Search results for: quantification accuracy

Authors: Sam Teek Ling, Norhashidah Hj. Mohd. Ali

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We investigate the formulation and implementation of new explicit group iterative methods in solving the two-dimensional Poisson equation with Dirichlet boundary conditions. The methods are derived from a fourth order compact nine point finite difference discretization. The methods are compared with the existing second order standard five point formula to show the dramatic improvement in computed accuracy. Numerical experiments are presented to illustrate the effectiveness of the proposed methods.

Keywords: explicit group iterative method, finite difference, fourth order compact, Poisson equation

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Authors: Eftychia Pappa, Heleni Vastardis, Christos Rahiotis, Andriani Vazaiou

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The aim of this study was to evaluate the prevalence of dry-mouth symptoms (xerostomia) and compare it with alterations in salivary characteristics of children and adolescents with type 1 diabetes (DM1), as measured with the use of chair-side saliva tests. This study also investigated the possible association between salivary dysfunction and incidence of caries, in relation to the level of metabolic control. A cross-sectional study was performed on young patients (6-18 years old) allocated among 3 groups: 40 patients poorly-controlled (DM1-A, HbA1c>8%), 40 well-controlled (DM1-B, HbA1c≤8%) and 40 age- and sex-matched healthy controls. The study was approved by the Research Ethics Committee of University of Athens and the parents signed written informed consent. All subjects were examined for dental caries, oral hygiene and salivary factors. Assessments of salivary function included self-reported xerostomia, quantification of resting and stimulated whole saliva flow rates, pH values, buffering capacity and saliva’s viscosity. Salivary characteristics were evaluated with the use of GC Saliva Check Buffer (3Μ ESPE). Data were analysed by chi-square and Kruskal-Wallis tests. Subjects with diabetes reported xerostomia more frequently than healthy controls (p<0.05). Unstimulated salivary flow rate and pH values remained significantly lower in DM1-A compared to DM1-B and controls. Low values of resting salivary flow rate were associated with a higher prevalence of dental caries in children and adolescents with poorly-controlled DM1 (p<0.05). The results suggested that diabetes-induced alterations in salivary characteristics are indicative of higher caries susceptibility of diabetics and chair-side saliva tests are a useful tool for the evaluation of caries risk assessment.

Keywords: caries risk assessment, saliva diagnostic tests, type 1 diabetes, xerostomia

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Authors: M. Abouelatta, A. Shaker, M. El-Banna, G. T. Sayah, C. Gontrand, A. Zekry

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In this paper, a methodology for physically modeling the intrinsic MOS part and the drift region of the n-channel Laterally Double-diffused MOSFET (LDMOS) is presented. The basic physical effects like velocity saturation, mobility reduction, and nonuniform impurity concentration in the channel are taken into consideration. The analytical model is implemented using MATLAB. A comparison of the simulations from technology computer aided design (TCAD) and that from the proposed analytical model, at room temperature, shows a satisfactory accuracy which is less than 5% for the whole voltage domain.

Keywords: LDMOS, MATLAB, RESURF, modeling, TCAD

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Gyorgy Deak, Marius C. Raischi, Lucian P. Georgescu, Tiberius M. Danalache, Elena Holban, Madalina G. Boboc, Monica Matei, Catalina Iticescu, Marius V. Olteanu, Stefan Zamfir, Gabriel Cornateanu

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At present, on the Lower Danube, different types of pressures have been identified that affect the anadromous sturgeons stocks with an impact that leads to their decline. This paper presents techniques and procedures used by Romanian experts in the tagging and monitoring of anadromous sturgeons, as well as unique results at international level obtained on the basis of an informational volume collected in over 7 years of monitoring on these species behavior (both for adults as well as for ultrasonically tagged juveniles) on the Lower Danube. The local impact of hydrotechnical constructions (bottom sill, maritime navigation channel), the global impact of the poaching phenomenon and the impact of the restocking programs with sturgeon juveniles were assessed. Thus, the bottom sill impact on the Bala branch, the Bastroe Channel (cross-border impact) and the poaching phenomenon at the level of the Lower Danube was analyzed on the basis of a unique informational volume obtained through the use of patented monitoring systems by the Romanian experts (DKTB respectively, DKMR-01T). At the same time, the results from the monitoring of ultrasonically tagged sturgeon juveniles from the 2015 repopulation program are presented. Conclusions resulting from research can ensure favorable premises for finding some conservation solutions for CITES-protected sturgeon species that have survived for millions of years, currently being 1 species on the brink of extinction - Russian sturgeon, 2 species in danger of extinction - Beluga sturgeon and Stellate sturgeon and 2 species already extinct from the Lower Danube, namely common sturgeon and ship sturgeon.

Keywords: Lower Danube, sturgeons monitoring (adults and juveniles), tagging, impact on conservation

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Authors: Mohammad Ali Oghabian, Reza Reiazi, Esmaeel Parsai, Mehdi Aghili, Ramin Jaberi

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In this project a new procedure has been introduced for utilizing digitally reconstructed radiograph from MRI images in Brachytherapy treatment planning. This procedure enables us to localize the tumor volume and delineate the extent of critical structures in vicinity of tumor volume. The aim of this project was to improve the accuracy of dose delivered to targets of interest in 2D treatment planning system.

Keywords: brachytherapy, cervix, digitally reconstructed radiographs, lymph node

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Authors: Arslan Ellahi, Syed Amjad Hussain, Fawaz Saleem Bokhari

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Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them.

Keywords: complexity, rapid evolution, swarm intelligence, dimensions

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Authors: Hoda A. Abdel Hafez

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Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: mining big data, big data, machine learning, telecommunication

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Authors: Sidrah Ahmed

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The river and lakes flows are modeled mathematically by shallow water equations that are depth-averaged Reynolds Averaged Navier-Stokes equations under Boussinesq approximation. The temperature stratification dynamics influence the water quality and mixing characteristics. It is mainly due to the atmospheric conditions including air temperature, wind velocity, and radiative forcing. The experimental observations are commonly taken along vertical scales and are not sufficient to estimate small turbulence effects of temperature variations induced characteristics of shallow flows. Wind shear stress over the water surface influence flow patterns, heat fluxes and thermodynamics of water bodies as well. Hence it is crucial to couple temperature gradients with shallow water model to estimate the atmospheric effects on flow patterns. The Ripa system has been introduced to study ocean currents as a variant of shallow water equations with addition of temperature variations within the flow. Ripa model is a hyperbolic system of partial differential equations because all the eigenvalues of the system’s Jacobian matrix are real and distinct. The time steps of a numerical scheme are estimated with the eigenvalues of the system. The solution to Riemann problem of the Ripa model is composed of shocks, contact and rarefaction waves. Solving Ripa model with Riemann initial data with the central schemes is difficult due to the eigen structure of the system.This works presents the comparison of four different finite difference schemes for the numerical solution of Riemann problem for Ripa model. These schemes include Lax-Friedrichs, Lax-Wendroff, MacCormack scheme and a higher order finite difference scheme with WENO method. The numerical flux functions in both dimensions are approximated according to these methods. The temporal accuracy is achieved by employing TVD Runge Kutta method. The numerical tests are presented to examine the accuracy and robustness of the applied methods. It is revealed that Lax-Freidrichs scheme produces results with oscillations while Lax-Wendroff and higher order difference scheme produce quite better results.

Keywords: finite difference schemes, Riemann problem, shallow water equations, temperature gradients

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Authors: Manon Giraud, Francois-Damien Delapierre, Guenaelle Jasmin-Lebras, Cecile Feraudet-Tarisse, Stephanie Simon, Claude Fermon

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Early diagnosis or detection of harmful substances at low level is a growing field of high interest. The ideal test should be cheap, easy to use, quick, reliable, specific, and with very low detection limit. Combining the high specificity of antibodies-functionalized magnetic beads used to immune-capture biologic objects and the high sensitivity of a GMR-based sensors, it is possible to even detect these biologic objects one by one, such as a cancerous cell, a bacteria or a disease biomarker. The simplicity of the detection process makes its use possible even for untrained staff. Giant Magneto Resistance (GMR) is a recently discovered effect consisting in the electrical resistance modification of some conductive layers when exposed to a magnetic field. This effect allows the detection of very low variations of magnetic field (typically a few tens of nanoTesla). Magnetic nanobeads coated with antibodies targeting the analytes are mixed with a biological sample (blood, saliva) and incubated for 45 min. Then the mixture is injected in a very simple microfluidic chip and circulates above a GMR sensor that detects changes in the surrounding magnetic field. Magnetic particles do not create a field sufficient to be detected. Therefore, only the biological objects surrounded by several antibodies-functionalized magnetic beads (that have been captured by the complementary antigens) are detected when they move above the sensor. Proof of concept has been carried out on NS1 mouse cancerous cells diluted in PBS which have been bonded to magnetic 200nm particles. Signals were detected in cells-containing samples while none were recorded for negative controls. Binary response was hence assessed for this first biological model. The precise quantification of the analytes and its detection in highly diluted solution is the step now in progress.

Keywords: early diagnosis, giant magnetoresistance, lab-on-a-chip, submicron particle

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Authors: Rajesh S. Mony, Vaidyaratnam Oushadhasala

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An attempt is made in this study to evolve a drug development modality based on classical Ayurvedic knowledge base as well as on modern scientific methodology. The present study involves (a) identification of a specific ailment condition, (b) the selection of a polyherbal formulation, (c) deciding suitable extraction procedure, (d) confirming the efficacy of the combination by in-vitro trials and (e) fixing up the recommended dose. The ailment segment selected is arthritic condition. The selected herbal combination is Kunturushka, Vibhitaki, Guggulu, Haridra, Maricha and Nirgundi. They were selected as per Classical Ayurvedic references, Authentified as per API (Ayurvedic Pharmacopeia of India), Extraction of each drug was done by different ratios of Hydroalcoholic menstrums, Invitro assessment of each extract after removing residual solvent for anti-Inflammatory, anti-arthritic activities (by UV-Vis. Spectrophotometer with positive control), Invitro assessment of each extract for COX enzyme inhibition (by UV-Vis. Spectrophotometer with positive control), Selection of the extracts was made having good in-vitro activity, Performed the QC testing of each selected extract including HPTLC, that is the in process QC specifications, h. Decision of the single dose with mixtures of selected extracts was made as per the level of in-vitro activity and available toxicology data, Quantification of major groups like Phenolics, Flavonoids, Alkaloids and Bitters was done with both standard Spectrophotometric and Gravimetric methods, Method for Marker assay was developed and validated by HPTLC and a good resolved HPTLC finger print was developed for the single dosage API (Active Pharmaceutical Ingredient mixture of extracts), Three batches was prepared to fix the in process and API (Active Pharmaceutical Ingredient) QC specifications.

Keywords: drug development, antiinflammatory, quality stardardisation, planar chromatography

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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Performance of different filtering approaches depends on modeling of dynamical system and algorithm structure. For modeling and smoothing the data the evaluation of posterior distribution in different filtering approach should be chosen carefully. In this paper different filtering approaches like filter KALMAN, EKF, UKF, EKS and smoother RTS is simulated in some trajectory tracking of path and accuracy and limitation of these approaches are explained. Then probability of model with different filters is compered and finally the effect of the noise variance to estimation is described with simulations results.

Keywords: Gaussian approximation, Kalman smoother, parameter estimation, noise variance

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Mayowa Adeyeye, Kehinde Adeyemo

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The direct free kick is considered a great opportunity to score a goal but this is not always the case amidst Nigerian and other elite footballers. This study, therefore, examined the extent to which an 8 weeks mental skill training programme is effective for improving accuracy in direct free kick in football. Sixty (n-60) students of Pepsi Football Academy participated in the study. They were randomly distributed into two groups of positive self-talk group (intervention n-30) and control group (n-30). The instrument used in the collection of data include a standard football goal post while the research materials include a dummy soccer wall, a cord, an improvised vanishing spray, a clipboard, writing materials, a recording sheet, a self-talk log book, six standard 5 football, cones, an audiotape and a compact disc. The Weinberge and Gould (2011) mental skills training manual was used. The reliability coefficient of the apparatus following a pilot study stood at 0.72. Before the commencement of the mental skills training programme, the participants were asked to take six simulated direct free kick. At the end of each physical skills training session after the pre-test, the researcher spent at least 15 minutes with the groups exposing them to the intervention. The mental skills training programme alongside physical skills training took place in two different locations for the different groups under study, these included Agege Stadium Main bowl Football Pitch (Imagery Group), and Ogba Ijaye (Control Group). The mental skills training programme lasted for eight weeks. After the completion of the mental skills training programme, all the participants were asked to take another six simulated direct free kick attempts using the same field used for the pre-test to determine the efficacy of the treatments. The pre-test and post-test data were analysed using inferential statistics of t-test, while the alpha level was set at 0.05. The result revealed significant differences in t-test for positive self-talk and control group. Based on the findings, it is recommended that athletes should be exposed to positive self-talk alongside their normal physical skills training for quality delivery of accurate direct free kick during training and competition.

Keywords: accuracy, direct free kick, pepsi football academy, positive self-talk

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Authors: Thanooja Naushad, Meena Natarajan, Tushar Vasant Kulkarni

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Background: The Lacey Assessment of Preterm Infants (LAPI) is used in clinical practice to identify premature babies at risk of neuromotor impairments, especially cerebral palsy. This study attempted to find the validity of the Lacey assessment of preterm infants to predict neuromotor outcomes of premature babies at 12 months corrected age and to compare its predictive ability with the brain ultrasound. Methods: This prospective cohort study included 89 preterm infants (45 females and 44 males) born below 35 weeks gestation who were admitted to the neonatal intensive care unit of a government hospital in Dubai. Initial assessment was done using the Lacey assessment after the babies reached 33 weeks postmenstrual age. Follow up assessment on neuromotor outcomes was done at 12 months (± 1 week) corrected age using two standardized outcome measures, i.e., infant neurological international battery and Alberta infant motor scale. Brain ultrasound data were collected retrospectively. Data were statistically analyzed, and the diagnostic accuracy of the Lacey assessment of preterm infants (LAPI) was calculated -when used alone and in combination with the brain ultrasound. Results: On comparison with brain ultrasound, the Lacey assessment showed superior specificity (96% vs. 77%), higher positive predictive value (57% vs. 22%), and higher positive likelihood ratio (18 vs. 3) to predict neuromotor outcomes at one year of age. The sensitivity of Lacey assessment was lower than brain ultrasound (66% vs. 83%), whereas specificity was similar (97% vs. 98%). A combination of Lacey assessment and brain ultrasound results showed higher sensitivity (80%), positive (66%), and negative (98%) predictive values, positive likelihood ratio (24), and test accuracy (95%) than Lacey assessment alone in predicting neurological outcomes. The negative predictive value of the Lacey assessment was similar to that of its combination with brain ultrasound (96%). Conclusion: Results of this study suggest that the Lacey assessment of preterm infants can be used as a supplementary assessment tool for premature babies in the neonatal intensive care unit. Due to its high specificity, Lacey assessment can be used to identify those babies at low risk of abnormal neuromotor outcomes at a later age. When used along with the findings of the brain ultrasound, Lacey assessment has better sensitivity to identify preterm babies at particular risk. These findings have applications in identifying premature babies who may benefit from early intervention services.

Keywords: brain ultrasound, lacey assessment of preterm infants, neuromotor outcomes, preterm

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Authors: Jihan Abdulazeez Ahmed, Adnan Mohsin Abdulazeez Brifcani

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This paper suggests a new internal architecture of holon based on feature selection model using the combination of Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is used to generate features while ANN is used as a classifier to evaluate the produced features. Proposed system is applied on the Wine data set, the statistical result proves that the proposed system is effective and has the ability to choose informative features with high accuracy.

Keywords: artificial neural network, bees algorithm, feature selection, Holon

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Authors: Yusra Mahfooz, Saleha Mehmood

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With increase in industrialization and urbanization, water contaminating rivers through effluents laden with diverse chemicals in developing countries. The study was based on the waste water quality of the four drains (Outfall, Gulshan -e- Ravi, Hudiara, and Babu Sabu) which enter into river Ravi in Lahore, Pakistan. Different pollution parameters were analyzed including pH, DO, BOD, COD, turbidity, EC, TSS, nitrates, phosphates, sulfates and fecal coliform. Approximately all the water parameters of drains were exceeded the permissible level of wastewater standards. In calculation of pollution load, Hudiara drains showed highest pollution load in terms of COD i.e. 429.86 tons/day while in Babu Sabu drain highest pollution load was calculated in terms of BOD i.e. 162.82 tons/day (due to industrial and sewage discharge in it). Lab scale treatment (oxidation ponds) was designed in order to treat the waste water of Babu Sabu drain, through combination of different algae species i.e. chaetomorphasutoria, sirogoniumsticticum and zygnema sp. Two different sizes of ponds (horizontal and vertical), and three different concentration of algal samples (25g/3L, 50g/3L, and 75g/3L) were selected. After 6 days of treatment, 80 to 97% removal efficiency was found in the pollution parameters. It was observed that in the vertical pond, maximum reduction achieved i.e. turbidity 62.12%, EC 79.3%, BOD 86.6%, COD 79.72%, FC 100%, nitrates 89.6%, sulphates 96.9% and phosphates 85.3%. While in the horizontal pond, the maximum reduction in pollutant parameters, turbidity 69.79%, EC 83%, BOD 88.5%, COD 83.01%, FC 100%, nitrates 89.8%, sulphates 97% and phosphates 86.3% was observed. Overall treatment showed that maximum reduction was carried out in 50g algae setup in the horizontal pond due to large surface area, after 6 days of treatment. Results concluded that algae-based treatment are most energy efficient, which can improve drains water quality in cost effective manners.

Keywords: oxidation pond, ravi pollution, river water quality, wastewater treatment

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Authors: Zachary Blanks, Solomon Sonya

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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

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Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik

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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.

Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes

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Authors: Kanika Saxena, Sunita Balani

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Purpose - The aim of the study is to compare the reported sustainability activities in areas of emission, water management and gender equality, currently undertaken by the seven major pharmaceutical companies. Methodology: The published corporate sustainability activity reports for the year 2017 for seven pharmaceutical companies have been studied. The two main criteria for the inclusion of pharmaceutical companies in this study are that they are globally recognized and active in the field of sustainability reporting. Company’s actions and initiatives have been grouped under three categories: (i) Emissions (ii) Water management (iii) Gender Equality in terms of employee workforce. Findings: Based on the sustainability reports, quantification and grading of the companies showed interesting results. Johnson & Johnson and Bayer are leading their activities under emissions and water management categories. The number of activities under emission and water management in case of Eli Lily, Roche, Sanofi, Pfizer and GlaxoSmithKline were 19, 16, 16, 11 and 6 respectively. Johnson & Johnson and Eli Lily are leading in taking the initiatives to curb the problem of emissions as compared with other 5 companies. Under the category of gender equality in terms of employee workforce, Eli Lily is leading the group of sampled companies with 47% of women employee workforce globally followed by Sanofi with 46.2% (42.2% of managers) female employees. It has also been observed that in some of the reports, gender diversification in the workforce has not been mentioned though the total number of employees were mentioned. Conclusion: This study could serve as the informative material for future in-depth industry-specific studies in order to find out the participation of the pharmaceutical companies in the reporting of the sustainability activities especially in reference to emission, water management and gender equality in the workforce. In addition to it, this can be helpful as a reference point for other companies in the pharmaceutical sector who are yet to explore the field of sustainability initiatives and reporting. Due to the limited scope of this study, only seven major players of the pharmaceutical sector who are active in the field of sustainability have been considered.

Keywords: emission, gender equality workforce, pharmaceutical, sustainability, water management

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Authors: S. A. Gayvoronsky, T. A. Ezangina

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The robust control system objects with interval-undermined parameters is considers in this paper. Initial information about the system is its characteristic polynomial with interval coefficients. On the basis of coefficient estimations of quality indices and criterion of the maximum stability degree, the methods of synthesis of a robust regulator parametric is developed. The example of the robust stabilization system synthesis of the rope tension is given in this article.

Keywords: interval polynomial, controller synthesis, analysis of quality factors, maximum degree of stability, robust degree of stability, robust oscillation, system accuracy

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Authors: Kwok W. Cheung

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A scientific interpretation of creation reconciling the beliefs of six literal days of creation and a 13.7-billion-year-old universe currently perceived by most modern cosmologists is proposed. We hypothesize that the reference timeframe of God’s creation is associated with some cosmic time different from the earth's time. We show that the scale factor of earth time to cosmic time can be determined by the solution of the Friedmann equations. Based on this scale factor and some basic assumptions, we derive a Cosmic Time Dilation model that harmonizes the literal meaning of creation days and scientific discoveries with remarkable accuracy.

Keywords: cosmological expansion, time dilation, creation, genesis, relativity, Big Bang, biblical hermeneutics

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Authors: Javad Nosratabadi

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This paper investigates the difference between firms' actual and expected employment along with the amount of loans invested by them. In addition, it examines the relationship between the amount of loans received by firms and wages. Empirically, using a causal effect estimation and firm-level data from a province in Iran between 2004 and 2011, the results show that there is a range of the loan amount for which firms' expected employment meets their actual one. In contrast, there is a gap between firms' actual and expected employment for any other loan amount. Furthermore, the result shows that there is a positive and significant relationship between the amount of loan invested by firms and wages.

Keywords: expected employment, actual employment, wage, loan

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Authors: Manuel A. Alonso-Tarajano, Roberto Mosca, Patrick Aloy

Abstract:

Protein kinases participate in a myriad of cellular processes of major biomedical interest. The in vivo substrate specificity of these enzymes is a process determined by several factors, and despite several years of research on the topic, is still far from being totally understood. In the present work, we have quantified the contributions to the kinase substrate specificity of i) the phosphorylation sites and their surrounding residues in the sequence and of ii) the association of kinases to adaptor or scaffold proteins. We have used position-specific scoring matrices (PSSMs), to represent the stretches of sequences phosphorylated by 93 families of kinases. We have found negative correlations between the number of sequences from which a PSSM is generated and the statistical significance and the performance of that PSSM. Using a subset of 22 statistically significant PSSMs, we have identified specificity determinant residues (SDRs) for 86% of the corresponding kinase families. Our results suggest that different SDRs can function as positive or negative elements of substrate recognition by the different families of kinases. Additionally, we have found that human proteins with known function as adaptors or scaffolds (kAS) tend to interact with a significantly large fraction of the substrates of the kinases to which they associate. Based on this characteristic we have identified a set of 279 potential adaptors/scaffolds (pAS) for human kinases, which is enriched in Pfam domains and functional terms tightly related to the proposed function. Moreover, our results show that for 74.6% of the kinase– pAS association found, the pAS colocalize with the substrates of the kinases they are associated to. Finally, we have found evidence suggesting that the association of kinases to adaptors and scaffolds, may contribute significantly to diminish the in vivo substrate crossed- specificity of protein kinases. In general, our results indicate the relevance of several SDRs for both the positive and negative selection of phosphorylation sites by kinase families and also suggest that the association of kinases to pAS proteins may be an important factor for the localization of the enzymes with their set of substrates.

Keywords: kinase, phosphorylation, substrate specificity, adaptors, scaffolds, cellular colocalization

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Authors: Gaohuizi Guo, Ning Zhang

Abstract:

Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA.

Keywords: firefly algorithm, hybrid algorithm, multi-objective optimization, sine cosine algorithm

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Authors: Ankush M. Dewle, Suditi Bhattacharya, Prachi R. Abhang, Savita Datar, Ajay J. Jog, Rupesh K. Srivastava, Geetanjali Tomar

Abstract:

Objectives: The aim of this study was to investigate the quality of mesenchymal stem cells (MSCs) obtained from ageing gingival tissues, in order to suggest their potential role in autologous stem cell therapy for old individuals. Methods: MSCs were isolated from gingival tissues of young (18-45 years) and old (above 45 years) donors by enzymatic digestion. MSCs were analysed for cfu-f, surface marker expression by flow-cytometry and multilineage differentiation potential. The angiogenic potential was compared in a chick embryo yolk sac membrane model. The aging and differentiation markers including SA-β-galactosidase and p21 respectively were analysed by staining and flow-cytometry analysis. Additionally, osteogenic markers such as glucocorticoid receptor (GR), vitamin D receptor (VDR) were measured by flow-cytometry and RT-qPCR was performed for quantification of osteogenic gene expression. Alizarin Red S and alkaline phosphatase (ALP) activity were also quantitated. Results: Gingival MSCs (GMSCs) from both the age groups were similar in their morphology and displayed cfu-f. They had similar expression of MSC surface markers and p21, comparable rate of proliferation and differentiated to all the four lineages. GMSCs from young donors had a higher adipogenic differentiation potential as compared to the old GMSCs. Moreover, these cells did not display a significant difference in ALP activity probably due to comparable expression of GR, VDR, and osteogenic genes. Conclusions: Ageing of GMSCs occurs at a much slower rate than stem cells from other sources. Thus we suggest GMSCs as an excellent candidate for autologous stem cell therapy in degenerative diseases of elderly individuals. Clinical Significance: GMSCs could help overcome the setbacks in clinical implementation of autologous stem cell therapy for regenerative medicine in all age group of patient.

Keywords: bone regeneration, cell therapy, senescence, stem cell

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Authors: H. İmrek, O. Corumluoglu, B. Akdemir, I. Sanlioglu

Abstract:

It is important to be able to determine the heading direction of a moving vehicle with respect to a distant location. Additionally, it is important to be able to direct a rotating platform on a moving vehicle towards a distant position or location on the earth surface, especially for applications such as determination of the Kaaba direction for daily Muslim prayers. GNNS offers some reasonable solutions. In this study, a functional model of such a directing system supported by GNNS is discussed, and an appropriate system is designed for these purposes. An application for directing system is done by using RTK and DGNSS. Accuracy estimations are given for this system.

Keywords: GNNS, orientation of rotating platform, vehicle orientation, prayer aid device

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Authors: Abdullah Alenezi, Mane Alsudrawi, Rafat Misak

Abstract:

Kuwait is faced with a potentially wide and harmful range of both natural and anthropogenic hazardous events such as dust storms, floods, fires, nuclear accidents, earthquakes, oil spills, tsunamis and other disasters. For Kuwait can be highly vulnerable to these complex environmental risks, an up-to-date and in-depth understanding of their typology, genesis, and impact on the Kuwaiti society is needed. Adequate anticipation and management of environmental crises further require a comprehensive system of decision support to the benefit of decision makers to further bridge the gap between (technical) risk understanding and public action. For that purpose, the Kuwait Institute for Scientific Research (KISR), intends to establish a decision support center for management of the environmental crisis in Kuwait. The center will support policy makers, stakeholders and national committees with technical information that helps them efficiently and effectively assess, monitor to manage environmental disasters using decision support tools. These tools will build on state of the art quantification and visualization techniques, such as remote sensing information, Geographical Information Systems (GIS), simulation and prediction models, early warning systems, etc. The center is conceived as a central facility which will be designed, operated and managed by KISR in coordination with national authorities and decision makers of the country. Our vision is that by 2035 the center will be recognized as a leading national source of scientific advice on national risk management in Kuwait and build unity of effort among Kuwaiti’s institutions, government agencies, public and private organizations through provision and sharing of information. The project team now focuses on capacity building through upgrading some KISR facilities manpower development, build strong collaboration with international alliance.

Keywords: decision support, environment, hazard, Kuwait

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

Abstract:

Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

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Authors: Long Kim Vu, Ban Dang Nguyen

Abstract:

In this paper, the finite element method (FEM) is utilized to simulate the comprehensive process including tightening, releasing and self-loosening of a bolt joint under transverse vibration. Following to the accurate geometry of helical threads, an absolutely hexahedral meshing is implemented. The accuracy of simulation process is verified and validated by comparison with the experimental results on clamping force-vibration relationship, which shows the sufficient correlation. Further analysis with different amplitude and frequency of transverse vibration is done to determine the dominant factor inducing the failure.

Keywords: bolt self-loosening, contact state, finite element method, FEM, helical thread modeling

Procedia PDF Downloads 202