Search results for: clusters

Authors: K. Vallianou, T. Alexopoulos, V. Plaka, M. K. Seleventi, V. Skanavis, C. Skanavis

Abstract:

The present research addresses the role of place attachment and emotions in community resiliency and recovery within the context of a disaster. Natural disasters represent a disruption in the normal functioning of a community, leading to a general feeling of disorientation. This study draws on the trauma caused by a natural hazard such as a forest fire. The changes of the sense of togetherness are being assessed. Finally this research determines how the place attachment of the inhabitants was affected during the reorientation process of the community. The case study area is Mati, a small coastal town in eastern Attica, Greece. The fire broke out on July 23^rd, 2018. A quantitative research was conducted through questionnaires via phone interviews, one year after the disaster, to address community resiliency in the long-run. The sample was composed of 159 participants from the rural community of Mati plus 120 coming from Skyros Island that was used as a control group. Inhabitants were prompted to answer items gauging their emotions related to the event, group identification and emotional significance of their community, and place attachment before and a year after the fire took place. Importantly, the community recovery and reorientation were examined within the context of a relative absence of government backing and official support. Emotions related to the event were aggregated into 4 clusters related to: activation/vigilance, distress/disorientation, indignation, and helplessness. The findings revealed a decrease in the level of place attachment in the impacted area of Mati as compared to the control group of Skyros Island. Importantly, initial distress caused by the fire prompted the residents to identify more with their community and to report more positive feelings toward their community. Moreover, a mediation analysis indicated that the positive effect of community cohesion on place attachment one year after the disaster was mediated by the positive feelings toward the community. Finally, place attachment contributes to enhanced optimism and a more positive perspective concerning Mati’s future prospects. Despite an insufficient state support to this affected area, the findings suggest an important role of emotions and place attachment during the process of recovery. Implications concerning the role of emotions and social dynamics in meshing place attachment during the disaster recovery process as well as community resiliency are discussed.

Keywords: community resilience, natural disasters, place attachment, wildfire

Procedia PDF Downloads 72

Authors: Ahlem Dhahri, Waleed Omri, Audrey Becuwe, Abdelwahed Omri

Abstract:

In industrial processes, decision-making ranges across different scales, from process control to supply chain management. The purchasing decision-making process in the supply chain is presently gaining more attention as a critical contributor to the company's strategic success. Given the scarcity of thorough summaries in the prior studies, this bibliometric analysis aims to adopt a meticulous approach to achieve quantitative knowledge on the constantly evolving subject of purchasing decision-making in supply chain management. Through bibliometric analysis, we examine a sample of 358 peer-reviewed articles from the Scopus database. VOSviewer and Gephi software were employed to analyze, combine, and visualize the data. Data analytic techniques, including citation network, page-rank analysis, co-citation, and publication trends, have been used to identify influential works and outline the discipline's intellectual structure. The outcomes of this descriptive analysis highlight the most prominent articles, authors, journals, and countries based on their citations and publications. The findings from the research illustrate an increase in the number of publications, exhibiting a slightly growing trend in this field. Co-citation analysis coupled with content analysis of the most cited articles identified five research themes mentioned as follows integrating sustainability into the supplier selection process, supplier selection under disruption risks assessment and mitigation strategies, Fuzzy MCDM approaches for supplier evaluation and selection, purchasing decision in vendor problems, decision-making techniques in supplier selection and order lot sizing problems. With the help of a graphic timeline, this exhaustive map of the field illustrates a visual representation of the evolution of publications that demonstrate a gradual shift from research interest in vendor selection problems to integrating sustainability in the supplier selection process. These clusters offer insights into a wide variety of purchasing methods and conceptual frameworks that have emerged; however, they have not been validated empirically. The findings suggest that future research would emerge with a greater depth of practical and empirical analysis to enrich the theories. These outcomes provide a powerful road map for further study in this area.

Keywords: bibliometric analysis, citation analysis, co-citation, Gephi, network analysis, purchasing, SCM, VOSviewer

Procedia PDF Downloads 57

Authors: Jiahao Tian, Michael D. Porter

Abstract:

Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval-censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of the week. This approach presented here provides accurate intensity estimates and can also uncover day-of-week clusters that share the same intensity patterns. Anticipating where and when crimes might occur is a key element to successful policing strategies. However, this task is complicated by the presence of interval-censored data. The censored data refers to the type of data that the event time is only known to lie within an interval instead of being observed exactly. This type of data is prevailing in the field of criminology because of the absence of victims for certain types of crime. Despite its importance, the research in temporal analysis of crime has lagged behind the spatial component. Inspired by the success of solving crime-related problems with a statistical approach, we propose a statistical model for the temporal intensity estimation of crime with censored data. The model is built on Poisson regression and has special penalty terms added to the likelihood. An EM algorithm was derived to obtain maximum likelihood estimates, and the resulting model shows superior performance to the competing model. Our research is in line with the smart policing initiative (SPI) proposed by the Bureau Justice of Assistance (BJA) as an effort to support law enforcement agencies in building evidence-based, data-driven law enforcement tactics. The goal is to identify strategic approaches that are effective in crime prevention and reduction. In our case, we allow agencies to deploy their resources for a relatively short period of time to achieve the maximum level of crime reduction. By analyzing a particular area within cities where data are available, our proposed approach could not only provide an accurate estimate of intensities for the time unit considered but a time-variation crime incidence pattern. Both will be helpful in the allocation of limited resources by either improving the existing patrol plan with the understanding of the discovery of the day of week cluster or supporting extra resources available.

Keywords: cluster detection, EM algorithm, interval censoring, intensity estimation

Procedia PDF Downloads 41

Authors: Fatemeh Moosavi, Tumelo Franck Nkoshwane

Abstract:

Peter Drucker the renowned management guru once said, “The best way to predict the future is to create it.” Mr. Drucker is also the man who placed human capital as the most vital resource of any institution. As such any institution bent on creating a better future, requires a competent human capital, one that is able to execute with efficiency and effectiveness the objective a society aspires to. Technology today is accelerating the rate at which many societies transition to knowledge based societies. In this accelerated paradigm, it is imperative that those in leadership establish a platform capable of sustaining the planned future; intellectual capital. The capitalist economy going into the future will not just be sustained by dollars and cents, but by individuals who possess the creativity to enterprise, innovate and create wealth from ideas. This calls for cities of the future, to have this premise at the heart of their future plan, if the objective of designing sustainable and liveable future cities will be realised. The knowledge economy, now transitioning to the creative economy, requires cities of the future to be ‘gardens’ of inspiration, to be places where knowledge, creativity, and innovation can thrive as these instruments are becoming critical assets for creating wealth in the new economic system. Developing nations must accept that learning is a lifelong process that requires keeping abreast with change and should invest in teaching people how to keep learning. The need to continuously update one’s knowledge, turn these cities into vibrant societies, where new ideas create knowledge and in turn enriches the quality of life of the residents. Cities of the future must have as one of their objectives, the ability to motivate their citizens to learn, share knowledge, evaluate the knowledge and use it to create wealth for a just society. The five functional factors suggested by Kevin Lynch;-vitality, meaning/sense, adaptability, access, control, and monitoring should form the basis on which policy makers and urban designers base their plans for future cities. The authors of this paper believe that developing nations “creative economy clusters”, cities where creative industries drive the need for constant new knowledge creating sustainable learning creative cities. Obviously the form, shape and size of these districts should be cognisant of the environmental, cultural and economic characteristics of each locale. Gaborone city in the republic of Botswana is presented as the case study for this paper.

Keywords: learning city, sustainable creative city, creative industry, good city form

Procedia PDF Downloads 268

Authors: Kanika Gupta, Ashok Kumar

Abstract:

Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development.

Keywords: biofilms literature, classifiers development, text mining, unsupervised learning approach, unstructured data, relational database

Procedia PDF Downloads 141

Authors: Justin Fu, Thomas Mazzuchi, Shahram Sarkani

Abstract:

A key factor in technological immaturity in defense weapons acquisition is lack of understanding critical integrations at the subsystem and component level. To address this shortfall, recent research in integration readiness level (IRL) combines with technology readiness level (TRL) to form a system readiness level (SRL). SRL can be enriched with more robust quantitative methods to provide the program manager a useful tool prior to committing to major weapons acquisition programs. This research harnesses previous mathematical models based on graph theory, Petri nets, and tropical algebra and proposes a modification of the desirable SRL mathematical properties such that a tightly integrated (multitude of interfaces) subsystem can display a lower SRL than an inherently less coupled subsystem. The synthesis of these methods informs an improved decision tool for the program manager to commit to expensive technology development. This research ties the separately developed manufacturing readiness level (MRL) into the network representation of the system and addresses shortfalls in previous frameworks, including the lack of integration weighting and the over-importance of a single extremely immature component. Tropical algebra (based on the minimum of a set of TRLs or IRLs) allows one low IRL or TRL value to diminish the SRL of the entire system, which may not be reflective of actuality if that component is not critical or tightly coupled. Integration connections can be weighted according to importance and readiness levels are modified to be a cardinal scale (based on an analytic hierarchy process). Integration arcs’ importance are dependent on the connected nodes and the additional integrations arcs connected to those nodes. Lack of integration is not represented by zero, but by a perfect integration maturity value. Naturally, the importance (or weight) of such an arc would be zero. To further explore the impact of grouping subsystems, a multi-objective genetic algorithm is then used to find various clusters or communities that can be optimized for the most representative subsystem SRL. This novel calculation is then benchmarked through simulation and using past defense acquisition program data, focusing on the newly introduced Middle Tier of Acquisition (rapidly field prototypes). The model remains a relatively simple, accessible tool, but at higher fidelity and validated with past data for the program manager to decide major defense acquisition program milestones.

Keywords: readiness, maturity, system, integration

Procedia PDF Downloads 58

Authors: E. O. Clarke, O. J. Aderinola, O. A. Adeboyejo, M. A. Anetekhai

Abstract:

Aims: The study is aimed at establishing the influence of some physical and chemical parameters on the abundance, distribution pattern and seasonal variations of the planktonic crustacean populations. Place and Duration of Study: A premier investigation into the dynamics of planktonic crustacean populations in Ologe lagoon was carried out from January 2011 to December 2012. Study Design: The study covered identification, temporal abundance, spatial distribution and diversity of the planktonic crustacea. Methodology: Standard techniques were used to collect samples from eleven stations covering five proximal satellite towns (Idoluwo, Oto, Ibiye, Obele, and Gbanko) bordering the lagoon. Data obtained were statistically analyzed using linear regression and hierarchical clustering. Results:Thirteen (13) planktonic crustacean populations were identified. Total percentage abundance was highest for Bosmina species (20%) and lowest for Polyphemus species (0.8%). The Pearson’s correlation coefficient (“r” values) between total planktonic crustacean population and some physical and chemical parameters showed that positive correlations having low level of significance occurred with salinity (r = 0.042) (sig = 0.184) and with surface water dissolved oxygen (r = 0.299) (sig = 0.155). Linear regression plots indicated that, the total population of planktonic crustacea were mainly influenced and only increased with an increase in value of surface water temperature (Rsq = 0.791) and conductivity (Rsq = 0.589). The total population of planktonic crustacea had a near neutral (zero correlation) with the surface water dissolved oxygen and thus, does not significantly change with the level of the surface water dissolved oxygen. The correlations were positive with NO3-N (midstream) at Ibiye (Rsq =0.022) and (downstream) Gbanko (Rsq =0.013), PO4-P at Ibiye (Rsq =0.258), K at Idoluwo (Rsq =0.295) and SO4-S at Oto (Rsq = 0.094) and Gbanko (Rsq = 0.457). The Berger-Parker Dominance Index (BPDI) showed that the most dominant species was Bosmina species (BPDI = 1.000), followed by Calanus species (BPDI = 1.254). Clusters by squared Euclidan distances using average linkage between groups showed proximities, transcending the borders of genera. Conclusion: The results revealed that planktonic crustacean population in Ologe lagoon undergo seasonal perturbations, were highly influenced by nutrient, metal and organic matter inputs from river Owoh, Agbara industrial estate and surrounding farmlands and were patchy in spatial distribution.

Keywords: diversity, dominance, perturbations, richness, crustacea, lagoon

Procedia PDF Downloads 688

Authors: Shengnan Chen, Shuhua Wang

Abstract:

Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

Procedia PDF Downloads 258

Authors: D. Supaporn, B. Julaluk

Abstract:

The purposes of this study were to study symptom cluster, functional status and quality of life of patients with chronic obstructive pulmonary disease (COPD), and to examine factors related to and predicting symptom cluster, functional status and quality of life of COPD patients. The sample was 180 COPD patients multi-stage random sampling from 4 hospitals in the eastern region, Thailand. The research instruments were 8 questionnaires and recorded forms measuring personal and illness data, co-morbidity, physical and psychological symptom, health status perception, social support, and regimen adherence, functional status and quality of life. Spearman rank and Pearson correlation coefficient, exploratory factors analysis and standard multiple regression were used to analyzed data. The findings revealed that two symptom clusters were generated: physical symptom cluster including dyspnea, fatigue and insomnia; and, psychological symptom cluster including anxiety and depression. Scores of physical symptom cluster was at moderate level while that of psychological symptom cluster was at low level. Scores on functional status, social support and overall regimen adherence were at good level whereas scores on quality of life and health status perception were at moderate level. Disease severity was positively related to physical symptom cluster, psychological symptom cluster and quality of life, and was negatively related to functional status at a moderate level (rs = .512, .509, .588 and -.611, respectively). Co-morbidity was positively related to physical symptom cluster and psychological symptom cluster at a low level (r = .179 and .176, respectively). Regimen adherence was negatively related to quality of life and psychological symptom cluster at a low level (r=-.277 and -.309, respectively), and was positively related to functional status at a moderate level (r=.331). Health status perception was negatively related to physical symptom cluster, psychological symptom cluster and quality of life at a moderate to high level (r = -.567, -.640 and -.721, respectively) and was positively related to functional status at a high level (r = .732). Social support was positively related to functional status (r=.235) and was negatively related to quality of life at a low level (r=-.178). Physical symptom cluster was negatively related to functional status (r= -.490) and was positively related to quality of life at a moderate level (r=.566). Psychological symptom cluster was negatively related to functional status and was positively related to quality of life at a moderate level (r= -.566 and .559, respectively). Disease severity, co-morbidity and health status perception could predict 40.2% of the variance of physical symptom cluster. Disease severity, co-morbidity, regimen adherence and health status perception could predict 49.8% of the variance of psychological symptom cluster. Co-morbidity, regimen adherence and health status perception could predict 65.0% of the variance of functional status. Disease severity, health status perception and physical symptom cluster could predict 60.0% of the variance of quality of life in COPD patients. The results of this study can be used for enhancing quality of life of COPD patients.

Keywords: chronic obstructive pulmonary disease, functional status, quality of life, symptom cluster

Procedia PDF Downloads 523

Authors: Madhat Abu Al-Naeem, Ismail Yusoff, Ng Tham Fatt, Yatimah Alias

Abstract:

Groundwater in Gaza strip is increasingly being exposed to anthropic and natural factors that seriously impacted the groundwater quality. Physiochemical data of groundwater can offer important information on changes in groundwater quality that can be useful in improving water management tactics. An integrative hydrochemical and statistical techniques (Hierarchical cluster analysis (HCA) and factor analysis (FA)) have been applied on the existence ten physiochemical data of 84 samples collected in (2000/2001) using STATA, AquaChem, and Surfer softwares to: 1) Provide valuable insight into the salinization sources and the hydrochemical processes controlling the chemistry of groundwater. 2) Differentiate the influence of natural processes and man-made activities. The recorded large diversity in water facies with dominance Na-Cl type that reveals a highly saline aquifer impacted by multiple complex hydrochemical processes. Based on WHO standards, only (15.5%) of the wells were suitable for drinking. HCA yielded three clusters. Cluster 1 is the highest in salinity, mainly due to the impact of Eocene saline water invasion mixed with human inputs. Cluster 2 is the lowest in salinity also due to Eocene saline water invasion but mixed with recent rainfall recharge and limited carbonate dissolution and nitrate pollution. Cluster 3 is similar in salinity to Cluster 2, but with a high diversity of facies due to the impact of many sources of salinity as sea water invasion, carbonate dissolution and human inputs. Factor analysis yielded two factors accounting for 88% of the total variance. Factor 1 (59%) is a salinization factor demonstrating the mixing contribution of natural saline water with human inputs. Factor 2 measure the hardness and pollution which explained 29% of the total variance. The negative relationship between the NO3- and pH may reveal a denitrification process in a heavy polluted aquifer recharged by a limited oxygenated rainfall. Multivariate statistical analysis combined with hydrochemical analysis indicate that the main factors controlling groundwater chemistry were Eocene saline invasion, seawater invasion, sewage invasion and rainfall recharge and the main hydrochemical processes were base ion and reverse ion exchange processes with clay minerals (water rock interactions), nitrification, carbonate dissolution and a limited denitrification process.

Keywords: dendrogram and cluster analysis, water facies, Eocene saline invasion and sea water invasion, nitrification and denitrification

Procedia PDF Downloads 334

Authors: Mohamed Alimoussa, Alice Porebski, Nicolas Vandenbroucke, Rachid Oulad Haj Thami, Sana El Fkihi

Abstract:

Color and texture are highly discriminant visual cues that provide an essential information in many types of images. Color texture representation and classification is therefore one of the most challenging problems in computer vision and image processing applications. Color textures can be represented in different color spaces by using multiple image descriptors which generate a high dimensional set of texture features. In order to reduce the dimensionality of the feature set, feature selection techniques can be used. The goal of feature selection is to find a relevant subset from an original feature space that can improve the accuracy and efficiency of a classification algorithm. Traditionally, feature selection is focused on removing irrelevant features, neglecting the possible redundancy between relevant ones. This is why some feature selection approaches prefer to use feature clustering analysis to aid and guide the search. These techniques can be divided into two categories. i) Feature clustering-based ranking algorithm uses feature clustering as an analysis that comes before feature ranking. Indeed, after dividing the feature set into groups, these approaches perform a feature ranking in order to select the most discriminant feature of each group. ii) Feature clustering-based subset search algorithms can use feature clustering following one of three strategies; as an initial step that comes before the search, binded and combined with the search or as the search alternative and replacement. In this paper, we propose a new feature clustering-based sequential selection approach for the purpose of color texture representation and classification. Our approach is a three step algorithm. First, irrelevant features are removed from the feature set thanks to a class-correlation measure. Then, introducing a new automatic feature clustering algorithm, the feature set is divided into several feature clusters. Finally, a sequential search algorithm, based on a filter model and a separability measure, builds a relevant and non redundant feature subset: at each step, a feature is selected and features of the same cluster are removed and thus not considered thereafter. This allows to significantly speed up the selection process since large number of redundant features are eliminated at each step. The proposed algorithm uses the clustering algorithm binded and combined with the search. Experiments using a combination of two well known texture descriptors, namely Haralick features extracted from Reduced Size Chromatic Co-occurence Matrices (RSCCMs) and features extracted from Local Binary patterns (LBP) image histograms, on five color texture data sets, Outex, NewBarktex, Parquet, Stex and USPtex demonstrate the efficiency of our method compared to seven of the state of the art methods in terms of accuracy and computation time.

Keywords: feature selection, color texture classification, feature clustering, color LBP, chromatic cooccurrence matrix

Procedia PDF Downloads 100

Authors: Paramita Banerjee, K. R. S. Chandrakumar, G. P. Das

Abstract:

Bulk MgH2 has drawn much attention for the purpose of hydrogen storage because of its high hydrogen storage capacity (~7.7 wt %) as well as low cost and abundant availability. However, its practical usage has been hindered because of its high hydrogen desorption enthalpy (~0.8 eV/H2 molecule), which results in an undesirable desorption temperature of 3000C at 1 bar H2 pressure. To surmount the limitations of bulk MgH2 for the purpose of hydrogen storage, a detailed first-principles density functional theory (DFT) based study on the structure and stability of neutral (Mgm) and positively charged (Mgm+) Mg nanoclusters of different sizes (m = 2, 4, 8 and 12), as well as their interaction with molecular hydrogen (H2), is reported here. It has been found that due to the absence of d-electrons within the Mg atoms, hydrogen remained in molecular form even after its interaction with neutral and charged Mg nanoclusters. Interestingly, the H2 molecules do not enter into the interstitial positions of the nanoclusters. Rather, they remain on the surface by ornamenting these nanoclusters and forming new structures with a gravimetric density higher than 15 wt %. Our observation is that the inclusion of Grimme’s DFT-D3 dispersion correction in this weakly interacting system has a significant effect on binding of the H2 molecules with these nanoclusters. The dispersion corrected interaction energy (IE) values (0.1-0.14 eV/H2 molecule) fall in the right energy window, that is ideal for hydrogen storage. These IE values are further verified by using high-level coupled-cluster calculations with non-iterative triples corrections i.e. CCSD(T), (which has been considered to be a highly accurate quantum chemical method) and thereby confirming the accuracy of our ‘dispersion correction’ incorporated DFT calculations. The significance of the polarization and dispersion energy in binding of the H2 molecules are confirmed by performing energy decomposition analysis (EDA). A total of 16, 24, 32 and 36 H2 molecules can be attached to the neutral and charged nanoclusters of size m = 2, 4, 8 and 12 respectively. Ab-initio molecular dynamics (AIMD) simulation shows that the outermost H2 molecules are desorbed at a rather low temperature viz. 150 K (-1230C) which is expected. However, complete dehydrogenation of these nanoclusters occur at around 1000C. Most importantly, the host nanoclusters remain stable up to ~500 K (2270C). All these results on the adsorption and desorption of molecular hydrogen with neutral and charged Mg nanocluster systems indicate towards the possibility of reducing the dehydrogenation temperature of bulk MgH2 by designing new Mg-based nano materials which will be able to adsorb molecular hydrogen via this weak Mg-H2 interaction, rather than the strong Mg-H bonding. Notwithstanding the fact that in practical applications, these interactions will be further complicated by the effect of substrates as well as interactions with other clusters, the present study has implications on our fundamental understanding to this problem.

Keywords: density functional theory, DFT, hydrogen storage, molecular dynamics, molecular hydrogen adsorption, nanoclusters, physisorption

Procedia PDF Downloads 397

Authors: Rose Antony, Vivekanand B. Khanapuri, Karuna Jain

Abstract:

Online groceries have transformed the way the supply chains are managed. These are facing numerous challenges in terms of product wastages, low margins, long breakeven to achieve and low market penetration to mention a few. The e-grocery chains need to overcome these challenges in order to survive the competition. The purpose of this paper is to carry out a structural analysis of the enablers in e-grocery chains by applying Interpretive Structural Modeling (ISM) and MICMAC analysis in the Indian context. The research design is descriptive-explanatory in nature. The enablers have been identified from the literature and through semi-structured interviews conducted among the managers having relevant experience in e-grocery supply chains. The experts have been contacted through professional/social networks by adopting a purposive snowball sampling technique. The interviews have been transcribed, and manual coding is carried using open and axial coding method. The key enablers are categorized into themes, and the contextual relationship between these and the performance measures is sought from the Industry veterans. Using ISM, the hierarchical model of the enablers is developed and MICMAC analysis identifies the driver and dependence powers. Based on the driver-dependence power the enablers are categorized into four clusters namely independent, autonomous, dependent and linkage. The analysis found that information technology (IT) and manpower training acts as key enablers towards reducing the lead time and enhancing the online service quality. Many of the enablers fall under the linkage cluster viz., frequent software updating, branding, the number of delivery boys, order processing, benchmarking, product freshness and customized applications for different stakeholders, depicting these as critical in online food/grocery supply chains. Considering the perishability nature of the product being handled, the impact of the enablers on the product quality is also identified. Hence, study aids as a tool to identify and prioritize the vital enablers in the e-grocery supply chain. The work is perhaps unique, which identifies the complex relationships among the supply chain enablers in fresh food for e-groceries and linking them to the performance measures. It contributes to the knowledge of supply chain management in general and e-retailing in particular. The approach focus on the fresh food supply chains in the Indian context and hence will be applicable in developing economies context, where supply chains are evolving.

Keywords: interpretive structural modelling (ISM), India, online grocery, retail operations, supply chain management

Procedia PDF Downloads 178

Authors: Asma Omri, Noureddine Benothman, Sofiane Sayahi, Fethi Tlili, Hichem Besbes

Abstract:

Cycling, a popular mode of transportation, can also be perilous due to cyclists' vulnerability to collisions with vehicles and obstacles. This paper presents an innovative cyclist safety system based on radar technology designed to offer real-time collision risk warnings to cyclists. The system incorporates a low-power radar sensor affixed to the bicycle and connected to a microcontroller. It leverages radar point cloud detections, a clustering algorithm, and a supervised classifier. These algorithms are optimized for efficiency to run on the TI’s AWR 1843 BOOST radar, utilizing a coarse classification approach distinguishing between cars, trucks, two-wheeled vehicles, and other objects. To enhance the performance of clustering techniques, we propose a 2-Level clustering approach. This approach builds on the state-of-the-art Density-based spatial clustering of applications with noise (DBSCAN). The objective is to first cluster objects based on their velocity, then refine the analysis by clustering based on position. The initial level identifies groups of objects with similar velocities and movement patterns. The subsequent level refines the analysis by considering the spatial distribution of these objects. The clusters obtained from the first level serve as input for the second level of clustering. Our proposed technique surpasses the classical DBSCAN algorithm in terms of geometrical metrics, including homogeneity, completeness, and V-score. Relevant cluster features are extracted and utilized to classify objects using an SVM classifier. Potential obstacles are identified based on their velocity and proximity to the cyclist. To optimize the system, we used the View of Delft dataset for hyperparameter selection and SVM classifier training. The system's performance was assessed using our collected dataset of radar point clouds synchronized with a camera on an Nvidia Jetson Nano board. The radar-based cyclist safety system is a practical solution that can be easily installed on any bicycle and connected to smartphones or other devices, offering real-time feedback and navigation assistance to cyclists. We conducted experiments to validate the system's feasibility, achieving an impressive 85% accuracy in the classification task. This system has the potential to significantly reduce the number of accidents involving cyclists and enhance their safety on the road.

Keywords: 2-level clustering, coarse classification, cyclist safety, warning system based on radar technology

Procedia PDF Downloads 46

Authors: Marisol Warthon-Medina, Jenny Plumb, Ayoub Aljawaldeh, Mark Roe, Ailsa Welch, Maria Glibetic, Paul M. Finglas

Abstract:

Similar to Western Countries, the Eastern Mediterranean Region (EMR) also presents major public health issues associated with the increased consumption of sugar, fat, and salt. Therefore, one of the policies of the World Health Organization’s (WHO) EMR is to reduce the intake of salt, sugar, and fat (Saturated fatty acids, trans fatty acids) to address the risk of non-communicable diseases (i.e. diabetes, cardiovascular disease, cancer) and obesity. The project objective is to assess status and provide training and capacity development in the use of improved standardized methodologies for updated food composition data, dietary intake methods, use of suitable biomarkers of nutritional value and determine health outcomes in low and middle-income countries (LMIC). Training exchanges have been developed with clusters of countries created resulting from regional needs including Sudan, Egypt and Jordan; Tunisia, Morocco, and Mauritania; and other Middle Eastern countries. This capacity building will lead to the development and sustainability of up-to-date national and regional food composition databases in LMIC for use in dietary monitoring assessment in food and nutrient intakes. Workshops were organized to provide training and capacity development in the use of improved standardized methodologies for food composition and food intake. Training needs identified and short-term scientific missions organized for LMIC researchers including (1) training and knowledge exchange workshops, (2) short-term exchange of researchers, (3) development and application of protocols and (4) development of strategies to reduce sugar and fat intake. An initial training workshop, Morocco 2018 was attended by 25 participants from 10 EMR countries to review status and support development of regional food composition. 4 training exchanges are in progress. The use of improved standardized methodologies for food composition and dietary intake will produce robust measurements that will reinforce dietary monitoring and policy in LMIC. The capacity building from this project will lead to the development and sustainability of up-to-date national and regional food composition databases in EMR countries. Supported by the UK Medical Research Council, Global Challenges Research Fund, (MR/R019576/1), and the World Health Organization’s Eastern Mediterranean Region.

Keywords: dietary intake, food composition, low and middle-income countries, status.

Procedia PDF Downloads 127

Authors: Amit Gill, Lalith Wijerathne, Sebastian Poledna

Abstract:

This research presents the details of the implementation of high performance computing (HPC) extension of agent-based economic models (ABEMs) to simulate hundreds of millions of heterogeneous agents. ABEMs offer an alternative approach to study the economy as a dynamic system of interacting heterogeneous agents, and are gaining popularity as an alternative to standard economic models. Over the last decade, ABEMs have been increasingly applied to study various problems related to monetary policy, bank regulations, etc. When it comes to predicting the effects of local economic disruptions, like major disasters, changes in policies, exogenous shocks, etc., on the economy of the country or the region, it is pertinent to study how the disruptions cascade through every single economic entity affecting its decisions and interactions, and eventually affect the economic macro parameters. However, such simulations with hundreds of millions of agents are hindered by the lack of HPC enhanced ABEMs. In order to address this, a scalable Distributed Memory Parallel (DMP) implementation of ABEMs has been developed using message passing interface (MPI). A balanced distribution of computational load among MPI-processes (i.e. CPU cores) of computer clusters while taking all the interactions among agents into account is a major challenge for scalable DMP implementations. Economic agents interact on several random graphs, some of which are centralized (e.g. credit networks, etc.) whereas others are dense with random links (e.g. consumption markets, etc.). The agents are partitioned into mutually-exclusive subsets based on a representative employer-employee interaction graph, while the remaining graphs are made available at a minimum communication cost. To minimize the number of communications among MPI processes, real-life solutions like the introduction of recruitment agencies, sales outlets, local banks, and local branches of government in each MPI-process, are adopted. Efficient communication among MPI-processes is achieved by combining MPI derived data types with the new features of the latest MPI functions. Most of the communications are overlapped with computations, thereby significantly reducing the communication overhead. The current implementation is capable of simulating a small open economy. As an example, a single time step of a 1:1 scale model of Austria (i.e. about 9 million inhabitants and 600,000 businesses) can be simulated in 15 seconds. The implementation is further being enhanced to simulate 1:1 model of Euro-zone (i.e. 322 million agents).

Keywords: agent-based economic model, high performance computing, MPI-communication, MPI-process

Procedia PDF Downloads 103

Authors: Mohammed Fathima Shahanaaz, Shaik Fayazuddin, M. Uday Kiran

Abstract:

Groundwater quality is deteriorating day by day in different parts of the world due to various reasons, toxic chemicals are being discharged without proper treatment into inland water bodies and land which in turn add pollutants to the groundwater. In this kind of situation, the rural communities which do not have municipal drinking water have to rely on groundwater though it is polluted for various uses. Tannery industry is one of the major industry which provides economy and employment to India. Since most of the developed countries stopped using chemicals which are toxic, the tanning industry which uses chromium as its major element are being shifted towards developing countries. Most of the tanning industries in India can be found in clusters concentrated mainly in states of Tamilnadu, West Bengal, Uttar Pradesh and limited places of Punjab. Limited work is present in the case of tanneries of Warangal. There exists 18 group of tanneries in Desaipet, Enamamula region of Warangal, out of which 4 are involved in dry process and are low responsible for groundwater pollution. These units of tanneries are discharging their effluents after treatment into Sai Cheruvu. Though the treatment effluents are being discharged, the Sai Cheruvu is turned in to Pink colour, with higher levels of BOD, COD, chromium, chlorides, total hardness, TDS and sulphates. An attempt was made to analyse the groundwater samples around this polluted Sai Cheruvu region since literature shows that a single tannery can pollute groundwater to a radius of 7-8 kms from the point of disposal. Sample are collected from 6 different locations around Sai Cheruvu. Analysis was performed for determining various constituents in groundwater such as pH, EC, TDS, TH, Ca+2, Mg+2, HCO3-, Na+, K+, Cl-, SO42-, NO3-, F and Cr+6. The analysis of these constitutes gave values greater than permissible limits. Even chromium is also present in groundwater samples which is exceeding permissible limits People in Paidepally and Sardharpeta villages already stopped the usage of groundwater. They are buying bottle water for drinking purpose. Though they are not using groundwater for drinking purpose complaints are made about using this water for washing also. So treatment process should be adopted for groundwater which should be simple and efficient. In this study rice husk silica (RHS) is used to treat pollutants in groundwater with varying dosages of RHS and contact time. Rice husk is treated, dried and place in a muffle furnace for 6 hours at 650°C. Reduction is observed in total hardness, chlorides and chromium levels are observed after the application RHS. Pollutants reached permissible limits for 27.5mg/l and 50 mg/l of dosage for a contact time of 130 min at constant pH and temperature.

Keywords: chromium, groundwater, rice husk silica, tanning industries

Procedia PDF Downloads 174

Authors: Asrarul H. Jeelani

Abstract:

Arsenic contamination of groundwater and its’ health implications in lower Gangetic plain of Indian states started reporting in the 1980s. The same period was declared as the first water decade (1981-1990) to achieve ‘water for all.’ To fulfill the aim, the Indian government, with the support of international agencies installed millions of hand-pumps through water resources development programs. The hand-pumps improve the accessibility if the groundwater, but over-extraction of it increases the chances of mixing of trivalent arsenic which is more toxic than pentavalent arsenic of dug well water in Gangetic plain and has different physical manifestations. Now after three decades, Bihar (middle Gangetic plain) is also facing arsenic contamination of groundwater and its’ health implications. Objective: This interdisciplinary research attempts to understand the health and social implications of arsenicosis among different castes in Haldi Chhapra village and to find the association of ramifications with water resources development. Methodology: The Study used concurrent quantitative dominant mix method (QUAN+qual). The researcher had employed household survey, social mapping, interviews, and participatory interactions. However, the researcher used secondary data for retrospective analysis of hand-pumps and implications of arsenicosis. Findings: The study found 88.5% (115) household have hand-pumps as a source of water however 13.8% uses purified supplied water bottle and 3.6% uses combinations of hand-pump, bottled water and dug well water for drinking purposes. Among the population, 3.65% of individuals have arsenicosis, and 2.72% of children between the age group of 5 to 15 years are affected. The caste variable has also emerged through quantitative as well as geophysical locations analysis as 5.44% of arsenicosis manifested individual belong to scheduled caste (SC), 3.89% to extremely backward caste (EBC), 2.57% to backward caste (BC) and 3% to other. Among three clusters of arsenic poisoned locations, two belong to SC and EBC. The village as arsenic affected is being discriminated, whereas the affected individual is also facing discrimination, isolation, stigma, and problem in getting married. The forceful intervention to install hand-pumps in the first water decades and later restructuring of the dug well destroyed a conventional method of dug well cleaning. Conclusion: The common manifestation of arsenicosis has increased by 1.3% within six years of span in the village. This raised the need for setting up a proper surveillance system in the village. It is imperative to consider the social structure for arsenic mitigation program as this research reveals caste as a significant factor. The health and social implications found in the study; retrospectively analyzed as the collateral impact of water resource development programs in the village.

Keywords: arsenicosis, caste, collateral impact, water resources

Procedia PDF Downloads 76

Authors: Šárka Hradilová, Aleš Panáček, Radek Zbořil

Abstract:

Nanotechnologies are considered the most promising fields with high added value, brings new possibilities in various sectors from industry to medicine. With the growing of interest in nanomaterials and their applications, increasing nanoparticle production leads to increased exposure of people and environment with ‘human made’ nanoparticles. Nanoparticles (NPs) are clusters of atoms in the size range of 1–100 nm. Metal nanoparticles represent one of the most important and frequently used types of NPs due to their unique physical, chemical and biological properties, which significantly differ from those of bulk material. Biological properties including toxicity of metal nanoparticles are generally determined by their size, size distribution, shape, surface area, surface charge, surface chemistry, stability in the environment and ability to release metal ions. Therefore, the biological behavior of NPs and their possible adverse effect cannot be derived from the bulk form of material because nanoparticles show unique properties and interactions with biological systems just due to their nanodimensions. Silver and gold NPs are intensively studied and used. Both can be used for instance in surface enhanced Raman spectroscopy, a considerable number of applications of silver NPs is associated with antibacterial effects, while gold NPs are associated with cancer treatment and bio imaging. Antibacterial effects of silver ions are known for centuries. Silver ions and silver-based compounds are highly toxic to microorganisms. Toxic properties of silver NPs are intensively studied, but the mechanism of cytoxicity is not fully understood. While silver NPs are considered toxic, gold NPs are referred to as toxic but also innocuous for eukaryotic cells. Therefore, gold NPs are used in various biological applications without a risk of cell damaging, even when we want to suppress the growth of cancer cells. Thus, gold NPs are toxic or harmless. Because most studies comparing particles of various sizes prepared in various ways, and testing is performed on different cell lines, it is very difficult to generalize. The novelty and significance of our research is focused to the complex biological effects of silver and gold NPs prepared by the same method, have the same parameters and the same stabilizer. That is why we can compare the biological effects of pure nanometals themselves based on their chemical nature without the influence of other variable. Aim of our study therefore is to compare the cytotoxic effect of two types of noble metal NPs focusing on the mechanisms that contribute to cytotoxicity. The study was conducted on murine fibroblasts by selected common used tests. Each of these tests monitors the selected area related to toxicity and together provides a comprehensive view on the issue of interactions of nanoparticles and living cells.

Keywords: cytotoxicity, gold nanoparticles, mechanism of cytotoxicity, silver nanoparticles

Procedia PDF Downloads 222

Authors: Emilija Tribocka

Abstract:

This contribution presents parts of an ongoing study of a diachronic relationship between two weak verb classes in Norwegian, the a-class (cf. the paradigm of ‘throw’: kasta – kastar – kasta – kasta) and the e-class (cf. the paradigm of ‘buy’: kjøpa – kjøper – kjøpte – kjøpt). The study investigates inflection class shifts between the two classes with Old Norse, the ancestor of Modern Norwegian, as a starting point. Examination of inflection in 38 verbs in four chosen dialect areas (106 places of attestations) demonstrates that the shifts from the a-class to the e-class are widespread to varying degrees in three out of four investigated areas and are more common than the shifts in the opposite direction. The diachronic productivity of the e-class is unexpected for several reasons. There is general agreement that type frequency is an important factor influencing productivity. The a-class (53% of all weak verbs) was more type frequent in Old Norse than the e-class (42% of all weak verbs). Thus, given the type frequency, the expansion of the e-class is unexpected. Furthermore, in the ‘core’ areas of expanded e-class inflection, the shifts disregard phonological principles creating forms with uncomfortable consonant clusters, e.g., fiskte instead of fiska, the preterit of fiska ‘fish’. Later on, these forms may be contracted, i.e., fiskte > fiste. In this contribution, two factors influencing the shifts are presented: phonological form and token frequency. Verbs with the stem ending in a consonant cluster, particularly when the cluster ends in -t, hardly ever shift to the e-class. As a matter of fact, verbs with this structure belonging to the e-class in Old Norse shift to the a-class in Modern Norwegian, e.g., ON e-class verb skipta ‘change’ shifts to the a-class. This shift occurs as a result of the lack of morpho-phonological transparency between the stem and the preterit suffix of the e-class, -te. As there is a phonological fusion between the stem ending in -t and the suffix beginning in -t, the transparent a-class inflection is chosen. Token frequency plays an important role in the shifts, too, in some dialects. In one of the investigated areas, the most token frequent verbs of the ON e-class remain in the e-class (e.g., høyra ‘hear’, leva ‘live’, kjøpa ‘buy’), while less frequent verbs may shift to the a-class. Furthermore, the results indicate that the shift from the a-class to the e-class occurs in some of the most token frequent verbs of the ON a-class in this area, e.g., lika ‘like’, lova ‘promise’, svara ‘answer’. The latter is unexpected as frequent items tend to remain stable. This study presents a case of unexpected productivity, demonstrating that minor patterns can grow and outdo major patterns. Thus, type frequency is not the only factor that determines productivity. The study addresses the role of phonological form and token frequency in the spread of inflection patterns.

Keywords: inflection class, productivity, token frequency, phonological form

Procedia PDF Downloads 27

Authors: Jing-Yang Chung, Chi-Wei Liao, Jing Li, Bor Kae Chang, Cheng-Yu Wang

Abstract:

Chemical hydride ammonia borane (AB, NH3BH3) draws attentions to hydrogen energy researches for its high theoretical gravimetrical capacity (19.6 wt%). Nevertheless, the elevated AB decomposition temperatures (Td) and unwanted byproducts are main hurdles in practical application. It was reported that the byproducts and Td can be reduced with nanoconfinement technique, in which AB molecules are confined in porous materials, such as porous carbon, zeolite, metal-organic frameworks (MOFs), etc. Although nanoconfinement empirically shows effectiveness on hydrogen generation temperature reduction in AB, the theoretical mechanism is debatable. Low Td was reported in AB@IRMOF-1 (Zn4O(BDC)3, BDC = benzenedicarboxylate), where Zn atoms form closed metal clusters secondary building unit (SBU) with no exposed active sites. Other than nanosized hydride, it was also observed that catalyst addition facilitates AB decomposition in the composite of Li-catalyzed carbon CMK-3, MOF JUC-32-Y with exposed Y3+, etc. It is believed that nanosized AB is critical for lowering Td, while active sites eliminate byproducts. Nonetheless, some researchers claimed that it is the catalytic sites that are the critical factor to reduce Td, instead of the hydride size. The group physically ground AB with ZIF-8 (zeolitic imidazolate frameworks, (Zn(2-methylimidazolate)2)), and found similar reduced Td phenomenon, even though AB molecules were not ‘confined’ or forming nanoparticles by physical hand grinding. It shows the catalytic reaction, not nanoconfinement, leads to AB dehydrogenation promotion. In this research, we explored the possible criteria of hydrogen production temperature from nanoconfined AB in MOFs with different pore sizes and active sites. MOFs with metal SBU such as Zn (IRMOF), Zr (UiO), and Al (MIL-53), accompanying with various organic ligands (BDC and BPDC; BPDC = biphenyldicarboxylate) were modified with AB. Excess MOFs were used for AB size constrained in micropores estimated by revisiting Horvath-Kawazoe model. AB dissolved in methanol was added to MOFs crystalline with MOF pore volume to AB ratio 4:1, and the slurry was dried under vacuum to collect AB@MOF powders. With TPD-MS (temperature programmed desorption with mass spectroscopy), we observed Td was reduced with smaller MOF pores. For example, it was reduced from 100°C to 64°C when MOF micropore ~1 nm, while ~90°C with pore size up to 5 nm. The behavior of Td as a function of AB crystalline radius obeys thermodynamics when the Gibbs free energy of AB decomposition is zero, and no obvious correlation with metal type was observed. In conclusion, we discovered Td of AB is proportional to the reciprocal of MOF pore size, possibly stronger than the effect of active sites.

Keywords: ammonia borane, chemical hydride, metal-organic framework, nanoconfinement

Procedia PDF Downloads 157

Authors: Helen Nelly Naiga, Jane Frances Zalwango, Saudah N. Kizito, Brian Agaba, Brenda N Simbwa, Maria Goretti Zalwango, Richard Migisha, Benon Kwesiga, Daniel Kadobera, Alex Ario Riolexus, Sarah Paige, Julie R. Harris

Abstract:

Background: Traditional community beliefs and practices can facilitate the spread of Ebola virus during outbreaks. On September 20, 2022, Uganda declared a Sudan Virus Disease (SVD) outbreak after a case was confirmed in Mubende District. During September–November 2022, the outbreak spread to eight additional districts. We investigated the role of community beliefs and practices in the spread of SUDV in Uganda in 2022. Methods: A qualitative study was conducted in Mubende, Kassanda, and Kyegegwa districts in February 2023. We conducted nine focus group discussions (FGDs) and six key informant interviews (KIIs). FGDs included SVD survivors, household members of SVD patients, traditional healers, religious leaders, and community leaders. Key informants included community, political, and religious leaders, traditional healers, and health workers. We asked about community beliefs and practices to understand if and how they contributed to the spread of SUDV. Interviews were recorded, translated, transcribed, and analyzed thematically. Results: Frequently-reported themes included beliefs that the community deaths, later found to be due to SVD, were the result of witchcraft or poisoning. Key informants reported that SVD patients frequently first consulted traditional healers or spiritual leaders before seeking formal healthcare, and noted that traditional healers treated patients with signs and symptoms of SVD without protective measures. Additional themes included religious leaders conducting laying-on-of-hands prayers for SVD patients and symptomatic contacts, SVD patients and their symptomatic contacts hiding in friends’ homes, and exhumation of SVD patients originally buried in safe and dignified burials, to enable traditional burials. Conclusion: Multiple community beliefs and practices likely promoted SVD outbreak spread during the 2022 outbreak in Uganda. Engaging traditional and spiritual healers early during similar outbreaks through risk communication and community engagement efforts could facilitate outbreak control. Targeted community messaging, including clear biological explanations for clusters of deaths and information on the dangers of exhuming bodies of SVD patients, could similarly facilitate improved control in future outbreaks in Uganda.

Keywords: Ebola, Sudan virus, outbreak, beliefs, traditional

Procedia PDF Downloads 33

Authors: Narjis Kahtoon

Abstract:

The issue of the wage discrimination on the basis of gender and social problem has been a significant research problem for several decades. Whereas lots of have explored reasons for the persistence of an inequality in the wages of male and female, none has successfully explained away the entire differentiation. The wage discrimination on the basis of gender and social problem of working women is a global issue. Although inequality in political and economic and social make-up of countries all over the world, the gender wage discrimination, and social constraint is present. The aim of the research is to examine the gender wage discrimination and social constraint from an international perspective and to determine whether any pattern exists among cultural dimensions of a country and the man and women remuneration gap in Readymade Garment Sector of Pakistan. Population growth rate is significant indicator used to explain the change in population and play a crucial point in the economic development of a country. In Pakistan, readymade garment sector consists of small, medium and large sized firms. With an estimated 30 percent of the workforce in textile- Garment is females’. Readymade garment industry is a labor intensive industry and relies on the skills of individual workers and provides highest value addition in the textile sector. In the Garment sector, female workers are concentrated in poorly paid, labor-intensive down-stream production (readymade garments, linen, towels, etc.), while male workers dominate capital- intensive (ginning, spinning and weaving) processes. Gender wage discrimination and social constraint are reality in Pakistan Labor Market. This research allows us not only to properly detect the size of gender wage discrimination and social constraint but to also fully understand its consequences in readymade garment sector of Pakistan. Furthermore, research will evaluated this measure for the three main clusters like Lahore, Karachi, and Faisalabad. These data contain complete details of male and female workers and supervisors in the readymade garment sector of Pakistan. These sources of information provide a unique opportunity to reanalyze the previous finding in the literature. The regression analysis focused on the standard 'Mincerian' earning equation and estimates it separately by gender, the research will also imply the cultural dimensions developed by Hofstede (2001) to profile a country’s cultural status and compare those cultural dimensions to the wage inequalities. Readymade garment of Pakistan is one of the important sectors since its products have huge demand at home and abroad. These researches will a major influence on the measures undertaken to design a public policy regarding wage discrimination and social constraint in readymade garment sector of Pakistan.

Keywords: gender wage differentials, decomposition, garment, cultural

Procedia PDF Downloads 178

Authors: Guanyu Jiang, Donghai Xu, Huanteng Liu

Abstract:

After the Fukushima-Daiichi accident, the development of accident tolerant fuel cladding materials to improve reactor safety has become a hot topic in the field of nuclear industry. ZrO₂ has a satisfactory neutron economy and can guarantee the fission chain reaction process, which enables it to be a promising coating for zirconium alloy cladding. Maintaining a good corrosion resistance in primary coolant loop during normal operations of Pressurized Water Reactors is a prerequisite for ZrO₂ as a protective coating on zirconium alloy cladding. Research on the corrosion performance of ZrO₂ coating in nuclear water chemistry is relatively scarce, and existing reports failed to provide an in-depth explanation for the failure causes of ZrO₂ coating. Herein, a detailed corrosion process of ZrO₂ coating in lithiated water at 360 °C and 18.5 MPa was proposed based on experimental research and molecular dynamics simulation. Lithiated water with different LiOH solutions in the present work was deaerated and had a dissolved oxygen concentration of < 10 ppb. The concentration of Li (as LiOH) was determined to be 2.3 ppm, 70 ppm, and 500 ppm, respectively. Corrosion tests were conducted in a static autoclave. Modeling and corresponding calculations were operated on Materials Studio software. The calculation of adsorption energy and dynamics parameters were undertaken by the Energy task and Dynamics task of the Forcite module, respectively. The protective effect and failure mechanism of ZrO₂ coating on Zry-4 under varied LiOH concentrations was further revealed by comparison with the coating corrosion performance in pure water (namely 0 ppm Li). ZrO₂ coating provided a favorable corrosion protection with the occurrence of localized corrosion at low LiOH concentrations. Factors influencing corrosion resistance mainly include pitting corrosion extension, enhanced Li+ permeation, short-circuit diffusion of O²⁻ and ZrO₂ phase transformation. In highly-concentrated LiOH solutions, intergranular corrosion, internal oxidation, and perforation resulted in coating failure. Zr ions were released to coating surface to form flocculent ZrO₂ and ZrO₂ clusters due to the strong diffusion and dissolution tendency of α-Zr in the Zry-4 substrate. Considering that primary water of Pressurized Water Reactors usually includes 2.3 ppm Li, the stability of ZrO₂ make itself a candidate fuel cladding coating material. Under unfavorable conditions with high Li concentrations, more boric acid should be added to alleviate caustic corrosion of ZrO₂ coating once it is used. This work can provide some references to understand the service behavior of nuclear coatings under variable water chemistry conditions and promote the in-pile application of ZrO₂ coating.

Keywords: ZrO₂ coating, Zry-4, corrosion behavior, failure mechanism, LiOH concentration

Procedia PDF Downloads 27

Authors: M. François, L. Sigot, C. Vallières

Abstract:

Volatile Organic Compounds (VOCs) are a real health issue, particularly in domestic indoor environments. Among these VOCs, acetaldehyde is frequently monitored in dwellings ‘air, especially due to smoking and spontaneous emissions from the new wall and soil coverings. It is responsible for respiratory complaints and is classified as possibly carcinogenic to humans. Adsorption processes are commonly used to remove VOCs from the air. Metal-Organic Frameworks (MOFs) are a promising type of material for high adsorption performance. These hybrid porous materials composed of metal inorganic clusters and organic ligands are interesting thanks to their high porosity and surface area. The HKUST-1 (also referred to as MOF-199) is a copper-based MOF with the formula [Cu₃(BTC)₂(H₂O)₃]n (BTC = benzene-1,3,5-tricarboxylate) and exhibits unsaturated metal sites that can be attractive sites for adsorption. The objective of this study is to investigate the impact of HKUST-1 pretreatment on acetaldehyde adsorption. Thus, dynamic adsorption experiments were conducted in 1 cm diameter glass column packed with 2 cm MOF bed height. MOF were sieved to 630 µm - 1 mm. The feed gas (Co = 460 ppmv ± 5 ppmv) was obtained by diluting a 1000 ppmv acetaldehyde gas cylinder in air. The gas flow rate was set to 0.7 L/min (to guarantee a suitable linear velocity). Acetaldehyde concentration was monitored online by gas chromatography coupled with a flame ionization detector (GC-FID). Breakthrough curves must allow to understand the interactions between the MOF and the pollutant as well as the impact of the HKUST-1 humidity in the adsorption process. Consequently, different MOF water content conditions were tested, from a dry material with 7 % water content (dark blue color) to water saturated state with approximately 35 % water content (turquoise color). The rough material – without any pretreatment – containing 30 % water serves as a reference. First, conclusions can be drawn from the comparison of the evolution of the ratio of the column outlet concentration (C) on the inlet concentration (Co) as a function of time for different HKUST-1 pretreatments. The shape of the breakthrough curves is significantly different. The saturation of the rough material is slower (20 h to reach saturation) than that of the dried material (2 h). However, the breakthrough time defined for C/Co = 10 % appears earlier in the case of the rough material (0.75 h) compared to the dried HKUST-1 (1.4 h). Another notable difference is the shape of the curve before the breakthrough at 10 %. An abrupt increase of the outlet concentration is observed for the material with the lower humidity in comparison to a smooth increase for the rough material. Thus, the water content plays a significant role on the breakthrough kinetics. This study aims to understand what can explain the shape of the breakthrough curves associated to the pretreatments of HKUST-1 and which mechanisms take place in the adsorption process between the MOF, the pollutant, and the water.

Keywords: acetaldehyde, dynamic adsorption, HKUST-1, pretreatment influence

Procedia PDF Downloads 208

Authors: Karolina Ordon, Sandrine Coste, Malgorzata Makowska-Janusik, Abdelhadi Kassiba

Abstract:

Photocatalytic processes play key role in the production of a new source of energy (as hydrogen), design of self-cleaning surfaces or for the environment preservation. The most challenging task deals with the purification of water distinguished by high efficiency. In the mentioned process, organic pollutants in solutions are decomposed to the simple, non-toxic compounds as H2O and CO2. The most known photocatalytic materials are ZnO, CdS and TiO2 semiconductors with a particular involvement of TiO2 as an efficient photocatalysts even with a high band gap equal to 3.2 eV which exploit only UV radiation from solar emitted spectrum. However, promising material with visible light induced photoactivity was searched through the monoclinic polytype of BiVO4 which has energy gap about 2.4 eV. As required in heterogeneous photocatalysis, the high contact surface is required. Also, BiVO4 as photocatalyst can be optimized by increasing its surface area by achieving the mesoporous structure synthesize. The main goal of the present work consists in the synthesis and characterization of BiVO4 mesoporous thin film. The synthesis method based on sol-gel was carried out using a standard surfactants such as P123 and F127. The thin film was deposited by spin and dip coating method. Then, the structural analysis of the obtained material was performed thanks to X-ray diffraction (XRD) and Raman spectroscopy. The surface of resulting structure was investigated using a scanning electron microscopy (SEM). The computer simulations based on modeling the optical and electronic properties of bulk BiVO4 by using DFT (density functional theory) methodology were carried out. The semiempirical parameterized method PM6 was used to compute the physical properties of BiVO4 nanostructures. The Raman and IR absorption spectra were also measured for synthesized mesoporous material, and the results were compared with the theoretical predictions. The simulations of nanostructured BiVO4 have pointed out the occurrence of quantum confinement for nanosized clusters leading to widening of the band gap. This result overcame the relevance of nanosized objects to harvest wide part of the solar spectrum. Also, a balance was searched experimentally through the mesoporous nature of the films devoted to enhancing the contact surface as required for heterogeneous catalysis without to lower the nanocrystallite size under some critical sizes inducing an increased band gap. The present contribution will discuss the relevant features of the mesoporous films with respect to their photocatalytic responses.

Keywords: bismuth vanadate, photocatalysis, thin film, quantum-chemical calculations

Procedia PDF Downloads 300

Authors: Dorothy Ebere Adimora

Abstract:

The study investigated the predictive power of home environment and peer pressure on disruptive behaviour and risky sexual behaviour of Secondary School Class Two Adolescents in Enugu State, Nigeria. The design of the study is a cross sectional survey of correlational study. The study was carried out in the six Education zones in Enugu state, Nigeria. Enugu State is divided into six education zones, namely Agbani, Awgu, Enugu, Nsukka, Obollo-Afor and Udi. The population for the study was all the 31,680 senior secondary class two adolescents in 285 secondary schools in Enugu State, Nigeria in 2014/2015 academic session. The target population was students in SSS.2 senior secondary class two. They constitute one-sixth of the entire student population in the state. The sample of the study was 528, a multi stage sampling technique was employed to draw the sample. Four research questions and four null hypotheses guided the study. The instruments for data collection were an interview session and a structured questionnaire of four clusters, they are; home environment, peer pressure, risky sexual behaviour and disruptive behaviour disorder questionnaires. The instruments were validated by 3 experts, two in psychology and one in measurement and Evaluation in Faculty of Education, University of Nigeria, Nsukka. The reliability coefficient of the instruments was ascertained by subjection to field trial. The adolescents were asked to complete the questionnaire on their home environment, peer pressure, disruptive behaviour disorder and risky sexual behaviours. The risky sexual behaviours were ascertained based on interview conducted on their actual sexual practice within the past 12 months. The research questions were analyzed using Pearson r and R-square, while the hypotheses were tested using ANOVA and multiple regression analysis at 0.05 level of significance. The results of this survey revealed that the adolescents are sexually active in very young ages. The mean age at sexual debut for the adolescents covered in this survey is a pointer to the fact that some of them started engaging in sexual activities long ago. It was also found that the adolescents engage in disruptive behaviour as a result of their poor home environment factors and association with negative peers. Based on the findings, it was recommended that the adolescents should be exposed to enhanced home environment such as parents’ responsiveness, organization of the environment, availability of appropriate learning materials, opportunities for daily stimulation and to offer a proper guidance to these adolescents to avoid negative peer influence which could result in risky sexual behaviour and disruptive behaviour disorder.

Keywords: parenting, peer group, adolescents, sexuality, conduct disorder

Procedia PDF Downloads 449

Authors: Jaroslav Kraus

Abstract:

Spatial information about demographic processes are a standard part of outputs in the Czech Republic. That was also the case of Population and Housing Census which was held on 2011. This is a starting point for a follow up study devoted to two basic types of households: single person households and households of one completed family. Single person households and one family households create more than 80 percent of all households, but the share and spatial structure is in long-term changing. The increase of single households is results of long-term fertility decrease and divorce increase, but also possibility of separate living. There are regions in the Czech Republic with traditional demographic behavior, and regions like capital Prague and some others with changing pattern. Population census is based - according to international standards - on the concept of currently living population. Three types of geospatial approaches will be used for analysis: (i) firstly measures of geographic distribution, (ii) secondly mapping clusters to identify the locations of statistically significant hot spots, cold spots, spatial outliers, and similar features and (iii) finally analyzing pattern approach as a starting point for more in-depth analyses (geospatial regression) in the future will be also applied. For analysis of this type of data, number of households by types should be distinct objects. All events in a meaningful delimited study region (e.g. municipalities) will be included in an analysis. Commonly produced measures of central tendency and spread will include: identification of the location of the center of the point set (by NUTS3 level); identification of the median center and standard distance, weighted standard distance and standard deviational ellipses will be also used. Identifying that clustering exists in census households datasets does not provide a detailed picture of the nature and pattern of clustering but will be helpful to apply simple hot-spot (and cold spot) identification techniques to such datasets. Once the spatial structure of households will be determined, any particular measure of autocorrelation can be constructed by defining a way of measuring the difference between location attribute values. The most widely used measure is Moran’s I that will be applied to municipal units where numerical ratio is calculated. Local statistics arise naturally out of any of the methods for measuring spatial autocorrelation and will be applied to development of localized variants of almost any standard summary statistic. Local Moran’s I will give an indication of household data homogeneity and diversity on a municipal level.

Keywords: census, geo-demography, households, the Czech Republic

Procedia PDF Downloads 81

Authors: Maxim Kravchenko

Abstract:

The paper looks into the structure and contents of the concept of empire in the social consciousness of modern Americans. To construct the model of this socially and politically relevant concept we have conducted an experiment with respondents born and living in the USA. Empire is seen as a historic notion describing such entities as the British empire, the Russian empire, the Ottoman empire and others. It seems that the democratic regime adopted by most countries worldwide is incompatible with imperial status of a country. Yet there are countries which tend to dominate in the contemporary world and though they are not routinely referred to as empires, in many respects they are reminiscent of historical empires. Thus, the central hypothesis of the study is that the concept of empire is cultivated in some states through the intermediary of the mass media though it undergoes a certain transformation to meet the expectations of a democratic society. The transformation implies that certain components which were historically embedded in its structure are drawn to the margins of the hierarchical structure of the concept whereas other components tend to become central to the concept. This process can be referred to as restructuration of the concept of empire. To verify this hypothesis we have conducted a study which falls into two stages. First we looked into the definition of empire featured in dictionaries, the dominant conceptual components of empire are: importance, territory/lands, recognition, independence, authority/power, supreme/absolute. However, the analysis of 100 articles from American newspapers chosen at random revealed that authors rarely use the word «empire» in its basic meaning (7%). More often «empire» is used when speaking about countries, which no longer exist or when speaking about some corporations (like Apple or Google). At the second stage of the study we conducted an associative experiment with the citizens of the USA aged 19 to 45. The purpose of the experiment was to find out the dominant components of the concept of empire and to construct the model of the transformed concept. The experiment stipulated that respondents should give the first association, which crosses their mind, on reading such stimulus phrases as “strong military”, “strong economy” and others. The list of stimuli features various words and phrases associated with empire including the words representing the dominant components of the concept of empire. Then the associations provided by the respondents were classified into thematic clusters. For instance, the associations to the stimulus “strong military” were compartmentalized into three groups: 1) a country with strong military forces (North Korea, the USA, Russia, China); 2) negative impression of strong military (war, anarchy, conflict); 3) positive impression of strong military (peace, safety, responsibility). The experiment findings suggest that the concept of empire is currently undergoing a transformation which brings about a number of changes. Among them predominance of positively assessed components of the concept; emergence of two poles in the structure of the concept, that is “hero” vs. “enemy”; marginalization of any negatively assessed components.

Keywords: associative experiment, conceptual components, empire, restructurasation of the concept

Procedia PDF Downloads 286

Authors: Fabiana Franceschi, Martha Cobo, Manuel Figueredo

Abstract:

Bogotá, the capital of Colombia, is its largest city and one of the most polluted in Latin America due to the fast economic growth over the last ten years. Bogotá has been affected by high pollution events which led to the high concentration of PM10 and NO2, exceeding the local 24-hour legal limits (100 and 150 g/m3 each). The most important pollutants in the city are PM10 and PM2.5 (which are associated with respiratory and cardiovascular problems) and it is known that their concentrations in the atmosphere depend on the local meteorological factors. Therefore, it is necessary to establish a relationship between the meteorological variables and the concentrations of the atmospheric pollutants such as PM10, PM2.5, CO, SO2, NO2 and O3. This study aims to determine the interrelations between meteorological variables and air pollutants in Bogotá, using data mining techniques. Data from 13 monitoring stations were collected from the Bogotá Air Quality Monitoring Network within the period 2010-2015. The Principal Component Analysis (PCA) algorithm was applied to obtain primary relations between all the parameters, and afterwards, the K-means clustering technique was implemented to corroborate those relations found previously and to find patterns in the data. PCA was also used on a per shift basis (morning, afternoon, night and early morning) to validate possible variation of the previous trends and a per year basis to verify that the identified trends have remained throughout the study time. Results demonstrated that wind speed, wind direction, temperature, and NO2 are the most influencing factors on PM10 concentrations. Furthermore, it was confirmed that high humidity episodes increased PM2,5 levels. It was also found that there are direct proportional relationships between O3 levels and wind speed and radiation, while there is an inverse relationship between O3 levels and humidity. Concentrations of SO2 increases with the presence of PM10 and decreases with the wind speed and wind direction. They proved as well that there is a decreasing trend of pollutant concentrations over the last five years. Also, in rainy periods (March-June and September-December) some trends regarding precipitations were stronger. Results obtained with K-means demonstrated that it was possible to find patterns on the data, and they also showed similar conditions and data distribution among Carvajal, Tunal and Puente Aranda stations, and also between Parque Simon Bolivar and las Ferias. It was verified that the aforementioned trends prevailed during the study period by applying the same technique per year. It was concluded that PCA algorithm is useful to establish preliminary relationships among variables, and K-means clustering to find patterns in the data and understanding its distribution. The discovery of patterns in the data allows using these clusters as an input to an Artificial Neural Network prediction model.

Keywords: air pollution, air quality modelling, data mining, particulate matter

Procedia PDF Downloads 232