Search results for: models synthesis

Authors: L. Yettou

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In this study, the calculations of proton emission spectra produced by ⁶³Cu(n,xp) and ⁶⁵Cu(n,xp) reactions are used in the framework of preequilibrium models using the EMPIRE code and TALYS code. Exciton Model predidtions combined with the Kalbach angular distribution systematics and the Hybrid Monte Carlo Simulation (HMS) were used. The effects of levels densities and those of a-parameter have been investigated for our calculations. The comparison with experimental data shows clear improvement over the Exciton Model and HMS calculations.

Keywords: Preequilibrium models , level density, level density a-parameter., Empire code, Talys code.

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Harish Rajak, Kamlesh Raghuwanshi

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Histone deacetylase (HDAC) are promising target for cancer treatment. The panobinostat (Farydak; Novartis; approved by USFDA in 2015) and chidamide (Epidaza; Chipscreen Biosciences; approved by China FDA in 2014) are the novel HDAC inhibitors ratified for the treatment of patients with multiple myeloma and peripheral T cell lymphoma, respectively. On the other hand, two other HDAC inhibitors, Vorinostat (SAHA; approved by USFDA in 2006) and Romidepsin (FK228; approved by USFDA in 2009) are already in market for the treatment of cutaneous T-cell lymphoma. Several hydroxamic acid based HDAC inhibitors i.e., belinostat, givinostat, PCI24781 and JNJ26481585 are in clinical trials. HDAC inhibitors consist of three pharmacophoric features - an aromatic cap group, zinc binding group (ZBG) and a linker chain connecting cap group to ZBG. Herein, we report synthesis, characterization and biological evaluation of HDAC inhibitors possessing substituted isatin moiety as cap group which recognize the surface of active enzyme pocket and thiosemicarbazide moiety incorporated as linker group responsible for connecting cap group to ZBG (hydroxamic acid). Several analogues were found to inhibit HDAC and cellular proliferation of Hela cervical cancer cells with GI50 values in the micro molar range. Some of the compounds exhibited promising results in vitro antiproliferative studies. Attempts were also made to establish the structure activity relationship among synthesized HDAC inhibitors.

Keywords: HDAC inhibitors, hydroxamic acid derivatives, isatin derivatives, antiproliferative activity, docking

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: Abderrazek Ben Maatoug, Ibrahim Fatnassi, Wassim Ben Ayed

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In this paper we investigate the issue of market risk quantification for Middle East and North Africa (MENA) Islamic market equity. We use Value-at-Risk (VaR) as a measure of potential risk in Islamic stock market, for long and short position, based on Riskmetrics model and the conditional parametric ARCH class model volatility with normal, student and skewed student distribution. The sample consist of daily data for the 2006-2014 of 11 Islamic stock markets indices. We conduct Kupiec and Engle and Manganelli tests to evaluate the performance for each model. The main finding of our empirical results show that (i) the superior performance of VaR models based on the Student and skewed Student distribution, for the significance level of α=1% , for all Islamic stock market indices, and for both long and short trading positions (ii) Risk Metrics model, and VaR model based on conditional volatility with normal distribution provides the best accurate VaR estimations for both long and short trading positions for a significance level of α=5%.

Keywords: value-at-risk, risk management, islamic finance, GARCH models

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Authors: Fares Laouacheria, Said Kechida, Moncef Chabi

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The objective of the study was based on the hydrological routing modelling for the continuous monitoring of the hydrological situation in the Moudjar river catchment, especially during floods with Hydrologic Engineering Center–Hydrologic Modelling Systems (HEC-HMS). The HEC-GeoHMS was used to transform data from geographic information system (GIS) to HEC-HMS for delineating and modelling the catchment river in order to estimate the runoff volume, which is used as inputs to the hydrological routing model. Two hydrological routing models were used, namely Muskingum and Muskingum routing models, for conducting this study. In this study, a comparison between the parameters of the Muskingum and Muskingum-Cunge routing models in HEC-HMS was used for modelling flood routing in the Moudjar river catchment and determining the relationship between these parameters and the physical characteristics of the river. The results indicate that the effects of input parameters such as the weighting factor "X" and travel time "K" on the output results are more significant, where the Muskingum routing model was more sensitive to input parameters than the Muskingum-Cunge routing model. This study can contribute to understand and improve the knowledge of the mechanisms of river floods, especially in ungauged river catchments.

Keywords: HEC-HMS, hydrological modelling, Muskingum routing model, Muskingum-Cunge routing model

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Authors: Heba Gamal Abd Elhaleem Elsayed, Nour F Attia

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The extensive use of textile and textile based materials in various applications including industrial applications are increasing regularly due to their interesting properties which require rapid development in their functions to be adapted to these applications [1-3]. Herein, green, new and renewable smart coating was developed for furniture textile fabrics. Facile and single step method was used for synthesis of green coating based on mandarin peel and chitosan. As, the mandarin peel as fruit waste material was dried, grinded and directly dispersed in chitosan solution producing new green coating composite and then coated on textile fabrics. The mass loadings of green mandarin peel powder was varied on 20-70 wt% and optimized. Thermal stability of coated textile fabrics was enhanced and char yield was improved compared to uncoated one. The charring effect of mandarin peel powder coated samples was significantly enhanced anticipating good flame retardancy effect. The tensile strength of the coated textile fabrics was improved achieved 35% improvement compared to uncoated sample. The interaction between the renewable coating and textile was evaluated. The morphology of uncoated and coated textile fabrics was studied using microscopic technique. Additionally, based on thermal properties of mandarin peel powder it could be promising flame retardant for textile fabrics. This study open new avenues for finishing textile fabrics with enhanced thermal, flame retardancy and mechanical properties with cost-effective and renewable green and effective coating

Keywords: flame retardant , Thermal Properties, Textile Coating , Renewable Textile

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Authors: Asmaa M. Fahim

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In this elucidation, we synthesized different heterocyclic compounds attached to Benzene sulfonamide moiety via (E)-N-(4-(3-(4-bromophenyl)acryloyl)phenyl)-4-methyl benzene sulfonamide which is obtained from Nucleophilic substitution reaction between 4-methylbenzene sulfonyl chloride and 1-(4-aminophenyl)ethan-1-one in pyridine to get N-(4-acetyl phenyl)-4-methyl benzenesulfonamide which reacted 4-bromobenzal dehyde undergoes aldol condensation in NaOH to afford the corresponding chalchone 4. Moreover, the reactivity of chalchone 4 showed several active methylene derivatives utilized the pressurized microwave irradiation as a green energy resource. Chalcone 4 was allowed to react with ethyl cyanoacetate and acetylacetone, respectively, at 70 °C with pressure under microwave reaction condition to afford the 5-cyano-6-oxo-1,2,5,6-tetrahydropyridin-2-yl)-4-methylbenzenesulfonamide 6 and N-(4'-acetyl-4''-bromo-5'-oxo-2',3',4',5'-tetrahydro-[1,1':3',1''-terphenyl]-4-yl)-4-methylbenzenesulfonamide 8 derivatives. Moreover, the reactivity of this sulphonamide chalchone with NH2NH2 in EtOH and acetic acid, which gave 2,5-dihydro-1H-imidazol-4-yl)-4-methyl benzenesulfonamide, 1H-pyrazol-3-yl)-4-methyl and reactivity with NH2OH.HCl gave isoxazol-3-yl)-4-methylbenzenesulfonamide derivatives. The synthesized compounds were screened for their ADME properties and directed to antitumor activity on HepG2 hepatocellular carcinoma and MCF-7 breast cancer and exhibited excellent behavior against standard drugs; these results were confirmed through molecular simulations with different proteins. Additionally, the Density Functional Theory analysis of optimized structures investigated their physical descriptors, FMO, ESP and MEP, which correlated with biological evaluation.

Keywords: synthesis, green chemistry, antitumor activity, DFT study

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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In this paper, quantitative structure-retention relationships (QSRR) analysis was applied in order to correlate in silico biological and lipophilicity molecular descriptors with retention values for the set of selected s-triazine herbicides. In silico generated biological and lipophilicity descriptors were discriminated using generalized pair correlation method (GPCM). According to this method, the significant difference between independent variables can be noticed regardless almost equal correlation with dependent variable. Using established multiple linear regression (MLR) models some biological characteristics could be predicted. Established MLR models were evaluated statistically and the most suitable models were selected and ranked using sum of ranking differences (SRD) method. In this method, as reference values, average experimentally obtained values are used. Additionally, using SRD method, similarities among investigated s-triazine herbicides can be noticed. These analysis were conducted in order to characterize selected s-triazine herbicides for future investigations regarding their biodegradability. This study is financially supported by COST action TD1305.

Keywords: descriptors, generalized pair correlation method, pesticides, sum of ranking differences

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Authors: Payam Haghparast, Mikhail V. Sorin, Hakim Nesreddine

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The complex oblique shock phenomenon can be simply assumed as a normal shock at the constant area section to simulate a sharp pressure increase and velocity decrease in 1-D thermodynamic models. The assumed normal shock location is one of the greatest sources of error in ejector thermodynamic models. Most researchers consider an arbitrary location without justifying it. Our study compares the effect of normal shock place on ejector dimensions in 1-D models. To this aim, two different ejector experimental test benches, a constant area-mixing ejector (CAM) and a constant pressure-mixing (CPM) are considered, with different known geometries, operating conditions and working fluids (R245fa, R141b). In the first step, in order to evaluate the real value of the efficiencies in the different ejector parts and critical back pressure, a CFD model was built and validated by experimental data for two types of ejectors. These reference data are then used as input to the 1D model to calculate the lengths and the diameters of the ejectors. Afterwards, the design output geometry calculated by the 1D model is compared directly with the corresponding experimental geometry. It was found that there is a good agreement between the ejector dimensions obtained by the 1D model, for both CAM and CPM, with experimental ejector data. Furthermore, it is shown that normal shock place affects only the constant area length as it is proven that the inlet normal shock assumption results in more accurate length. Taking into account previous 1D models, the results suggest the use of the assumed normal shock location at the inlet of the constant area duct to design the supersonic ejectors.

Keywords: 1D model, constant area-mixing, constant pressure-mixing, normal shock location, ejector dimensions

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Authors: Arifuzzaman Nayeem, Tafannum Torsha, Tanvir Manzur, Shaurav Alam

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Performance evaluation of reinforced concrete (RC) beams subjected to accidental fire is significant for post-fire capacity measurement. Mechanical properties of any RC beam degrade due to heating since the strength and modulus of concrete and reinforcement suffer considerable reduction under elevated temperatures. Moreover, fire-induced thermal dilation and shrinkage cause internal stresses within the concrete and eventually result in cracking, spalling, and loss of stiffness, which ultimately leads to lower service life. However, conducting full-scale comprehensive experimental investigation for RC beams exposed to fire is difficult and cost-intensive, where the finite element (FE) based numerical study can provide an economical alternative for evaluating the post-fire capacity of RC beams. In this study, an attempt has been made to study the fire behavior of RC beams using FE software package ABAQUS under different durations of fire. The damaged plasticity model of concrete in ABAQUS was used to simulate behavior RC beams. The effect of temperature on strength and modulus of concrete and steel was simulated following relevant Eurocodes. Initially, the result of FE models was validated using several experimental results from available scholarly articles. It was found that the response of the developed FE models matched quite well with the experimental outcome for beams without heat. The FE analysis of beams subjected to fire showed some deviation from the experimental results, particularly in terms of stiffness degradation. However, the ultimate strength and deflection of FE models were similar to that of experimental values. The developed FE models, thus, exhibited the good potential to predict the fire behavior of RC beams. Once validated, FE models were then used to analyze several RC beams having different strengths (ranged between 20 MPa and 50 MPa) exposed to the standard fire curve (ASTM E119) for different durations. The post-fire performance of RC beams was investigated in terms of load-deflection behavior, flexural strength, and deflection characteristics.

Keywords: fire durations, flexural strength, post fire capacity, reinforced concrete beam, standard fire

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Authors: Khaled Alshamari

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Synthesized partially phosphorylated polyvinyl phosphate derivative (PPVP) was functionalized to extract Zirconium (IV) from Egyptian zircon sand. The specifications for the PPVP composite were approved effectively via different techniques, namely, FT-IR, XPS, BET, EDX, TGA, HNMR, C-NMR, GC-MS, XRD and ICP-OES analyses, which demonstrated a satisfactory synthesis of PPVP and zircon dissolution from Egyptian zircon sand. Factors controlling parameters, such as pH values, shaking time, initial zirconium concentration, PPVP dose, nitrate ions concentration, co-ions, temperature and eluting agents, have been optimized. At 25 ◦C, pH 0, 20 min shaking, 0.05 mol/L zirconium ions and 0.5 mol/L nitrate ions, PPVP has an exciting preservation potential of 195 mg/g, equivalent to 390 mg/L zirconium ions. From the extraction–distribution isotherm, the practical outcomes of Langmuir’s modeling are better than the Freundlich model, with a theoretical value of 196.07 mg/g, which is more in line with the experimental results of 195 mg/g. The zirconium ions adsorption onto the PPVP composite follows the pseudo-second-order kinetics with a theoretical capacity value of 204.08 mg/g. According to thermodynamic potential, the extraction process was expected to be an exothermic, spontaneous and beneficial extraction at low temperatures. The thermodynamic parameters ∆S (−0.03 kJ/mol), ∆H (−12.22 kJ/mol) and ∆G were also considered. As the temperature grows, ∆G values increase from −2.948 kJ/mol at 298 K to −1.941 kJ/mol at 338 K. Zirconium ions may be eluted from the working loaded PPVP by 0.025M HNO₃, with a 99% efficiency rate. It was found that zirconium ions revealed good separation factors towards some co-ions such as Hf⁴+ (28.82), Fe³+ (10.64), Ti⁴+ (28.82), V⁵+ (86.46) and U⁶+ (68.17). A successful alkali fusion technique with NaOH flux followed by the extraction with PPVP is used to obtain a high-purity zirconia concentrate with a zircon content of 72.77 % and a purity of 98.29%. As a result of this, the improved factors could finally be used.

Keywords: zirconium extraction, partially phosphorylated polyvinyl phosphate (PPVP), acidic medium, zircon

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Authors: Shweta Verma

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Background: Epilepsy is a chronic non-communicable central nervous system (CNS) disorder which affects a large population of all ages. Different classes of drugs are used for the treatment of this neurological disorder, but due to augmented drug resistance and side effects, these drugs become incompetent. Therefore, we design the synthesis of ten new derivatives of Phenytoin. The moiety of Phenytoin was hybridized with different phenols by using three step approach. The synthesized molecules were then investigated for different physicochemical parameters, such as Log P values using diverse software programs and to predict the potential to cross the blood-brain barrier. Objective: The Phenytoin derivatives were designed, synthesized, and characterized to meet the structural necessities indispensable for antiepileptic activity. Method: Firstly, the chloroacetylation of the 5,5-diphenyl hydantoin was carried out, and then various substituted phenols were added to it. The synthesized compounds were characterized and evaluated for antianxiety activity by elevated plus maze method and antiepileptic activity by using subcutaneous pentylenetetrazole (scPTZ) and maximal electroshock (MES) models and neurotoxicity. Result: The number of derivatives of 5,5-diphenyl hydantoin was developed and optimized. The number of parameters was optimized which reveal that the compound containing chloro group such as C3 and C6 showed imperative potential when compared with the standard drug Diazepam. Other compounds containing nitro and methyl group were also found to possess activity. Conclusion: It was summarized that the new compounds of 5,5-diphenyl hydantoin derivatives were synthesized. The results of the data show that the compound containing chloro group is more potent for CNS activity. The new compounds have the probability of being optimized further to engender new scaffolds to treat various CNS disorders.

Keywords: phenytoin, parameters, CNS activity, blood-brain barrier, Log P, CNS active

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: prediction model, sensitivity analysis, simulation method, USMLE

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Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

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An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

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Authors: K. O'Malley

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Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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Authors: Maryam Safrai, Tewfik Mahdi

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This article presents the stochastic modeling of the Richelieu River flood in Quebec, Canada, occurred in the spring of 2011. With the aid of the one-dimensional Watershed Modeling System (WMS (v.10.1) and HEC-RAS (v.4.1) as a flood simulator, the delineation of the probabilistic flooded areas was considered. Based on the Monte Carlo method, WMS (v.10.1) delineated the probabilistic flooded areas with corresponding occurrence percentages. Furthermore, results of this one-dimensional model were compared with the results of two-dimensional model (SRH-2D) for the evaluation of efficiency and precision of each applied model. Based on this comparison, computational process in two-dimensional model is longer and more complicated versus brief one-dimensional one. Although, two-dimensional models are more accurate than one-dimensional method, but according to existing modellers, delineation of probabilistic flooded areas based on Monte Carlo method is achievable via one-dimensional modeler. The applied software in this case study greatly responded to verify the research objectives. As a result, flood risk maps of the Richelieu River with the two applied models (1d, 2d) could elucidate the flood risk factors in hydrological, hydraulic, and managerial terms.

Keywords: flood modeling, HEC-RAS, model comparison, Monte Carlo simulation, probabilistic flooded area, SRH-2D, WMS

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Authors: Bachir Redouane, Dib Nihel, Bedrane Sumeya, Blanco Ginesa, Calvino José Juan

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Furfural, a key biomass-derived platform compound, holds significant potential for biofuel production and the synthesis of high-value intermediates. This study investigates the hydrogenation-condensation reaction of furfural issued from lignocellulosique biomass with isopropyl alcohol to produce isopropylfurfuryl ether (iPFE), a next-generation synfuel with a high-octane number. iPFE’s water stability and resistance to methanol absorption make it a sustainable alternative to conventional gasoline additives, offering comparable performance. The catalyst used in this reaction is based on NiAl layered double hydroxides (LDH), with zirconium incorporated to enhance the distribution and structure of active sites. Gold (Au) was deposited on the NiAlZr-LDH support to improve selectivity and yield. The addition of Zr improved the thermal and mechanical stability of the catalyst, while the Au modification further increased selectivity toward iPFE. Extensive catalytic experiments were conducted to optimize reaction conditions, including temperature, hydrogen pressure, and Au loading, to maximize iPFE yield. The results demonstrate a high conversion rate of furfural, exceeding 90% under optimal conditions, with enhanced selectivity toward iPFE. Moreover, iPFE was shown to have a higher-octane number compared to traditional furfuryl ethers, making it a highly promising candidate for advanced fuel applications.

Keywords: Au-NiAlZr-LDH, biofuels, furfural, green chemistry, hydrogenation, isopropylfurfuryl ether, octane number.

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Authors: Komal Kumar, Sreedevi Upadhyayula

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In this study, a two-stage process was established for the synthesis of HMF esters using ionic liquid acid catalyst. Ionic liquid catalyst with different strength of the Bronsted acidity was prepared in the laboratory and characterized using 1H NMR, FT-IR, and 13C NMR spectroscopy. Solid acid catalyst from the ionic liquid catalyst was prepared using the immobilization method. The acidity of the synthesized acid catalyst was measured using Hammett function and titration method. Catalytic performance was evaluated for the biomass conversion to 5-hydroxymethylfurfural (5-HMF) and levulinic acid (LA) in methyl isobutyl ketone (MIBK)-water biphasic system. A good yield of 5-HMF and LA was found at the different composition of MIBK: Water. In the case of MIBK: Water ratio 10:1, good yield of 5-HMF was observed at ambient temperature 150˚C. Upgrading of 5-HMF into monoesters from the reaction of 5-HMF and reactants using biomass-derived monoacid were performed. Ionic liquid catalyst with -SO₃H functional group was found to be best efficient in comparative of a solid acid catalyst for the esterification reaction and biomass conversion. A good yield of 5-HMF esters with high 5-HMF conversion was found to be at 105˚C using the best active catalyst. In this process, process A was the hydrothermal conversion of cellulose and monomer into 5-HMF and LA using acid catalyst. And the process B was the esterification followed by using similar acid catalyst. All monoesters of 5-HMF synthesized here can be used in chemical, cross linker for adhesive or coatings and pharmaceutical industry. A theoretical density functional theory (DFT) study for the optimization of the ionic liquid structure was performed using the Gaussian 09 program to find out the minimum energy configuration of ionic liquid catalyst.

Keywords: biomass conversion, 5-HMF, Ionic liquid, HMF ester

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Authors: Mohammad Reza Fazeli

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Nowadays, the fourth industry, known as the digital transformation of industries, is seen as one of the top subjects in the history of structural revolution, which has led to the high-tech and tactical dominance of the organization. In the face of these profits, the undefined and non-transparent nature of the after-effects of investing in digital transformation has hindered many organizations from attempting this area of this industry. One of the important frameworks in the field of understanding digital transformation in all organizations is the maturity model of digital transformation. This model includes two main parts of digital transformation maturity dimensions and digital transformation maturity stages. Mediating factors of digital maturity and organizational performance at the individual (e.g., motivations, attitudes) and at the organizational level (e.g., organizational culture) should be considered. For successful technology adoption processes, organizational development and human resources must go hand in hand and be supported by a sound communication strategy. Maturity models are developed to help organizations by providing broad guidance and a roadmap for improvement. However, as a result of a systematic review of the literature and its analysis, it was observed that none of the 18 maturity models in the field of digital transformation fully meet all the criteria of appropriateness, completeness, clarity, and objectivity. A maturity assessment framework potentially helps systematize assessment processes that create opportunities for change in processes and organizations enabled by digital initiatives and long-term improvements at the project portfolio level. Cultural characteristics reflecting digital culture are not systematically integrated, and specific digital maturity models for the service sector are less clearly presented. It is also clearly evident that research on the maturity of digital transformation as a holistic concept is scarce and needs more attention in future research.

Keywords: digital transformation, organizational performance, maturity models, maturity assessment

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Authors: Zohreh Moghaddas, Morteza Yazdani, Farhad Hosseinzadeh

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Nowadays, data envelopment analysis (DEA) models featuring network structures have gained widespread usage for evaluating the performance of production systems and activities (Decision-Making Units (DMUs)) across diverse fields. By examining the relationships between the internal stages of the network, these models offer valuable insights to managers and decision-makers regarding the performance of each stage and its impact on the overall network. To further empower system decision-makers, the inverse data envelopment analysis (IDEA) model has been introduced. This model allows the estimation of crucial information for estimating parameters while keeping the efficiency score unchanged or improved, enabling analysis of the sensitivity of system inputs or outputs according to managers' preferences. This empowers managers to apply their preferences and policies on resources, such as inputs and outputs, and analyze various aspects like production, resource allocation processes, and resource efficiency enhancement within the system. The results obtained can be instrumental in making informed decisions in the future. The top result of this study is an analysis of infeasibility and incorrect estimation that may arise in the theory and application of the inverse model of data envelopment analysis with network structures. By addressing these pitfalls, novel protocols are proposed to circumvent these shortcomings effectively. Subsequently, several theoretical and applied problems are examined and resolved through insightful case studies.

Keywords: inverse models of data envelopment analysis, series network, estimation of inputs and outputs, efficiency, resource allocation, sensitivity analysis, infeasibility

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Authors: Chuan Yang, Jing Bie, Panagiotis Psimoulis, Zhong Wang

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Modelling and understanding vehicle volume distribution over the urban network are essential for urban design and transport planning. The space syntax approach was widely applied as the main conceptual and methodological framework for contemporary vehicle volume models with the help of the statistical method of multiple regression analysis (MRA). However, the MRA model with space syntax variables shows a limitation in vehicle volume predicting in accounting for the crossed effect of the urban configurational characters and socio-economic factors. The aim of this paper is to construct models by interacting with the combined impact of the street network structure and socio-economic factors. In this paper, we present a multilevel linear (ML) and an agent-based (AB) vehicle volume model at an urban scale interacting with space syntax theoretical framework. The ML model allowed random effects of urban configurational characteristics in different urban contexts. And the AB model was developed with the incorporation of transformed space syntax components of the MRA models into the agents’ spatial behaviour. Three models were implemented in the same urban environment. The ML model exhibit superiority over the original MRA model in identifying the relative impacts of the configurational characters and macro-scale socio-economic factors that shape vehicle movement distribution over the city. Compared with the ML model, the suggested AB model represented the ability to estimate vehicle volume in the urban network considering the combined effects of configurational characters and land-use patterns at the street segment level.

Keywords: space syntax, vehicle volume modeling, multilevel model, agent-based model

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

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Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: J. R. Wang, H. T. Lin, Y. S. Tseng, W. Y. Li, H. C. Chen, S. W. Chen, C. Shih

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TRACE is developed by U.S. NRC for the nuclear power plants (NPPs) safety analysis. We focus on the establishment and application of TRACE/FRAPTRAN/SNAP models for Chinshan NPP (BWR/4) spent fuel pool in this research. The geometry is 12.17 m × 7.87 m × 11.61 m for the spent fuel pool. In this study, there are three TRACE/SNAP models: one-channel, two-channel, and multi-channel TRACE/SNAP model. Additionally, the cooling system failure of the spent fuel pool was simulated and analyzed by using the above models. According to the analysis results, the peak cladding temperature response was more accurate in the multi-channel TRACE/SNAP model. The results depicted that the uncovered of the fuels occurred at 2.7 day after the cooling system failed. In order to estimate the detailed fuel rods performance, FRAPTRAN code was used in this research. According to the results of FRAPTRAN, the highest cladding temperature located on the node 21 of the fuel rod (the highest node at node 23) and the cladding burst roughly after 3.7 day.

Keywords: TRACE, FRAPTRAN, BWR, spent fuel pool

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Authors: Gyula I. Tóth, Shaho Abdalla

Abstract:

Even though numerical simulations indeed have a significant precursory/supportive role in exploring the disordered phase displaying no long-range order in pattern formation models, studying the stability properties of this phase and determining the order of the ordered-disordered phase transition in these models necessitate an analytical description of the disordered phase. First, we will present the results of a comprehensive statistical analysis of a large number (1,000-10,000) of numerical simulations in the Swift-Hohenberg model, where the bulk disordered (or amorphous) phase is stable. We will show that the average free energy density (over configurations) converges, while the variance of the energy density vanishes with increasing system size in numerical simulations, which suggest that the disordered phase is a thermodynamic phase (i.e., its properties are independent of the configuration in the macroscopic limit). Furthermore, the structural analysis of this phase in the Fourier space suggests that the phase can be modeled by a colored isotropic Gaussian noise, where any instant of the noise describes a possible configuration. Based on these results, we developed the general mathematical framework of finding a pool of solutions to partial differential equations in the sense of continuous probability measure, which we will present briefly. Applying the general idea to the Swift-Hohenberg model we show, that the amorphous phase can be found, and its properties can be determined analytically. As the general mathematical framework is not restricted to continuum theories, we hope that the proposed methodology will open a new chapter in studying disordered phases.

Keywords: fundamental theory, mathematical physics, continuum models, analytical description

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Authors: Okpaneje Onyinye Theresa, Ugwu Laeticia Udodiri, Okereke Ngozi Agatha, Okoli Nonso Livinus

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This work is on the synthesis and study of the optical characterization of polyaniline/nickel sulphide nanocomposite. Polyaniline (PANI) and nickel sulphide (NiS) nanoparticles were synthesized by oxidative chemical polymerization and sol-gel method. The polyaniline nickel sulphide nanocomposites with various concentrations of NiS were synthesized by in-situ polymerization of aniline monomer. In each case, the nickel sulphide nanoparticles were uniformly dispersed in the aniline hydrochloride before the initiation of oxidative chemical polymerization using ammonium persulphate. The samples formed were subjected to optical characterization using an ultraviolet (UV)-visible light (VIS) spectrophotometer (model: 756S UV – VIS). Optical analysis of the synthesized nanoparticles and nanocomposites showed absorption of radiation within VIS regions. The Tauc model was used to obtain the optical band gap. Energy band gap values of PANI and NiS were found to be 2.50 eV and 1.95 eV, respectively. PANI/NiSnanocomposites has an energy band gap that decreased from 2.25 eV to 1.90 eV as the amount of NiS increased (from 0.5g to 2.0g). These optical results showed that these nanocomposites are potential materials to be considered in solar cells and optoelectronics devices. The structural analysis confirmed the formation of polyaniline and hexagonal nickel sulphide with an average crystallite size of 25.521 nm, while average crystallite sizes of PANI/NiSnanocomposites ranged from 19.458 nm to 25.108 nm. Average particle sizes obtained from the SEM images ranged from 23.24 nm to 51.88 nm. Compositional results confirmed the presence of desired elements that made up the nanoparticles and nanocomposites.

Keywords: polyaniline, nickel sulphide, polyaniline-nickel sulphide nanocomposite, optical characterization, structural analysis, morphological properties, compositional properties

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Authors: S. Boukais, A. Nekmouche, N. Khelil, A. Kezmane

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The first intent of this study is to develop a finite element model that can predict correctly the behavior of the reinforced concrete column. Second aim is to use the finite element model to investigate and evaluate the effect of the strengthening method by jacketing of the reinforced concrete column, by considering different interface contact between the old and the new concrete. Four models were evaluated, one by considering perfect contact, the other three models by using friction coefficient of 0.1, 0.3 and 0.5. The simulation was carried out by using Abaqus software. The obtained results show that the jacketing reinforcement led to significant increase of the global performance of the behavior of the simulated reinforced concrete column.

Keywords: strengthening, jacketing, rienforced concrete column, Abaqus, simulation

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Authors: Debananda Mohapatra, Subramanya Badrayyana, Smrutiranjan Parida

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Recognizing the upcoming era of carbon nanostructures and their revolutionary applications, we investigated the formation and supercapacitor application of highly pure and hydrophilic carbon nano-onions (CNOs) by economical one-step flame-synthesis procedure. The facile and scalable method uses easily available organic carbon source such as clarified butter, restricting the use of any catalyst, sophisticated instrumentation, high vacuum and post processing purification procedure. The active material was conformally coated onto a locally available cotton wipe by “sonicating and drying” process to obtain novel, lightweight, inexpensive, flexible, binder-free electrodes with strong adhesion between nanoparticles and porous wipe. This interesting electrode with CNO as the active material delivers a specific capacitance of 102.16 F/g, the energy density of 14.18 Wh/kg and power density of 2448 W/kg which are the highest values reported so far in symmetrical two electrode cell configuration with 1M Na2SO4 as an electrolyte. Incorporation of CuO nanoparticles to these functionalized CNOs by one-step hydrothermal method add up to a significant specific capacitance of 420 F/g with deliverable energy and power density at 58.33 Wh/kg and 4228 W/kg, respectively. The free standing CNOs, as well as CNO-CuO composite electrode, showed an excellent cyclic performance and stability retaining 95 and 90% initial capacitance even after 5000 charge-discharge cycles at a current density of 5 A/g. This work presents a new platform for high performance supercapacitors for next generation wearable electronic devices.

Keywords: binder-free, flame synthesis, flexible, carbon nano-onion

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Authors: F. D. Konstandakopoulou, G. A. Papagiannopoulos, N. G. Pnevmatikos, G. D. Hatzigeorgiou

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This paper examines the effects of pile-soil-structure interaction on the dynamic response of offshore platforms under the action of near-fault earthquakes. Two offshore platforms models are investigated, one with completely fixed supports and one with piles which are clamped into deformable layered soil. The soil deformability for the second model is simulated using non-linear springs. These platform models are subjected to near-fault seismic ground motions. The role of fault mechanism on platforms’ response is additionally investigated, while the study also examines the effects of different angles of incidence of seismic records on the maximum response of each platform.

Keywords: hazard analysis, offshore platforms, earthquakes, safety

Procedia PDF Downloads 148