Search

Objective: To evaluate the prevalence of psychiatric comorbidities in children with epilepsy (CWE) and to assess their impact on the quality of life (QOL). Methods: All CWE with a normal Intelligence Quotient (IQ) were assessed for psychiatric commorbidities using the Revised Child Anxiety and Depression Scale (RCADS) for anxiety and depression, and Strengths and Difficulties Questionnaire (SDQ) for behavioral and emotional problems. Quality of Life in Children with Epilepsy (QOLCE-31) scale was used to assess the quality of life at enrolment and was repeated again after appropriate intervention for comorbidities at 3 months. Results: Twenty-two (24.4%), 18 (20.0%), 35 (38.9%), 32 (35.56), 26 (28.89), and 12 (13.34) children met the clinical threshold for social phobia, major depression, generalized anxiety, separation anxiety, conduct problem and peer problem, respectively. After appropriate intervention for the co-morbidities, improvement was noted in the quality of life. Conclusion: Psychiatric co-morbidities are common in children with epilepsy and these contribute to the poor QOL. [ABSTRACT FROM AUTHOR]

Per‑oral endoscopic myotomy (POEM) is the preferred endoscopic modality for managing achalasia cardia. However, there are no recommendations on the role of POEM in non‑achalasia esophageal dysmotility disorders (NAEMD), including esophagogastric junction outflow obstruction (EGJOO), distal esophageal spasm (DES), and hypercontractile esophagus (HE). The present systematic review and meta-analysis aimed to assess the safety and efficacy of POEM in the treatment of NAEMD. MEDLINE, Embase, and Scopus were searched from inception to August 2023 for studies analyzing the outcome of POEM in NAEMD. Clinical success and adverse events were the main outcomes assessed. The event rates and their 95% confidence interval were calculated using a random effects model. A total of 11 studies with 271 patients were included in the final analysis. The pooled clinical success rate with POEM in NAEMD was 86.9% (82.9–90.9). On subgroup analysis, the pooled clinical success rates of POEM in DES and EGJOO were 97.8% (90.9–100.0) and 92.7% (86.3–95.1), which were significantly higher than in HE 81.2% (73.5–88.8). Data from limited studies showed that the pooled rate of improvement in dysphagia and chest pain was 88.5% (83.0–93.9) and 87.4% (80.5–94.4). The pooled incidence of overall AEs and serious AEs was 12.6% (5.7–19.5) and 0.3% (0.0–1.9), respectively. On follow-up, the pooled incidence of new-onset heartburn was 18.7% (11.1–26.2). POEM is a safe and efficacious treatment modality for the management of NAEMD with a lower clinical success in patients with HE. Further large-scale studies are required to validate the findings of the present analysis. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive three-step framework for fault classification in underground cable systems. The first step involves the implementation of the Cumulative Sum (CUSUM) technique to detect the fault instances, which will help in precisely tracking the affected portions. The second step focuses on parameter estimation techniques, followed by the third step, which incorporates classification techniques. The study employs a range of evolutionary optimization algorithms, including Ant Colony Optimization, Bacteria Foraging Optimization, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. These algorithms are effectively employed to track and estimate parameters like amplitude, phase, frequency, and damping factor associated with various types of permanent faults, such as L-G fault, L-L fault, L-L-G fault, and L-L-L-G fault generated in the underground cable. The optimized parameters extracted through these algorithms are utilized as features within a Support Vector Classifier for fault classification. The overall accuracy of the classifier using these features is reported to be 0.944, which is quite high and indicates a strong performance in classifying faults. The study includes a brief comparison of the performance of these bio-inspired algorithms. The effectiveness of the tracking algorithms is validated through the use of laboratory-tested current signals. In addition, the signals are subjected to varying signal-to-noise ratios to evaluate the robustness of the tracking algorithm. A comparative analysis is performed to estimate the accurate parameters and highlights the significant tracking ability of the Particle Swarm Optimization (PSO) algorithm, which outperforms other algorithms.The Root Mean Squared Percentage Error (RMSPE) for the L-G fault in PSO is 0.3082, showing superior performance compared to other algorithms. [ABSTRACT FROM AUTHOR]

The use of machine learning (ML) models have become a crucial factor in the growing field of healthcare, ushering in a new era of medical research and diagnosis. This study rigorously reviews research publications published in reputable journals during the last five years. The pace and dynamic nature of machine learning in the healthcare domains demonstrated by the arduous criteria, which are used to sort through these articles. Disease-centric analysis uncovered a wide range of deep learning and machine learning models which are designed to address particular medical problems. Convolutional neural networks (CNNs), one of the most complex deep learning architectures, coexist with more conventional statistical models like logistic regression and support vector machines. CNNs are particularly prominent when it comes to disorders that need picture processing, which highlights the significant influence of deep learning in deciphering complex medical patterns. The popularity of ensemble methods, such as Random Forest, Gradient Boosting, and AdaBoost, indicates that their ability to combine predictive capability and strengthen model resilience is well acknowledged. Hybrid techniques, which integrate the advantages of many models, provide novel approaches to tackle distinct healthcare problems. This research also sheds light on a nuanced approach for model selection, wherein deep learning models performs well with huge datasets and image analysis, while statistical and ensemble models provides better results with numerical and categorical data. The adaptability needed in healthcare analytics is shown by hybrid models, which frequently combine standard models for classification with deep learning for feature extraction. The present review can endow problems related to ML in healthcare domain, possible solutions, potential directions and some knowledge to the researchers working in this field. [ABSTRACT FROM AUTHOR]

Estimation of compressive strength (CS) of concrete is an active area of research in the domain of manufacturing engineering. The concrete ingredients are disseminated arbitrarily over the whole concrete matrix and the relationships among them are a complex nonlinear system, thus challenging to model. Soft computing techniques are found better to statistical methods applied for prediction of CS. However, sophisticated prediction models are still missing and need to be explored. Random vector functional link network (RVFLN) is an efficient algorithm with low time complexity and is able to handle complex domain problems without compromising on accuracy. It assigns input-hidden layer weights and bias randomly without further modification and computes output layer weights iteratively by gradient methods or non-iteratively by least square methods. Though it is applied on solving various engineering problems, its application on CS prediction of concrete is scarce. The learning ability, structural simplicity, and computational efficiency of RVFLN motivated us to investigate its efficiency on compressive strength prediction. In this article, we develop a RVFLN-based forecast for estimation of CS concrete. A publicly available dataset from UCI repository is used to develop and access the performance of the model. For comparative analysis, few other models such as FLANN, MLP, RBFNN, MLR, and ARIMA are also developed and used for the forecasting considering samples with curing ages at 14, 28, 56, and 91 days. All the models are evaluated in terms of MAPE, ARV, U of Theil's statistics (UT), NMSE, and execution time. Considering the four-sample series, RVFLN attained an average MAPE of 0.5341, ARV of 0.0982, UT of 0.3239, and NMSE of 0.1188 which are smaller than other models. The study revealed that RVFLN is quite capable in modeling and predicting the CS data. Outcomes of comparative studies and statistical significance tests are in favor of RVFLN-based forecasting. [ABSTRACT FROM AUTHOR]

The optical characteristics of colored dissolved organic matter (CDOM) serve as a convenient tool for evaluating coastal processes, e.g., river runoff, anthropogenic inputs, primary production, and bacterial/photochemical processes. We conducted a study on the seasonal and spatial variability of absorbance and fluorescence characteristics of CDOM and nutrients in the coastal waters near the Gauthami estuary of River Godavari, the largest peninsular river of India, for a year. The surface aCDOM(350) showed a significant inverse relation with salinity in the coastal region, indicating a conservative mixing of marine and terrestrial end members. The aCDOM(350) was not conservative in the offshore (100 m isobath) waters due to enrichment by secondary sources. Seasonal variability in optical properties indicated diverse sources for CDOM, as revealed by principal component analysis. The excitation-emission matrix (EEM) spectra followed by parallel factor analysis (EEM-PARAFAC) revealed four distinct fluorophores. The tyrosine (B) fluorophore showed a predominant increase in the post-monsoon season (October to January), while tryptophan (T) was relatively more enriched, coincident with nutrient enrichment and transparency increase during the early monsoon phase (July). The biological index (BIX), which reflects recent photosynthetic activity, also displayed relatively higher values during the early monsoon. The humic fluorophores A and M, and humification index (HIX) were relatively enriched during the later phase of monsoon (July–October). HIX was > 4 in a few samples of the offshore region (100-m isobath) and indicated a probable contamination from drill-mud (bentonite) used in hydrocarbon exploration. During the monsoon, the relationship between T and B with CDOM was not evident due to the masking of B fluorescence in intact protein. However, during the post-monsoon (POM) and pre-monsoon (PRM) periods, this masking effect was not observed, likely due to protein degradation via bacterial and photochemical processes, respectively. Temporal variability in nutrients indicated that high ammonium levels were produced during POM (OM bacterial degradation), and high nitrite levels were observed during PRM (due to primary production). This study provides foundational insights into the use of CDOM for understanding the impact of diverse environmental, river discharge, and anthropogenic factors on coastal ecosystems. [ABSTRACT FROM AUTHOR]

The irregular movement of cryptocurrency market makes effective price forecasting a challenging task. Price fluctuations in cryptocurrencies often appear to be arbitrary that has been a hot topic. Though various statistical and econometric forecasting models exists, still there is lack of advanced artificial intelligence models to explain behaviour of such price fluctuations. Artificial neural networks (ANNs) are data-driven models and can effectively handle complex nonlinear functions in presence of abundant data. However, optimal parameter tuning of such models with conventional back propagation-based learning entail domain expertise, higher computational cost, and yield inferior accuracy thus, makes its use tough. Contrast to this, metaheuristic-based ANN training has been emerging as an efficient learning paradigm. This article constructs few optimal ANNs through three efficient metaheuristics with less control parameters such as fireworks algorithm (FWA), chemical reaction optimization (CRO), and the teaching–learning based optimization (TLBO) separately. The role of a metaheuristic is to investigate the near-optimal weights and thresholds of an ANN of solitary hidden layer and thereby ensuring a higher degree of accuracy. The hybrid models are then used to simulate and predict the behaviour of four fast growing cryptocurrencies such as Bitcoin, Litecoin, Ethereum, and Ripple. Various experiments are carried out using real time cryptocurrency data and hybrid ANNs through four performance measures. We undertake a comparative performance analysis of forecasting models and Friedman tests to demonstrate the superiority and statistical significance. In particular, ANN trained with CRO, TLBO, and FWA obtained an average rank of 1, 2, and 2.75 respectively. [ABSTRACT FROM AUTHOR]

In the recent years, extreme temperature i.e. cold in winter and warm in summer has been observed in the coastal Odisha state in the east part of India. Hydro-meteorological disasters such as floods, droughts, heat waves and cold spells being experienced nowadays are unprecedented in living memory. In this work the climate variability over the coastal Odisha region is quantified considering the temperature parameter. A climate departure index (CDI) of three versions (simple, absolute and least-square) are calculated using monthly, annually and seasonally maximum, minimum as well as mean temperature form seven stations in the costal Odisha for the period 1969 to 2018 for assessing the degree of normal/unusual/extreme of a particular year in compared to the long-term average for the specific coast under consideration. Temperature time series data for the period 1969 to 2018 are homogenized by using the relative comparison between station observations and composite reference series by calculating CDI. Results indicates that CDI values of least-square on all cases are comparatively greater than simple and absolute CDIs. Least-square method is the appropriate method for determining the category of a year or a month or a season in terms of the temperature variation. Analysis based on mean temperature infers that only twenty extreme seasons are observed in monsoon season while three normal are observed during the month of October i.e., in post-monsoon season over the coastal Odisha. The relationship of climatic event including El Nino or La Nina feature of a particular year with respect to CDI is also investigated. The climatic variability is very high on some of the years based on analysis of index values of annual average of the temperatures (maximum, minimum and mean) over a region. [ABSTRACT FROM AUTHOR]

The selective fixation of CO2 and epoxides into cyclic carbonates offers an atom-economical approach towards promising utilization of anthropogenic CO2 gas. This work demonstrates the development of recyclable organic–inorganic hybrid TBAI@CuO catalyst containing quaternary ammonium (TBAI) salt as active organic groups and CuO flowers as the inorganic component. Initially, CuO flowers was synthesized using a simple oxidation-precipitation method followed by immobilization of TBAI groups using impregnation method. The impregnated TBAI groups functioned as the active basic centers, whereas CuO flowers with effective surface area provided heterogeneity, Lewis acidic sites, facilitated better dispersion and strong interaction of TBAI. Resultantly, hybrid TBAI@CuO catalyst consisted of dual-active sites which played a decisive role in manipulating catalytic activity without requirement of a co-catalyst. With styrene oxide and CO2 as model substrates, the present catalyst system delivered 95% conversion and 97% selectivity towards styrene carbonate under solvent-free and milder reaction conditions when compared to other single metal oxide catalysts. The homogeneously dispersed TBAI groups in strong interaction with CuO flowers provided simultaneous access to both Lewis acidic as well as basic sites and hence resulted in superior catalytic activity. Meanwhile, hybrid TBAI@CuO catalyst showed catalytic adequacy for various terminal and internal epoxides. This catalytic system also presented good reusability without prominent loss in activity and no leaching of active TBAI groups. Based on characterization results, a plausible reaction mechanism was predicted to support the cycloaddition reaction with hybrid TBAI@CuO catalyst. It was proposed that inorganic Lewis acidic CuO activated the epoxide whereas the immobilized TBAI groups activated the inert CO2 molecule and stabilized the epoxide after ring opening. Therefore, the CuO flowers acting in concert with the immobilized TBAI groups showed exceptional acid–base cooperativity and provided high selectivity towards cyclic carbonates. The hybrid TBAI@CuO catalyst in this effort bridges the gap between homogeneous and heterogeneous catalysis for sustainable transformation of CO2 and epoxides to cyclic carbonates under solvent-free and comparatively mild reaction conditions [ABSTRACT FROM AUTHOR]

A three-dimensional mathematical hydrodynamic model associated with surface wave radiation by a floating rectangular box-type structure due to heave, sway, and roll motions in finite water depth is investigated based on small amplitude water wave theory and linear structural response. The analytical expressions for the radiation potentials, wave forces, and hydrodynamic coefficients are presented based on matched eigenfunction expansion method (MEFEM). The correctness of the analytical results of wave forces is compared with the construction of a numerical model using the open-source boundary element method code NEMOH. In addition, the present result is compared with the existing published experimental results available in the literature. The effects of the different design parameters on the floating box-type rectangular structure are studied by analyzing the vertical wave force, horizontal wave force, torque, added mass, and damping coefficients due to the heave, sway, and roll motions, and the comparison analysis between the forces is also analyzed in detail. Further, the effect of reflection and transmission coefficients by varying the structural width and drafts are analyzed. [ABSTRACT FROM AUTHOR]

This study attempts to accelerate the learning ability of an artificial electric field algorithm (AEFA) by attributing it with two mechanisms: elitism and opposition-based learning. Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far, and opposition-based learning helps enhance its exploration ability. The new version of the AEFA, called elitist opposition leaning-based AEFA (EOAEFA), retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning. Hence, the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence. Higher-order neural networks (HONNs) have single-layer adjustable parameters, fast learning, a robust fault tolerance, and good approximation ability compared with multilayer neural networks. They consider a higher order of input signals, increased the dimensionality of inputs through functional expansion and could thus discriminate between them. However, determining the number of expansion units in HONNs along with their associated parameters (i.e., weight and threshold) is a bottleneck in the design of such networks. Here, we used EOAEFA to design two HONNs, namely, a pi-sigma neural network and a functional link artificial neural network, called EOAEFA-PSNN and EOAEFA-FLN, respectively, in a fully automated manner. The proposed models were evaluated on financial time-series datasets, focusing on predicting four closing prices, four exchange rates, and three energy prices. Experiments, comparative studies, and statistical tests were conducted to establish the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

A Simulink-based model is designed to simulate biomass pyrolysis as a case study. The Simulink model has a separate database for system models, material properties, and thermodynamic models. The data flow has been standardised and straightforward to use with user-supplied data. The simulation results are validated with the results from Aspen Plus. Given that Aspen Plus is licensed software cast-aside, the scope for young researchers to design and analyse the performance of a process. The purpose is not an alternative to Aspen Plus but to serve the ease of accessibility and work. The present case study considers biomass pyrolysis in a bubbling bed reactor. The screw feeder is considered, in practice, to accomplish continuous introduction of feed biomass to the bed. The bubbling bed realises vigorous mixing of solid-phase; a continuous stirred tank reactor (CSTR) model is more suitable to represent the pyrolysis reaction dynamics. The pyrolysis reactions were proposed and applied to model a pyrolysis reactor. This process analysis is based on operating factors such as reaction temperature, residence duration, biomass-wood type, and reactor type. To evaluate the foregoing findings, the results of other researchers' process analyses are compared. Pyro-char, a by-product of pyro-oil manufacture, aids in retaining moisture and nutrients in forest soils and has been an aspect of environmental importance. Hence, this research focuses on determining the precise reaction for biomass pyrolysis in a CSTR with high oil yield rates, char yields, and biomass conversion. [ABSTRACT FROM AUTHOR]

Background and Aim: Non-specific isolated terminal ileum abnormalities (NSITIA) namely erosions, ulcer and nodularity are frequent findings on ileal examination during colonoscopy. Their clinical significance and management are uncertain. Methodology: A pilot randomized clinical trial comparing combination antimicrobial therapy (oral Rifaximin 550 mg twice daily for two weeks; Albendazole 400 mg orally as a single dose; Tinidazole 1 gm twice daily for three days i.e. Group A) with symptomatic treatment (Group B) was performed in patients with NSITIA, which was diagnosed on the basis of colonoscopy and histopathology features. The primary outcome measure was mucosal healing on follow-up ileocolonoscopy at three months of randomization. Additionally, clinical, endoscopic and histological findings were noted at baseline and after a follow-up of three months. Results: Total 60 patients with NSITIA were randomized. The most prevalent symptoms were abdominal discomfort (n = 37, 61.6%), diarrhea (n = 25, 41.6%) and constipation (n = 24, 40%). The incidence of ulcers, nodularity and erosions were (n = 18, 62.1%), (n = 8, 27.6%) and (n = 3, 10.34%) in group A and (n = 18, 58%), (n = 9, 29%), (n = 4, 13%) in group B, respectively. After a mean follow-up duration of 3.36 ± 0.27 months, both groups showed comparable resolution in clinical symptoms (n = 24, 92.4% vs. n = 24, 88.8%, p = 0.954), ileocolonoscopic findings (n = 23, 88.5% vs. n = 22, 81.5%, p = 0.765) and histological characteristics (n = 20, 76.5% vs. n = 19, 70.4%, p = 0.806). Conclusion: The clinical, endoscopic and histopathological remission occurs in most patients with NSITIA. The use of antimicrobials including antibiotic, antiprotozoal and anthelminthic therapy did not have any impact on the rate of mucosal healing in these patients. Our study is a pilot study and has some limitations such as small sample size and lack of complete small bowel workup in all patients, which leaves a possibility of undetected ulcers proximal to the terminal ileum. Clinical trial registration: This study has been registered in India's clinical trial registry under the registration number CTRI/2020/02/023459). [ABSTRACT FROM AUTHOR]

The financial market volatility has been a focus of study for experts over past decades. While stockbrokers and investors expect reliable projections of future stock indices, it instead displays unpredictable, complicated, and nonlinear reactions which paves path towards designing accurate prediction mechanisms for approximate stock market behaviour. Many computational intelligence methods have been used in the field of economic forecasting alongside more conventional methods. Yet, a trustworthy forecasting depends heavily on the correct selection of an efficient model and optimum set of model hyperparameters. In this regard, artificial neural networks (ANNs) specifically higher-order neural networks (HONNs) have shown notable performance in modelling the chaotic behaviour of financial data and predicting future data. Improved precision and nonlinear decision boundaries are two benefits of using HONN. They can store more data, map features beautifully with weights that are configurable on a single layer, learn quickly, and even tackle complex real-world situations. Besides, evolutionary optimization algorithms (EOA) are widely employed for adjusting HONN parameters thus, enhancing their generalization capability. HONNs are trained with several flavours of evolutionary learning algorithms to discover optimal solutions for various real-world complex engineering problems. The purpose of this article is to explore HONN and EOA based financial forecasts from the last two decades and see how well these forecasts performed in terms of making accurate and reliable financial predictions. We conducted a rigorous survey on state-of-the-art HONN and EOA based methods used for financial forecasting collected from reliable and reputed sources, analysed their applicability and performability, identified their strength and limitations, and suggested few constructive criticisms. Findings of this study may assist researchers of this field in adopting such forecasting methods appropriately. [ABSTRACT FROM AUTHOR]

Background: A survey was executed to estimate the extent of entomophagy practiced by the tribal population in Nagaland. From the survey, two consumed stink bugs, namely Tessaratoma javanica and Coridius singhalanus, were assessed. Nutrient contents were assessed following the AOAC guidelines and elements were quantised using ICP-OES. Results: About 55% of the people surveyed consumed T. javanica, and 49% were consumers of C. singhalanus. The survey also indicated that the non-consumer respondents did not have any aversion towards the consumption of these insects. Biochemical analysis revealed that except for carbohydrate and moisture contents, all the other evaluated parameters (ash, crude fats, crude protein, and fibre content) were found to be comparatively higher in C. singhalanus, although the difference was significant (p ≤ 0.05) only for crude fat, carbohydrate, and fibre content. Trace elements analysis revealed that calcium (107.74 ± 0.213 mg/100 g), iron (22.17 ± 0.080 mg/100 g), and sodium (120.34 ± 0.214 mg/100 g) contents were significantly higher in T. javanica, whereas magnesium (76.06 ± 0.166 mg/100 g) content was significantly higher in C. singhalanus. Conclusions: Both the bugs show good nutritive contents and are liked for their distinctive smell and flavour. [ABSTRACT FROM AUTHOR]

Soft clay generally cannot be directly used as subgrade material due to its poor engineering characteristics. The application of rice husk ash (RHA) for the solidification of problematic soil contributes to the recycling of waste and reduces carbon emissions. A series of mechanical, chemical, and microscopic tests were conducted to assess the early effectiveness of RHA in stabilizing soft clay. The pH and conductivity Ec also were measured to evaluate the effect of active ions. The results show that the use of RHA alone can only improve the compressive strength qu of soft clay to a certain extent. However, it cannot improve the soaked strength of the solidified soil. Carbide slag (CS) and metakaolin (MK) as activators were used to modify RHA, which significantly improved the compressive strength, shear properties, and soaked strength of soft clay. During initial curing, the compressive strength qu and cohesion c showed a marked correlation with pH and Ec. This is closely related to the dissolution and consumption behaviors of more abundant active ions contributed by the active oxides in the modified RHA. Finally, combined with XRD, SEM, EDS, and MIP tests indicated that the mechanical properties and microstructure improvement of soft clay can be attributed to the early physical reinforcement and meshing effect due to the porous granular structure of RHA. Subsequently generated CaCO3 crystals and C–S–H/C–A–S–H cementitious hydrates played a crucial role through carbonization and pozzolanic reactions supported by more abundant active ions. [ABSTRACT FROM AUTHOR]

Increase in seismic events around the world has necessitated the evaluation of soil liquefaction potential since it adversely influences the stability of adjacent structures and poses a threat to lives and property. The first step in evaluating the liquefaction potential involves the estimation of the in situ state of sand and the state parameter as an index to characterize the behavior of sand. Due to the difficulty in obtaining high-quality undisturbed sandy samples for laboratory testing and evaluation, simplified equations based on the cone penetration test (CPT) are used for reasonable estimation of field behavior. In the present study, an optimized group method of data handling (GMDH)-type neural network was proposed to estimate the state parameter from CPT data obtained from the historical liquefaction database. A comparison was made between the measured and the predicted values of the state parameter to evaluate the performance of the proposed GMDH neural network method. A sensitivity analysis of the proposed model was also carried out to study the effect of input variables on the output variable of the proposed model. Additionally, the evaluation of in situ state and liquefaction potential of sand based on the state parameter was also presented and compared with the existing methods. Overall, the use of the GMDH model in evaluating the in situ state of sand and subsequent liquefaction potential assessment has shown promising results. [ABSTRACT FROM AUTHOR]

Bitcoin prediction is a recent area of research interest and growing fast. Since its inception, in a short period of time Bitcoin got wide popularity and considered as an investment asset. At present it is the most successful cryptocurrency compared to other altcoins dispersed in the world economy. The Bitcoin prices fluctuate like other stock markets due to inherent volatility. The investors' confidence on Bitcoin rising fast and have been reflected on its prices. Though few computational intelligence methods are available, sophisticated methodologies for accurate prediction of Bitcoin are still lacking and need to be explored. Functional link neural network (FLN) is a flat network, offers lower computational complexity, and achieves enhanced input–output nonlinearity mapping through functional expansion of input signals. Basis functions such as Legendre, Trigonometric, Laguerre, and Chebyshev are commonly used polynomials in FLN for expansion of input signal dimension. In this article along with the weight and bias vector of FLNs, optimal number of polynomial functions for each category of basis function is selected by genetic algorithm during training process rather fixing them earlier. Therefore, an optimal FLN structure is crafted on fly from exploitation of training data. The optimal FLN models are used to predict the daily, weekly, and monthly closing prices of Bitcoin. A comparative study among optimal FLNs is carried out using evaluation metrics such as MAPE, NMSE, ARV, and U of Theil's statistics. Finally, outcomes from experimental and comparative studies suggested the superiority of optimal FLN models for Bitcoin closing price prediction. [ABSTRACT FROM AUTHOR]

The recent Covid-19 pandemic has brought about drastic changes in the teaching-learning process. We witnessed a paradigm shift from conventional classrooms to virtual online learning. Although online learning is not a new concept, the covid-19 pandemic situation came with subsequent restrictions that shook the entire school educational system. Education has become highly uncertain for the time being, especially in India. This has been more evident in rural and village areas where the teaching-learning process does not include technology-enabled methods. Over one year, we have observed that conducting theory classes through online conferencing and virtual classroom software systems has become very common, and students are coping with it. However, in an online environment, students miss out on the practical aspects, i.e., laboratory experiments which are an essential part of science and engineering education. Software-based virtual labs are available, but they lack the real feel of performing an experiment, especially getting familiar with the laboratory apparatus. In this context, the present article proposes an innovative teaching-learning system that helps conduct chemistry volumetric experiments through a hardware-enabled platform named avatar-shell. The proposed system helps students develop titration skills using the actual apparatus. However, while performing experiments with the avatar-shell, we don't need to use real chemicals; instead, we use ordinary water. The proposed virtual reality chemistry laboratory system comprises two units—the administrator module for teachers to configure the experiment with appropriate molarity associated with a solution and the simulator module for students to perform the volumetric analysis. The simulator module avatar-shell comprises a high sensitive weight sensor, which communicates with the administrator module and verifies the correctness during every instance of titration, avatar-shell can connect to the administrator module via the internet, and teachers can monitor the experiment through a mobile-based application. [ABSTRACT FROM AUTHOR]

Quite a good number of population-based meta-heuristics based on mimicking natural phenomena are observed in the literature in resolving varieties of complex optimization problems. They are widely used in search of the optimal model parameters of artificial neural networks (ANNs). However, efficiencies of these are mostly dependent on fine tuning algorithm-specific parameters. Rao algorithms are metaphor-less meta-heuristics which do not need any algorithm-specific parameters. Functional link artificial neural network (FLANN) is a flat network and possesses the ability of mapping input–output nonlinear relationships by using amplification in input vector dimension. This article attempts to observe the efficacy of Rao algorithms on searching the most favorable parameters of FLANN, thus forming hybrid models termed as Rao algorithm-based FLANNs (RAFLANNs). The models are evaluated on forecasting five stock markets such as NASDAQ, BSE, DJIA, HSI, and NIKKEI. The RAFLANNs performances are compared with that of variations of FLANN (i.e., FLANN based on gradient descent, multi-verse optimizer, monarch butterfly optimization and genetic algorithm) and conventional models (i.e., MLP, SVM and ARIMA). The proposed models are found better in terms of prediction accuracy, computation time and statistical significance test. [ABSTRACT FROM AUTHOR]

The SPEEK-PVA-Silica hybrid membranes are prepared by solution casting method. The physical, chemical and electrical properties of the polymer membranes are studied by FESEM, FTIR, XRD, DSC, TGA, DMA, water and methanol uptake capacity, Ion Exchange Capacity (IEC), Degree of Sulfonation (DS), hydration number (λ), void volume fraction (%), methanol permeability and proton conductivity. The void volume fraction and density of the polymer membrane increases with increase in the silica content of the membrane which decreases glass transition temperature. At 30 °C, the maximum proton conductivity is found for SPS-3 membrane (3.8 × 10–2 S/cm) which is much higher than recast SPEEK membrane (2.9 × 10–2 S/cm). The proton conductivity of the polymer membrane is dominated by free water facilitated vehicular mechanism. The improvement of the conductivity of the proton in the silica filled polymer membrane at medium to higher temperature is due to the strong interaction between the silica and water resulting in a reduction in the loss of evaporation of water as well as an improvement in the bound water content of the polymer channel, which is regulated by the Grotthuss mechanism. The reduction of methanol permeability and increment in membrane selectivity clearly confirms that the silica filled polymer have excellent methanol blocking capacity which is beneficial for direct methanol fuel cell design. [ABSTRACT FROM AUTHOR]

The coefficient of consolidation (ch) of clay interpreted based on piezocone penetration test (CPTU) usually deviates from the actual values. This can be due to the inherent variability of ch, model uncertainty in the CPTU-based interpretation model, and test errors. Considering the importance of ch in designing the structures over clays, Bayesian inference approach combined with total probability theorem was proposed in this study to integrate the prior knowledge of ch with the project-specific test data to obtain the site-specific distributions of ch. Subsequently, a large number of equivalent samples were generated from the updated distributions of ch by Markov Chain Monte Carlo simulation. Then, Bayesian model comparison approach was developed to quantitatively compare the model proposed by Torstensson (model M1) with the model proposed by Teh and Houlsby (model M2). Finally, a sensitivity analysis was performed to study the effect of model uncertainty and data quantity on the updated distributions of ch. The results showed that model M2 was significantly better than model M1 after considering the rigidity index (Ir). The updated distributions of ch approximately exhibited the log-normal distribution, especially for model M2, which was less affected by the model uncertainty and data quantity than model M1. The proposed Bayesian approach in this study gives the updated distributions of ch from a limited number of test data and recommends model M2 for calculating ch. [ABSTRACT FROM AUTHOR]

This paper aims to develop an automated web application to generate validated daily effective reproduction numbers (Rt) which can be used to examine the effects of super-spreading events due to mass gatherings and the effectiveness of the various Movement Control Order (MCO) stringency levels on the outbreak progression of COVID-19 in Malaysia. The effective reproduction number, Rt, was estimated by adopting and modifying an Rt estimation algorithm using a validated distribution mean of 3.96 and standard deviation of 4.75 with a seven-day sliding window. The Rt values generated were validated using thea moving window SEIR model with a negative binomial likelihood fitted using methods from the Bayesian inferential framework. A Pearson's correlation between the Rt values estimated by the algorithm and the SEIR model was r = 0.70, p < 0.001 and r = 0.81, p < 0.001 during the validation period The Rt increased to reach the highest values at 3.40 (95% CI 1.47, 6.14) and 1.72 (95% CI 1.54, 1.90) due to the Sri Petaling and Sabah electoral process during the second and third waves of COVID-19 respectively. The MCOs was able to reduce the Rt values by 63.2 to 77.1% and 37.0 to 47.0% during the second and third waves of COVID-19, respectively. Mass gathering events were one of the important drivers of the COVID-19 outbreak in Malaysia. However, COVID-19 transmission can be fuelled by noncompliance to Standard Operating Procedure, population mobility, ventilation and environmental factors. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven approach to classify and locate the faults occurring in underground distribution cables using a CNN-LSTM-based deep learning architecture. A sliding window method is adopted, using the current and voltage signal patches as inputs. The combination of various system parameters is considered to generalize the performance. Additionally, Gaussian noise is added to resemble and extend the approach to practical scenarios. The trained model is evaluated with the data from a new simulation, and the results confirm the generalizability of the adopted method. A comparison is made with other feasible approaches, which show that the adopted method is preferable to achieve better performance. Further, to ease the extension of the proposed method for different systems with similar configurations, parameters of the trained deep learning networks are exploited with the help of transfer learning. [ABSTRACT FROM AUTHOR]

In the recent times, many studies have been devoted toward rectangular excavations but only a few studies have considered the "corner effect" in the optimized design of soil nail wall-retaining pile-anchor cable supporting systems, especially in large-scale deep foundation pit environments excavated by the central-island technique. Corner effect not only increases the construction costs but also may pose a risk to the safety and stability of such pits. In this paper, changes in the lateral displacement of retaining pile, soil at 1 m away from the foundation pit, crown beam, and the settlement of ground surface and surrounding buildings were extensively investigated based on the field measurements and numerical simulations of a large-scale deep foundation pit in Gaoxin zone, Xi'an, China. In addition, the supporting structure was optimized by considering the lateral influence zone of the corner effect. The optimization scheme proposed in this study not only satisfies the safety requirements of foundation pit supports, but also reduces the construction costs. [ABSTRACT FROM AUTHOR]

Buffer/backfill material is an important engineering barrier in a deep geological repository of high-level radioactive waste (HLW). Its thermo-hydro-mechanical (THM) performance is very important for the safe and stable operation of the HLW repository system. Natural graphite powder mixed with sodium bentonite forms a buffer/backfill material that can dissipate heat quickly and provide strong isolation. In this paper, the THM characteristics of bentonite–sand–graphite–polypropylene fiber (BSGF) mixtures, used as a buffer/backfill material, were studied through a series of laboratory tests. The influence of graphite and polypropylene fiber contents on thermal conductivity, swelling pressure, hydraulic conductivity, and strength properties of BSGF mixtures with different sand contents was analyzed. Experimental results indicated that the graphite content, the maximum graphite mesh number, and the initial dry density of bentonite–graphite mixtures influenced the thermal conductivity of bentonite–graphite mixtures. The addition of polypropylene fiber was found to enhance the shear strength and inhibit cracking without significantly affecting the expansivity, permeability, and thermal conductivity of the BSGF mixtures. This study provides a new buffer/backfill material that can improve the stability, functionality, and thermal efficiency of the HLW repository. [ABSTRACT FROM AUTHOR]

The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions' dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas. [ABSTRACT FROM AUTHOR]

Under the combination of heavy loading and high moisture content, the metastable structure of natural loess can easily lead to uneven settlement and damage the overlying infrastructure. Using traditional binder such as cement has harmful impact on the environment, especially due to resource consumption and carbon emission. This research has identified the feasibility of using red mud waste as a partial replacement of cement for loess subgrade treatment in terms of dynamic properties and environmental impact. The performance of loess treated with a combination of waste red mud (RM) and small amount of cement additive (C) is evaluated by considering the complex engineering geological conditions. The results show that dynamic stress (σd) and moisture content (w) have a more significant influence on the dynamic properties of RMC-treated loess compared with confining pressure (σ3) and loading frequency (f). Higher w shows a remarkable reduction in the dynamic load resistance of treated loess, yet the addition of RMC still can improve the microstructure and water sensitivity of loess. Specifically, the failure dynamic stress (σdf) and the maximum dynamic elastic modulus (Edmax) of the treated loess at higher w are found to be 100% and 400% higher than those of untreated loess respectively. RMC treatment also improved the dynamic cohesive (cd) value from 23.2 to 173.6 kPa compared with untreated loess. In addition, the leaching toxicity and radiation of RMC-treated loess indicate that it does not pose any risk to the groundwater. Finally, revised Monismith model has been developed based on the proposed formula for predicting power index b, which can be capable of describing the long-term deformation stability under cyclic loading. [ABSTRACT FROM AUTHOR]

The system of multiple agents working in coordination for a given task has several advantages on faster completion, fault-tolerance, etc. To develop a multi-agent graph exploration technique, the following are defined: 1) the way agents communicate, 2) initial placement of agents and 3) the role of each agent in a systematic search of (unexplored) vertices/edges in the graph. However, the general concepts and requirements are not developed. Hence, we attempt to generalize the multi-agent system for graph exploration. The graph may or may not be known a priori. The proposed representation for the multi-agent system is aimed to provide general conditions for declaring completion and finite time completion applicable to any multi-agent system for graph exploration. Results are proved using linear algebraic concepts on graphs. We also propose modifications in the incidence matrix as a data structure for organized information exchange between the agents. Further, a generic algorithm for the system of multiple agents exploring a graph is developed. The algorithm is based on the proposed generalized framework and modified incidence matrix. Simulation videos show the applicability of proposed generalization in exploring a graph using multiple agents for various combinations of initial placements of agents, search strategy, and interagent communication method. Further, We use the generalization to show the application of the proposed method in multi-robot exploration and map building of an unknown environment represented by a graph. [ABSTRACT FROM AUTHOR]

Pressuremeter test (PMT) is conducted to obtain effective soil parameters such as limit pressure (PL) and pressuremeter modulus (Ep) that are frequently used in calculating foundation bearing capacity, settlement, and foundation behavior. However, the application of PMT in China was limited due to higher cost and time. There is a need for identifying a suitable method and establish models to predict reliable PL and Ep for interpreting or cross-checking soil parameters. Piezocone test (CPTU) offers an ideal test method to develop correlation models since it is widely adopted for geotechnical investigations in China. In this study, artificial neural networks (ANN) have been used to develop CPTU-PMT correlations. A total of 92 sets of sandy soil and 65 sets of clayey soil data from four testing sites were collected using CPTU and PMT. ANN was employed to develop 4 models, half of them considering effective overburden stress ( σ v 0 ' ), for predicting PL and Ep from CPTU data. The obtained ANN models were validated using the measured values of PL and Ep from pressuremeter tests and also the predicted values based on previous correlations. The comparison results show that PL and Ep values predicted by ANN models proposed in this study are more consistent with the measured values at testing sites. Additionally, foundation settlements were measured from a load test and compared with predictive settlements obtained using PL and Ep estimated by the proposed ANN correlation models. The results have shown that the CPTU results can be used to accurately predict PMT parameters and derive settlements. [ABSTRACT FROM AUTHOR]

The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, β t and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19. [ABSTRACT FROM AUTHOR]

Rescuing Hardware from malware attacks is a great challenge today. Moreover detecting the presence of malicious intrusion using low-cost techniques is very challenging especially when it is believed that hardware Trojans are integrated into the rarely excited nodes. Though logical testing is admitted to be the accurate way to check the functional correctness of the circuit, the method becomes inefficient for circuits requiring humongous number of test patterns to activate the hardware trojan. This paper proposes a probabilistic approach to generate a set of test patterns that activate the Trojan explicitly by observing the incorrect responses to the applied test patterns. Our experimental result in ISCAS'85 benchmark circuits show that the test patterns generated by our approach enclose all nets where there is a high chance for trojan insertion. We stress that though our results are circuit specific, the proposed approach is generic and hence it can be applied to any digital circuit. In fact, we demonstrate that for the C880 circuit, our approach requires only 217 inputs to be tested whereas the naive approach needs 260 test patterns. In addition to the experimental results, we use a game-theoretic framework to show the effectiveness of our approach in generating trojan activating test patterns compared to naive and ATPG testing process. [ABSTRACT FROM AUTHOR]

Chlorella vulgaris is used for food and feed applications due to its nutraceutical, antioxidant and anticancer properties. An airlift photobioreactor comprising transparent draft tube was used for C. vulgaris cultivation. The effect of reactor parameters like hydrodynamics (0.3–1.5 vvm), light intensity (85–400 μmol m−2 s−1), photoperiod (12–24 h) and gas-phase carbon dioxide (CO2) concentration (5–15% v/v) were evaluated on microalgae and associated bacterial growth, biochemical profile; with special emphasis on ω-3, ω-6 fatty acids, and vitamin B12. The optimal growth of C. vulgaris without CO2 supplementation was observed at 1.2 vvm, which was associated with higher algal productivity, chlorophyll, vitamin B12 content, and bacterial load along with 72% of nitrate removal. The higher light intensity (400 μmol m−2 s−1) and photoperiod (24:0) increased biomass productivity and ω-3 fatty acid content (in lipid) up to 2–3 fold. The elevated levels of gas-phase CO2 concentration (15% v/v) enhanced EPA content up to 7% and biomass productivity up to 171 mg L−1 day−1. However, the increase in CO2 concentration lowered vitamin B12 content (up to 30%) and bacterial load (2–3 log). Also, all the cultivation conditions favoured desirable ω-6/ω-3 ratio(in the range of 1–2). [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability. Further, choosing the optimal number of hidden nodes for a network usually requires intensive human intervention, which may lead to an ill-conditioned situation. In this context, chemical reaction optimization (CRO) is a meta-heuristic paradigm with increased success in a large number of application areas. It is characterized by faster convergence capability and requires fewer tunable parameters. This study develops a learning framework combining the advantages of ELM and CRO, called extreme learning with chemical reaction optimization (ELCRO). ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy. We evaluate its performance by predicting the daily volatility and closing prices of BSE indices. Additionally, its performance is compared with three other similarly developed models—ELM based on particle swarm optimization, genetic algorithm, and gradient descent—and find the performance of the proposed algorithm superior. Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model. Hence, this model can be used as a promising tool for financial forecasting. [ABSTRACT FROM AUTHOR]

A pyridylhydrazone incorporating an anthracene moiety, designated as HLAnc, has been synthesized in order to examine its coordination behaviour towards rhodium(III). The complex [RhLAnc(PPh3)2Cl2], incorporating a four-membered metallacycle, has been isolated and authenticated by a single-crystal X-ray diffraction study. The complex shows interesting redox and optoelectronic properties, and to better understand these, theoretical investigations have been performed using density functional theory (DFT) and time-dependent DFT. The visible excitation for the complex arises from primarily mixed singlet-manifold 1ILCT and 1LLCT transitions. [ABSTRACT FROM AUTHOR]

Prediction of stock index remains a challenging task of the financial time series prediction process. Random fluctuations in the stock index make it difficult to predict. Usually the time series prediction is based on the observations of past trend over a period of time. In general, the curve the time series data follows has a linear part and a non-linear part. Prediction of the linear part with past history is not a difficult task, but the prediction of non linear segments is difficult. Though different non-linear prediction models are in use, but their prediction accuracy does not improve beyond a certain level. It is observed that close enough data positions are more informative where as far away data positions mislead prediction of such non linear segments. Apart from the existing data positions, exploration of few more close enough data positions enhance the prediction accuracy of the non-linear segments significantly. In this study, an evolutionary virtual data position (EVDP) exploration method for financial time series is proposed. It uses multilayer perceptron and genetic algorithm to build this model. Performance of the proposed model is compared with three deterministic methods such as linear, Lagrange and Taylor interpolation as well as two stochastic methods such as Uniform and Gaussian method. Ten different stock indices from across the globe are used for this experiment and it is observed that in majority of the cases performance of the proposed EVDP exploration method is better. Some stylized facts exhibited by the financial time series are also documented. [ABSTRACT FROM AUTHOR]

Research on natural hazards in the recent decades is driven by advent of new tools and technologies for risk assessment, management and mitigation measures. We thank Springer and editorial staff of Natural Hazards for the invitation to serve as the Guest Editors for this Special Issue of Natural Hazards: Climate Change and Coastal Vulnerability. Guest Editors: U. C. Mohanty Prasad K. Bhaskaran M. Mohapatra Sarat Chandra Sahu Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

Abstract: A comparative life cycle assessment of CO2 capture potential of microalgae, Scenedesmus dimorphus, during autotrophic cultivation in closed airlift photobioreactor and open raceway pond under Indian conditions has been carried out. The LCA inventory inputs for the both the systems were entirely based on primary experimental data obtained by algal cultivation in 1000-L pilot-scale raceway pond and 5-L airlift photobioreactor, respectively. The primary energy demand, environmental impacts and Net CO2 capture efficiency of both the systems were evaluated. The results indicated that the primary energy demand and GWP of airlift photobioreactor were 3.7-fold higher than raceway pond. The NERCO2 (defined as net emission ratio of CO2 and estimated as ratio of CO2 sequestered/CO2 emitted) of raceway pond was fourfold higher as compared to airlift photobioreactor; however, both the cultivation systems were observed to be net negative in terms of CO2 sequestration. However, the process can become net CO2 positive, with up to fourfold and 7.5-fold enhancement in biomass productivities (wrt base value) for raceway pond and airlift photobioreactor, respectively. Further, LCA predictions indicated that utilization of energy-efficient motors with lower specific energy consumptions for microalgae cultivation would facilitate algal cultivation systems to achieve net CO2-positive sequestration. [ABSTRACT FROM AUTHOR]

Interactions between surface gravity wave and submerged horizontal flexible structures are studied under the assumption of small amplitude water wave theory and structural response. The generalized dispersion relation associated with surface gravity wave interaction with submerged horizontal flexible plate is analyzed to understand the characteristics of the two propagating modes due to the presence of the free surface and submerged horizontal plate. The phase and group velocities are studied in order to analyze the effect of submerged flexible plate on gravity wave motion. The expansion formulae based on Green’s function technique and eigenfunction expansion method using Fourier transform with appropriate orthogonal mode-coupling relation associated with surface gravity wavemaker problems are derived and compared in both the cases of water of finite and infinite depths. The usefulness of the expansion formulae is demonstrated by deriving the solution for surface gravity wave interaction with submerged articulated flexible plate in water of finite depth. Several numerical results on reflection and transmission coefficients related to submerged flexible plate are presented in order to understand the effect of submerged flexible structure on surface wave motion in different cases. [ABSTRACT FROM AUTHOR]

The freshwater green algae, Scenedesmus obtusus, was cultivated in a 3.4 L airlift photobioreactor. The hydrodynamic parameters were estimated at different inlet gas flow rates (1, 2, 3, and 4 LPM) and their subsequent impact on the growth and biochemical characteristics of microalgae was studied. The biomass concentration and productivity increased with an increase in flow rates from 1 to 4 LPM. A maximum of 0.07 g L day productivity of biomass was attained at 3 LPM. An increase of total carbohydrate content from 19.6 to 26.4% was noticed with increment in the inlet flow rate of gas from 1 to 4 LPM. Major variations in total fatty acid content were not observed. The impact of light irradiance on growth and biochemical characteristics of S. obtusus was also evaluated. A maximum biomass productivity of 0.103 g L day was attained at an illumination of 150 μmol m s under continuous light. The major fatty acids reported were palmitic acid (C16:0), α-linolenic acid (C18:3), linoleic acid (C18:2), and oleic acid (C18:1). Biodiesel properties of the microalgae were estimated under various culture conditions. The light profile inside the airlift reactor was experimentally measured and the predictive modelling of light profile was also attempted. [ABSTRACT FROM AUTHOR]