Search results for: hierarchical geospatial enquiry

Authors: Debasis Sarkar

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Development of a Smart City would depend upon the development of its infrastructure in a smart way. Primarily based on the ideology of the fourth industrial revolution a Smart City project should have Smart governance, smart health care, smart building, smart transportation, smart mobility, smart energy, smart technology and smart citizen. Considering the Indian scenario of current state of cities in India, it has become very essential to decide the specific parameters which would govern the development of a Smart City project. It has been observed that there are significant parameters beyond Information and Communication Technology (ICT), which govern the development of a Smart City project. This paper is an attempt to identify the Critical Success Factors (CSF) which are significantly responsible for the development of a Smart City project in Western India. Responses to questionnaire survey were analyzed on basis of Likert scale. They were further critically evaluated with help of Factor Comparison Method (FCM) and Analytical Hierarchy Process (AHP). The project authorities need to incorporate Building Information Modeling (BIM) to make the smart city project more collaborative. To make the project more sustainable, use of flyash in the concrete used, reduced usage of cement and steel, use of alternate fuels like biodiesel is recommended.

Keywords: analytical hierarchical process, building information modeling, critical success factors, factor comparison method

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Authors: Fayyaz Rasool, Shakeela Parveen

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The studies on genetic diversity of Labeo rohita by using molecular markers were carried out to investigate the genetic structure by RAPAD marker and the levels of polymorphism and similarity amongst the different groups of five populations of wild and farmed types. The samples were collected from different five locations as representatives of wild and hatchery raised populations. RAPAD data for Jaccard’s coefficient by following the un-weighted Pair Group Method with Arithmetic Mean (UPGMA) for Hierarchical Clustering of the similar groups on the basis of similarity amongst the genotypes and the dendrogram generated divided the randomly selected individuals of the five populations into three classes/clusters. The variance decomposition for the optimal classification values remained as 52.11% for within class variation, while 47.89% for the between class differences. The Principal Component Analysis (PCA) for grouping of the different genotypes from the different environmental conditions was done by Spearman Varimax rotation method for bi-plot generation of the co-occurrence of the same genotypes with similar genetic properties and specificity of different primers indicated clearly that the increase in the number of factors or components was correlated with the decrease in eigenvalues. The Kaiser Criterion based upon the eigenvalues greater than one, first two main factors accounted for 58.177% of cumulative variability.

Keywords: variation, clustering, PCA, wild, hatchery, RAPAD, Labeo rohita

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Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Wei Wang, Yilun Xu

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With the development of knowledge economy era, deepening the mechanism of cooperation and adhering to sharing and win-win cooperation has become new direction of urban development nowadays. In recent years, innovative collaborations between cities are becoming more and more frequent, whose influence on urban network space has aroused many scholars' attention. Taking 46 cities in China as the research object, the paper builds the connectivity of innovative network between cities and the linkages of urban external innovation using patent cooperation data among cities, and explores urban network space in China by the application of GIS, which is a beneficial exploration to the study of social network space in China in the era of information network. The result shows that the urban innovative network space and geographical entity space exist differences, and the linkages of external innovation are not entirely related to the city innovative capacity and the level of economy development. However, urban innovative network space and geographical entity space are similar in hierarchical clustering. They have both formed Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta three metropolitan areas and Beijing-Shenzhen-Shanghai-Hangzhou four core cities, which lead the development of innovative network space in China.

Keywords: innovative collaboration, urban network space, the connectivity of innovative network, the linkages of external innovation

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Authors: Javier Sandoval Bustamante, Pardis Sheikhzadeh, Vijayanarasimha Hindupur Pakka

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In the pursuit of achieving net-zero carbon emissions, the integration of low carbon technologies into electricity distribution networks is paramount. This paper delves into the critical assessment of how the integration of low carbon technologies, such as heat pumps, electric vehicle chargers, and photovoltaic systems, impacts the infrastructure and operation of electricity distribution networks. The study employs rigorous methodologies, including power flow analysis and headroom analysis, to evaluate the feasibility and implications of integrating these technologies into existing distribution systems. Furthermore, the research utilizes Local Area Energy Planning (LAEP) methodologies to guide local authorities and distribution network operators in formulating effective plans to meet regional and national decarbonization objectives. Geospatial analysis techniques, coupled with building physics and electric energy systems modeling, are employed to develop geographic datasets aimed at informing the deployment of low carbon technologies at the local level. Drawing upon insights from the Local Energy Net Zero Accelerator (LENZA) project, a comprehensive case study illustrates the practical application of these methodologies in assessing the rollout potential of LCTs. The findings not only shed light on the technical feasibility of integrating low carbon technologies but also provide valuable insights into the broader transition towards a sustainable and electrified energy future. This paper contributes to the advancement of knowledge in power electrical engineering by providing empirical evidence and methodologies to support the integration of low carbon technologies into electricity distribution networks. The insights gained are instrumental for policymakers, utility companies, and stakeholders involved in navigating the complex challenges of energy transition and achieving long-term sustainability goals.

Keywords: energy planning, energy systems, digital twins, power flow analysis, headroom analysis

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Authors: Jefferson Hernandez, Juan Padilla

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Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach.

Keywords: price elasticity, volume, correlation structures, Bayesian models

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Authors: Edward Fosu

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Green innovation has emerged as a significant environmental concern across the world. Green innovation refers to the utilization of technological developments that facilitate energy savings and waste material recycling. The stakeholder theory and resourced-based theory were used to examine how stakeholders' expectations affect corporate green innovation activities and how corporate innovation initiatives affect the corporate image and social performance. This study used structural equation modelling (SEM) and hierarchical regression to test the effects of environmental corporate social responsibility on social performance through mediators: green innovation and corporate image. A quantitative design was employed using data from Chinese companies in Ghana for this study. The study assessed. The results revealed that environmental practices promote corporate social performance (β = 0.070, t = 1.974, p = 0.049), positively affect green product innovation (β = 0.251, t = 7.478, p < 0.001), and has direct effect on green process innovation (β = 0.174, t = 6.192, p < 0.001). Green product innovation and green process innovation significantly promote corporate image respectively (β = 0.089, t = 2.581, p = 0.010), (β = 0.089, t = 2.367, p = 0.018). Corporate image has significant direct effects on corporate social performance (β = 0.146, t = 4.256, p < 0.001). Corporate environmental practices have an impact on the development of green products and processes which promote companies’ social performance. Additionally, evidence supports that corporate image influences companies’ social performance.

Keywords: environmental CSR, corporate image, green innovation, coprorate social performance

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Authors: Shashi Bhushan, Sucharita Sen

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In India, forest and people are inextricably linked since millions of people live adjacent to or within protected areas and harvest forest products. Indian forest has their own legacy to sustain by its own climatic nature with several social, economic and cultural activities. People surrounding forest areas are not only dependent on this resource for their livelihoods but also for the other source, like religious ceremonies, social customs and herbal medicines, which are determined by the forest like agricultural land, groundwater level, and soil fertility. The assumption that fuelwood and fodder extraction, which is the part of local livelihood leads to deforestation, has so far been the dominant mainstream views in deforestation discourses. Given the occupational division across social groups in Kaimur reserve forest, the differential nature of dependence of forest resources is important to understand. This paper attempts to assess the nature of dependence and impact of forest degradation on rural households across various social groups. Also, an additional element that is added to the enquiry is the way degradation of forests leading to scarcity of forest-based resources impacts the patterns of dependence across various social groups. Change in forest area calculated through land use land cover analysis using remote sensing technique and examination of different economic activities carried out by the households that are forest-based was collected by primary survey in Kaimur reserve forest of state of Bihar in India. The general finding indicates that the Scheduled Tribe and Scheduled Caste communities, the most socially and economically deprived sections of the rural society are involved in a significant way in collection of fuelwood, fodder, and fruits, both for self-consumption and sale in the market while other groups of society uses fuelwood, fruit, and fodder for self-use only. Depending on the local forest resources for fuelwood consumption was the primary need for all social groups due to easy accessibility and lack of alternative energy source. In last four decades, degradation of forest made a direct impact on rural community mediated through the socio-economic structure, resulting in a shift from forest-based occupations to cultivation and manual labour in agricultural and non-agricultural activities. Thus there is a need to review the policies with respect to the ‘community forest management’ since this study clearly throws up the fact that engagement with and dependence on forest resources is socially differentiated. Thus tying the degree of dependence and forest management becomes extremely important from the view of ‘sustainable’ forest resource management. The statization of forest resources also has to keep in view the intrinsic way in which the forest-dependent population interacts with the forest.

Keywords: forest degradation, livelihood, social groups, tribal community

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Authors: Shuwen Wu, Zhi LI

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Atomically dispersed Fe‒N₄ catalysts are proven as promising alternatives to commercial Pt/C for the oxygen reduction reaction. Most reported Fe‒N₄ catalysts suffer from inferior O‒O bond-breaking capability due to superoxo-like O₂ adsorption, though the isolated dual-atomic metal sites strategy is extensively adopted. Atomic Fe clusters hold greater promise for promoting O‒O bond cleavage by forming peroxo-like O₂ adsorption. However, the excessively strong binding strength between Fe clusters and oxygenated intermediates sacrifices the activity. Here, we first report a Fex/Cu‒N@CF catalyst with atomic Fe clusters functionalized by adjacent single Cu‒N₄ sites anchoring on a porous carbon nanofiber membrane. The theoretical calculation indicates that the single Cu‒N₄ sites can modulate the electronic configuration of Fe clusters to reduce O₂* protonation reaction free energy, which ultimately enhances the electrocatalytic performance. Particularly, the Cu‒N₄ sites can increase the overlaps between the d orbitals of Fe and p orbitals of O to accelerate O‒O cleavage in OOH*. As a result, this unique atomic catalyst exhibits a half potential (E1/2) of 0.944 V in an alkaline medium exceeding that of commercial Pt/C, whereas acidic performance E1/2 = 0.815 V is comparable to Pt/C. This work shows the great potential of single atoms for improvements in atomic cluster catalysts.

Keywords: Hierarchical porous fibers, atomic Fe clusters, Cu single atoms, oxygen reduction reaction; O-O bond cleavage

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Authors: Sarish Rehman, Kishwar Khan, Yanglong Hou

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Among the existed myriad energy-storage technologies, lithium–sulfur batteries (LSBs) show the appealing potential for the ubiquitous growth of next-generation electrical energy storage application, owing to their unparalleled theoretical energy density of 2600 Wh/kg that is over five times larger than that of conventional lithium-ion batteries (LIBs). Despite its significant advances, its large scale implementations are plagued by multitude issues: particularly the intrinsic insulating nature of the sulfur (10-30 S/cm), mechanical degradation of the cathode due to large volume changes of sulfur up to 80 % during cycling and loss of active material (producing polysulfide shuttle effect). We design a unique structure, namely silicon/silica (Si/SiO2) crosslink with hierarchical porous carbon spheres (Si/SiO2@C), and use it as a new and efficient sulfur host to prepare Si/SiO2@C-S hybrid spheres to solve the hurdle of the polysulfides dissolution. As results of intriguing structural advantages developed hybrids spheres, it acts as efficient polysulfides reservoir for enhancing lithium sulfur battery (LSB) in the terms of capacity, rate ability and cycling stability via combined chemical and physical effects.

Keywords: high specific surface area, high power density, high content of sulfur, lithium sulfur battery

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Authors: Vithul Kalki

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The mainstream political economy failed to explain the nature and causes of systemic failures and thus to compare and comprehend how contemporary capitalist systems work. An alternative research framework of Critical Macro-Finance (CMF) is an attempt to collaborate political theory with post-Keynesian economics with an objective to find answers to unresolved questions that emerged since the international financial crisis and repeated failures of capital systems. This unorthodox approach brings out four main propositions, namely : (a) that the adoption of American financial practices has anchored financial globalization in market-based finance; (b) that global finance is a set of interconnected, hierarchical balance sheets, increasingly subject to time-critical liquidity; (c) that credit creation in market-based finance involves new forms of money; and (d) that market-based finance structurally requires a de-risking state capable both of protecting systemic liabilities and creating new investment opportunities. The ongoing discussion of CMF literature is yet to be tested or even fully framed. This qualitative paper will critically examine the CMF framework and will engage in discussions aiming to connect the CMF with theories of capitalism in a wider context to bring a holistic approach for analyzing contemporary financial capitalism.

Keywords: critical macro-finance, capitalism, financial system, comparative political economy

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Authors: Bohsiu Wu

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This study compares the differences in social dynamics between rural and urban areas in California to explain homicides involving family members. It is hypothesized that rural homicides are better explained by social isolation and lack of intervention resources, whereas urban homicides are attributed to social disadvantage factors. Several critical social dynamics including social isolation, social disadvantages, acculturation, and intervention resources were entered in a hierarchical linear model (HLM) to examine whether county-level factors affect how each specific dynamic performs at the ZIP code level, a proxy measure for communities. Homicide data are from the Supplementary Homicide Report for all 58 counties in California from 1997 to 1999. Predictors at both the county and ZIP code levels are derived from the 2000 US census. Preliminary results from a HLM analysis show that social isolation is a significant but moderate predictor to explain rural family homicide and various social disadvantage factors are significant factors accounting for urban family homicide. Acculturation has little impact. Rurality and urbanity appear to interact with various social dynamics in explaining family homicide. The implications for prevention at both the county and community level as well as directions for future study on the differences between rural and urban locales are explored in the paper.

Keywords: communities, family, HLM, homicide, rural, urban

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Karthik Mittal

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This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics.

Keywords: breast cancer, computational biology, unsupervised machine learning, k-means, PCA

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Authors: Yong Wee Foo, Lai Meng Tang

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Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper proposes a novel approach to empower students to actively explore the inner workings of deep neural networks by combining the student-centered learning approach of flipped classroom models with the investigative power of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Combining the two factors is vital in fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: deep learning, explainable AI, AI visualization, representation learning

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Authors: Zahid Khalil, Saad Ul Haque, Asif Khan

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Decision making about identifying suitable sites for any project by considering different parameters is difficult. Using GIS and Multi-Criteria Analysis (MCA) can make it easy for those projects. This technology has proved to be an efficient and adequate in acquiring the desired information. In this study, GIS and MCA were employed to identify the suitable sites for small dams in Dadu Tehsil, Sindh. The GIS software is used to create all the spatial parameters for the analysis. The parameters that derived are slope, drainage density, rainfall, land use / land cover, soil groups, Curve Number (CN) and runoff index with a spatial resolution of 30m. The data used for deriving above layers include 30-meter resolution SRTM DEM, Landsat 8 imagery, and rainfall from National Centre of Environment Prediction (NCEP) and soil data from World Harmonized Soil Data (WHSD). Land use/Land cover map is derived from Landsat 8 using supervised classification. Slope, drainage network and watershed are delineated by terrain processing of DEM. The Soil Conservation Services (SCS) method is implemented to estimate the surface runoff from the rainfall. Prior to this, SCS-CN grid is developed by integrating the soil and land use/land cover raster. These layers with some technical and ecological constraints are assigned weights on the basis of suitability criteria. The pairwise comparison method, also known as Analytical Hierarchy Process (AHP) is taken into account as MCA for assigning weights on each decision element. All the parameters and group of parameters are integrated using weighted overlay in GIS environment to produce suitable sites for the Dams. The resultant layer is then classified into four classes namely, best suitable, suitable, moderate and less suitable. This study reveals a contribution to decision-making about suitable sites analysis for small dams using geospatial data with minimal amount of ground data. This suitability maps can be helpful for water resource management organizations in determination of feasible rainwater harvesting structures (RWH).

Keywords: Remote sensing, GIS, AHP, RWH

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Authors: Charmaine Lamiel, Van Hoa Nguyen, Marjorie Baynosa, Jae-Jin Shim

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The depletion of non-renewable sources had led to the continuous development of various energy storage systems in order to cope with the world’s demand in energy. Supercapacitors have attracted considerable attention because they can store more energy than conventional capacitors and have higher power density than batteries. The combination of carbon-based material and metal chalcogenides are now being considered in response to the search for active electrode materials exhibiting high electrochemical performance. In this study, a hierarchical mesoporous carbon sphere@nickel cobalt sulfide (CS@Ni-Co-S) core-shell was synthesized using a simple hydrothermal method. The CS@Ni-Co-S core-shell microstructures exhibited a high capacitance of 724.4 F g−1 at 2 A g−1 in a 6 M KOH electrolyte. Good specific retention of 86.1% and high Coulombic efficiency of 97.9% was obtained after 2000 charge-discharge cycles. The electrode exhibited a high energy density of 58.0 Wh kg−1 (1440 W kg−1) and high power density of 7200 W kg−1 (34.2 Wh kg−1). The reaction involved green synthesis without further sulfurization or post-heat treatment. Through this study, a cost-effective and facile synthesis of CS@Ni-Co-S as an active electrode showed favorable electrochemical performance.

Keywords: carbon sphere, electrochemical, hydrothermal, nickel cobalt sulfide, supercapacitor

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Authors: Dina Ahmed Selim, Eman Shawky Anwar, Rasha Mohamed Abu El-Khair

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A rapid, simple and efficient method with minimal sample treatment was developed for authentication of Garcinia cambogia fruit peel powder, along with determining undeclared active pharmaceutical ingredients (APIs) in its herbal slimming dietary supplements using near infrared spectroscopy combined with chemometrics. Five featured adulterants, including sibutramine, metformin, orlistat, ephedrine, and theophylline are selected as target compounds. The Near infrared spectral data matrix of authentic Garcinia cambogia fruit peel and specimens degraded by intentional contamination with the five selected APIs was subjected to hierarchical clustering analysis to investigate their bundling figure. SIMCA models were established to ensure the genuiness of Garcinia cambogia fruit peel which resulted in perfect classification of all tested specimens. Adulterated samples were utilized for construction of PLSR models based on different APIs contents at minute levels of fraud practices (LOQ < 0.2% w/w).The suggested approach can be applied to enhance and guarantee the safety and quality of Garcinia fruit peel powder as raw material and in dietary supplements.

Keywords: Garcinia cambogia, Quality control, NIR spectroscopy, Chemometrics

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Authors: Francis Sichimba

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Literature has increasingly acknowledged the important role that grandparents play in child care with evidence highlighting differences in grand-parental investment between countries and cultures. However, there are very few systematic cross cultural studies on grandparents’ participation in child care. Thus, we decided to conduct this study in Zambia and the Netherlands because the two countries differ rather drastically socially and culturally. The objective of this study was to investigate grand-parental involvement in child care in Zambia and the Netherlands. In line with the general objective, four hypotheses were formulated using nationality, family size, social economic status (SES), attachment security as independent variables. The study sample consisted of 411 undergraduate students from the University of Zambia and the University of Leiden. A questionnaire was used to measure grand-parental involvement in child care. Results indicated that grandparent involvement in child care was prevalent in both Zambia and Netherlands. However, as predicted it was found that Zambian grandparents (M = 9.69, SD=2.40) provided more care for their grandchildren compared to their Dutch counterparts (M = 7.80, SD=3.31) even after controlling for parents being alive. Using hierarchical logistic regression analysis the study revealed that nationality and attachment-related avoidance were significant predictors of grand-parental involvement in child care. It was concluded that grand-parental care is a great resource in offering complementary care in both countries.

Keywords: attachment, care, grand-parenting involvement, social economic status

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Authors: H. Beisch, J. Marx, S. Garlof, R. Shvets, I. I. Grygorchak, A. Kityk, B. Fiedler

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Carbon materials, especially three-dimensional carbon foams, show very high potential in the application as electrode material for energy storage systems such as batteries and supercapacitors with unique fast charging and discharging times. Regarding their high specific surface areas (SSA) high specific capacities can be reached. Globugraphite is a newly developed carbon foam with an interconnected globular carbon morphology. Especially, this foam has a statistically distributed hierarchical pore structure resulting from the manufacturing process based on sintered ceramic templates which are synthetized during a final chemical vapor deposition (CVD) process. For morphology characterization scanning electron (SEM) and transmission electron microscopy (TEM) is used. In addition, the SSA is carried out by nitrogen adsorption combined with the Brunauer–Emmett–Teller (BET) theory. Electrochemical measurements in organic and inorganic electrolyte provide high energy densities and power densities resulting from ion absorption by forming an electrochemical double layer. All values are summarized in a Ragone Diagram. Finally, power densities up to 833 W/kg and energy densities up to 48 Wh/kg could be achieved. The corresponding SSA is between 376 m²/g and 859 m²/g. For organic electrolyte a specific capacity of 71 F/g at a density of 20 mg/cm³ was achieved.

Keywords: BET, CVD process, electron microscopy, Ragone diagram

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Authors: Ana Paula Cavalcanti Furtado, Silvio Meira

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Context: The quality of a software product can be directly influenced by the quality of its development process. Therefore, immature or ad-hoc test processes are means that are unsuited for introducing systematic test automation, and should not be used to support improving the quality of software. Objective: In order to conduct this research, the benefits and limitations of and gaps in automating software testing had to be assessed in order to identify the best practices and to propose a strategy for systematically introducing test automation into software development processes. Method: To conduct this research, an exploratory bibliographical survey was undertaken so as to underpin the search by theory and the recent literature. After defining the proposal, two case studies were conducted so as to analyze the proposal in a real-world environment. In addition, the proposal was also assessed through a focus group with specialists in the field. Results: The proposal of a Framework for Automating Software Testing (FAST), which is a theoretical framework consisting of a hierarchical structure to introduce test automation. Conclusion: The findings of this research showed that the absence of systematic processes is one of the factors that hinder the introduction of test automation. Based on the results of the case studies, FAST can be considered as a satisfactory alternative that lies within the scope of introducing and maintaining test automation in software development.

Keywords: software process improvement, software quality, software testing, test automation

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Authors: Omid Kalatpour, Mohammadreza Ajdari

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This article studied the possible causes of human error-induced incidents in the petrochemical industry maintenance activities by using Human Factors Analysis and Classification System (HFACS). The purpose of the study was anticipating and identifying these causes and proposing corrective and preventive actions. Maintenance department in a petrochemical company was selected for research. A checklist of human error-induced incidents was developed based on four HFACS main levels and nineteen sub-groups. Hierarchical task analysis (HTA) technique was used to identify maintenance activities and tasks. The main causes of possible incidents were identified by checklist and recorded. Corrective and preventive actions were defined depending on priority. Analyzing the worksheets of 444 activities in four levels of HFACS showed 37.6% of the causes were at the level of unsafe actions, 27.5% at the level of unsafe supervision, 20.9% at the level of preconditions for unsafe acts and 14% of the causes were at the level of organizational effects. The HFACS sub-groups showed errors (24.36%) inadequate supervision (14.89%) and violations (13.26%) with the most frequency. According to findings of this study, increasing the training effectiveness of operators and supervision improvement respectively are the most important measures in decreasing the human error-induced incidents in petrochemical industry maintenance.

Keywords: human error, petrochemical industry, maintenance, HFACS

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Authors: Nivedita Ghosh

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In his critique of Lefebvre’s view that ‘technological capacities’ are class-dependent, Francois Hetman argues that technology today is participatory, allowing the entry of individuals from different levels of social stratification. As a result, we are entering into an era of technology operators or ‘clerks’ who become the new decision-makers because of the knowledge they possess of the use of technologies. In response to Hetman’s thesis, this paper argues that knowledge of technology, while indeed providing a momentary space for decision-making, does not necessarily restructure social hierarchies. Through case studies presented from the world of Indian documentary filmmaking, this paper puts forth the view that Hetman’s clerks, despite being technologically advanced, do not break into the filmmaking hierarchical order. This remains true even for a situation where technical knowledge rests most with those in the lowest rungs of the filmmaking ladder. Instead, technological knowledge provides the space for other kinds of relationships to evolve, such as those of ‘trusting the technician’ or ‘admiration for the technician’s work’. Furthermore, what continues to define documentary filmmaking hierarchy is conceptualization capacities of the practitioners, which are influenced by a similarity in socio-cultural backgrounds and film school training accessible primarily to the filmmakers instead of the technicians. Accordingly, the paper concludes with the argument that more than ‘technological-capacities’, it is ‘conceptualization capacities’ which are class-dependent, especially when we study the field of documentary filmmaking.

Keywords: documentary filmmaking, India, technology, knowledge, hierarchy

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Authors: Hiripurage Mallika Sandamali Dissanayaka

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Butterflies are found everywhere on Earth, helping flowering plants reproduce through pollination. Wetlands perform many valuable functions such as providing wildlife habitat. Diyasaru Wetland Park was chosen as the study site. It is located in a highly urbanized area of Sri Jayawardenepura Kotte, Sri Lanka. A distribution map was prepared to increase butterfly habitat in the urbanized area, and research was conducted to determine the most suitable sections for using it. As this wetland has footpaths for walking, line transect surveys were used to mark species within the sampling area, and directly observed species were recorded. All data collection was done from 0900 to 1200 hours and 1300 to 1600 hours and fieldwork was done from 11 February 2020 to 20 January 2021. ED binoculars (10.5x45), DSLR cameras (Canon EOS/EFS5 mm 3.5-5.6), and Garmin GPS (Etrex 10) were used to observe butterfly species, identify locations, and take photographs as evidence. Analyzing their habitats using GIS (ArcGIS Pro) to identify their distribution within the park premises, the distribution density of the known size of the population was calculated for each point by kernel density, and local similarity values were calculated for each pair of corresponding features through hotspot analysis, and cell values were determined by inverse distance weighting (IDW) using a linearly weighted combination of a set of sample points. According to the maps prepared to predict the distribution of butterflies in this park, the high level of distribution or favorable areas were near flower gardens and meadows, but some individual species prefer habitats that are more suitable for their life activities, so they live in other areas. Sixty-six (66) species belonging to six (6) families have been recorded in the premises. Sixty (60) species of least concern (LC), two (2) near threatened (NT), and four (4) vulnerable (VU) species have been recorded, and several new species, such as Plum Judy (Abisara echerius), were reported. The outcome of the study will form the basis for decision-making by the Sri Lanka Land Development (SLLD) Corporation for the future development and maintenance of the park.

Keywords: wetland, Lepidoptera, habitat, urban, west

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Authors: Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac Kuzmanović

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Yina A. Cifuentes Triana, Andrés M. Pinzón Velásco, Marío E. Velásquez Lozano

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In this work the sequencing and genome characterization of a natural isolate of Saccharomyces cerevisiae yeast (strain 202-3), identified with potential for the production of second generation ethanol from sugarcane bagasse hydrolysates is presented. This strain was selected because its capability to consume xylose during the fermentation of sugarcane bagasse hydrolysates, taking into account that many strains of S. cerevisiae are incapable of processing this sugar. This advantage and other prominent positive aspects during fermentation profiles evaluated in bagasse hydrolysates made the strain 202-3 a candidate strain to improve the production of second-generation ethanol, which was proposed as a first step to study the strain at the genomic level. The molecular characterization was carried out by genome sequencing with the Illumina HiSeq 2000 platform paired end; the assembly was performed with different programs, finally choosing the assembler ABYSS with kmer 89. Gene prediction was developed with the approach of hidden Markov models with Augustus. The genes identified were scored based on similarity with public databases of nucleotide and protein. Records were organized from ontological functions at different hierarchical levels, which identified central metabolic functions and roles of the S. cerevisiae strain 202-3, highlighting the presence of four possible new proteins, two of them probably associated with the positive consumption of xylose.

Keywords: cellulosic ethanol, Saccharomyces cerevisiae, genome sequencing, xylose consumption

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Authors: Bayar Shahab

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The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems, and issues of this new era have been found and are being found like no other time in history. Brain-computer interface so-called BCI has opened the door to several new research areas and have been able to provide solutions to critical and important issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair or even a car and neurotechnology enabled the rehabilitation of the lost memory, etc. This review work presents state-of-the-art methods and improvements of canonical correlation analyses (CCA), which is an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said in a different way, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers to understand the most state-of-the-art methods available in this field with their pros and cons, along with their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the prominent methods used in this field in a hierarchical way (2) explaining pros and cons of each method and their performance (3) presenting the gaps that exist at the end of each method that can open the understanding and doors to new research and/or improvements.

Keywords: BCI, CCA, SSVEP, EEG

Procedia PDF Downloads 122

Authors: Arturo S. Daag, Ira Karrel D. L. San Jose, Mike Gabriel G. Pedrosa, Ken Adrian C. Villarias, Rayfred P. Ingeniero, Cyrah Gale H. Rocamora, Margarita P. Dizon, Roland Joseph B. De Leon, Teresito C. Bacolcol

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The province of Leyte consists of various geomorphological landforms: These are: a) landforms of tectonic origin transect large part of the volcanic centers in upper Ormoc area; b) landforms of volcanic origin, several inactive volcanic centers located in Upper Ormoc are transected by Philippine Fault; c) landforms of volcano-denudational and denudational slopes dominates the area where most of the earthquake-induced landslide occurred; and d) Colluvium and alluvial deposits dominate the foot slope of Ormoc and Jaro-Pastrana plain. Earthquake ground acceleration and geotechnical properties of various landforms are crucial for landslide studies. To generate the landslide critical acceleration model of sliding block, various data were considered, these are: geotechnical data (i.e., soil and rock strength parameters), slope, topographic wetness index (TWI), landslide inventory, soil map, geologic maps for the calculation of the factor of safety. Horizontal-to-vertical spectral ratio (HVSR) surveying methods, refraction microtremor (ReMi), and three-component microtremor (3CMT) were conducted to measure site period and surface wave velocity as well as to create a soil thickness model. Critical acceleration model of various geomorphological unit using Remote Sensing, field geotechnical, geophysical, and geospatial data collected from the areas affected by the 06 July 2017 M6.5 Leyte earthquake. Spatial analysis of earthquake-induced landslide from the 06 July 2017, were then performed to assess the relationship between the calculated critical acceleration and peak ground acceleration. The observed trends proved helpful in establishing the role of critical acceleration as a determining factor in the distribution of co-seismic landslides.

Keywords: earthquake-induced landslide, remote sensing, geomorphology, seismic response

Procedia PDF Downloads 75

Authors: Tshepang Tshube

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The primary purpose of this study was to examine student-athletes’ experiences, particularly an in-depth account of balancing school and sport. The secondary objective was to assess student-athletes’ susceptibility to the effects of the “dumb-jock” stereotype threat and also determine the strength of athletic and academic identity as predicated by the extent to which stereotype is perceived by student-athletes. Sub-objectives are (a) examine support structures available for student-athletes in their respective academic institutions, (b) to establish the most effective ways to address student-athletes’ learning needs, (c) to establish crucial entourage members who play a pivotal role in student-athletes’ academic pursuits, (d) and unique and effective ways lecturers and coaches can contribute to student-athletes’ learning experiences. To achieve the above stated objectives, the study used a mixed methods approach. A total of 110 student-athletes from colleges and universities in Botswana completed an online survey that was followed by semi-structured interviews with eight student-athletes, and four coaches. The online survey assessed student-athletes’ demographic variables, measured athletic (AIMS), academic (modified from AIMS) identities, and perceived stereotype threat. Student-athletes reported a slightly higher academic identity (M=5.9, SD= .85) compared to athletic identity (M=5.4, SD=1.0). Student-athletes reported a moderate mean (M=3.6, SD=.82) just above the midpoint of the 7-point scale for stereotype threat. A univariate ANOVA was conducted to determine if there was any significant difference between university and college brackets in Botswana with regard to three variables: athletic identity, student identity and stereotype threat. The only significant difference was in the academic identity (Post Hoc-Tukey Student Identity: Bracket A < Bracket B, Bracket C) with Bracket A schools being the least athletically competitive. Bracket C and B are the most athletically competitive brackets in Botswana. Follow-up interviews with student-athletes and coaches were conducted. All interviews lasted an average of 55 minutes. Following all the interviews, all recordings were transcribed which is an obvious first step in qualitative data analysis process. The researcher and an independent academic with experience in qualitative research independently listened to all recordings of the interviews and read the transcripts several times. Qualitative data results indicate that even though student-athletes reported a slightly higher student identity, there are parallels between sports and academic structures on college campuses. Results also provide evidence of lack of academic support for student-athletes. It is therefore crucial for student-athletes to have access to academic support services (e.g., tutoring, flexible study times, and reduced academic loads) to meet their academic needs. Coaches and lecturers play a fundamental role in sporting student-athletes. Coaches and professors’ academic efficacy on student-athletes enhances student-athletes’ academic confidence. Results are discussed within the stereotype threat theory.

Keywords: athletic identity, colligiate sport, sterotype threat, student athletes

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Authors: Seemal Mazhar Khan, Tahira Mubashar

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The present research aimed to investigate the relationship in perceived organizational justice, organizational trust and employee engagement in bank employees. It was hypothesized: there is likely to be a relationship in perceived organizational justices, organizational trust and employee engagement; perceived organizational justice and organizational trust are likely to predict employee engagement; there is likely to be effect of bank type and designation on perceived organizational justice, organizational trust and employee engagement. The sample consisted of 150 bank employees (50 from government, 50 from private and 50 from privatized banks) selected from different banks in Lahore, Pakistan. Correlational research design was used to conduct this study. Perceived Organizational Justices Questionnaire, Organizational Trust Questionnaire and Employee Engagement Scale were used for assessment. Pearson product moment correlation, hierarchical regression and multivariate analysis of covariance were applied. Results showed a positive significant relationship in perceived organizational justice and organizational engagement and there were also a positive significant relation between organizational trust and job and organizational engagement. Results showed that organizational trust predicts organizational engagement after controlling the effect of age, marital status and socio-economic status and there is a significant interaction effect of bank type and designation level on organizational trust in bank employees. The findings of the research can serve as a platform for the awareness of important antecedents of employee engagement and organizations can inculcate trust for better and improved engagement of its employees, thereby, enhancing the productivity of their employees.

Keywords: bank employees, organizational engagement, perceived organizational justice, trust

Procedia PDF Downloads 368