DAM using data from July 1, 2021, to March 31, 2022. This period is chosen due to significant price volatility in Greece, driven by its dependence on natural gas and limited interconnection capacity with larger European grids. The analysis covers two phases: pre-conflict (January 1, 2022, to February 23, 2022) and post-conflict (February 24, 2022, to March 31, 2022), following the Russian-Ukraine conflict that initiated an energy crisis. We use the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) for evaluation, as well as the Direction of Change (DoC) measure to assess the accuracy of price movement predictions. Our findings suggest that forecasters need to apply all strategies across different horizons and models. Different strategies may be required for different horizons to optimize both accuracy and directional predictions, ensuring more reliable forecasts.

Keywords: short-term electricity price forecast, forecast strategies, forecast horizons, recursive strategy, direct strategy

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

Abstract:

This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Laura Boschis, Elena D. Ozzello, Enzo Mastromatteo

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Point of care diagnostic finds growing interest in medicine and agri-food because of faster intervention and prevention. EliChip is a microfluidic platform to perform Point of Care immunoenzymatic assay based on ready-to-use kits and a portable instrument to manage fluidics and read reliable quantitative results. Thanks to miniaturization, analyses are faster and more sensible than conventional ELISA. EliChip is one of the crucial assets of the Europen-founded Flamingo project for in-line measuring inflammatory markers.

Keywords: lab on chip, point of care, immunoenzymatic analysis, synovial arthritis

Procedia PDF Downloads 184

Authors: Jaspreet Kaur, Parminder Nain, Vipin Saini, Sumitra Dahiya

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Elaeocarpus sphaericus has been traditionally used in the Indian traditional medicine system for the treatment of stress, anxiety, depression, palpitation, epilepsy, migraine and lack of concentration. The study was investigated to evaluate the neurological potential such as anxiolytic, muscle relaxant and sedative activity of methanolic extract of Elaeocarpus sphaericus stem bark (MEESSB) in mice. Preliminary phytochemical screening and acute oral toxicity of MEESSB was carried out by using standard methods. The anxiety was induced by employing Elevated Plus-Maze (EPM), Light and Dark Test (LDT), Open Field Test (OFT) and Social Interaction test (SIT). The motor coordination and sedative effect was also observed by using actophotometer, rota-rod apparatus and ketamine-induced sleeping time, respectively. Animals were treated with different doses of MEESSB (i.e.100, 200, 400 and 800 mg/kg orally) and diazepam (2 mg/kg i.p) for 21 days. Brain neurotransmitters like dopamine, serotonin and nor-epinephrine level were estimated by validated methods. Preliminary phytochemical analysis of the extract revealed the presence of tannins, phytosterols, steroids and alkaloids. In the acute toxicity studies, MEESSB was found to be non-toxic and with no mortality. In anxiolytic studies, the different doses of MEESSB showed a significant (p<0.05) effect on EPM and LDT. In OFT and SIT, a significant (p<0.05) increase in ambulation, rearing and social interaction time was observed. In the case of motor coordination activity, the MEESSB does not cause any significant effect on the latency to fall off from the rotarod bar as compared to the control group. Moreover, no significant effects on ketamine-induced sleep latency and total sleeping time induced by ketamine were observed. Results of neurotransmitter estimation revealed the increased concentration of dopamine, whereas the level of serotonin and nor-epinephrine was found to be decreased in the mice brain, with MEESSB at dose 800 mg/kg only. The study has validated the folkloric use of the plant as an anxiolytic in Indian traditional medicine while also suggesting potential usefulness in the treatment of stress and anxiety without causing sedation.

Keywords: anxiolytic, behavior experiments, brain neurotransmitters, elaeocarpus sphaericus

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Authors: Reza Bazargan lari, Mohammad H. Fattahi

Abstract:

Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for pre-processing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based pre-processing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN

Procedia PDF Downloads 368