Search results for: drug property prediction

Authors: E. Akpinar, A. Erol, M.F. Cakir

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Precast reinforced concrete (RC) structures are excellent alternatives for construction world all over the globe, thanks to their rapid erection phase, ease mounting process, better quality and reasonable prices. Such structures are rather popular for industrial buildings. For the sake of economic importance of such industrial buildings as well as significance of safety, like every other type of structures, performance assessment and structural risk analysis are important. Fragility curves are powerful tools for damage projection and assessment for any sort of building as well as precast structures. In this study, a comparative review of current knowledge on fragility analysis of industrial precast RC structures were presented and findings in previous studies were compiled. Effects of different structural variables, parameters and building geometries as well as soil conditions on fragility analysis of precast structures are reviewed. It was aimed to briefly present the information in the literature about the procedure of damage probability prediction including fragility curves for such industrial facilities. It is found that determination of the aforementioned structural parameters as well as selecting analysis procedure are critically important for damage prediction of industrial precast RC structures using fragility curves.

Keywords: damage prediction, fragility curve, industrial buildings, precast reinforced concrete structures

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Dedjo Yao Simon, Takahiro Saito, Norikazu Inuzuka, Ikuo Sugiyama

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Abidjan city (Republic of Ivory Coast) is an emerging megacity and an urban coastal area where the number of floods reported is on a rapid increase due to climate change and unplanned urbanization. However, comprehensive disaster mitigation plans, policies, and financial resources are still lacking as the population ignores the extent and location of the flood zones; making them unprepared to mitigate the damages. Considering the existing condition, this paper aims to discuss an approach for flood risk reduction in Cocody Commune through value capture strategy and flood risk communication. Using geospatial techniques and hydrological simulation, we start our study by delineating flood zones and depths under several return periods in the study area. Then, through a questionnaire a field survey is conducted in order to validate the flood maps, to estimate the flood risk and to collect some sample of the opinion of residents on how the flood risk information disclosure could affect the values of property located inside and outside the flood zones. The results indicate that the study area is highly vulnerable to 5-year floods and more, which can cause serious harm to human lives and to properties as demonstrated by the extent of the 5-year flood of 2014. Also, it is revealed there is a high probability that the values of property located within flood zones could decline, and the values of surrounding property in the safe area could increase when risk information disclosure commences. However in order to raise public awareness of flood disaster and to prevent future housing promotion in high-risk prospective areas, flood risk information should be disseminated through the establishment of an early warning system. In order to reduce the effect of risk information disclosure and to protect the values of property within the high-risk zone, we propose that property tax increments in flood free zones should be captured and be utilized for infrastructure development and to maintain the early warning system that will benefit people living in flood prone areas. Through this case study, it is shown that combination of value capture strategy and risk communication could be an effective tool to educate citizen and to invest in flood risk reduction in emerging countries.

Keywords: Cocody-Abidjan, flood, geospatial techniques, risk communication, value capture

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Abdul Musaweer Habib, Habibul Hasan Mazumder, Saiful Islam, Sohel Sikder, Omar Faruk Sikder

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The plethora of genome sequence information of bacteria in recent times has ushered in many novel strategies for antibacterial drug discovery and facilitated medical science to take up the challenge of the increasing resistance of pathogenic bacteria to current antibiotics. In this study, we adopted subtractive genomics approach to analyze the whole genome sequence of the Fusobacterium nucleatum, a human oral pathogen having association with colorectal cancer. Our study divulged 1499 proteins of Fusobacterium nucleatum, which has no homolog in human genome. These proteins were subjected to screening further by using the Database of Essential Genes (DEG) that resulted in the identification of 32 vitally important proteins for the bacterium. Subsequent analysis of the identified pivotal proteins, using the KEGG Automated Annotation Server (KAAS) resulted in sorting 3 key enzymes of F. nucleatum that may be good candidates as potential drug targets, since they are unique for the bacterium and absent in humans. In addition, we have demonstrated the 3-D structure of these three proteins. Finally, determination of ligand binding sites of the key proteins as well as screening for functional inhibitors that best fitted with the ligands sites were conducted to discover effective novel therapeutic compounds against Fusobacterium nucleatum.

Keywords: colorectal cancer, drug target, Fusobacterium nucleatum, homology modeling, ligands

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Authors: Ekrem Canli, Thomas Glade

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The precise prediction of rainfall in space and time is a key element to most landslide early warning systems. Unfortunately, the spatial variability of rainfall in many early warning applications is often disregarded. A common simplification is to use uniformly distributed rainfall to characterize aerial rainfall intensity. With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on kriging. Because the application of kriging is usually a labor intensive task, a simplified and consequently automated variogram modeling procedure was applied to up-to-date rainfall data. The entire workflow was carried out purely with open source technology. Validation results, albeit promising, pointed out the challenges that are involved in pure distance based, automated geostatistical interpolation techniques for ever-changing environmental phenomena over short temporal and spatial extent.

Keywords: kriging, landslide early warning system, spatial rainfall prediction, variogram modelling, web scraping

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Authors: Kacper Tomasz Kröber-Mulawa

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This paper will address the topic of data ownership from an economic perspective, and examples of economic limitations of data property rights will be provided, which have been identified using methods and approaches of economic analysis of law. To properly build a background for the economic focus, in the beginning a short perspective of data and data ownership in the EU’s legal system will be provided. It will include a short introduction to its political and social importance and highlight relevant viewpoints. This will stress the importance of a Single Market for data but also far-reaching regulations of data governance and privacy (including the distinction of personal and non-personal data, data held by public bodies and private businesses). The main discussion of this paper will build upon the briefly referred to legal basis as well as methods and approaches of economic analysis of law.

Keywords: antitrust, data, data ownership, digital economy, property rights

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Authors: Takashi Ueda, Shinichi Enoki

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Forging parts is used to automobiles. Because they have high strength and it is possible to press them into complicated shape. When it is possible to manufacture hollow forging parts, it leads to reduce weight of the automobiles. But, hollow forging parts are confined to axisymmetrical shape. Hollow forging parts that were pressed to complicated shape are expected. Therefore, we forge a blank that aluminum alloy was inserted in stainless steel. After that, we can provide complex forging parts that are reduced weight, if it is possible to be melted the aluminum alloy away by using different of melting points. It is necessary to establish heat forging analysis method on blank consist of stainless steel and aluminum alloy. Because, this forging is different from conventional forging and this technology is not confirmed. In this study, we compared forging experiment with numerical analysis on the view point of forming load and shape after forming and establish how to set the material temperatures of two metals and material property of stainless steel on the analysis method. Consequently, temperature difference of stainless steel and aluminum alloy was obtained by experiment. We got material property of stainless steel on forging experimental by compression tests. We had compared numerical analysis that was used the temperature difference of two metals and the material property of stainless steel on forging experimental with forging experiment. Forging analysis method on blank consist of two metals was established by result of numerical analysis having agreed with result of forging experiment.

Keywords: forging, lightweight, analysis, hollow

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Authors: Ritu Shitak

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Humans are social mammals, of the primate order. Our biology, behaviour, and pathologies are unique to us. In our desire to understand, reduce solitary confinement one source of information is the many reports of social isolation of other social mammals, especially primates. A behavioural study was conducted in the department of pharmacology at Indira Gandhi Medical College, Shimla in Himachal Pradesh province in India using white albino mice. Different behavioural parameters were observed by using open field, tail suspension, tests for aggressive behaviour and social interactions and the effect of isolation was studied. The results were evaluated and the standard statistics were applied. The said study was done to establish facts that isolation itself impairs social behaviour and can lead to alcohol dependence as well as related drug dependence.

Keywords: social isolation, albino mice, drug dependence, isolation on social behaviour

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Authors: Olusola Omolola Olafuyi, Michael Coleman, Raj Kumar Singh Badhan

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Introduction: Malaria in pregnancy has morbidity and mortality implication on both mother and unborn child. Piperaquine (PQ) based antimalarial treatment is emerging as a choice antimalarial for pregnant women in the face of resistance to current antimalarial treatment recommendation in pregnancy. Physiological and biochemical changes in pregnant women may affect the pharmacokinetics of the antimalarial drug in these. In malaria endemic regions other infectious diseases like HIV/AIDs are prevalent. Pregnant women who are co-infected with malaria and HIV/AID are at even more greater risk of death not only due to complications of the diseases but also due to drug-drug interactions (DDIs) between antimalarials (AMT) and antiretroviral (ARVs). In this study, physiologically based pharmacokinetic (PBPK) modelling was used to investigate the effect of physiological and biochemical changes on the impact of ARV mediated DDIs in pregnant women in three countries. Method: A PBPK model for PQ was developed on SimCYP® using published physicochemical and pharmacokinetic data of PQ from literature, this was validated in three customized population groups from Thailand, Sudan and Papua New Guinea with clinical data. Validation of PQ model was also done in presence of interaction with efavirenz (pre-validated on SimCYP®). Different albumin levels and pregnancy stages was simulated in the presence of interaction with standard doses of efavirenz and ritonavir. PQ day 7 concentration of 30ng/ml was used as the efficacy endpoint for PQ treatment.. Results: The median day 7 concentration of PQ remained virtually consistent throughout pregnancy and were satisfactory across the three population groups ranging from 26-34.1ng/ml; this implied the efficacy of PQ throughout pregnancy. DDI interaction with ritonavir and efavirenz resulted in modest effect on the day 7 concentrations of PQ with AUCratio ranging from 0.56-0.8 and 1.64-1.79 for efavirenz and ritonavir respectively over 10-40 gestational weeks, however, a reduction in human serum albumin level reflective of severe malaria resulted in significantly reduced the number of subjects attaining the PQ day 7 concentration in the presence of both DDIs. The model demonstrated that the DDI between PQ and ARV in pregnant women with different malaria severities can alter the pharmacokinetic of PQ.

Keywords: antiretroviral, malaria, piperaquine, pregnancy, physiologically-based pharmacokinetics

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Londeka Ntuli, Frasia Oosthuizen

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Background: Tenefovir disiproxil fumarate (TDF) is a nephrotoxic drug and has been proven to contribute to renal insufficiency necessitating intensive monitoring and management of adverse effects arising from prolonged exposure to the drug. TDF is one of the preferred first-line drugs used in combination therapy in most regions. There are estimated 300 000 patients being initiated on the Efavirenz/TDF/Emtricitabine first-line regimen annually in South Africa. It is against this background that this study aims to investigate the effects of TDF on renal sufficiency of HIV positive patients. Methodology: A retrospective quantitative study was conducted, analysing clinical charts of HIV positive patient’s older than 18 years of age and on a TDF-containing regimen for more than 1 year. Data were obtained from the analysis of patient files and was transcribed into Microsoft® Excel® spreadsheet. Extracted data were coded, categorised and analysed using STATA®. Results: A total of 275 patient files were included in this study. Renal function started decreasing after 3 months of treatment (with 93.5% patients having a normal EGFR), and kept on decreasing as time progressed with only 39.6% normal renal function at year 4. Additional risk factors for renal insufficiency included age below 25, female gender, and additional medication. Conclusion: It is clear from this study that the use of TDF necessitates intensive monitoring and management of adverse effects arising from prolonged exposure to the drug. The findings from this study generated pertinent information on the safety profile of the drug TDF in a resource-limited setting of a public health institution. The appropriate management is of tremendous importance in the South African context where the majority of HIV positive individuals are on the TDF containing regimen; thus it is beneficial to ascertain the possible level of toxicities these patients may be experiencing.

Keywords: renal insufficiency, tenefovir, HIV, risk factors

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Authors: Mattia Testuzza

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Public health is a social endeavour, which involves many different actors: from extremely stratified, structured health systems to unofficial networks of people and knowledge. Health and diseases are an intertwined individual and social experiences. Both patients and health workers navigate this public space through relations of trust. Trust in healthcare goes from the personal trust between a patient and her/his doctor to the trust of both the patient and the health worker in the medical knowledge and the healthcare system. Trust it is not a given, but it is continuously negotiated, given and gained. The key to understand these essential relations of trust in health is to recognise them as a social practice, which therefore implies agency and power. In these terms, health is constantly public and made public, as trust emerges as a meaningfully political phenomenon. Trust as a power relation can be observed at play in the implementation of public health policies such as the WHO’s Directly-Observed Theraphy Short-course (DOTS), and with the increasing concern for drug-resistance that tuberculosis pose, looking at the role of trust in the healthcare delivery system and implementation of public health policies becomes significantly relevant. The ethnographic fieldwork was carried out in four months through observation of the daily practices at the National Tuberculosis Center of Nepal, and semi-structured interviews with MultiDrug-Resistant Tuberculosis (MDR-TB) patients at different stages of the treatment, their relatives, MDR-TB specialised nurses, and doctors. Throughout the research, the role which trust plays in tuberculosis treatment emerged as one fundamental ax that cuts through all the different factors intertwined with drug-resistance development, unfolding a tension between the DOTS policy, which undermines trust, and the day-to-day healthcare relations and practices which cannot function without trust. Trust also stands out as a key component of the solutions to unforeseen issues which develop from the overall uncertainty of the context - for example, political instability and extreme poverty - in which tuberculosis treatment is carried out in Nepal.

Keywords: trust, tuberculosis, drug-resistance, politics of health

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Authors: Salwa Mohamed Salah Eldin, Howida Kamal Ibrahim

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Introduction: Major depressive disorder affects high proportion of the world’s population presenting cost load in health care. Extended release venlafaxine is more convenient and could reduce discontinuation syndrome. The once daily dosing also reduces the potential for adverse events such as nausea due to reduced Cmax. Venlafaxine is an effective first-line agent in the treatment of depression. A once daily formulation was designed to enhance patient compliance. Complexing with a resin was suggested to improve loading of the water soluble drug. The formulated systems were thoroughly evaluated in vitro to prove superiority to previous trials and were compared to the commercial extended release product in experimental animals. Materials and Methods: Venlafaxine-resinates were prepared using Dowex®50WX4-400 and Dowex®50WX8-100 at drug to resin weight ratio of 1: 1. The prepared resinates were evaluated for their drug content, particle shape and surface properties and in vitro release profile in gradient pH. The release kinetics and mechanism were evaluated. Venlafaxine-Dowex® resinates were encapsulated using O/W solvent evaporation technique. Poly-ε-caprolactone, Poly(D, L-lactide-co-glycolide) ester, Poly(D, L-lactide) ester and Eudragit®RS100 were used as coating polymers alone and in combination. Drug-resinate microcapsules were evaluated for morphology, entrapment efficiency and in-vitro release profile. The selected formula was tested in rabbits using a randomized, single-dose, 2-way crossover study against Effexor-XR tablets under fasting condition. Results and Discussion: The equilibrium time was 30 min for Dowex®50WX4-400 and 90 min for Dowex®50WX8-100. The percentage drug loaded was 93.96 and 83.56% for both resins, respectively. Both drug-Dowex® resintes were efficient in sustaining venlafaxine release in comparison to the free drug (up to 8h.). Dowex®50WX4-400 based venlafaxine-resinate was selected for further encapsulation to optimize the release profile for once daily dosing and to lower the burst effect. The selected formula (coated with a mixture of Eudragit RS and PLGA in a ratio of 50/50) was chosen by applying a group of mathematical equations according to targeted values. It recorded the minimum burst effect, the maximum MDT (Mean dissolution time) and a Q24h (percentage drug released after 24 hours) between 95 and 100%. The 90% confidence intervals for the test/reference mean ratio of the log-transformed data of AUC0–24 and AUC0−∞ are within (0.8–1.25), which satisfies the bioequivalence criteria. Conclusion: The optimized formula could be a promising extended release form of the water soluble, short half lived venlafaxine. Being a multiple unit formulation, it lowers the probability of dose dumping and reduces the inter-subject variability in absorption.

Keywords: biodegradable polymers, cation-exchange resin, microencapsulation, venlafaxine hcl

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Authors: Emy Nayana Pinto, Denise Bomtempo Birche De Carvalho

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In the present study, the harmful use of illicit drugs is seen as a public health problem and as one of the expressions of the social question, since its consequences fall mainly on the poorer classes of the population. This perspective is a counterpoint to the dominant paradigm on illicit drug policy at the global level, whose centrality lies within the criminal justice arena. The 'drug problem' is internationally combated through fragmented approaches that focus its actions on banning and criminalizing users. In this sense, the research seeks to answer the following key questions: What are the influences of the prohibitionism in the recommendations of the United Nations (UN), the World Health Organization (WHO), and the formulation of drug policies in member countries? What are the actors that have been provoking the prospect of breaking with the prohibitionist paradigm? What is the WHO contribution to the rupture with the prohibitionist paradigm and the displacement of the drug problem in the field of public health? The general objective of this work is to seek evidence from the perspective of rupture with the prohibitionist paradigm in the field of drugs policies at the global and regional level, through analysis of documents of the World Health Organization (WHO), between the years of 2001 to 2016. The research was carried out in bibliographical and documentary sources. The bibliographic sources contributed to the approach with the object and the theoretical basis of the research. The documentary sources served to answer the research questions and evidence the existence of the perspective of change in drug policy. Twenty-two documents of the UN system were consulted, of which fifteen had the contribution of the World Health Organization (WHO). In addition to the documents that directly relate to the subject of the research, documents from various agencies, programs, and offices, such as the Joint United Nations Program on HIV/AIDS (UNAIDS) and the United Nations Office on Drugs and Crime (UNODC), which also has drugs as the central or transversal theme of its performance. The results showed that from the 2000s it was possible to find in the literature review and in the documentary analysis evidence of the critique of the prohibitionist paradigm parallel to the construction of a new perspective for drug policy at the global level and the displacement of criminal justice approaches for the scope of public health, with the adoption of alternative and pragmatic interventions based on human rights, scientific evidence and the reduction of social damages and health by the misuse of illicit drugs.

Keywords: illicit drugs, international organizations, prohibitionism, public health, World Health Organization

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: H. Tayefih, F. farajzade ahari, F. Zarghami, V. Zeighamian, N. Zarghami, Y. Pilehvar-soltanahmadi

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Breast cancer is the most frequently occurring cancer among women throughout the world. Natural compounds such as curcumin hold promise to treat a variety of cancers including breast cancer. However, curcumin's therapeutic application is limited, due to its rapid degradation and poor aqueous solubility. On the other hand, previous studies have stated that drug delivery using nanoparticles might improve the therapeutic response to anticancer drugs. Poly (N-isopropylacrylamide-co-methacrylic acid) (PNIPAAm–MAA) is one of the hydrogel copolymers utilized in the drug delivery system for cancer therapy. The aim of this study was to examine the cytotoxic potential of curcumin encapsulated within the NIPAAm-MAA nanoparticle, on the MCF-7 breast cancer cell line. In this work, polymeric nanoparticles were synthesized through the free radical mechanism, and curcumin was encapsulated into NIPAAm-MAA nanoparticles. Then, the cytotoxic effect of curcumin-loaded NIPAAm-MAA on the MCF-7 breast cancer cell line was measured by MTT assays. The evaluation of the results showed that curcumin-loaded NIPAAm-MAA has more cytotoxic effect on the MCF-7 cell line and efficiently inhibited the growth of the breast cancer cell population, compared with free curcumin. In conclusion, this study indicates that curcumin-loaded NIPAAm-MAA suppresses the growth of the MCF-7 cell line. Overall, it is concluded that encapsulating curcumin into the NIPAAm-MAA copolymer could open up new avenues for breast cancer treatment.

Keywords: PNIPAAm-MAA, breast cancer, curcumin, drug delivery

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Authors: Mehrzad Mohabbi Yadollahi, Ramazan Demirboğa, Majid Atashafrazeh

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Geopolymers are highly complex materials which involve many variables which makes modeling its properties very difficult. There is no systematic approach in mix design for Geopolymers. Since the amounts of silica modulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength an ANFIS (Adaptive neuro fuzzy inference system) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of ANFIS for predicting the compressive strength has been studied. Consequently, ANFIS can be used for geopolymer compressive strength prediction with acceptable accuracy.

Keywords: geopolymer, ANFIS, compressive strength, mix design

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Muhammad Fakhrul Arrazi

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Ibn Taymiyyah is a great figure in Islam. His works have become the reference for many Muslims in implementing the fiqh of Ibadah and Muamalat. This article reviews the concepts that Ibn Taymiyyah has initiated in Halal and Haram, long before the books on Halal and Haram are written by contemporary scholars. There are at least four concepts of Halal and Haram ever spawned by Ibn Taymiyyah. First, the belief of a jurist (Faqih) in a matter that is Haram does not necessarily make the matter Haram. Haram arises from the Quran, Sunnah, Ijma’ and Qiyas as the tarjih. Due to the different opinions among the ulama, we should revisit this concept. Second, if a Muslim involves in a transaction (Muamalat), believes it permissible and gets money from such transaction, then it is legal for other Muslims to transact with the property of this Muslim brother, even if he does not believe that the transactions made by his Muslims brother are permissible. Third, Haram is divided into two; first is Haram because of the nature of an object, such as carrion, blood, and pork. If it is mixed with water or food and alters their taste, color, and smell, the food and water become Haram. Second is Haram because of the way it is obtained such as a stolen item and a broken aqad. If it is mixed with the halal property, the property does not automatically become Haram. Fourth, a treasure whose owners cannot be traced back then it is used for the benefit of the ummah. This study used the secondary data from the classics books by Ibn Taymiyyah, particularly those entailing his views on Halal and Haram. The data were then analyzed by using thematic and comparative approach. It is found that most of the concepts proposed by Ibn Taymiyyah in Halal and Haram correspond the majority’s views in the schools. However, some of his concepts are also in contrary to other scholars. His concepts will benefit the ummah, should it be applied to the contemporary issues.

Keywords: fiqh Muamalat, halal, haram, Ibn Taymiyyah

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Authors: A. Brahma

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Drying is a phenomenon that accompanies the hardening of hydraulic materials. It can, if it is not prevented, lead to significant spontaneous dimensional variations, which the cracking is one of events. In this context, cracking promotes the transport of aggressive agents in the material, which can affect the durability of concrete structures. Drying shrinkage develops over a long period almost 30 years although most occurred during the first three years. Drying shrinkage stabilizes when the material is water balance with the external environment. The drying shrinkage of cementitious materials is due to the formation of capillary tensions in the pores of the material, which has the consequences of bringing the solid walls of each other. Knowledge of the shrinkage characteristics of concrete is a necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable shrinkage movement in reinforced or prestressed concrete and the appropriate steps can be taken in design to accommodate this movement. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes in consideration the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: drying, hydraulic concretes, shrinkage, modeling, prediction

Procedia PDF Downloads 337

Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

Abstract:

Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming, and resource-intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials.

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine, pattern recognition algorithms, ethanol treatment

Procedia PDF Downloads 37

Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

Abstract:

The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

Procedia PDF Downloads 188

Authors: Asma’a Mahfoud, Abu Bakar Md. Sultan, Abdul Azim Abd, Norhayati Mohd Ali, Novia Admodisastro

Abstract:

Obfuscation is a practice to make something difficult and complicated. Programming code is ordinarily obfuscated to protect the intellectual property (IP) and prevent the attacker from reverse engineering (RE) a copyrighted software program. Obfuscation may involve encrypting some or all the code, transforming out potentially revealing data, renaming useful classes and variables (identifiers) names to meaningless labels, or adding unused or meaningless code to an application binary. Obfuscation techniques were not performing effectively recently as the reversing tools are able to break the obfuscated code. We propose in this paper a hybrid obfuscation technique that contains three approaches of renaming. Experimentation was conducted to test the effectiveness of the proposed technique. The experimentation has presented a promising result, where the reversing tools were not able to read the code.

Keywords: intellectual property, obfuscation, software security, reverse engineering

Procedia PDF Downloads 146

Authors: Vijayakameswara Rao Neralla, Jueun Jeon, Jae Hyung Park

Abstract:

To develop a potential nanocarrier for diagnosis and treatment of rheumatoid arthritis (RA), we prepared a hyaluronic acid (HA)-5β-cholanic acid (CA) conjugate with an acid-labile ketal linker. This conjugate could self-assemble in aqueous conditions to produce pH-responsive HA-CA nanoparticles as potential carriers of the anti-inflammatory drug methotrexate (MTX). MTX was rapidly released from nanoparticles under inflamed synovial tissue in RA. In vitro cytotoxicity data showed that pH-responsive HA-CA nanoparticles were non-toxic to RAW 264.7 cells. In vivo biodistribution results confirmed that, after their systemic administration, pH-responsive HA-CA nanoparticles selectively accumulated in the inflamed joints of collagen-induced arthritis mice. These results indicate that pH-responsive HA-CA nanoparticles represent a promising candidate as a drug carrier for RA therapy.

Keywords: rheumatoid arthritis, hyaluronic acid, nanocarrier, self-assembly, MTX

Procedia PDF Downloads 289

Authors: Shashi Sharma, V. K. Katiyar, Uaday Singh

Abstract:

The ability to manipulate magnetic particles in fluid flows by means of inhomogeneous magnetic fields is used in a wide range of biomedical applications including magnetic drug targeting (MDT). In MDT, magnetic carrier particles bounded with drug molecules are injected into the vascular system up-stream from the malignant tissue and attracted or retained at the specific region in the body with the help of an external magnetic field. Although the concept of MDT has been around for many years, however, wide spread acceptance of the technique is still looming despite the fact that it has shown some promise in both in vivo and clinical studies. This is because traditional MDT has some inherent limitations. Typically, the magnetic force is not very strong and it is also very short ranged. Since the magnetic force must overcome rather large hydrodynamic forces in the body, MDT applications have been limited to sites located close to the surface of the skin. Even in this most favorable situation, studies have shown that it is difficult to collect appreciable amounts of the MDCPs at the target site. To overcome these limitations of the traditional MDT approach, Ritter and co-workers reported the implant assisted magnetic drug targeting (IA-MDT). In IA-MDT, the magnetic implants are placed strategically at the target site to greatly and locally increase the magnetic force on MDCPs and help to attract and retain the MDCPs at the targeted region. In the present work, we develop a mathematical model to study the capturing of magnetic nanoparticles flowing in a fluid in an implant assisted cylindrical channel under the magnetic field. A coil of ferromagnetic SS 430 has been implanted inside the cylindrical channel to enhance the capturing of magnetic nanoparticles under the magnetic field. The dominant magnetic and drag forces, which significantly affect the capturing of nanoparticles, are incorporated in the model. It is observed through model results that capture efficiency increases from 23 to 51 % as we increase the magnetic field from 0.1 to 0.5 T, respectively. The increase in capture efficiency by increase in magnetic field is because as the magnetic field increases, the magnetization force, which is attractive in nature and responsible to attract or capture the magnetic particles, increases and results the capturing of large number of magnetic particles due to high strength of attractive magnetic force.

Keywords: capture efficiency, implant assisted-magnetic drug targeting (IA-MDT), magnetic nanoparticles, modelling

Procedia PDF Downloads 462

Authors: CarlyWill Sloan

Abstract:

Racial disparities in criminal justice outcomes are well-documented. However, there is little evidence on the extent to which racial bias by prosecutors is responsible for these disparities. This paper tests for racial bias in conviction by prosecutors. To identify effects, this paper leverages as good as random variation in prosecutor race using detailed administrative data on the case assignment process and case outcomes in New York County, New York. This paper shows that the assignment of an opposite-race prosecutor leads to a 5 percentage point (~ 8 percent) increase in the likelihood of conviction for property crimes. There is no evidence of effects for other types of crimes. Additional results indicate decreased dismissals by opposite-race prosecutors likely drive my property crime estimates.

Keywords: criminal justice, discrimination, prosecutors, racial disparities

Procedia PDF Downloads 191

Authors: Mihika Shivkumar, Krishna Kumar, C. Perisamy

Abstract:

3D printing is a method of manufacturing wherein materials, such as plastic or metal, are deposited in layers one on top of the other to produce a three dimensional object. 3D printing is most commonly associated with creating engineering prototypes. However, its applications in the field of human health care have been frequently disregarded. Medical applications for 3D printing are expanding rapidly and are envisaged to revolutionize health care. Medical applications for 3D printing, both present and its potential, can be categorized broadly, including: creation of customized prosthetics tissue and organ fabrication; creation of implants, and anatomical models and pharmaceutical research regarding drug dosage forms. This piece breaks down bioprinting in the healthcare sector. It focuses on the better subtle elements of every particular point, including how 3D printing functions in the present, its impediments, and future applications in the health care sector.

Keywords: bio-printing, prototype, drug delivery, organ regeneration

Procedia PDF Downloads 271

Authors: H. Bonakdari, I. Ebtehaj

Abstract:

The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

Procedia PDF Downloads 218

Authors: Huafeng Wei

Abstract:

Dementia in Alzheimer’s Disease (AD) is the number one cause of dementia internationally, without effective treatments. Increasing evidence suggest that disruption of intracellular calcium homeostasis, primarily pathological elevation of cytosol and mitochondria but reduction of endoplasmic reticulum (ER) calcium concentrations, play critical upstream roles on multiple pathologies and associated neurodegeneration, impaired neurogenesis, synapse, and cognitive dysfunction in various AD preclinical studies. The last federal drug agency (FDA) approved drug for AD dementia treatment, memantine, exert its therapeutic effects by ameliorating N-methyl-D-aspartate (NMDA) glutamate receptor overactivation and subsequent calcium dysregulation. More research works are needed to develop other drugs targeting calcium dysregulation at multiple pharmacological acting sites for future effective AD dementia treatment. Particularly, calcium channel blockers for the treatment of hypertension and dantrolene for the treatment of muscle spasm and malignant hyperthermia can be repurposed for this purpose. In our own research work, intranasal administration of dantrolene significantly increased its brain concentrations and durations, rendering it a more effective therapeutic drug with less side effects for chronic AD dementia treatment. This review summarizesthe progress of various studies repurposing drugs targeting calcium dysregulation for future effective AD dementia treatment as potentially disease-modifying drugs.

Keywords: alzheimer, calcium, cognitive dysfunction, dementia, neurodegeneration, neurogenesis

Procedia PDF Downloads 182