Search results for: protein tertiary structure prediction

Authors: T. Vikneshvaran, A. Aminudin, U. Alyaa Hashim, Waziralilah N. Fathiah, D. Shakirah Shukor

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This paper presents the experimental study conducted on a structure of three-floor height building model. Most vibrations are undesirable and can cause damages to the buildings, machines and people all around us. The vibration wave from earthquakes, construction and winds have high potential to bring damage to the buildings. Excessive vibrations can result in structural and machinery failures. This failure is related to the human life and environment around it. The effect of vibration which causes failure and damage to the high rise buildings can be controlled in real life by implementing tuned mass damper (TMD) into the structure of the buildings. This research aims to study the effect and performance improvement achieved by applying TMD into the building structure. A structure model of three degrees of freedom (3DOF) is designed to demonstrate the performance of TMD to the designed model. The model designed is the physical representation of actual building structure in real life. It is constructed at a reduced scale and will be used for the experiment. Thus, the result obtained will be more accurate to compared with the real life effect. Based on the result from experimental study, by applying TMD to the structure model, the forces of vibration and the displacement mode of the building reduced. Thus, the reduced in vibration of the building helps to maintain the good condition of the building.

Keywords: degrees-of-freedom, displacement mode, natural frequency, tuned mass damper

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Authors: S. H. Bae, D. W. Cho, W. B. Jeong, J. R. Cho

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Fatigue fracture of an aluminum welded structure is a phenomenon frequently occurring from pores in a weld. In order to grasp the state of the welded structure in operation in real time, the acceleration signal of the structure is measured. At this time, the vibration characteristic of the signal according to the fatigue load is an important parameter of the state diagnosis. This paper was an experimental study on the variation of vibration characteristics of welded beams with vibration aging (especially bending vibration). First simple beams were produced according to welding conditions. Each beam was vibrated and measured beam's PSD (power spectral density) according to the degree of aging. Also, modal testing was conducted to compare the transfer functions of welded beams. Testing result shows that the natural frequencies of the beam changed with the vibration aging due to the change of stiffness in welding part and its stiffness was estimated by the finite element method.

Keywords: modal testing, natural frequency, vibration aging, welded structure

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Authors: Panomporn Wisuthseriwong, Hatairuk Tungkasen, Siyaporn Namsongsan, Chanikarn Srinark, Mayuva Youngsabanant-Areekijseree

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The objective of the present study was to evaluate the morphology of porcine cumulus-oocyte complexes (pCOCs) and follicular fluid during follicular development. The samples were obtained from local slaughterhouses in Nakorn Pathom Province, Thailand. Pigs were classified as either in the follicular phase or luteal phase. Porcine follicles (n = 3,510) were categorized as small (1-3 mm in diameters; n=2,910), medium (4-6 mm in diameters; n=530) and large (7-8 mm in diameters; n=70). Then pCOCs and follicular fluid were collected. Finally, we found that the oocytes can be categorized into intact cumulus cells layer oocyte, multi-cumulus cells layer oocyte, partial cumulus cells layer oocyte, completely denuded oocyte and degenerated oocyte. They showed high percentage of intact and multi-cumulus cells layer oocytes from small follicles (54.68%) medium follicles (69.06%) and large follicles (68.57%), which have high potential to develop into matured oocytes in vitro. Protein composition of the follicular fluid was separated by SDS-PAGE technique. The result shows that the protein molecular weight in the small and medium follicles are 23, 50, 66, 75, 92, 100, 132, 163, 225 and >225 kDa. Meanwhile, protein molecular weight in large follicles are 12, 16, 23, 50, 66, 75, 92, 100, 132, 163, 225 and >225 kDa. All proteins play an important role in promotion and regulation on development, maturation of oocytes and regulation of ovulation. We conclude that the results of discovery can be used porcine secretion proteins for supplement in IVM/IVF technology. Acknowledgements: The project was funded by a grant from Silpakorn University Research and Development Institute (SURDI) and Faculty of Science, Silpakorn University, Thailand.

Keywords: porcine follicles, porcine oocyte, follicular fluid, SDS-PAGE

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Authors: ShuHui Yang, ChingChing Lu

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Measuring event-related potentials (ERPs) has been fundamental to our understanding of how people process language. One specific ERP component, a P600, has been hypothesized to be associated with syntactic reanalysis processes. We, however, propose that the P600 is not restricted to reanalysis processes, but is the index of the structural pattern processing. To investigate the structural pattern processing, we utilized the effects of stimulus degradation in structural priming. To put it another way, there was no P600 effect if the structure of the prime was the same with the structure of the target. Otherwise, there would be a P600 effect if the structure were different between the prime and the target. In the experiment, twenty-two participants were presented with four sentences of Tang poetry. All of the first two sentences, being prime, were conducted with SVO+VP. The last two sentences, being the target, were divided into three types. Type one of the targets was SVO+VP. Type two of the targets was SVO+VPVP. Type three of the targets was VP+VP. The result showed that both of the targets, SVO+VPVP and VP+VP, elicited positive-going brainwave, a P600 effect, at 600~900ms time window. Furthermore, the P600 component was lager for the target’ VP+VP’ than the target’ SVO+VPVP’. That meant the more dissimilar the structure was, the lager the P600 effect we got. These results indicate that P600 was the index of the structure processing, and it would affect the P600 effect intensity with the degrees of structural heterogeneity.

Keywords: ERPs, P600, structural pattern, structural priming, Tang poetry

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Authors: Andrew Laming, John Hattie, Mark Wilson

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Nine Queensland Independent high schools provided deidentified student-matched ATAR and NAPLAN data for all 1217 ATAR graduates since 2020 who also sat NAPLAN at the school. Graduating cohorts from the nine schools contained a mean 100 ATAR graduates with previous NAPLAN data from their school. Excluded were vocational students (mean=27) and any ATAR graduates without NAPLAN data (mean=20). Based on Index of Community Socio-Educational Access (ICSEA) prediction, all schools had larger that predicted proportions of their students graduating with ATARs. There were an additional 173 students not releasing their ATARs to their school (14%), requiring this data to be inferred by schools. Gain was established by first converting each student’s strongest NAPLAN domain to a statewide percentile, then subtracting this result from final ATAR. The resulting ‘percentile shift’ was corrected for plausible ATAR participation at each NAPLAN level. Strongest NAPLAN domain had the highest correlation with ATAR (R2=0.58). RESULTS School mean NAPLAN scores fitted ICSEA closely (R2=0.97). Schools achieved a mean cohort gain of two ATAR rankings, but only 66% of students gained. This ranged from 46% of top-NAPLAN decile students gaining, rising to 75% achieving gains outside the top decile. The 54% of top-decile students whose ATAR fell short of prediction lost a mean 4.0 percentiles (or 6.2 percentiles prior to correction for regression to the mean). 71% of students in smaller schools gained, compared to 63% in larger schools. NAPLAN variability in each of the 13 ICSEA1100 cohorts was 17%, with both intra-school and inter-school variation of these values extremely low (0.3% to 1.8%). Mean ATAR change between years in each school was just 1.1 ATAR ranks. This suggests consecutive school cohorts and ICSEA-similar schools share very similar distributions and outcomes over time. Quantile analysis of the NAPLAN/ATAR revealed heteroscedasticity, but splines offered little additional benefit over simple linear regression. The NAPLAN/ATAR R2 was 0.33. DISCUSSION Standardised data like NAPLAN and ATAR offer educators a simple no-cost progression metric to analyse performance in conjunction with their internal test results. Change is expressed in percentiles, or ATAR shift per student, which is layperson intuitive. Findings may also reduce ATAR/vocational stream mismatch, reveal proportions of cohorts meeting or falling short of expectation and demonstrate by how much. Finally, ‘crashed’ ATARs well below expectation are revealed, which schools can reasonably work to minimise. The percentile shift method is neither value-add nor a growth percentile. In the absence of exit NAPLAN testing, this metric is unable to discriminate academic gain from legitimate ATAR-maximizing strategies. But by controlling for ICSEA, ATAR proportion variation and student mobility, it uncovers progression to ATAR metrics which are not currently publicly available. However achieved, ATAR maximisation is a sought-after private good. So long as standardised nationwide data is available, this analysis offers useful analytics for educators and reasonable predictivity when counselling subsequent cohorts about their ATAR prospects.  

Keywords: NAPLAN, ATAR, analytics, measurement, gain, performance, data, percentile, value-added, high school, numeracy, reading comprehension, variability, regression to the mean

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Authors: M. Heidari, A. Safekordi, A. Zamaniyan, E. Ganji Babakhani, M. Amanipour

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Perovskite-type membrane Ba_0.5Sr_0.5Ce_0.9Y_0.1O_3-δ (BSCY) was successfully synthesized by liquid citrate method. The hydrogen permeation and stability of BSCY perovskite-type membranes were studied at high temperatures. The phase structure of the powder was characterized by X-ray diffraction (XRD). Scanning electron microscopy (SEM) was used to characterize microstructures of the membrane sintered under various conditions. SEM results showed that increasing in sintering temperature, formed dense membrane with clear grains. XRD results for BSCY membrane that sintered in 1150 °C indicated single phase perovskite structure with orthorhombic configuration, and SEM results showed dense structure with clear grain size which is suitable for permeation tests. Partial substitution of Sr with Ba in SCY structure improved the hydrogen permeation flux through the membrane due to the larger ionic radius of Ba²⁺. BSCY membrane shows high hydrogen permeation flux of 1.6 ml/min.cm² at 900 °C and partial pressure of 0.6.

Keywords: hydrogen separation, perovskite, proton conducting membrane.

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Authors: Lan Nguyen, Lam Cao Van

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Thuan Phuoc is a suspension bridge built in Danang city, Vietnam. Because this bridge locates near the estuary, its structure has degraded rapidly. Many cracks have currently occurred on most of the concrete piers of the curved approach spans. This paper aims to present the results of diagnostic analysis of causes for cracks as well as some calculations for strengthening piers by carbon fiber reinforced polymer (CFRP). Besides, it describes how to use concrete nonlinear analysis software ATENA to diagnostically analyze cracks, strengthening designs. Basing on the results of studying the map of distributing crack on Thuan Phuoc bridge’s concrete piers is analyzed by the software ATENA is suitable for the real conditions and CFRP would be the best solution to strengthen piers in a sound and fast way.

Keywords: ATENA, bridge pier strengthening, carbon fiber reinforced polymer (CFRP), crack prediction analysis

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Xu Chen, Zhao Caiqi

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In addition to the advantages of light weight, resistant corrosion and ease of processing, aluminum is also applied to the long-span spatial structures. However, the elastic modulus of aluminum is lower than that of the steel. This paper combines the high performance aluminum honeycomb panel with the aluminum latticed shell, forming a new panel-and-rod composite shell structure. Through comparative analysis between the static and dynamic performance, the conclusion that the structure of composite shell is noticeably superior to the structure combined before.

Keywords: combination of aluminum honeycomb panel, rod latticed shell, dynamic performence, response spectrum analysis, seismic properties

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Authors: Hoda M. Eid, Abir Nachar, Farah Thong, Gary Sweeney, Pierre S. Haddad

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Caffeic acid methyl ester (CAME) and caffeic ethyl esters (CAEE) were previously reported to potently stimulate glucose uptake in cultured C2C12 skeletal muscle cells via insulin-independent mechanisms involving the activation of adenosine monophosphate-activated protein kinase (AMPK). In the present study, we investigated the effect of the two compounds on the translocation of glucose transporter GLUT4 in L6 skeletal muscle cells. The cells were treated with the optimum non-toxic concentration (50 µM) of either CAME or CAEE for 18 h. Levels of GLUT4myc at the cell surface were measured by O-phenylenediamine dihydrochloride (OPD) assay. The effects of CAME and CAEE on GLUT1 and GLUT4 protein content were also measured by western immunoblot. Our results show that CAME and CAEE significantly increased glucose uptake, GLUT4 translocation and GLUT4 protein content. Furthermore, the effect of the two CA esters on two insulin-sensitive cell lines: H4IIE rat hepatoma and 3T3-L1 adipocytes were investigated. CAME and CAEE reduced the enzymatic activity of the key hepatic gluconeogenic enzyme glucose-6-phosphatase in a concentration-dependent manner. In addition, they exerted a concentration-dependent antiadipogenic effect on 3T3-L1 cells. Mitotic clonal expansion (MCE), a prerequisite for adipocytes differentiation was also concentration-dependently inhibited. The two compounds abrogated lipid droplet accumulation, blocked MCE and maintained cells in fibroblast-like state when applied at the maximum non-toxic concentration (100 µM). In addition, the expression of the early key adipogenic transcription factors CCAAT enhancer-binding protein beta (C/EBP-β) and the master regulator of adipogenesis peroxisome-proliferator-activated receptor gamma (PPAR-γ) were inhibited. We, therefore, conclude that CAME and CAEE exert pleiotropic benefits in several insulin-sensitive cell lines through insulin-independent mechanisms involving AMPK, hence they may treat obesity, diabetes and other metabolic diseases.

Keywords: type 2 diabetes mellitus, insulin resistance, GLUT4, Akt, AMPK.

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Authors: Moudi Almousa

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This research paper explores Saudi Arabian female consumers’ experiences using 3D body scanning technology and their level of acceptance of possible market applications of this technology to adopt for apparel online shopping. Data was collected for 82 women after being scanned then viewed a short video explaining three possible scenarios of 3D body scanning applications, which include size prediction, customization, and virtual try-on, before completing the survey questionnaire. Although respondents have strong positive responses towards the scanning experience, the majority were concerned about their privacy during the scanning process. The results indicated that size prediction and virtual try on had greater market application potential and a higher chance of crossing the gap based on consumer interest. The results of the study also indicated a strong positive correlation between respondents’ concern with inability to try on apparel products in online environments and their willingness to use the 3D possible market applications.

Keywords: 3D body scanning, market applications, online, apparel fit

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Authors: Thidathit Prachanukool, Chaiyaporn Yuksen, Welawat Tienpratarn, Sorravit Savatmongkorngul, Panvilai Tangkulpanich, Chetsadakon Jenpanitpong, Yuranan Phootothum, Malivan Phontabtim, Promphet Nuanprom

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Background: Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to developed the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods: This study was diagnostic, retrospectively cross-sectional and exploratory model at the Emergency Medicine Department in Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN)>75%, and the pain duration before presentation. The predictive model and prediction score for ruptured appendicitis was developed by multivariable logistic regression analysis. Result: During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score > 6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion: Using the Ramathibodi Welawat Ruptured Appendicitis Score. (RAMA WeRA Score) developed in this study, a score of > 6 was associated with ruptured appendicitis.

Keywords: predictive model, risk score, ruptured appendicitis, emergency room

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Authors: O. Eren, L. Zakar

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Recycling steel building components is key to the sustainability of a structure’s end-of-life, as it is the most economical solution. In this paper the effects of usage of recycled steel material in tall buildings aspects are investigated.

Keywords: building, recycled material, steel, structure

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Authors: Wang Haining, Zhang Hong

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In China, rapid urbanization needs more and more buildings constructed for the growing population in cities. With the help of the methodology which contains investigation, contrastive analysis, design based on component with BIM and experiment before real construction, this research based on mobile light structure system, trying to the sustainable problems partly in present China by systematic study. The system cannot replace the permanent heavy structure completely. So the goal is the improvement of the whole building system by the addition of light structure. This house system uses modularized envelopes and standardized connections, which are pre-fabricated and assembled in factories and transported like containers. Aluminum is used as the structural material in this system, and inorganic thermal insulation material used in the envelope, which have high fireproof properties. The relationship between manufactory and construction of the system is progressive hierarchy. They exist as First Industrial, Second Industrial, Third Industrial and Site Assembly Stage. It could maximize the land usage capacity by fully exploit the area where normal permanent architecture can't take advantage of. Not only the building system itself especially the thermal isolated materials used and active solar photovoltaic system equipped can save energy, but also the way of product development is sustainable.

Keywords: aluminum house, light Structure, rapid assembly, repeat construction

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Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

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Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

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Authors: M. Kashiripoor

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Existing indicators for sustainable urban development do not identify the features of cities’ planning structures and their architecture. Iranian city has special relevance problem of assessing the conformity of their planning and development of the concept of sustainable development. Based on theoretical sources, the author concludes that, despite the existence of common indicators for sustainable development of settlements, specialized evaluation criteria city structure planning has not been developed. He is trying to fill this gap and put forward a system of indicators characterizing the level of development of the architectural-planning structure of the city. The proposed system of indicators is designed based on technical and economic urban standard indicators from different countries. Alternative designing systems and requirements of modern rating systems like LEED-ND comprise a criterion for evaluation of urban structures in accordance with principles of "Green" building and New Urbanism. Urban development trends are close in spirit of sustainable development and developed under its influence. The study allowed concluding that a system of indicators to identify the relevant architectural-planning structure of the city, requirements of sustainable development, should be adapted to the conditions of each country, particularly in Iran. The article attempts typology proposed indicators, which are presented in tabular form and are divided into two types: planning and spatial. This article discusses the known indicators of sustainable development and proposed specific system of indicators characterizing the level of development of architectural-planning structure of the city. This article examines indicators for evaluating level of city' planning structure development. The proposed system of indicators is derived from the urban planning standards and rating systems such as LEED-ND, BREEAM Community and CASBEE-UD.

Keywords: architectural-planning structure of cities, urban planning indicators, urban space indicators, urban development

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Jingwen Ou

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Writing for academic purposes in a second or foreign language is one of the most important and the most demanding skills to be mastered by non-native speakers. Traditionally, the EAP writing instruction at the tertiary level encompasses the teaching of academic genre knowledge, more specifically, the disciplinary writing conventions, the rhetorical functions, and specific linguistic features. However, one of the main sources of challenges in English academic writing for L2 students at the tertiary level can still be found in proficiency in academic discourse, especially vocabulary, academic register, and organization. Data-Driven Learning (DDL) is defined as “a pedagogical approach featuring direct learner engagement with corpus data”. In the past two decades, the rising popularity of the application of the data-driven learning (DDL) approach in the field of EAP writing teaching has been noticed. Such a combination has not only transformed traditional pedagogy aided by published DDL guidebooks in classroom use but also triggered global research on corpus use in EAP classrooms. This study endeavors to delineate a systematic review of research in the intersection of DDL and EAP writing instruction by conducting a systematic literature review on both indirect and direct DDL practice in EAP writing instructional settings in China. Furthermore, the review provides a synthesis of significant discoveries emanating from prior research investigations concerning Chinese university students’ perception of Data-Driven Learning (DDL) and the subsequent impact on their academic writing performance following corpus-based training. Research papers were selected from Scopus-indexed journals and core journals from two main Chinese academic databases (CNKI and Wanfang) published in both English and Chinese over the last ten years based on keyword searches. Results indicated an insufficiency of empirical DDL research despite a noticeable upward trend in corpus research on discourse analysis and indirect corpus applications for material design by language teachers. Research on the direct use of corpora and corpus tools in DDL, particularly in combination with genre-based EAP teaching, remains a relatively small fraction of the whole body of research in Chinese higher education settings. Such scarcity is highly related to the prevailing absence of systematic training in English academic writing registers within most Chinese universities' EAP syllabi due to the Chinese English Medium Instruction policy, where only English major students are mandated to submit English dissertations. Findings also revealed that Chinese learners still held mixed attitudes towards corpus tools influenced by learner differences, limited access to language corpora, and insufficient pre-training on corpus theoretical concepts, despite their improvements in final academic writing performance.

Keywords: corpus linguistics, data-driven learning, EAP, tertiary education in China

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Authors: C. Puangsawat, C. Limotai, P. Srikhachin

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Bio-psycho-social caring was required for promoting the quality of life of the patients with epilepsy (PWE), despite controlled seizures. Multifaceted issues emerge at the epilepsy clinic. Unpredicted seizures, antiepileptic drug compliance problems/adverse effects, psychiatric, and social problems are all needed to be explored and managed. The Nursing Service System (NSS) at the tertiary epilepsy clinic (TEC) was consequently developed for improving the clinical care for PWE. Case manager concept was integrated as the framework guiding the processes and strategies used for developing the NSS as well as the roles of the multidisciplinary team at the clinic. This study aimed to report the outcomes of the developed NSS integrated case manager concept. The processes of our developed NSS program included 1) screening for patient’s problems using questionnaire prior to seeing epileptologists i.e., assessing the patient’s risk to develop acute seizures at the clinic, issues related to medication use, and uncovered psychiatric and social problems; and 2) assigning the patients at risk to be evaluated and managed by appropriate team. Nurses specializing in epilepsy in coordination with the multidisciplinary team implemented the NSS to promote coordinated work among the team which consists of epileptologists, nurses, pharmacists, psychologists, and social workers. Determination of the role of each person and their responsibilities along with joint care plan were clearly established. One year after implementation, the rate of acute seizure occurrence at the clinic was decreased, and satisfactory feedback from the patients was received. In order to achieve an optimal goal to promote self-management behaviors in PWE, continuing the NSS and systematic assessment of its effectiveness is required.

Keywords: case manager concept, nursing service system, patients with epilepsy, quality of life

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Authors: Wahyu Widodo, Imbang Dwi Rahayu, Adi Sutanto

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The purpose of this research was to analyze the effect of the fermented organic feed to dry matter, ash, organic matter, protein, fat and crude fiber contents of kampong chicken meat. The research had conducted at January until June, 2016. One hundreds chickens were used in this research. Experimental method and completely randomized design were used to support this research. We had 4 treatment namely P0: organic feed without fermentation, P1: Organic feed with fermented rice bran, P2: Organic feed with fermented corn, P3: Organic feed with fermented rice bran and corn with 5 replication. The conclusion was the treatment had not a significant effect in the dry matter, ash, organic matter and protein contents of chicken meat. On the other hand, it had a significant effect in the fat and crude fiber contents of chicken meat.

Keywords: corn, fermented organic feed, nutritive contents, rice bran

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Authors: Filipa Cortez, Vanessa Pereira

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Introduction: The main challenge of obesity treatment is long-term weight loss maintenance. The 3 phases method is a weight loss program that combines a low carb and moderately high-protein diet, food supplements and a weekly one-to-one consultation with a certified nutritionist. Sustained weight control is the ultimate goal of phase 3. Success criterion was the minimum loss of 10% of initial weight and its maintenance after 12 months. Objective: The aim of this study was to identify factors associated with successful weight loss maintenance after 12 months at the end of 3 phases method. Methods: The study included 199 subjects that achieved their weight loss goal (phase 3). Weight and body mass index (BMI) were obtained at the baseline and every week until the end of the program. Therapeutic adherence was measured weekly on a Likert scale from 1 to 5. Subjects were considered in compliance with nutritional recommendation and supplementation when their classification was ≥ 4. After 12 months of the method, the current weight and number of previous weight-loss attempts were collected by telephone interview. The statistical significance was assumed at p-values < 0.05. Statistical analyses were performed using SPSS TM software v.21. Results: 65.3% of subjects met the success criterion. The factors which displayed a significant weight loss maintenance prediction were: greater initial percentage weight loss (OR=1.44) during the weight loss intervention and a higher number of consultations in phase 3 (OR=1.10). Conclusion: These findings suggest that the percentage weight loss during the weight loss intervention and the number of consultations in phase 3 may facilitate maintenance of weight loss after the 3 phases method.

Keywords: obesity, weight maintenance, low-carbohydrate diet, dietary supplements

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Mokhtar Nikgoo

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Definition of bridge: A bridge is a structure that allows people to pass through the communication road with two points. There are many different types of bridges, each of which is designed to perform a specific function. This article introduces the concept, history, components, uses, types, construction methods, selected factors, damage factors and principles of bridge maintenance. A bridge is a structure to cross a passage such as a water, valley or road without blocking another path underneath. This structure makes it possible to pass obstacles that are difficult or impossible to pass. There are different designs for bridge construction, each of which is used for a particular function and condition. In the old definition, a bridge is an arch over a river, valley, or any type of passage that makes traffic possible. But today, in the topic of urban management, the bridge is considered as a structure to cross physical barriers, so that while using space (not just the surface of the earth), it can facilitate the passage and access to places. The useful life of bridges may be between 30 and 80 years depending on the location and the materials used. But with proper maintenance and improvement, their life may last for hundreds of years.

Keywords: bridge, road construction, surveying, transportation

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Jibran Abbasy, Rizwan Sultan, Ammar Humayun, Tabish Chawla

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Background: Hydatid disease caused by Echinococcus Granulosus affects liver in 70-90% of cases. Dogs are the definitive host while humans are the accidental host. Modalities used for its treatment are especially important for our population as the disease is endemic in many Asian countries. The aim of the study was to perform an audit of the various modalities used for treatment of hydatid disease of liver and the response to each modality in tertiary care center of Pakistan. Materials and Methods: Retrospective audit of patients diagnosed and treated for Hydatid disease of the liver at Aga Khan University Hospital from 1st January 2007 to 31st December 2014 was completed. All patients aged 16 and above were included. Patients who had extra hepatic disease and missing records were excluded. Outcome measures were morbidity, mortality and recurrence of the disease. Results: During the study period 56 patients were treated for isolated hepatic hydatid disease and were included. Mean age was 39 years with 48% being females and 52% males. Most common presenting complaint was abdominal pain seen in 53% of patients(n=41). Duration of symptoms was less than 6 months in 74% (n=38). Mostly right lobe was involved in 69% (n=38).Most common treatment modality used was surgery in 34 patients followed by PAIR in 14 patients while 8 patients were treated medically. At a median follow up of 34 months recurrence was seen in 2 patients treated with PAIR while no patient treated with surgery had recurrence with the median follow up of 20 months. While no morbidity and mortality were observed in PAIR, but in surgery 5 patients had morbidity while 1 patient had mortality. Conclusion: Our data is comparative to other studies in terms of morbidity, mortality, and recurrence. We had adequate follow up. In our study PAIR and surgery both are effective and have less complications and recurrence rate. Surgery is still the gold standard in terms of recurrence.

Keywords: echinococcous granulosus, puncture aspiration irrigation reaspiration (PAIR), surgery, hydatid disease

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Authors: Srilaxmi Gangula, MahaLakshmi Vinukonda, Neeraj Rao

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A wide band polarization insensitive metamaterial absorber with bandwidth enhancement in X and C band is proposed. The structure proposed here consists of a periodic unit cell of resonator arrangements in double layer. The proposed structure shows near unity absorption at frequencies of 6.21 GHz and 10.372 GHz spreading over a bandwidth of 1 GHz and 6.21 GHz respectively in X and C bands. The proposed metamaterial absorber is designed so as to increase the bandwidth. The proposed structure is also independent for TE and TM polarization. Because of its simple implementation, near unity absorption and wide bandwidth this dual band polarization insensitive metamaterial absorber can be used for EMI/EMC applications.

Keywords: absorber, C-band, metamaterial, multilayer, X-band

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Authors: Majid Mardaniyan Ghahfarokhi, Abdolhamid Habibi, Majid Mohammad Shahi

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The combination of exercise and diet as the most important strategy to reduce weight and control obesity-related factors, including Irisin, Insulin, and Glucose was raised. The aim of this study was to investigate the effect of aerobic exercise combined with four different diets on serum levels of Irisin, Insulin, and Glucose in overweight men. Methods: In this quasi-experimental study, 8 overweight men (BMI 29.23±0.47) with average age of (23±1.6) voluntarily participated in 4 sessions by one-week interval. The study was done in exercise physiology lab. In each session, subjects performed a 30 minutes treadmill test with 60-70% of maximum heart rate, after consuming a high carbohydrate, high-fat, high-protein and normal diet. For biochemical measurement, three blood samples were taken in fasting state, two hours after meals and after exercise Results: Statistical analysis of data showed that the serum levels of Irisin after consumption all four diets had been reduced which this reduce as a result of high-fat diet that were significantly (p ≤ 0/038). Serum concentration of Insulin and Glucose increased after consuming four diets. However, increase in serum Insulin and Glucose was significant only after consuming high-carbohydrate diet (Respectively p ≤ 0/001, p ≤ 0/042). In addition, during exercise after consuming all four regular diet, high carbohydrate, high-protein and high-fat, Irisin significant increased significantly (Respectively p ≤ 0/021, p ≤ 0/049, p ≤ 0/001, P ≤ 0/003), Insulin decreased significantly (Respectively p ≤ 0/002, p ≤ 0/001, p ≤ 0/001, p ≤ 0/002) and Glucose were significantly reduced (Respectively p ≤ 0/001, p ≤ 0/001, P ≤ 0/001, p ≤ 0/002). After aerobic activity following the consumption of a high protein diet the highest increase in irisin levels, and after aerobic exercise following consumption of high carbohydrate diet the greatest decrease in insulin and glucose levels were observed. Conclusion: It seems that diet alone and exercises following different consumption diets can have a significant effect on Irisin, Insulin, and Glucose serum levels in overweight young men.

Keywords: acute aerobic exercise, diet, irisin, overweight

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Authors: Vijay S. Katta, Vinod Kushwah, Rudraksh Tiwari, Mulayam Singh Gaur, Priti Dimri, Ashok Kumar Sharma

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The formation process of seismic activities because of abrupt slip on faults, tectonic plate moments due to accumulated stress in the Earth’s crust. The prediction of seismic activity is a very challenging task. We have studied the changes in surface latent heat temperatures which are observed prior to significant earthquakes have been investigated and could be considered for short term earthquake prediction. We analyzed the surface latent heat temperature (SLHT) variation for inland earthquakes occurred in Chamba, Himachal Pradesh (32.5 N, 76.1E, M-4.5, depth-5km) nearby the main boundary fault region, the data of SLHT have been taken from National Center for Environmental Prediction (NCEP). In this analysis, we have calculated daily variations with surface latent heat temperature (0C) in the range area 1⁰x1⁰ (~120/KM²) with the pixel covering epicenter of earthquake at the center for a three months period prior to and after the seismic activities. The mean value during that period has been considered in order to take account of the seasonal effect. The monthly mean has been subtracted from daily value to study anomalous behavior (∆SLHT) of SLHT during the earthquakes. The results found that the SLHTs adjacent the epicenters all are anomalous high value 3-5 days before the seismic activities. The abundant surface water and groundwater in the epicenter and its adjacent region can provide the necessary condition for the change of SLHT. To further confirm the reliability of SLHT anomaly, it is necessary to explore its physical mechanism in depth by more earthquakes cases.

Keywords: surface latent heat temperature, satellite data, earthquake, magnetic storm

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