Search results for: spectroscopy data analysis

Authors: Khalida Douibi, Rodrigo Balp, Solène Le Bars

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In the medical field, applications related to human experiments are frequently linked to reduced samples size, which makes the training of machine learning models quite sensitive and therefore not very robust nor generalizable. This is notably the case in Brain-Computer Interface (BCI) studies, where the sample size rarely exceeds 20 subjects or a few number of trials. To address this problem, several resampling approaches are often used during the data preparation phase, which is an overly critical step in a data science analysis process. One of the naive approaches that is usually applied by data scientists consists in the transformation of the entire database before the resampling phase. However, this can cause model’ s performance to be incorrectly estimated when making predictions on unseen data. In this paper, we explored the effect of data leakage observed during our BCI experiments for device control through the real-time classification of SSVEPs (Steady State Visually Evoked Potentials). We also studied potential ways to ensure optimal validation of the classifiers during the calibration phase to avoid overfitting. The results show that the scaling step is crucial for some algorithms, and it should be applied after the resampling phase to avoid data leackage and improve results.

Keywords: data leackage, data science, machine learning, SSVEP, BCI, overfitting

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Authors: Ismayanti Magfirah, Sartohadi Junun, Samodra Guruh

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Landslides are one of the most frequent and devastating natural disasters in Indonesia. Many studies have been conducted regarding this phenomenon. However, there is a lack of attention in the landslide inventory mapping. The natural condition (dense forest area) and the limited human and economic resources are some of the major problems in building landslide inventory in Indonesia. Considering the importance of landslide inventory data in susceptibility, hazard, and risk analysis, it is essential to generate landslide inventory based on available resources. In order to achieve this, the first thing we have to do is identify the landslides' location. The presence of Sentinel-1A and Sentinel-2A data gives new insights into land monitoring investigation. The free access, high spatial resolution, and short revisit time, make the data become one of the most trending open sources data used in landslide mapping. Sentinel-1A and Sentinel-2A data have been used broadly for landslide detection and landuse/landcover mapping. This study aims to generate landslide map by integrating Sentinel-1A and Sentinel-2A data use change detection method. The result will be validated by field investigation to make preliminary landslide inventory in the study area.

Keywords: change detection method, landslide inventory mapping, Sentinel-1A, Sentinel-2A

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Authors: Adrian W. Chew

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This paper argues that education policy analysis tends to be steered towards empiricist oriented approaches, which place emphasis on objective and measurable data. However, this paper argues that empiricist oriented approaches are generally based on inductive and/or deductive reasoning, which are unable to generate new ideas/knowledge. This paper will outline the logical structure of induction, deduction, and abduction, and argues that only abduction provides possibilities for the creation of new ideas/knowledge. This paper proposes the neologism of ‘informed guessing’ as a reformulation of abduction, and also as an approach to education policy analysis. On one side, the signifier ‘informed’ encapsulates the idea that abductive policy analysis needs to be informed by descriptive conceptualization theory to be able to make relations and connections between, and within, observed phenomenon and unobservable general structures. On the other side, the signifier ‘guessing’ captures the cyclical and unsystematic process of abduction. This paper will end with a brief example of utilising ‘informed guessing’ for a policy analysis of school choice lotteries in the United States.

Keywords: abductive reasoning, empiricism, informed guessing, policy analysis

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Authors: Aissa Belmeguenai, Oulaya Berrak, Khaled Mansouri

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In this work, efficiency implementation and security evaluation of the keystream generator of Achterbahn-128 for images encryption and decryption was introduced. The implementation for this simulated project is written with MATLAB.7.5. First of all, two different original images are used to validate the proposed design. The developed program is used to transform the original images data into digital image file. Finally, the proposed program is implemented to encrypt and decrypt images data. Several tests are done to prove the design performance, including visual tests and security evaluation.

Keywords: Achterbahn-128, keystream generator, stream cipher, image encryption, security analysis

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Authors: Clare Cunningham

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Whilst there is a strong global research base on the benefits of structured writing retreats and similar provisions, such as Shut Up and Write events, for academic staff and postgraduate researchers, very little has been published about the worth of such events for undergraduate students. This is despite the fact that, internationally, undergraduate student researchers experience similar pressures, distractions and feelings towards writing as those who are at more senior levels within the academy. This paper reports on a mixed-methods study with cohorts of third-year undergraduate students over the course of four academic years. This involved a range of research instruments adopted over the four years of the study. They include the administration of four questionnaires across three academic years, a collection of ethnographic recordings in the second year, and the collation of reflective journal entries and evaluations from all four years. The final two years of data collection took place during the period of Covid-19 restrictions when writing retreats moved to the virtual space which adds an additional dimension of interest to the analysis. The analysis involved the collation of quantitative questionnaire data to observe patterns in expressions of attitudes towards writing. Qualitative data were analysed thematically and used to corroborate and support the quantitative data when appropriate. The resulting data confirmed that one of the biggest challenges for undergraduate students mirrors those reported in the findings of studies focused on more experienced researchers. This is not surprising, especially given the number of undergraduate students who now work alongside their studies, as well as the increasing number who have caring responsibilities, but it has, as yet, been under-reported. The data showed that the groups of writing retreat participants all had very positive experiences, with accountability, a sense of community and procrastination avoidance some of the key aspects. The analysis revealed the sometimes transformative power of these events for a number of these students in terms of changing the way they viewed writing and themselves as writers. The data presented in this talk will support the proposal that retreats should much more widely be offered to undergraduate students across the world.

Keywords: academic writing, students, undergraduates, writing retreat

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Authors: Sima Shakoorjavan, Dawid Stawski, Somaye Akbari

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In this study, the adsorption-desorption behavior of poly(amidoamine) (PAMAM) as a polycation and poly (acrylic acid) (PAA) as a polyanion deposited on aminolyzed-PLA nonwoven through layer-by-layer technique (lbl) was studied. The adsorption-desorption behavior was monitored by UV adsorbance spectroscopy and turbidity tests of the waste polyelectrolytes after each deposition. Also, the drying between each deposition step was performed to study the effect of drying on adsorption-desorption behavior. According to UV adsorbance spectroscopy of the waste polyelectrolyte after each deposition, it was revealed that drying has a great effect on the deposition behavior of the next layer. Regarding the deposition of the second layer, drying caused more desorption and removal of the previously deposited layer since the turbidity and the absorbance of the waste increased in comparison to pure polyelectrolyte. To deposit the third layer, the same scenario occurred and drying caused more removal of the previously deposited layer. However, the deposition of the fourth layer drying after the deposition of the third layer did not affect the adsorption-desorption behavior. Since the adsorbance and turbidity of the samples that were dried and those that were not dried were the same. As a result, it seemed that deposition of the fourth layer could be the starting point where lbl reached its constant state. The decrease in adsorbance and remaining turbidity of the waste same as a pure polyelectrolyte can indicate that most portion of the polyelectrolyte was adsorbed onto the substrate rather than complex formation in the bath as the subsequence of the previous layer removal.

Keywords: Adsorption-desorption behavior, lbl technique, poly(amidoamine), poly (acrylic acid), weak polyelectrolytes

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Authors: Darlington Ashiegbu, Herman Johannes Potgieter

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The increasing utilization of Zinc Oxide (ZnO) as a better alternative to TiO₂ has been attributed to its wide bandgap (3.37eV), lower production cost, ability to absorb over a larger range of the UV-spectrum and higher efficiency in some cases. ZnO nanoparticles were synthesized via sol-gel process and calcined at 400ᵒC, 500ᵒC, and 650ᵒC. The as-synthesized nanoparticles were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and Brunauer–Emmett–Teller (BET) surface area measurement. Scanning electron micrograph revealed pseudo-spherical and rod-like morphologies and a high rate of agglomeration for the sample calcined at 650ᵒC, Brunnauer Emmett Teller (BET) surface area measurement was highest in the sample calcined at 500ᵒC, energy dispersive X-ray spectroscopy (EDS) results confirmed the purity of the samples as only Zn and O₂ were detected and X-ray diffraction (XRD) results revealed crystalline hexagonal wurtzite structure of the ZnO nanoparticles. All three samples were utilized in the degradation of ethylbenzene, and a UV-Vis spectrophotometer was utilized in monitoring degradation of ethylbenzene. The sample calcined at 500ᵒC had the highest surface area for reaction, lowest agglomeration and the highest photocatalytic activity in the degradation of ethylbenzene. This revealed temperature as a very important factor in improved and higher photocatalytic activity.

Keywords: ethylbenzene, pseudo-spherical, sol-gel, zinc oxide

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Authors: Chien-Hung Liu, Yu-Fen Chen

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This study used triangular laser probe and three-axial direction mobile platform for surface measurement, programmed it and applied it to real-time analytic statistics of different measured data. This structure was used to design a system integration program: using triangular laser probe for scattering or reflection non-contact measurement, transferring the captured signals to the computer through RS-232, and using RS-485 to control the three-axis platform for a wide range of measurement. The data captured by the laser probe are formed into a 3D surface. This study constructed an optical measurement application program in the concept of visual programming language. First, the signals are transmitted to the computer through RS-232/RS-485, and then the signals are stored and recorded in graphic interface timely. This programming concept analyzes various messages, and makes proper presentation graphs and data processing to provide the users with friendly graphic interfaces and data processing state monitoring, and identifies whether the present data are normal in graphic concept. The major functions of the measurement system developed by this study are thickness measurement, SPC, surface smoothness analysis, and analytical calculation of trend line. A result report can be made and printed promptly. This study measured different heights and surfaces successfully, performed on-line data analysis and processing effectively, and developed a man-machine interface for users to operate.

Keywords: laser probe, non-contact measurement, triangulation measurement principle, statistical process control, labVIEW

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Abdulrahman S. Alqahtani

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The revolution of computing and networks could revolutionise terrorism in the same way that it has brought about changes in other aspects of life. The modern technological era has faced countries with a new set of security challenges. There are many states and potential adversaries who have the potential and capacity in cyberspace, which makes them able to carry out cyber-attacks in the future. Some of them are currently conducting surveillance, gathering and analysis of technical information, and mapping of networks and nodes and infrastructure of opponents, which may be exploited in future conflicts. This poster presents the results of the quantitative study (survey) to test the validity of the proposed theoretical framework for the cyber terrorist threats. This theoretical framework will help to in-depth understand these new digital terrorist threats. It may also be a practical guide for managers and technicians in critical infrastructure, to understand and assess the threats they face. It might also be the foundation for building a national strategy to counter cyberterrorism. In the beginning, it provides basic information about the data. To purify the data, reliability and exploratory factor analysis, as well as confirmatory factor analysis (CFA) were performed. Then, Structural Equation Modelling (SEM) was utilised to test the final model of the theory and to assess the overall goodness-of-fit between the proposed model and the collected data set.

Keywords: cyberterrorism, critical infrastructure, , national security, theoretical framework, terrorism

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Mohamed A. Ghandourah, Walied M. Alarif, Nahed O. Bawakid

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Halogenated cyclic enynes and terpenoids are commonly identified among secondary metabolites of the genus Laurencia. Laurencian acetogenins are entirly C15 non-terpenoid haloethers with different carbocyclic nuclei; a specimen of the Red Sea red alga L. obtusa was investigated for its acetogenin content. The dichloromethane extract of the air-dried red algal material was fractionated on aluminum oxide column preparative thin-layer chromatography. Three new rare C12 acetogenin derivatives (1-3) were isolated from the organic extract obtained from Laurencia obtusa, collected from the territorial Red Sea water of Saudi Arabia. The structures of the isolated metabolites were established by means of spectroscopical data analyses. Examining the isolated compounds in activated human peripheral blood mononuclear cells (PBMC) revealed potent Anti-inflammatory activity as evidenced by inhibition of NFκB and release of other inflammatory mediators like TNF-α, IL-1β and IL-6.

Keywords: Red Sea, red algae, fatty acids, spectroscopy, anti-inflammatory

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Authors: H. Zouaoui, D. Abdi, B. Nessark, F. Habelhames, A. Bahloul

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Among the conjugated organic polymers, the polythiophenes constitute a particularly important class of conjugated polymers, which has been extensively studied for the relation between the geometrical structure and the optic and electronic properties, while the polythiophene is an intractable material. They are, furthermore, chemically and thermally stable materials, and are very attractive for exploitation of their physical properties. The polythiophenes are extensively studied due to the possibility of synthesizing low band gap materials by using substituted thiophenes as precursors. Low band gap polymers may convert visible light into electricity and some photoelectrochemical cells based on these materials have been prepared. Polythiophenes (PThs) are good candidates for polymer optoelectronic devices such as polymer solar cells (PSCs) polymer light-emitting diodes (PLEDs) field-effect transistors (FETs) electrochromics and biosensors. In this work, MnO2 has been synthesized by hydrothermal method and analyzed by infrared spectroscopy. The polybithiophene+MnO2 composite films were electrochemically prepared by cyclic voltammetry technic on a conductor glass substrate ITO (indium–tin-oxide). The composite films are characterized by cyclic voltammetry, impedance spectroscopy and photoelectrochemical analyses. The results confirmed the presence of manganese dioxide nanoparticles in the polymer layer. An application has been made by using these deposits as an electrode in a photoelectrochemical cell for measuring photocurrent tests. The composite films show a significant photocurrent intensity 80 μA.cm-2.

Keywords: polybithiophene, MnO2, photoelectrochemical cells, composite films

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Authors: Catur Sigit Hartanto, Anggraeni Anisa Wara Rahmayanti

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Objective: This research is aimed to assess the benefits of using hijab syar'i against female sexual abuse. Method: This research uses a quantitative study. The population is students in Semarang State University who wear hijab syar’i. The sampling technique uses the method of conformity. The retrieving data uses questionnaire on 30 female students as the sample. The data analysis uses descriptive analysis. Result: Using hijab syar’i provides benefits in preventing and minimizing female sexual abuse. Limitation: Respondents were limited to only 30 people.

Keywords: hijab syar’i, female, sexual abuse, student of Semarang State University

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Authors: Vijaykumar Patel, P. Bahadur

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Microstructural evolution of a cationic gemini surfactant 12-4-12 micelles in the presence of bile salts has been investigated using different techniques. A negative value of interaction parameter evaluated from surface tension measurements is a signature of strong synergistic interaction between oppositely charged surfactants. Both the bile salts compete with each other in inducing the micellar transition of 12-4-12 micelles depending on their hydrophobicity. Viscosity measurements disclose that loading of bile salts induces morphological changes in 12-4-12 micelles; sodium deoxycholate is more efficient in altering the aggregation behaviour of 12-4-12 micelles compared to sodium cholate and presents pronounced increase in viscosity and micellar growth which is suppressed at elevated temperatures. A remarkable growth of 12-4-12 micelles in the presence of sodium deoxycholate at low pH has been ascribed to the solubilization of bile acids formed in acidic medium. Small angle neutron scattering experiments provided size and shape of 12-4-12/bile salt mixed micelles are explicated on the basis of hydrophobicity of bile salts. The location of bile salts in micelle was determined from nuclear overhauser effect spectroscopy. The present study characterizes 12-4-12 gemini-bile salt mixed systems which significantly enriches our knowledge, and such a structural transition provides an opportunity to use these bioamphiphiles as delivery vehicles and in some pharmaceutical formulations.

Keywords: gemini surfactants, bile salts, SANS (small angle neutron scattering), NOESY (nuclear overhauser effect spectroscopy)

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Authors: Vladimir Hovhannisyan, Chen Yuan Dong

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Recently nanoparticles (NPs) have been introduced in biomedicine as effective agents for cancer-targeted drug delivery and noninvasive tissue imaging. The most important requirements to these agents are their non-toxicity, biocompatibility and stability. In view of these criteria, the zeolite (ZL) nanoparticles (NPs) may be considered as perfect candidates for biomedical applications. ZLs are crystalline aluminosilicates consisting of oxygen-sharing SiO4 and AlO4 tetrahedral groups united by common vertices in three-dimensional framework and containing pores with diameters from 0.3 to 1.2 nm. Generally, the behavior and physical properties of ZLs are studied by SEM, X-ray spectroscopy, and AFM, whereas optical spectroscopic and microscopic approaches are not effective enough, because of strong scattering in common ZL bulk materials and powders. The light scattering can be reduced by using of ZL NPs. ZL NPs have large external surface area, high dispersibility in both aqueous and organic solutions, high photo- and thermal stability, and exceptional ability to adsorb various molecules and atoms in their nanopores. In this report, using multiphoton microscopy and nonlinear spectroscopy, we investigate nonlinear optical properties of clinoptilolite type of ZL micro- and nanoparticles with average diameters of 2200 nm and 240 nm, correspondingly. Multiphoton imaging is achieved using a laser scanning microscope system (LSM 510 META, Zeiss, Germany) coupled to a femtosecond titanium:sapphire laser (repetition rate- 80 MHz, pulse duration-120 fs, radiation wavelength- 720-820 nm) (Tsunami, Spectra-Physics, CA). Two Zeiss, Plan-Neofluar objectives (air immersion 20×∕NA 0.5 and water immersion 40×∕NA 1.2) are used for imaging. For the detection of the nonlinear response, we use two detection channels with 380-400 nm and 435-700 nm spectral bandwidths. We demonstrate that ZL micro- and nanoparticles can produce nonlinear optical response under the near-infrared femtosecond laser excitation. The interaction of hypericine, chlorin e6 and other dyes with ZL NPs and their photodynamic activity is investigated. Particularly, multiphoton imaging shows that individual ZL NPs particles adsorb Zn-tetraporphyrin molecules, but do not adsorb fluorescein molecules. In addition, nonlinear spectral properties of ZL NPs in native biotissues are studied. Nonlinear microscopy and spectroscopy may open new perspectives in the research and application of ZL NP in biomedicine, and the results may help to introduce novel approaches into the clinical environment.

Keywords: multiphoton microscopy, nanoparticles, nonlinear optics, zeolite

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Matthieu-P. Schapranow

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Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

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Authors: Goksel Ezgi Guzey, Bihrat Onoz

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The scarcity of streamflow gauging stations and the increasing effects of global warming cause designing water management systems to be very difficult. This study is a significant contribution to assessing regional regression models for estimating streamflow. In this study, simulated meteorological data was related to the observed streamflow data from 1971 to 2020 for 33 stream gauging stations of the Euphrates-Tigris Basin. Ordinary least squares regression was used to predict flow for 2020-2100 with the simulated meteorological data. CORDEX- EURO and CORDEX-MENA domains were used with 0.11 and 0.22 grids, respectively, to estimate climate conditions under certain climate scenarios. Twelve meteorological variables simulated by two regional climate models, RCA4 and RegCM4, were used as independent variables in the ordinary least squares regression, where the observed streamflow was the dependent variable. The variability of streamflow was then calculated with 5-6 meteorological variables and watershed characteristics such as area and height prior to the application. Of the regression analysis of 31 stream gauging stations' data, the stations were subjected to a clustering analysis, which grouped the stations in two clusters in terms of their hydrometeorological properties. Two streamflow equations were found for the two clusters of stream gauging stations for every domain and every regional climate model, which increased the efficiency of streamflow estimation by a range of 10-15% for all the models. This study underlines the importance of homogeneity of a region in estimating streamflow not only in terms of the geographical location but also in terms of the meteorological characteristics of that region.

Keywords: hydrology, streamflow estimation, climate change, hydrologic modeling, HBV, hydropower

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Authors: Bisola Olumegbon

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Globally, there were about 229 million cases of malaria in 2022. The sub-Saharan African region accounted for 92% of the reported cases and 94% of deaths. Nigeria had the highest number of malaria cases and deaths, representing 27% of global cases. This scholarly project was a case study guided by the political determinants of health. Triangulation of data using thematic analysis was used to identify the political determinants of malaria in Nigeria and to understand how the concept of interaction contributes to the persistence of the disease. The analysis involved a deductive and inductive approach based on the literature review and the evidence of political determinants gathered in the data. Participants’ in-depth interviews were used to collect data from frontline personnel. Data triangulation was done using thematic analysis, a method used to identify patterns and themes in qualitative data. The study findings revealed a correlation between political determinants of health and malaria management efforts in Nigeria. Some influencing factors included voting challenges, inadequate funding, lack of health priority from the government, noncompliance among patients, and hurdles to effective communication. The findings suggest a need to deliberately increase dedication to the political agenda, provide sufficient financial resources, enhance communication, and active community involvement to address the persistent malaria endemic effectively. Further study is recommended to identify interventions to address identified factors of political determinants of health to reduce malaria in Nigeria. Such intervention must involve collaboration with diverse stakeholders such as policymakers, healthcare professionals, community leaders, and researchers.

Keywords: malaria, malaria management, health worker, stakeholders, political determinant of health

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Authors: M. Muneer, Waseem Raza

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Nano-crystalline materials of pure and metal-doped semiconducting materials have been successfully synthesized using sol gel and hydrothermal methods. The prepared materials were characterized by standard analytical techniques, i.e., XRD, SEM, EDX, UV–vis Spectroscopy and FTIR. The (XRD) analysis showed that the obtained particles are present in partial crystalline nature and exhibit no other impurity phase. The EDX and (SEM) images depicted that metals have been successfully loaded on the surface of the semiconductor. FTIR showed an additional absorption band at 910 cm−1, characteristic of absorption band indicating the incorporation of dopant into the lattice in addition to a broad and strong absorption band in the region of 410–580 cm−1 due to metal–O stretching. The UV–vis absorption spectra of synthesized particles indicate that the doping of metals into the lattice shift the absorption band towards the visible region. Thermal analysis, measurement of the synthesized sample showed that the thermal stability of pure semiconducting material is decreased due to increase in dopant concentration. The photocatalytic activity of the synthesized particles was studied by measuring the change in concentration of three different chromophoric dyes as a function of irradiation time. The photocatalytic activity of doped materials were found to increase with increase in dopant concentration.

Keywords: photocatalysis, metal doped semicondcutors, dye degradation, visible light active materials

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Authors: Bahram Fathi, Mahdi Khodaparast Mashhadi, Masuod Homayounifar

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According to 1404 forecasting documentation, among the most fundamental ways of Iran’s success in competition with other regional countries are innovations, efficiency enhancements and domestic productivity. Therefore, in this study, the energy consumption efficiency of Iran and the neighbor countries has been measured in the period between 2007-2012 considering the simultaneous economic activities, CO2 emission, and consumption of energy through data envelopment analysis of undesirable output. The results of the study indicated that the energy efficiency changes in both Iran and the average neighbor countries has been on a descending trend and Iran’s energy efficiency status is not desirable compared to the other countries in the region.

Keywords: energy efficiency, environmental, undesirable output, data envelopment analysis

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Authors: A. Brunel, A. Kernevez, F. Leclere, J. Trenteseaux

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Today, wine quality is only evaluated by wine experts with their own different personal tastes, even if they may agree on some common features. So producers do not have any unbiased way to independently assess the quality of their products. A tool is here proposed to evaluate wine quality by an objective ranking based upon the variables entering wine elaboration, and analysed through principal component analysis (PCA) method. Actual climatic data are compared by measuring the relative distance between each considered wine, out of which the general ranking is performed.

Keywords: wine, grape, weather conditions, rating, climate, principal component analysis, metric analysis

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Authors: Zhidong Zhang, Zhi-Chao Zhang

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This study examines adolescent psychological and behavioral problems. The Achenbach systems of empirically based assessment (ASEBA) were used as the instrument. The problem framework consists of internal, external and social behavioral problems which are theoretically developed based on about 113 items plus relevant background variables. In this study, the sample consist of 1,975 sixth and seventh grade students in Northeast China. Stratified random sampling method was used to collect the data, meaning that samples were from different school districts, schools, and classes. The researchers looked at both macro and micro effect. Therefore, multilevel analysis techniques were used in the data analysis. The parts of the research results indicated that the background variables such as extracurricular activities were directly related to students’ internal problems.

Keywords: behavioral problems, anxious/depressed problems, internalizing problems, mental health, under-represented groups, empirically-based assessment, hierarchical modeling, ASEBA, multilevel analysis

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Yosra Ben Salah, Wang Li Jun, Salem Bellil

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The village Hammem Zriba is a lost little paradise in the middle of a beautiful landscape that captures the eyes of every visitor. The village alone is a rich expression of different elements such as urban, architecture, technical and vernacular elements, as well as sociological, spiritual and religious behaviors. This heritage is in degrading conditions and is threatened by disappearing soon; thus, actions have to be taken as soon as possible to preserve this heritage, record, analyze and learn from its traditional ways of construction. The strategy of this study is to examine the architecture within the Berber society over a period of time and influenced by a certain location and its relationship to the social and cultural aspects; this research will focus on historical, environmental, social and cultural aspects influencing architecture. The contents of this paper should mainly be constructed by three successive layouts of historical view, a cultural view and an architectural view that will include the urban and domestic scale. This research relies on the integration of both theoretical and empirical investigations. On the theoretical level: A documentary analysis of secondary data is used. Documentary analysis means content analysis of the relevant documents that include books, journals, magazines, archival data, and field survey and observations. On the empirical level: analysis of these traditional ways of planning and house building will be carried out. Through the Analysis, three techniques will be employed to collect primary data. These techniques are; systematic analysis of the architectural drawings, quantitative analysis to the houses statistics, and a direct observation. Through this research, the technical, architectural and urban achievements of the Berber people who represent a part of the general history and architectural history will be emphasized. And on a second point the potential for the sustainability present in this traditional urban planning and housing to be used to formulate guidelines for modern urban and housing development.

Keywords: culture, history, traditional architecture, values

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Authors: Muna Al-Kalbani, Thuwayba Al Barwani, Otherine Neisler, Hussain Alkharusi, David Clayton, Humaira Al-Sulaimani, Mohammad Khan, Hamad Al-Yahmadi

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This study describes the psychometric properties and factor structure of the University Readiness Survey (URS). Survey data were collected from sample of 2652 students from Sultan Qaboos University. Exploratory factor analysis identified ten significant factors underlining the structure. The results of Confirmatory factor analysis showed a good fit to the data where the indices for the revised model were χ2(df = 1669) = 6093.4; CFI = 0.900; GFI =0.926; PCLOSE = 1.00 and RMSAE = 0.030 where each of these indices were above threshold. The overall value of Cronbach’s alpha was 0.899 indicating that the instrument score was reliable. Results imply that the URS is a valid measure describing the college readiness pattern among Sultan Qaboos University students and the Arabic version could be used by university counselors to identify students’ readiness factors. Nevertheless, further validation of the of the USR is recommended.

Keywords: college readiness, confirmatory factor analysis, reliability, validity

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Authors: Olga S. Volodko, Lidiya A. Kompaniets, Ludmila V. Gavrilova

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The method of empirical orthogonal functions is the method of data analysis with a complex spatial-temporal structure. This method allows us to decompose the data into a finite number of modes determined by empirically finding the eigenfunctions of data correlation matrix. The modes have different scales and can be associated with various physical processes. The empirical orthogonal function method has been widely used for the analysis of hydrophysical characteristics, for example, the analysis of sea surface temperatures in the Western North Atlantic, ocean surface currents in the North Carolina, the study of tropical wave disturbances etc. The method used in this study has been applied to the analysis of temperature and velocity measurements in saline Lake Shira (Southern Siberia, Russia). Shira is a shallow lake with the maximum depth of 25 m. The lake Shira can be considered as a closed water site because of it has one small river providing inflow and but it has no outflows. The main factor that causes the motion of fluid is variable wind flows. In summer the lake is strongly stratified by temperature and saline. Long-term measurements of the temperatures and currents were conducted at several points during summer 2014-2015. The temperature has been measured with an accuracy of 0.1 ºC. The data were analyzed using the empirical orthogonal function method in the real version. The first empirical eigenmode accounts for 70-80 % of the energy and can be interpreted as temperature distribution with a thermocline. A thermocline is a thermal layer where the temperature decreases rapidly from the mixed upper layer of the lake to much colder deep water. The higher order modes can be interpreted as oscillations induced by internal waves. The currents measurements were recorded using Acoustic Doppler Current Profilers 600 kHz and 1200 kHz. The data were analyzed using the empirical orthogonal function method in the complex version. The first empirical eigenmode accounts for about 40 % of the energy and corresponds to the Ekman spiral occurring in the case of a stationary homogeneous fluid. Other modes describe the effects associated with the stratification of fluids. The second and next empirical eigenmodes were associated with dynamical modes. These modes were obtained for a simplified model of inhomogeneous three-level fluid at a water site with a flat bottom.

Keywords: Ekman spiral, empirical orthogonal functions, data analysis, stratified fluid, thermocline

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Authors: Tambe Daniel Atem

Abstract:

Background: Clinical Research is an organized research on human beings intended to provide adequate information on the drug use as a therapeutic agent on its safety and efficacy. The significance of the study is to educate the global health and life science graduates in Clinical Research in depth to perform better as it involves testing drugs on human beings. Objectives: to provide an overall understanding of the scientific approach to the evaluation of new and existing medical interventions and to apply ethical and regulatory principles appropriate to any individual research. Methodology: It is based on – Primary data analysis and Secondary data analysis. Primary data analysis: means the collection of data from journals, the internet, and other online sources. Secondary data analysis: a survey was conducted with a questionnaire to interview the Clinical Research Professionals to understand the need of training to perform clinical trials globally. The questionnaire consisted details of the professionals working with the expertise. It also included the areas of clinical research which needed intense training before entering into hardcore clinical research domain. Results: The Clinical Trials market worldwide worth over USD 26 billion and the industry has employed an estimated 2,10,000 people in the US and over 70,000 in the U.K, and they form one-third of the total research and development staff. There are more than 2,50,000 vacant positions globally with salary variations in the regions for a Clinical Research Coordinator. R&D cost on new drug development is estimated at US$ 70-85 billion. The cost of doing clinical trials for a new drug is US$ 200-250 million. Due to an increase trained Clinical Research Professionals India has emerged as a global hub for clinical research. The Global Clinical Trial outsourcing opportunity in India in the pharmaceutical industry increased to more than $2 billion in 2014 due to increased outsourcing from U.S and Europe to India. Conclusion: Assessment of training need is recommended for newer Clinical Research Professionals and trial sites, especially prior the conduct of larger confirmatory clinical trials.

Keywords: clinical research, clinical trials, clinical research professionals

Procedia PDF Downloads 438