Search results for: cell morphology prediction

Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Yehia Manawi, Viktor Kochkodan, Muataz Hussien

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In this paper, the effect of adding Arabic Gum (AG) to the dope solutions of polyethersulfone (PES) was studied. The aim of adding AG is to enhance the properties of ultrafiltration membranes such as hydrophilicity, porosity and selectivity. several AG loading (0.1-3.0 wt.%) in PES/ N-Methyl-2-pyrrolidone (NMP) casting solutions were prepared to fabricate PES membranes using phase inversion technique. The surface morphology, hydrophilicity and selectivity of the cast PES/AG membranes were analyzed using scanning electron microscopy and contact angle measurements. The selectivity of the fabricated membranes was also tested by filtration of oil solutions (1 ppm) and found to show quite high removal efficiency. The effect of adding AG to PES membranes was found to increase the permeate flux and porosity as well as reducing surface roughness and the contact angle of the membranes.

Keywords: antifouling, Arabic gum, polyethersulfone membrane, ultrafiltration

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Authors: Frazer Kirk, Rohen Skiba, Pankaj Saxena

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The natural history of the QAV is poorly understood due to the exceeding rarity of the condition. Incidence rates vary between 0.00028-1%. Classically patients present with Aortic Regurgitation (AR) between 40-60 years of age experiencing palpitations, chest pain, or heart failure. (1, 2) Echocardiography is the mainstay of diagnosis for this condition; however, given the rarity of this condition, it can easily be overlooked, as demonstrated here. The case report that follows serves as a reminder of the condition to reduce the innate cognitive bias to overlook the diagnosis due to the availability heuristic. Intraoperative photography, echocardiographic and magnetic resonance imaging from this case for reference to demonstrate that while the diagnosis of Aortic regurgitation was recognized early, the valve morphology was underappreciated.

Keywords: quadricuspid aortic valve, cardiac surgery, echocardiography, congenital

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Authors: Alazar Nebyou, Sujata Pandit

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Lactic acid bacteria (LAB) are essential ingredients in probiotic foods, intestinal microflora, and dairy products that are capable of coping up with harsh gastrointestinal tract conditions and are available in a variety of environments. The objective of this study is to evaluate the probiotic property of LAB isolated from bovine milk. Milk samples were collected from local dairy farms. Samples were obtained using sterile test tubes and transported to a laboratory in the icebox for further biochemical characterization. Preliminary physiological and biochemical identification of LAB isolates was conducted by growing on MRS agar after ten-fold serial dilution. Seven of the best isolates were selected for the evaluation of the probiotic property. The LAB isolates were checked for resistance to antibiotics and their antimicrobial activity by disc diffusion assay and agar well diffusion assay respectively. Bile salt hydrolase activity of isolates was studied by growing isolates in a BSH medium with bile salt. Cell surface property of isolates was assayed by studying their autoaggregation and coaggregation percentage with S. aerues. All isolates were found BSH positive. In addition, BCM2 and BGM1 were susceptible to all antibiotic disks except BBM1 which was resistant to all antibiotic disks. BCM1 and BGM1 had the highest autoaggregation and coaggregation potential respectively. Since all LAB isolates showed gastrointestinal tolerance and good cell surface property they could be considered as good potential probiotic candidates for treatment and probiotic starter culture preparation.

Keywords: probiotic, aggregation, lactic acid bacteria, antimicrobial activity

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises

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Authors: Anjali Roy, Mirza S. Baig

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Atherosclerosis is a chronic inflammatory disease characterized by the formation of lipid rich plaque enriched with necrotic core, modified lipid accumulation, smooth muscle cells, endothelial cells, leucocytes and macrophages. Macrophage foam cells play a critical role in the occurrence and development of inflammatory atherosclerotic plaque. Foam cells are the fat-laden macrophages in the initial stage atherosclerotic lesion formation. Foam cells are an indication of plaque build-up, or atherosclerosis, which is commonly associated with increased risk of heart attack and stroke as a result of arterial narrowing and hardening. The mechanisms that drive atherosclerotic plaque progression remain largely unknown. Dissecting the molecular mechanism involved in process of macrophage foam cell formation will help to develop therapeutic interventions for atherosclerosis. To investigate the mechanism, we studied the role of nitric oxide synthase 1(NOS1)-mediated nitric oxide (NO) on low-density lipoprotein (LDL) uptake by bone marrow derived macrophages (BMDM). Using confocal microscopy, we found that incubation of macrophages with NOS1 inhibitor, TRIM (1-(2-Trifluoromethylphenyl) imidazole) or L-NAME (N omega-nitro-L-arginine methyl ester) prior to LDL treatment significantly reduces the LDL uptake by BMDM. Further, addition of NO donor (DEA NONOate) in NOS1 inhibitor treated macrophages recovers the LDL uptake. Our data strongly suggest that NOS1 derived NO regulates LDL uptake by macrophages and foam cell formation. Moreover, we also checked proinflammatory cytokine mRNA expression through real time PCR in BMDM treated with LDL and copper oxidized LDL (OxLDL) in presences and absences of inhibitor. Normal LDL does not evoke cytokine expression whereas OxLDL induced proinflammatory cytokine expression which significantly reduced in presences of NOS1 inhibitor. Rapid NOS-1-derived NO and its stable derivative formation act as signaling agents for inducible NOS-2 expression in endothelial cells, leading to endothelial vascular wall lining disruption and dysfunctioning. This study highlights the role of NOS1 as critical players of foam cell formation and would reveal much about the key molecular proteins involved in atherosclerosis. Thus, targeting NOS1 would be a useful strategy in reducing LDL uptake by macrophages at early stage of disease and hence dampening the atherosclerosis progression.

Keywords: atherosclerosis, NOS1, inflammation, oxidized LDL

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Authors: Nidhi Sharotri, Dhiraj Sud

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Water pollution by pesticides constitutes a serious ecological problem due to their potential toxicity and bioaccumulation. The widespread use of pesticides in industry and agriculture along with their resistance to natural decomposition, biodegradation, chemical and photochemical degradation under typical environmental conditions has resulted in the emergence of these chemicals and their transformed products in natural water. Among AOP’s, heterogeneous photocatalysis using TiO2 as photocatalyst appears as the most emerging destructive technology for mineralization of the pollutant in aquatic streams. Among the various semiconductors (TiO2, ZnO, CdS, FeTiO3, MnTiO3, SrTiO2 and SnO2), TiO2 has proven to be the most efficient photocatalyst for environmental applications due to its biological and chemical inertness, high photo reactivity, non-toxicity, and photo stability. Semiconductor photocatalysts are characterized by an electronic band structure in which valence band and conduction band are separated by a band gap, i.e. a region of forbidden energy. Semiconductor based photocatalysts produces e-/h+ pairs which have been employed for degradation of organic pollutants. The present paper focuses on modification of TiO2 photocatalyst in order to shift its absorption edge towards longer wavelength to make it active under natural light. Semiconductor TiO2 photocatalysts was prepared by doping with anion (N), cation (Mn) and double doped (Mn, N) using greener approach. Titanium isopropoxide is used as titania precursor and ethanedithiol, hydroxyl amine hydrochloride, manganous chloride as sulphur, nitrogen and manganese precursors respectively. Synthesized doped TiO2 nanomaterials are characterized for surface morphology (SEM, TEM), crystallinity (XRD) and optical properties (absorption spectra and band gap). EPR data confirms the substitutional incorporation of Mn2+ in TiO2 lattice. The doping influences the phase transformation of rutile and anatase phase crystal and thereby the absorption spectrum changes were observed. The effect of variation of reaction parameters such as solvent, reaction time and calcination temperature on the yield, surface morphology and optical properties was also investigated. The TEM studies show the particle size of nanomaterials varies from 10-50 nm. The calculated band gap of nanomaterials varies from 2.30-2.60 eV. The photocatalytic degradation of organic pollutant organophosphate pesticide (Quinalphos) has been investigated by studying the changes in UV absorption spectrum and the promising results were obtained under visible light. The complete mineralization of quinalphos has occurred as no intermediates were recorded after 8 hrs of degradation confirmed from the HPLC studies.

Keywords: quinalphos, doped-TiO2, mineralization, EPR

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: B. Chandar, M. K. Ramasamy, P. Madasamy

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The discovery and development of newer antibiotics are limited with the increase in resistance of such multi-drug resistant bacteria creating the need for alternative new therapeutic agents. The recently discovered New Delhi Metallo-betalactamase-1 (NDM-1), which confers antibiotic resistance to most of the currently available β-lactams, except colistin and tigecycline, is a global concern. Several antibacterial drugs approved are natural products or their semisynthetic derivatives, but plant extracts remain to be explored to find molecules that are effective against NDM-1 bacteria. Therefore, it is necessary to explore the possibility of finding new and effective antibacterial compounds against NDM-1 bacteria. In the present study, we have screened a diverse set South Indian plant species, and report most plant species as a potential source for antimicrobial compounds against NDM-1 bacteria. Ethanol extracts from the leaves of taxonomically diverse South Indian medicinal plants were screened for antibacterial activity against NDM-1 E. coli using streak plate method. Among the plant screened against NDM-1 E. coli, the ethanol extracts from many plant extracts showed minimum bactericidal concentration between 5 and 15 mg /ml and MIC between 2.54 and 5.12 mg/ml. These extracts also showed a potent synergistic effect when combined with antibiotics colistin and tetracycline. Combretum albidum that was effective was taken for further analysis. At 5mg/L concentration, these extracts inhibited the NDM-1 enzyme in vitro, and residual activity for Combretum albidum was 33.09%. Treatment of NDM-1 E. coli with the extracts disrupted the cell wall integrity and caused 89.7% cell death. The plant extract of Combretum albidum that was effective was subjected to fractionation and the fraction was further subjected to HPLC, LC-MS for identification of antibacterial compound.

Keywords: antibacterial activity, combretum albidum, Escherichia coli, NDM-1

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Authors: Man Guo, Wenyong Tan

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By combining spatial syntax with data obtained from field visits, this paper interprets the internal relationship between spatial morphology and spatial cognition in Lidukou Village. By comparing the obtained data, it is recognized that the spatial integration degree of Lidukou Village is positively correlated with the spatial cognitive intention of local villagers. The part with a higher spatial cognitive degree within the village is distributed along the axis mainly composed of Shuxiang Road. And the accessibility of historical relics is weak, and there is no systematic relationship between them. Aiming at the morphological problem of Lidukou Village, optimization strategies have been proposed from multiple perspectives, such as optimizing spatial mechanisms and shaping spatial nodes.

Keywords: traditional villages, spatial syntax, spatial integration degree, morphological problem

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Authors: J. Iqbal, Z. Zara

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Five new Acceptor-Donor-Acceptor (A-D-A) type small donor molecules (M1-M5) namely; dimethyl cyanoacetate terthiophene di(methylthiophene) dibenzosilole (DMCAO3TBS) (M1), dimelononitrile terthiophene di(methylthiophene) dibenzosilole (DMCNTBS) (M2), dimethyl rhodanine terthiophene di(methylthiophene) dibenzosilole (DMRTBS) (M3), dimelanonitrile terthiophene di(methylthiophene) methyl fluorene (DMCNTF) (M4) and dimethyl rhodanine terthiophene di(methylthiophene) methyl fluorine (DMRTF) (M5) were designed and theoretically explored their electronic, photophysical and geometrical properties via DFT best functional MPW1PW91/6-311G (d,p) level of theory with respect to reference molecules dioctyl cyanoacetate terthiophene di(octylthiophene) dioctylfluorene (DCAO3TF) (Ra) and dioctyl cyanoacetate terthiophene di(octylthiophene) octylcarbazole (DCAO3TCz) (Rb). Among the designed donor molecules (M1-M5), M2 and M4 represented lowest band gap value (2.480 eV and 2.47 eV) with distinctive broad absorption peak at 598 and 601 nm in chloroform due to the presence of stronger electron withdrawing acceptor molecule which pulls the λmax value towards red shift. Theoretically estimated reorganization energies of these molecules recommended excellent property of charge mobility. The designed donor molecules M1-M5, demonstrated lower λe value with reference to their λh, showing that these molecules could be ideal candidates for the transfer of electron with and M2, M4 are best among these as champion molecules with having lowest λe (0.006 D and 0.005 D respectively). Additionally, the Voc of M2 and M4 are 2.01 eV and 1.85 eV respectively with reference respect to PCBM. Thus, our present investigation suggested that our designed donor molecules (M1-M5) are suitable candidates for the solar cell and proposed for high and better performance for the small molecule based solar cell devices.

Keywords: dibenzisilol, donor materials, hole mobility, organic solar cells

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Authors: Amir Saber Gharamaleki, Beitollah Alipour, Zeinab Faghfoori, Ahmad YariKhosroushahi

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Colorectal cancer (CRC) is one of the most prevalent cancers and intestinal microbial community plays an important role in colorectal tumorigenesis. Probiotics have recently been assessed as effective anti-proliferative agents and thus this study was performed to examine whether CRC undergo apoptosis by treating with isolated Iranian native dairy yeast, Kluyveromyces marxianus YAS, secretion metabolites. The cytotoxicity assessments on cells (HT-29, Caco-2) were accomplished through 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as well as qualitative DAPI (4',6-diamidino-2-phenylindole staining) and quantitative (flow cytometry assessments) evaluations of apoptosis. To evaluate the main mechanism of apoptosis, Real time PCR method was applied. Kluyveromyces marxianus YAS secretions (IC50) showed significant cytotoxicity against HT-29 and Caco-2 cancer cell lines (66.57 % and 66.34 % apoptosis) similar to 5-Fluorouracil (5-FU) while apoptosis only was developed in 27.57 % of KDR normal cells. The prophylactic effects of Kluyveromyces marxianus (PTCC 5195), as a reference yeast, was not similar to Kluyveromyces marxianus YAS indicating strain dependency of bioactivities on CRC disease prevention. Based on real time PCR results, the main cytotoxicity is related to apoptosis phenomenon and the core related mechanism is depended on the overexpression of BAX, CASP 9, CASP 8 and CASP 3 inducing apoptosis genes. However, several investigations should be conducted to precisely determine the effective compounds to be used as anticancer therapeutics in the future.

Keywords: anticancer, anti-proliferative, apoptosis, cytotoxicity, yeast

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Authors: Mr. Liju Elias, A. Chitharanjan Hegde

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Ni-W alloy coatings have been developed galvanostatically on copper substrate from tri-sodium citrate bath, using glycerol as the additive. The deposition conditions for production of Ni-W coatings have been optimized for peak performance of their electrocatalytic activity, namely hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The corrosion behavior of the coatings were tested under working conditions of electrocatalysis (1M KOH). Electrocatalytic behaviours were tested by cyclic voltammetry and chrono-potentiometry techniques. Experimental results demonstrated that Ni-W coatings at low and high current densities (c. d.) showing superior performance for OER and HER respectively. The increased electrocatalytic activity for HER with increase of deposition c. d. was attributed to the phase structure, surface morphology and chemical composition of the coatings, confirmed by XRD, SEM and EDX analysis, respectively. The dependency of hardness and thickness of the coatings on HER and OER were examined, and results were discussed.

Keywords: electrocatalytic behavior, HER, Ni-W alloy, OER

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Abigail Tan, Heng Liang Tan, Vanessa Ding, James Hui, Eng Hin Lee, Andre Choo

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Introduction: Comparative oncology investigates the similarities in spontaneous carcinogenesis between humans and animals, in order to identify treatments that can benefit these patients. Companion animals (CA), like canines and felines, are of special interest when it comes to studying human cancers due to their exposure to the same environmental factors and develop tumours with similar features. The purpose of this study is to explore the cross-reactivity of monoclonal antibodies (mAbs) across cancers in humans and CA. Material and Methods: A panel of CA mAbs generated in the lab was screened on multiple human cancer cell lines through flow cytometry to identify for positive binders. Shortlisted candidates were then characterised by biochemical and functional assays e.g., antibody-drug conjugate (ADC) and western blot assays, including glycan studies. Results: Candidate mAb KP52 was generated from whole-cell immunisation using feline mammary carcinoma. KP52 showed strong positive binding to human cancer cells, such as breast cancer and ovarian cancer. Furthermore, KP52 demonstrated strong killing ( > 50%) as an ADC with Saporin as the payload. Western blot results revealed the molecular weight of the antigen targets to be approximately 45kD and 50kD under reduced conditions. Glycan studies suggest that the epitope is glycan in nature, specifically an O-linked glycan. Conclusion: Candidate mAb KP52 has a therapeutic potential as an ADC against feline mammary cancer, human ovarian cancer, human mammary cancer, human pancreatic cancer, and human gastric cancer.

Keywords: ADC, comparative oncology, mAb, therapeutic

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Authors: Navid Mosallaei, Mahmoud Reza Jaafari, Mohammad Yahya Hanafi-Bojd, Shiva Golmohammadzadeh, Bizhan Malaekeh-Nikouei

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Background: Docetaxel (DTX), a potent anticancer drug derived from the European yew tree, is effective against various human cancers by inhibiting microtubule depolymerization. Solid lipid nanoparticles (SLNs) have gained attention as drug carriers for enhancing drug effectiveness and safety. SLNs, submicron-sized lipid-based particles, can passively target tumors through the "enhanced permeability and retention" (EPR) effect, providing stability, drug protection, and controlled release while being biocompatible. Methods: The SLN formulation included biodegradable lipids (Compritol and Precirol), hydrogenated soy phosphatidylcholine (H-SPC) as a lipophilic co-surfactant, and Poloxamer 188 as a non-ionic polymeric stabilizer. Two SLN preparation techniques, probe sonication and microemulsion, were assessed. Characterization encompassed SLNs' morphology, particle size, zeta potential, matrix, and encapsulation efficacy. In-vitro cytotoxicity and cellular uptake studies were conducted using mouse colorectal (C-26) and human malignant melanoma (A-375) cell lines, comparing SLN-DTX with Taxotere®. In-vivo studies evaluated tumor inhibitory efficacy and survival in mice with colorectal (C-26) tumors, comparing SLNDTX withTaxotere®. Results: SLN-DTX demonstrated stability, with an average size of 180 nm and a low polydispersity index (PDI) of 0.2 and encapsulation efficacy of 98.0 ± 0.1%. Differential scanning calorimetry (DSC) suggested amorphous encapsulation of DTX within SLNs. In vitro studies revealed that SLN-DTX exhibited nearly equivalent cytotoxicity to Taxotere®, depending on concentration and exposure time. Cellular uptake studies demonstrated superior intracellular DTX accumulation with SLN-DTX. In a C-26 mouse model, SLN-DTX at 10 mg/kg outperformed Taxotere® at 10 and 20 mg/kg, with no significant differences in body weight changes and a remarkably high survival rate of 60%. Conclusion: This study concludes that SLN-DTX, prepared using the probe sonication, offers stability and enhanced therapeutic effects. It displayed almost same in vitro cytotoxicity to Taxotere® but showed superior cellular uptake. In a mouse model, SLN-DTX effectively inhibited tumor growth, with 10 mg/kg outperforming even 20 mg/kg of Taxotere®, without adverse body weight changes and with higher survival rates. This suggests that SLN-DTX has the potential to reduce adverse effects while maintaining or enhancing docetaxel's therapeutic profile, making it a promising drug delivery strategy suitable for industrialization.

Keywords: docetaxel, Taxotere®, solid lipid nanoparticles, enhanced permeability and retention effect, drug delivery, cancer chemotherapy, cytotoxicity, cellular uptake, tumor inhibition

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Authors: Deybith Venegas-Rojas, Sercan Keskin, Svenja Riekeberg, Sana Azim, Stephanie Manz, R. J. Dwayne Miller, Hoc Khiem Trieu

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Liquid Transmission Electron Microscopy (TEM) is a growing area with a broad range of applications from physics and chemistry to material engineering and biology, in which it is possible to image in-situ unseen phenomena. For this, a nanofluidic device is used to insert the nanoflow with the sample inside the microscope in order to keep the liquid encapsulated because of the high vacuum. In the last years, Si3N4 windows have been widely used because of its mechanical stability and low imaging contrast. Nevertheless, the pressure difference between the inside fluid and the outside vacuum in the TEM generates bulging in the windows. This increases the imaged fluid volume, which decreases the signal to noise ratio (SNR), limiting the achievable spatial resolution. With the proposed device, the membrane is fortified with a microstructure capable of stand higher pressure differences, and almost removing completely the bulging. A theoretical study is presented with Finite Element Method (FEM) simulations which provide a deep understanding of the membrane mechanical conditions and proves the effectiveness of this novel concept. Bulging and von Mises Stress were studied for different membrane dimensions, geometries, materials, and thicknesses. The microfabrication of the device was made with a thin wafer coated with thin layers of SiO2 and Si3N4. After the lithography process, these layers were etched (reactive ion etching and buffered oxide etch (BOE) respectively). After that, the microstructure was etched (deep reactive ion etching). Then the back side SiO2 was etched (BOE) and the array of free-standing micro-windows was obtained. Additionally, a Pyrex wafer was patterned with windows, and inlets/outlets, and bonded (anodic bonding) to the Si side to facilitate the thin wafer handling. Later, a thin spacer is sputtered and patterned with microchannels and trenches to guide the nanoflow with the samples. This approach reduces considerably the common bulging problem of the window, improving the SNR, contrast and spatial resolution, increasing substantially the mechanical stability of the windows, allowing a larger viewing area. These developments lead to a wider range of applications of liquid TEM, expanding the spectrum of possible experiments in the field.

Keywords: liquid cell, liquid transmission electron microscopy, nanofluidics, nanofluidic cell, thin films

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Authors: Sundaram Chandrasekaran, Seung Hyun Hur

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Exploring strategies for oxygen vacancy engineering under mild conditions and understanding the relationship between dislocations and photoelectrochemical (PEC) cell performance are challenging issues for designing high performance PEC devices. Therefore, it is very important to understand that how the oxygen vacancies (VO) or other defect states affect the performance of the photocatalyst in photoelectric transfer. So far, it has been found that defects in nano or micro crystals can have two possible significances on the PEC performance. Firstly, an electron-hole pair produced at the interface of photoelectrode and electrolyte can recombine at the defect centers under illumination of light, thereby reducing the PEC performances. On the other hand, the defects could lead to a higher light absorption in the longer wavelength region and may act as energy centers for the water splitting reaction that can improve the PEC performances. Even if the dislocation growth of ZnO has been verified by the full density functional theory (DFT) calculations and local density approximation calculations (LDA), it requires further studies to correlate the structures of ZnO and PEC performances. Exploring the hybrid structures composed of graphene oxide (GO) and ZnO nanostructures offer not only the vision of how the complex structure form from a simple starting materials but also the tools to improve PEC performances by understanding the underlying mechanisms of mutual interactions. As there are few studies for the ZnO growth with other materials and the growth mechanism in those cases has not been clearly explored yet, it is very important to understand the fundamental growth process of nanomaterials with the specific materials, so that rational and controllable syntheses of efficient ZnO-based hybrid materials can be designed to prepare nanostructures that can exhibit significant PEC performances. Herein, we fabricated various ZnO nanostructures such as hollow sphere, bucky bowl, nanorod and triangle, investigated their pH dependent growth mechanism, and correlated the PEC performances with them. Especially, the origin of well-controlled dislocation-driven growth and its transformation mechanism of ZnO nanorods to triangles on the GO surface were discussed in detail. Surprisingly, the addition of GO during the synthesis process not only tunes the morphology of ZnO nanocrystals and also creates more oxygen vacancies (oxygen defects) in the lattice of ZnO, which obviously suggest that the oxygen vacancies be created by the redox reaction between GO and ZnO in which the surface oxygen is extracted from the surface of ZnO by the functional groups of GO. On the basis of our experimental and theoretical analysis, the detailed mechanism for the formation of specific structural shapes and oxygen vacancies via dislocation, and its impact in PEC performances are explored. In water splitting performance, the maximum photocurrent density of GO-ZnO triangles was 1.517mA/cm-2 (under UV light ~ 360 nm) vs. RHE with high incident photon to current conversion Efficiency (IPCE) of 10.41%, which is the highest among all samples fabricated in this study and also one of the highest IPCE reported so far obtained from GO-ZnO triangular shaped photocatalyst.

Keywords: dislocation driven growth, zinc oxide, graphene oxide, water splitting

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Authors: Akan C. Offong, E. J. Anthony, Vasilije Manovic

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A modified steady-state numerical model is developed for the electrochemical reduction of CO₂ to formic acid. The numerical model achieves a CD (current density) (~60 mA/cm²), FE-faradaic efficiency (~98%) and conversion (~80%) for CO₂ electro-reduction to formic acid in a microfluidic cell. The model integrates charge and species transport, mass conservation, and momentum with electrochemistry. Specifically, the influences of Bi-Sn based nanoparticle catalyst (on the cathode surface) at different mole fractions and 1-ethyl-3-methyl imidazolium tetra-fluoroborate ([EMIM][BF₄]) electrolyte, on CD, FE and CO₂ conversion to formic acid is studied. The reaction is carried out at a constant concentration of electrolyte (85% v/v., [EMIM][BF₄]). Based on the mass transfer characteristics analysis (concentration contours), mole ratio 0.5:0.5 Bi-Sn catalyst displays the highest CO₂ mole consumption in the cathode gas channel. After validating with experimental data (polarisation curves) from literature, extensive simulations reveal performance measure: CD, FE and CO₂ conversion. Increasing the negative cathode potential increases the current densities for both formic acid and H₂ formations. However, H₂ formations are minimal as a result of insufficient hydrogen ions in the ionic liquid electrolyte. Moreover, the limited hydrogen ions have a negative effect on formic acid CD. As CO₂ flow rate increases, CD, FE and CO₂ conversion increases.

Keywords: carbon dioxide, electro-chemical reduction, ionic liquids, microfluidics, modelling

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Authors: Elene Zhuravliova, Tamar Barbakadze, Irina Kalandadze, Elnari Zaalishvili, Lali Shanshiashvili, David Mikeladze

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Background: Multiple sclerosis (MS) is an inflammatory, neurodegenerative disease, which is accompanied by demyelination and autoimmune response to myelin proteins. Among post-translational modifications, which mediate the modulation of inflammatory pathways during MS, methylation is the main one. The methylation of DNA, also amino acids lysine and arginine, occurs in the cell. It was found that decreased trans-methylation is associated with neuroinflammatory diseases. Therefore, abnormal regulation of the methyl cycle could induce demyelination through the action on PAD (peptidyl-arginine-deiminase) gene promoter. PAD takes part in protein citrullination and targets myelin basic protein (MBP), which is affected during demyelination. To determine whether MBP charge isomers are changing the methyl cycle, we have estimated the concentrations of methyl cycle metabolites in MBP-activated primary astrocytes and oligodendrocytes. For this purpose, the action of the citrullinated MBP- C8 and the most cationic MBP-C1 isomers on the primary cells were investigated. Methods: Primary oligodendrocyte and astrocyte cell cultures were prepared from whole brains of 2-day-old Wistar rats. The methyl cycle metabolites, including homocysteine, S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH), were estimated by HPLC analysis using fluorescence detection and prior derivatization. Results: We found that the action of MBP-C8 and MBP-C1 induces a decrease in the concentration of both methyl cycle metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), in astrocytes compared to the control cells. As for oligodendrocytes, the concentration of SAM was increased by the addition of MBP-C1, while MBP-C8 has no significant effect. As for SAH, its concentration was increased compared to the control cells by the action of both MBP-C1 and MBP-C8. A significant increase in homocysteine concentration was observed by the action of the MBP-C8 isomer in both oligodendrocytes and astrocytes. Conclusion: These data suggest that MBP charge isomers change the concentration of methyl cycle metabolites. MBP-C8 citrullinated isomer causes elevation of homocysteine in astrocytes and oligodendrocytes, which may be the reason for decreased astrocyte proliferation and increased oligodendrocyte cell death which takes place in neurodegenerative processes. Elevated homocysteine levels and subsequent abnormal regulation of methyl cycles in oligodendrocytes possibly change the methylation of DNA that activates PAD gene promoter and induces the synthesis of PAD, which in turn provokes the process of citrullination, which is the accompanying process of demyelination. Acknowledgment: This research was supported by the SRNSF Georgia RF17_534 grant.

Keywords: myelin basic protein, astrocytes, methyl cycle metabolites, homocysteine, oligodendrocytes

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Authors: Daniel Manaye Kabtamu, Anteneh Wodaje Bayeh

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With the development and utilization of new energy technology, people’s demand for large-scale energy storage system has become increasingly urgent. Vanadium redox flow battery (VRFB) is one of the most promising technologies for grid-scale energy storage applications because of numerous attractive features, such as long cycle life, high safety, and flexible design. However, the relatively low energy efficiency and high production cost of the VRFB still limit its practical implementations. It is of great attention to enhance its energy efficiency and reduce its cost. One of the main components of VRFB that can impressively impact the efficiency and final cost is the electrode materials, which provide the reactions sites for redox couples (V₂₊/V³⁺ and VO²⁺/VO₂⁺). Graphite felt (GF) is a typical carbon-based material commonly employed as electrode for VRFB due to low-cost, good chemical and mechanical stability. However, pristine GF exhibits insufficient wettability, low specific surface area, and poor kinetics reversibility, leading to low energy efficiency of the battery. Therefore, it is crucial to further modify the GF electrode to improve its electrochemical performance towards VRFB by employing active electrocatalysts, such as less expensive metal oxides. This study successfully fabricates low-cost plate-like bismuth vanadate (BiVO₄) material through a simple one-step hydrothermal route, employed as an electrocatalyst to adorn the GF for use as the negative electrode in VRFB. The experimental results show that BiVO₄-3h exhibits the optimal electrocatalytic activity and reversibility for the vanadium redox couples among all samples. The energy efficiency of the VRFB cell assembled with BiVO₄-decorated GF as the negative electrode is found to be 75.42% at 100 mA cm−2, which is about 10.24% more efficient than that of the cell assembled with heat-treated graphite felt (HT-GF) electrode. The possible reasons for the activity enhancement can be ascribed to the existence of oxygen vacancies in the BiVO₄ lattice structure and the relatively high surface area of BiVO₄, which provide more active sites for facilitating the vanadium redox reactions. Furthermore, the BiVO₄-GF electrode obstructs the competitive irreversible hydrogen evolution reaction on the negative side of the cell, and it also has better wettability. Impressively, BiVO₄-GF as the negative electrode shows good stability over 100 cycles. Thus, BiVO₄-GF is a promising negative electrode candidate for practical VRFB applications.

Keywords: BiVO₄ electrocatalyst, electrochemical energy storage, graphite felt, vanadium redox flow battery

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Authors: Yuxi Zhu, Zhenqian Chen

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It is of great interest to investigate the thermal properties of non-precious metal catalysts for Proton exchange membrane fuel cell (PEMFC) based on the thermal management requirements. Due to the low symmetry of materials, to accurately obtain the thermal conductivity of materials, it is necessary to obtain the second and third order force constants by combining density functional theory and machine learning interatomic potential. To be specific, the interatomic force constants are obtained by moment tensor potential (MTP), which is trained by the computational trajectory of Ab initio molecular dynamics (AIMD) at 50, 300, 600, and 900 K for 1 ps each, with a time step of 1 fs in the AIMD computation. And then the thermal conductivity can be obtained by solving the Boltzmann transport equation. In this paper, the thermal transport properties of single metal atom catalysts are studied for the first time to our best knowledge by machine-learning interatomic potential (MLIP). Results show that the single metal atom catalysts exhibit anisotropic thermal conductivities and partially exhibit good thermal conductivity. The average lattice thermal conductivities of G-FeN₄, G-CoN₄ and G-NiN₄ at 300 K are 88.61 W/mK, 205.32 W/mK and 210.57 W/mK, respectively. While other single metal atom catalysts show low thermal conductivity due to their low phonon lifetime. The results also show that low-frequency phonons (0-10 THz) dominate thermal transport properties. The results provide theoretical insights into the application of single metal atom catalysts in thermal management.

Keywords: proton exchange membrane fuel cell, single metal atom catalysts, density functional theory, thermal conductivity, machine-learning interatomic potential

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: O. H. Gebril, N. A. Meguid

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Background: Iron had been a focus of interest recently as a main exaggerating factor for oxidative stresses in the central nervous system and a link to various neurological disorders is suspected. Many studies with various techniques showed evidence of disturbed iron-related proteins in the cell in human and animal models of neurodegenerative disorders. Also, linkage to significant pathological changes had been evidenced e.g. apoptosis and cell signaling. On the other hand, the role of iron in neurodevelopmental disorders is still unclear. With increasing prevalence of autism worldwide, some changes in iron parameters and its stores were documented in many studies. This study includes Haemochromatosis HFE gene polymorphisms (p.H63D and p.C282Y) and ferroportin gene (SLC40A1) Q248H polymorphism in autism and control children. Materials and Methods: Whole genome DNA was extracted; p.H63D and p.C282Y genotyping was studied using specific sequence amplification followed by restriction enzyme digestion on a sample of autism patients (25 cases) and twenty controls. Results: The p.H63D is seen more than the C282Y among both autism and control samples, with no significant association of p.H63D or p.C282Y polymorphism and autism was revealed. Also, no association with Q248H polymorphism was evidenced. Conclusion: The study results do not prove the role of cellular iron genes polymorphisms as risk factors for neurodevelopmental disorders, and in turn highlights the specificity of cellular iron related pathways in neurodegeneration. These results demand further gene expression studies to elucidate the main pathophysiological pathways that are disturbed in autism and other neurodevelopmental disorders.

Keywords: iron, neurodevelopmental, oxidative stress, haemohromatosis, ferroportin, genes

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Authors: Ndukwe, Chinedu O.

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Background: There are only a few epidemiological studies published on skin disorders in the elderly within the Nigerian context and none from the Southeast Region of the country. In addition, none of these studies has considered the pattern and frequency of histopathologically diagnosed geriatric skin lesions. Hence, we attempted to determine the frequency as well as the age and gender distributions of histologically diagnosed dermatological diseases in the geriatric population from skin biopsies submitted to the histopathology department of a tertiary care hospital in Southeast Nigeria. Material and methods: This is a cross-sectional retrospective hospital-based study involving all skin biopsies of patients 60 years and above, received at the Department of Histopathology, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria from January 2004 to December 2019. Results: During the study period, 751 skin biopsies were received in the histopathology department. Of these, 142 were from patients who were older than 60 years. Thus, the overall share of geriatric patients was 18.9%. The mean age at presentation was 71.1 ± 8.6 years. The M: F was 1:1 and most of the patients belonged to the age group of 60–69 years (69 cases, 48.6%). The mean age of the male patients was 72.1±9.5 years. In the female patients, it was 70.1±7.5 years. The commonest disease category was neoplasms (91, 64.1%). Most neoplasms were malignant. There were 67/142 (47.2%) malignant lesions. Commonest was Squamous cell carcinoma (SCC) (30 cases) which is 21.1% of all geriatric skin biopsies and 44.8% of malignant skin biopsies. This is closely followed by melanoma (29 cases). Conclusion: Malignant neoplasms, benign neoplasms and papulosquamous disorders are the three commonest histologically diagnosed skin lesions in our geriatric population. The commonest skin malignancies in this group of patients are squamous cell carcinoma and malignant melanoma.

Keywords: geriatric, skin, Nigeria, histopathology

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Authors: Francisco Pedroza-Montero, Jesús Naín Pedroza-Montero, Diego Soto-Puebla, Osiris Alvarez-Bajo, Beatriz Castaneda, Sofía Navarro-Espinoza, Martín Pedroza-Montero

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Nanodiamonds have been widely studied for their physical properties, including chemical inertness, biocompatibility, optical transparency from the ultraviolet to the infrared region, high thermal conductivity, and mechanical strength. In this work, we studied how the fluorescence spectrum of nanodiamonds quenches concerning the concentration in aqueous solutions systematically ranging from 0.1 to 10 mg/mL. Our results demonstrated a non-linear fluorescence quenching as the concentration increases for both of the NV zero-phonon lines; the 5 mg/mL concentration shows the maximum fluorescence emission. Furthermore, this behaviour is theoretically explained as an electronic recombination process that modulates the intensity in the NV centres. Finally, to gain more insight, the FRET methodology is used to determine the fluorescence efficiency in terms of the fluorophores' separation distance. Thus, the concentration level is simulated as follows, a small distance between nanodiamonds would be considered a highly concentrated system, whereas a large distance would mean a low concentrated one. Although the 5 mg/mL concentration shows the maximum intensity, our main interest is focused on the concentration of 0.5 mg/mL, which our studies demonstrate the optimal human cell viability (99%). In this respect, this concentration has the feature of being as biocompatible as water giving the possibility to internalize it in cells without harming the living media. To this end, not only can we track nanodiamonds on the surface or inside the cell with excellent precision due to their fluorescent intensity, but also, we can perform thermometry tests transforming a fluorescence contrast image into a temperature contrast image.

Keywords: nanodiamonds, fluorescence spectroscopy, concentration, bioimaging, thermometry

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Authors: Francisco Dos Santos, Diogo Fonseca-Pereira, Sílvia Arroz-Madeira, Henrique Veiga-Fernandes

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Haematopoiesis is a developmental process that generates all blood cell lineages in health and disease. This relies on quiescent haematopoietic stem cells (HSCs) that are able to differentiate, self renew and expand upon physiological demand. HSCs have great interest in regenerative medicine, including haematological malignancies, immunodeficiencies and metabolic disorders. However, the limited yield from existing HSC sources drives the global need for reliable techniques to expand harvested HSCs at high quality and sufficient quantities. With the extensive use of cord blood progenitors for clinical applications, there is a demand for a safe and efficient expansion protocol that is able to overcome the limitations of the cord blood as a source of HSC. StemCell2MAXTM developed a technology that enhances the survival, proliferation and transplantation efficiency of HSC, leading the way to a more widespread use of HSC for research and clinical purposes. StemCell2MAXTM MIX is a solution that improves HSC expansion up to 20x, while preserving stemness, when compared to state-of-the-art. In a recent study by a leading cord blood bank, StemCell2MAX MIX was shown to support a selective 100-fold expansion of CD34+ Hematopoietic Stem and Progenitor Cells (when compared to a 10-fold expansion of Total Nucleated Cells), while maintaining their multipotent differentiative potential as assessed by CFU assays. The technology developed by StemCell2MAXTM opens new horizons for the usage of expanded hematopoietic progenitors for both research purposes (including quality and functional assays in Cord Blood Banks) and clinical applications.

Keywords: cord blood, expansion, hematopoietic stem cell, transplantation

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Authors: Sanjeev Das

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In the present investigation, magnesium strips were produced by twin roll casting (TRC) and melt conditioned twin roll casting (MC-TRC) processes. The microstructures showed uniform fine equiaxed grain morphology in the case of MC-TRC cast samples. In the case of TRC samples elongated grains with centerline segregation was observed. Further investigation showed both the process has different solidification mechanism. Tensile tests were performed at 250–400ºC for both TRC and MCTRC samples. At 250ºC, MC-TRC sample showed significant improvement in strength and ductility. However, at higher temperatures the tensile properties were almost comparable, despite of TRC samples having larger grains compared to MC-TRC samples. It was observed that homogenized MC-TRC samples were easily hot stamped compared to TRC samples.

Keywords: MC-TRC, magnesium alloy, solidification, nucleation

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Authors: Akhtar Rasool, Tasneem Zahra Rizvi

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In this study, a series of the cadmium sulphide quantum dots/polyaniline nanocomposites with varying compositions were prepared by in-situ polymerization technique and were characterized using X-ray diffraction and Fourier transform infrared spectroscopy. The surface morphology was studied by scanning electron microscopy. UV-Visible spectroscopy was used to find out the energy band gap of the nanoparticles and the nanocomposites. Temperature dependence of DC electrical conductivity and temperature and frequency dependence of AC conductivity were investigated to study the charge transport mechanism in the nanocomposites. DC conductivity was found to be a typical for a semiconducting behavior following Mott’s 1D variable range hoping model. The frequency dependent AC conductivity followed the universal power law.

Keywords: conducting polymers, nanocomposites, polyaniline composites, quantum dots

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

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