Search results for: LCA tools and data

Authors: Mina Samizadeh

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In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems.

Keywords: keyword extraction, n-gram extraction, text summarization, topic clustering, semantic analysis

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Authors: Balamurugan L.

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Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

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Authors: Pilar Rey del Castillo

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This document describes the building of an average indicator of the general sentiments about the future exposed in the newspapers in Spain. The raw data are collected through the scraping of the Digital Periodical and Newspaper Library website. Basic tools of natural language processing are later applied to the collected information to evaluate the sentiment strength of each word in the texts using a polarized dictionary. The last step consists of summarizing these sentiments to produce daily indices. The results are a first insight into the applicability of these techniques to produce periodic sentiment indicators.

Keywords: natural language processing, periodic indicator, sentiment analysis, web scraping

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Authors: Enes Ergün, Sedat Geli̇bolu

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The purpose of this study is to determine the relationship between self-efficacy and subjective well-being among university students. We are aiming to determine whether self efficacy of university students predicts their subjective well-being and if there is a statistically significant difference among boys and girls in this context. Sample of this study consists of 245 university students from Çanakkale, ages ranging between 17 and 24. 72% (n=171) of the participants were girls and 28% (n=69) boys. Three different scales were utilized as data collection tools that Life Satisfaction Scale, General Self-Efficacy Scale, and Positive Negative Experiences Scale. Pearson correlation coefficient, independent sample t test and simple linear regression were used for data analyses. Results showed that well-being is significantly correlated with self-efficacy and self-efficacy is a statistically significant predictor of well-being too. In terms of gender differences, there is no significant difference between self-efficacy scores of boys and girls which shows the same case with well being scores, as well. Fostering university students' academic, social and emotional self-efficacy will increase their well-being which is very important for young adults especially their freshman years.

Keywords: positive psychology, self-efficacy, subjective well being, university students

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Authors: K. Myška, L. Pilařová

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The article deals with possibilities of using cheap mobile devices with the combination of free or open source software tools as an alternative to professional hardware and software equipment. Especially in social work, it is important to find cheap yet functional solution that can compete with complex but expensive solutions for digitizing paper materials. Our research was focused on the analysis of cheap and affordable solutions for digitizing the most frequently used paper materials that are being commonly used by terrain workers in social work. We used comparative analysis as a research method. Social workers need to process data from paper forms quite often. It is still more affordable, time and cost-effective to use paper forms to get feedback in many cases. Collecting data from paper quizzes and questionnaires can be done with the help of professional scanners and software. These technologies are very powerful and have advanced options for digitizing and processing digitized data, but are also very expensive. According to results of our study, the combination of open source software and mobile phone or cheap scanner can be considered as a cost-effective alternative to professional equipment.

Keywords: digitalization, e-learning, mobile devices, questionnaire

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Authors: Nyoman Wijana, Chandra Arha

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Disclosures of Intellectual Capital (IC) is a presentation of corporate information assets that are not recorded in the financial statements. This disclosures is very helpful because it provides inform corporate assets are intangible. In the new economic era, the company's intangible assets will determine company's competitive advantage. This study aimed to examine the effect of IC disclosures on market capitalization growth. Observational studies conducted over ten years in 2002-2011. The purpose of this study was to determine the effect for last ten years. One hundred samples of the company's largest market capitalization in 2011 traced back to last ten years. Data that used, are in 2011, 2008, 2005, and 2002 Method that’s used for acquiring the data is content analysis. The analytical method used is Ordinanary Least Square (OLS) and analysis tools are e views 7 This software using Pooled Least Square estimation parameters are specifically designed for panel data. The results of testing analysis showed inconsistent expression levels affect the growth of the market capitalization in each year of observation. The results of this study are expected to motivate the public company in Indonesia to do more voluntary IC disclosures and encourage regulators to make regulations in a comprehensive manner so that all categories of the IC must be disclosed by the company.

Keywords: IC disclosures, market capitalization growth, analytical method, OLS

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Authors: Parvin Ghafarian, Mohammadreza Mohammadpur Panchah, Mehri Fallahi

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Synoptic patterns from surface up to tropopause are very important for forecasting the weather and atmospheric conditions. There are many tools to prepare and analyze these maps. Reanalysis data and the outputs of numerical weather prediction models, satellite images, meteorological radar, and weather station data are used in world forecasting centers to predict the weather. The forecasting extreme precipitating on the southern coast of the Caspian Sea (CS) is the main issue due to complex topography. Also, there are different types of climate in these areas. In this research, we used two reanalysis data such as ECMWF Reanalysis 5th Generation Description (ERA5) and National Centers for Environmental Prediction /National Center for Atmospheric Research (NCEP/NCAR) for verification of the numerical model. ERA5 is the latest version of ECMWF. The temporal resolution of ERA5 is hourly, and the NCEP/NCAR is every six hours. Some atmospheric parameters such as mean sea level pressure, geopotential height, relative humidity, wind speed and direction, sea surface temperature, etc. were selected and analyzed. Some different type of precipitation (rain and snow) was selected. The results showed that the NCEP/NCAR has more ability to demonstrate the intensity of the atmospheric system. The ERA5 is suitable for extract the value of parameters for specific point. Also, ERA5 is appropriate to analyze the snowfall events over CS (snow cover and snow depth). Sea surface temperature has the main role to generate instability over CS, especially when the cold air pass from the CS. Sea surface temperature of NCEP/NCAR product has low resolution near coast. However, both data were able to detect meteorological synoptic patterns that led to heavy rainfall over CS. However, due to the time lag, they are not suitable for forecast centers. The application of these two data is for research and verification of meteorological models. Finally, ERA5 has a better resolution, respect to NCEP/NCAR reanalysis data, but NCEP/NCAR data is available from 1948 and appropriate for long term research.

Keywords: synoptic patterns, heavy precipitation, reanalysis data, snow

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Authors: Sibel Yoleri

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In this research it is aimed to determine the adjustment of students who attend the first class at elementary school to school in terms of several variables. The study group of the research consists of 286 students (131 female, 155 male) who continue attending the first class of elementary school in 2013-2014 academic year, in the city center of Uşak. In the research, ‘Personal Information Form’ and ‘Walker-Mcconnell Scale of Social Competence and School Adjustment’ have been used as data collection tools. In the analysis of data, the t-test has been applied in the independent groups to determine whether the sampling group students’ scores of school adjustment differ according to the sex variable or not. For the evaluation of data identified as not showing normal distribution, Mann Whitney U test has been applied for paired comparison, Kruskal Wallis H test has been used for multiple comparisons. In the research, all the statistical processes have been evaluated bidirectional and the level of significance has been accepted as .05. According to the results gathered from the research, a meaningful difference could not been identified in the level of students’ adjustment to school in terms of sex variable. At the end of the research, it is identified that the adjustment level of the students who have started school at the age of seven is higher than the ones who have started school at the age of five and the adjustment level of the students who have preschool education before the elementary school is higher than the ones who have not taken.

Keywords: starting school, preschool education, school adjustment, Walker-Mcconnell Scale

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Authors: Reza Taherlouei Safa, Mohammad Aboonajmi

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Agriculture plays an essential role in providing food for the world's population. It also offers numerous benefits to countries, including non-food products, transportation, and environmental balance. Precision agriculture, which employs advanced tools to monitor variability and manage inputs, can help achieve these benefits. The increasing demand for food security puts pressure on decision-makers to ensure sufficient food production worldwide. To support sustainable agriculture, unmanned aerial vehicles (UAVs) can be utilized to manage farms and increase yields. This paper aims to provide an understanding of UAV usage and its applications in agriculture. The objective is to review the various applications of UAVs in agriculture. Based on a comprehensive review of existing research, it was found that different sensors provide varying analyses for agriculture applications. Therefore, the purpose of the project must be determined before using UAV technology for better data quality and analysis. In conclusion, identifying a suitable sensor and UAV is crucial to gather accurate data and precise analysis when using UAVs in agriculture.

Keywords: drone, precision agriculture, farmer income, UAV

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Authors: Abdoulie K. Ceesay

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Within the sequence of the whole genome, it is known that 99.9% of the human genome is similar, whilst our difference lies in just 0.1%. Among these minor dissimilarities, the most common type of genetic variations that occurs in a population is SNP, which arises due to nucleotide substitution in a protein sequence that leads to protein destabilization, alteration in dynamics, and other physio-chemical properties’ distortions. While causing variations, they are equally responsible for our difference in the way we respond to a treatment or a disease, including various cancer types. There are two types of SNPs; synonymous single nucleotide polymorphism (sSNP) and non-synonymous single nucleotide polymorphism (nsSNP). sSNP occur in the gene coding region without causing a change in the encoded amino acid, while nsSNP is deleterious due to its replacement of a nucleotide residue in the gene sequence that results in a change in the encoded amino acid. Predicting the effects of cancer related nsSNPs on protein stability, function, and dynamics is important due to the significance of phenotype-genotype association of cancer. In this thesis, Data of 5 oncogenes (ONGs) (AKT1, ALK, ERBB2, KRAS, BRAF) and 5 tumor suppressor genes (TSGs) (ESR1, CASP8, TET2, PALB2, PTEN) were retrieved from ClinVar. Five common in silico tools; Polyphen, Provean, Mutation Assessor, Suspect, and FATHMM, were used to predict and categorize nsSNPs as deleterious, benign, or neutral. To understand the impact of each variation on the phenotype, Maestro, PremPS, Cupsat, and mCSM-NA in silico structural prediction tools were used. This study comprises of in-depth analysis of 10 cancer gene variants downloaded from Clinvar. Various analysis of the genes was conducted to derive a meaningful conclusion from the data. Research done indicated that pathogenic variants are more common among ONGs. Our research also shows that pathogenic and destabilizing variants are more common among ONGs than TSGs. Moreover, our data indicated that ALK(409) and BRAF(86) has higher benign count among ONGs; whilst among TSGs, PALB2(1308) and PTEN(318) genes have higher benign counts. Looking at the individual cancer genes predisposition or frequencies of causing cancer according to our research data, KRAS(76%), BRAF(55%), and ERBB2(36%) among ONGs; and PTEN(29%) and ESR1(17%) among TSGs have higher tendencies of causing cancer. Obtained results can shed light to the future research in order to pave new frontiers in cancer therapies.

Keywords: tumor suppressor genes (TSGs), oncogenes (ONGs), non synonymous single nucleotide polymorphism (nsSNP), single nucleotide polymorphism (SNP)

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Authors: Muhammad Shahzad Iqbal, Zobia Sarwar, Salah-ud-Din

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Tomato spotted wilt virus (TSWV) belongs to the genus Tospoviruses (family Bunyaviridae). It is one of the most devastating pathogens of tomato (Solanum Lycopersicum) and heavily damages the crop yield each year around the globe. In this study, we retrieved 329 mature miRNA sequences from two microRNA databases (miRBase and miRSoldb) and checked the putative target sites in the downloaded-genome sequence of TSWV. A consensus of three miRNA target prediction tools (RNA22, miRanda and psRNATarget) was used to screen the false-positive microRNAs targeting sites in the TSWV genome. These tools calculated different target sites by calculating minimum free energy (mfe), site-complementarity, minimum folding energy and other microRNA-mRNA binding factors. R language was used to plot the predicted target-site data. All the genes having possible target sites for different miRNAs were screened by building a consensus table. Out of these 329 mature miRNAs predicted by three algorithms, only eight miRNAs met all the criteria/threshold specifications. MC-Fold and MC-Sym were used to predict three-dimensional structures of miRNAs and further analyzed in USCF chimera to visualize the structural and conformational changes before and after microRNA-mRNA interactions. The results of the current study show that the predicted eight miRNAs could further be evaluated by in vitro experiments to develop TSWV-resistant transgenic tomato plants in the future.

Keywords: tomato spotted wild virus (TSWV), Solanum lycopersicum, plant virus, miRNAs, microRNA target prediction, mRNA

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Authors: Al. I. Diwedar, Moheb Iskander, Mohamed Yossef, Ahmed ElKut, Noha Fouad, Radwa Fathy, Mustafa M. Almaghraby, Amira Samir, Ahmed Romya, Nourhan Hassan, Asmaa Abo Zed, Bas Reijmerink, Julien Groenenboom

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Coastal problems are stressing the coastal environment due to its complexity. The dynamic interaction between the sea and the land results in serious problems that threaten coastal areas worldwide, in addition to human interventions and activities. This makes the coastal environment highly vulnerable to natural processes like flooding, erosion, and the impact of human activities as pollution. Protecting and preserving this vulnerable coastal zone with its valuable ecosystems calls for addressing the coastal problems. This, in the end, will support the sustainability of the coastal communities and maintain the current and future generations. Consequently applying suitable management strategies and sustainable development that consider the unique characteristics of the coastal system is a must. The coastal management philosophy aims to solve the conflicts of interest between human development activities and this dynamic nature. Modeling emerges as a successful tool that provides support to decision-makers, engineers, and researchers for better management practices. Modeling tools proved that it is accurate and reliable in prediction. With its capability to integrate data from various sources such as bathymetric surveys, satellite images, and meteorological data, it offers the possibility for engineers and scientists to understand this complex dynamic system and get in-depth into the interaction between both the natural and human-induced factors. This enables decision-makers to make informed choices and develop effective strategies for sustainable development and risk mitigation of the coastal zone. The application of modeling tools supports the evaluation of various scenarios by affording the possibility to simulate and forecast different coastal processes from the hydrodynamic and wave actions and the resulting flooding and erosion. The state-of-the-art application of modeling tools in coastal management allows for better understanding and predicting coastal processes, optimizing infrastructure planning and design, supporting ecosystem-based approaches, assessing climate change impacts, managing hazards, and finally facilitating stakeholder engagement. This paper emphasizes the role of hydraulic models in enhancing the management of coastal problems by discussing the diverse applications of modeling in coastal management. It highlights the modelling role in understanding complex coastal processes, and predicting outcomes. The importance of informing decision-makers with modeling results which gives technical and scientific support to achieve sustainable coastal development and protection.

Keywords: coastal problems, coastal management, hydraulic model, numerical model, physical model

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Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Youn-Hwan Kim, Jae-Won Moon, Hae-Joong Kim

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This paper introduces the results of the study on the development of accelerated life test methods for the motor used in machine tools. In recent years, as well as efficiency for motors, there is a growing need for research on life expectancy of motors. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. This paper describes the equipment development procedure for the highly accelerated life test (HALT) of the 12kW three-phase squirrel-cage induction motors (SCIMs). After the test, the lifetime analysis was carried out, and it is compared with the life expectancy by finite element method (FEM) and bearing theory.

Keywords: acceleration coefficient, bearing, HALT, life expectancy, motor

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Authors: Caroline Labib

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This paper reports on a qualitative study exploring the approaches teachers used to integrate computers or smart tablets into their program planning. Their aim was to integrate ICT into children’s play, thereby supporting children’s learning and development. Data was collected in preschool settings in Melbourne in 2016. Interviews with teachers, observations of teacher interactions with children and copies of teachers’ planning and observation documents informed the study. The paper looks closely at findings from two early childhood settings and focuses on exploring the differing approaches two EC teachers have adopted when integrating iPad or computers into their settings. Data analysis revealed three key approaches which have been labelled: free digital play, guided digital play and teacher-led digital use. Importantly, teacher decisions were influenced by the interplay between the opportunities that the ICT tools offered, the teachers’ prior knowledge and experience about ICT and children’s learning needs and contexts. This paper is a snapshot of two early childhood settings, and further research will encompass data from six more early childhood settings in Victoria with the aim of exploring a wide range of motivating factors for early childhood teachers trying to integrate ICT into their programs.

Keywords: early childhood education (ECE), digital play, information and communication technologies (ICT), play, and teachers' interaction approaches

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Authors: Ronita Singh, Yaman Pattanaik, Soham Lalwala

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In today’s highly competitive business environment, every organization is striving towards lean manufacturing systems to achieve lower Production Lead Times, lower costs, less inventory and overall improvement in supply chains efficiency. Based on the similar idea, this paper presents the practical application of Value Stream Mapping (VSM) tool and ECRS (Eliminate, Combine, Reduce, and Simplify) technique in the receipt section of the material management center of an oil refinery. A value stream is an assortment of all actions (value added as well as non-value added) that are required to bring a product through the essential flows, starting with raw material and ending with the customer. For drawing current state value stream mapping, all relevant data of the receipt cycle has been collected and analyzed. Then analysis of current state map has been done for determining the type and quantum of waste at every stage which helped in ascertaining as to how far the warehouse is from the concept of lean manufacturing. From the results achieved by current VSM, it was observed that the two processes- Preparation of GRN (Goods Receipt Number) and Preparation of UD (Usage Decision) are both bottle neck operations and have higher cycle time. This root cause analysis of various types of waste helped in designing a strategy for step-wise implementation of lean tools. The future state thus created a lean flow of materials at the warehouse center, reducing the lead time of the receipt cycle from 11 days to 7 days and increasing overall efficiency by 27.27%.

Keywords: current VSM, ECRS, future VSM, receipt cycle, supply chain, VSM

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: business intelligence, business intelligence capability, decision making, decision quality

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Authors: Pouria Ghidi

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Creating convergence in trade is very important, but in practice, this seems out of reach due to the conflict of interests and views of countries. The most important mission of UNCITRAL is to standardize and modernize international trade law through legislative and non-legislative tools on various issues of international trade law between governments. Unfortunately, the performance of governments has shown that, except in some cases, unity is not welcomed. Therefore, although unification is envisaged as a goal, it is more practical to create convergence between countries. In a variety of ways, UNCITRAL seeks to create a kind of common ground between influential actors in the international trade law system that approaches a degree of convergence of views. Accordingly, this realization seeks to find these mechanisms and their impact on creating convergence among actors in the field of international trade. In other words, this study seeks to address the question of what tools the UN Commission on International Trade Law uses to develop the convergence of rules and regulations in this area, which groups it targets, and at what levels they work.

Keywords: UNCITRAL, harmonization, unification in interpretation, international trade law, model laws

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Authors: Paulo Mendonca, Nzar Faiq Naqeshbandi

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The objective of this study is to propose an interactive design environment that outlines the underlying computational framework to reach self-supporting overhangs. The research demonstrates the digital printability of overhangs taking into consideration factors related to the geometry design, the material used, the applied support, and the printing set-up of slicing and the extruder inclination. Parametric design tools can contribute to the design phase, form-finding, and stability optimization of self-supporting structures while printing in order to hold the components in place until they are sufficiently advanced to support themselves. The challenge is to ensure the stability of the printed parts in the critical inclinations during the whole fabrication process. Facilitating the identification of parameterization will allow to predict and optimize the process. Later, in the light of the previous findings, some guidelines of simulations and physical tests are given to be conducted for estimating the structural and functional performance.

Keywords: additive manufacturing, overhangs, self-support overhangs, printability, parametric tools

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Ladda Thiamwong, Renata Komalasari

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Falls are the leading cause of disability and hospitalization in low-income older adults. Fear of falling is present in 20% to 85 % of older adults and has been identified as an independent risk factor of fall risk, activity restriction, and loss of independence. About 12% of American older adults have subjective cognitive decline. Cognitive impairment is also an established factor of fall risk. However, it is unclear whether measures of fear of falling and subjective cognitive decline have the greatest association with fall risk in low-income older adults. The aim of this study was to evaluate the association between fear of falling, subjective cognitive decline-functional performance (SCD-FP), and fall risk using simple screening tools. In this cross-section study, we collected data from community-dwelling older adults 60 years or older in low-income settings in Central Florida, and 86 participants were included in the data analysis. Fear of falling was assessed by the Short Fall Efficacy Scale- International (Short FES-I) with seven items. Subjective cognitive decline-functional performance (SCD-FP) was assessed by a self-reported experience of worsening or more frequent confusion or memory loss in the past 12 months and its functional implications. Fall risk was evaluated by the Centers for Disease Control and Prevention (CDC)'s Stay Independent checklist with 12 items. The majority of participants were female, and more than half of the participants were African American. More than half of the participants had a higher school degree or higher, and less than 20% had no financial problems. Less than 30% of the participants perceived their general health as very good- excellent. More than half of the participants lived alone, and less than 15% lived with a partner or spouse. About 60% of the participants had hypertension, 40% had diabetes, 16% had cancer, and 50% had arthritis. About 30% of the participants had difficulty walking up ten steps without resting, more than 40% felt unsteady when walking, and 30% had been advised to use a cane or walker to get around safely. Regression analysis showed that fall risk was associated with fear of falling ( = .524, p <.001) and subjective cognitive decline-functional performance ( = .465, p =.027). The structure coefficient showed that fear of falling (rs2 = .922) was a stronger predictor of fall risk than subjective cognitive decline-functional performance (rs2= .200). Fear of falling and subjective cognitive decline-functional performance are growing public health issues, and addressing those issues is a public priority. Proactive screening for fear of falling and subjective cognitive decline-functional performance is critical in fall prevention. A combination of all three self-reported tools (Short FES-I, SCD-FP, and CDC's Stay Independent checklist) takes less than 5 minutes to complete. Primary care providers or public health professionals should consider including these tools to screen fear of falling and subjective cognitive decline-functional performance as part of fall risk assessment, especially in low-income settings. Thus, encouraging older adults and healthcare professionals to discuss fear of falling, subjective cognitive decline, and fall risk during routine medical office visits.

Keywords: falls, fall risk, fear of falling, cognition, subjective cognitive decline, low-income, older adults, community, screening, nursing, primary care

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Authors: Muhammad A. Ishaq, S. U. R. Aliyu

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This paper on an exposition of Islamic fiscal policy attempts to discuss the basic principles of Islamic fiscal policy in an Islamic economy. The paper presents a number of definitions of the subject matter, its nature and its tools of application. Government spending, taxation and public borrowings were identified as the tools of the policy. The paper identifies zakat both as a veritable source of revenue and a major instrument of economic stabilization. Furthermore, the paper presents an algebraic 2-sector and 3-sector models from the basic Keynesian model. The paper posits that in view of uniqueness of its instruments, absence of interest rate in the economy and the policy’s derive towards socioeconomic justice and redistribution, Islamic fiscal policy is capable of stabilizing Islamic economy and ushering it into the path of long term economic growth and prosperity.

Keywords: automatic built-in-stabilizers, government spending, Islamic fiscal policy, taxation, zakat

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Authors: Jin Fan, M. Ala Saadeghvaziri

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Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: bridge, construction, drones, infrastructure, information

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Authors: Marwa Mamdouh Shaban, Nagat Saied Habib, Shireen Ezz El-Din Taha, Eman Mahmoud Seif El-Naser

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Background: Dental nurses and dentists were constantly exposed to a number of specific work related health risk factors which develop and intensify with years. Awareness regarding these work-related health risk factors and implementation of preventive health care measures could provide a safe work environment for all dental nurses and dentists. Aim of the study: to assess the work-related health risk factors among dental nurses and dentists and preventive health care measures applied among dental nurses and dentists. Research design: A descriptive design was utilized. Sample: Convenience sample of 50 dental nurses and 150 dentists were included in the current study. Setting: This study was conducted at the dental clinics at faculty of oral and dental medicine, Al-Kasr Al Ainy Hospital. Tools of data collection: Three tools were developed, tested for clarity, and feasibility: a-Socio-demographic data sheet, b-Work-related health risk factors questionnaire, and c-structured observational checklist. Results: The most common work risk factors prevailing among dental nurses were emotional exhaustion (82%), low back pain (76%) and latex allergy (62%) and the most common work risk factors prevailing among dentists were percutaneous exposure incident (100%), emotional exhaustion (100%) and low back pain (93.3%). Also, statistically significant negative correlation (r=-0.274, at p = 0.045) between the incidence of chemical health risk factors and application of chemical preventive measures among dental nurses. A statistically significant negative correlation (r=-0.177, at p = 0.030) between the incidences of mechanical health risk factors among dentists and application of mechanical preventive measures. Conclusion: The studied dental nurses and dentists exposed to many work related health risk factors as latex allergy, percutaneous exposure incidents, low back pain and emotional exhaustion related to inappropriate application of preventive health care measures. Recommendation: Raise awareness of dental nurses and dentists about work-related health risk factors, design and implement health education program for preventive health care measures.

Keywords: work-related risk factors, preventive measures, nurses, dentists

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Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Suleiman Obeidat, Nabeel Mandahawi

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In this work, the packaging process for a move is improved. The customers of this move need their household stuff to be moved from their current house to the new one with minimum damage, in an organized manner, on time and with the minimum cost. Our goal was to improve the process between 10% and 20% time efficiency, 90% reduction in damaged parts and an acceptable improvement in the cost of the total move process. The expected ROI was 833%. Many improvement techniques have been used in terms of the way the boxes are prepared, their preparation cost, packing the goods, labeling them and moving them to a place for moving out. DMAIC technique is used in this work: SIPOC diagram, value stream map of “As Is” process, Root Cause Analysis, Maps of “Future State” and “Ideal State” and an Improvement Plan. A value of ROI=624% is obtained which is lower than the expected value of 833%. The work explains the techniques of improvement and the deficiencies in the old process.

Keywords: packaging, lean tools, six sigma, DMAIC methodology, SIPOC

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Authors: Sofia Sehli, Zainab El Ouafi, Casey Eddington, Soumaya Jbara, Kasambula Arthur Shem, Islam El Jaddaoui, Ayorinde Afolayan, Olaitan I. Awe, Allissa Dillman, Hassan Ghazal

Abstract:

The ability to promptly sequence whole genomes at a relatively low cost has revolutionized the way we study the microbiome. Microbiologists are no longer limited to studying what can be grown in a laboratory and instead are given the opportunity to rapidly identify the makeup of microbial communities in a wide variety of environments. Analyzing whole genome sequencing (WGS) data is a complex process that involves multiple moving parts and might be rather unintuitive for scientists that don’t typically work with this type of data. Thus, to help lower the barrier for less-computationally inclined individuals, TAXAPRO was developed at the first Omics Codeathon held virtually by the African Society for Bioinformatics and Computational Biology (ASBCB) in June 2021. TAXAPRO is an advanced metagenomics pipeline that accurately assembles organelle genomes from whole-genome sequencing data. TAXAPRO seamlessly combines WGS analysis tools to create a pipeline that automatically processes raw WGS data and presents organism abundance information in both a tabular and graphical format. TAXAPRO was evaluated using COVID-19 patient gut microbiome data. Analysis performed by TAXAPRO demonstrated a high abundance of Clostridia and Bacteroidia genera and a low abundance of Proteobacteria genera relative to others in the gut microbiome of patients hospitalized with COVID-19, consistent with the original findings derived using a different analysis methodology. This provides crucial evidence that the TAXAPRO workflow dispenses reliable organism abundance information overnight without the hassle of performing the analysis manually.

Keywords: metagenomics, shotgun metagenomic sequence analysis, COVID-19, pipeline, bioinformatics

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Authors: Naser Ahmadi Sani, Karim Solaimani, Lida Razaghnia, Jalal Zandi

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In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.

Keywords: Haraz basin, change detection, land-use, satellite data

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Authors: Chana Pavneet

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Background: In more recent years in dentistry, a greater emphasis has been placed on competency-based education (CBE) programmes. Undergraduate and postgraduate curriculums have been reformed to reflect these changes, and adopting a CBE approach has shown to be beneficial to trainees and places an emphasis on continuous lifelong learning. The literature is vast; however, very little work has been done specifically to the assessment of competence in dentistry and even less so in oral surgery. The majority of the literature tends to opinion pieces. Some small-scale studies have been undertaken in this area researching assessment tools which can be used to assess competence in oral surgery. However, there is a lack of general consensus on the preferable assessment methods. The aim of this review is to identify the assessment methods available and their usefulness. Methods: Electronic databases (Medline, Embase, and the Cochrane Database of systematic reviews) were searched. PRISMA guidelines were followed to identify relevant papers. Abstracts of studies were reviewed, and if they met the inclusion criteria, they were included in the review. Papers were reviewed against the critical appraisal skills programme (CASP) checklist and medical education research quality instrument (MERQSI) to assess their quality and identify any bias in a systematic manner. The validity and reliability of each assessment method or tool were assessed. Results: A number of assessment methods were identified, including self-assessment, peer assessment, and direct observation of skills by someone senior. Senior assessment tended to be the preferred method, followed by self-assessment and, finally, peer assessment. The level of training was shown to affect the preferred assessment method, with one study finding peer assessment more useful in postgraduate trainees as opposed to undergraduate trainees. Numerous tools for assessment were identified, including a checklist scale and a global rating scale. Both had their strengths and weaknesses, but the evidence was more favourable for global rating scales in terms of reliability, applicability to more clinical situations, and easier to use for examiners. Studies also looked into trainees’ opinions on assessment tools. Logbooks were not found to be significant in measuring the competence of trainees. Conclusion: There is limited literature exploring the methods and tools which assess the competence of oral surgery trainees. Current evidence shows that the most favourable assessment method and tool may differ depending on the stage of training. More research is required in this area to streamline assessment methods and tools.

Keywords: competence, oral surgery, assessment, trainees, education

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