Search results for: grey clustering

Authors: Ahmed Bahieldin, Ahmed Atef, Jamal S. M. Sabir, Nour O. Gadalla, Sherif Edris, Ahmed M. Alzohairy, Nezar A. Radhwan, Mohammed N. Baeshen, Ahmed M. Ramadan, Hala F. Eissa, Sabah M. Hassan, Nabih A. Baeshen, Osama Abuzinadah, Magdy A. Al-Kordy, Fotouh M. El-Domyati, Robert K. Jansen

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Wild salt-tolerant barley (Hordeum spontaneum) is the ancestor of cultivated barley (Hordeum vulgare or H. vulgare). Although the cultivated barley genome is well studied, little is known about genome structure and function of its wild ancestor. In the present study, RNA-Seq analysis was performed on young leaves of wild barley treated with salt (500 mM NaCl) at four different time intervals. Transcriptome sequencing yielded 103 to 115 million reads for all replicates of each treatment, corresponding to over 10 billion nucleotides per sample. Of the total reads, between 74.8 and 80.3% could be mapped and 77.4 to 81.7% of the transcripts were found in the H. vulgare unigene database (unigene-mapped). The unmapped wild barley reads for all treatments and replicates were assembled de novo and the resulting contigs were used as a new reference genome. This resultedin94.3 to 95.3%oftheunmapped reads mapping to the new reference. The number of differentially expressed transcripts was 9277, 3861 of which were uni gene-mapped. The annotated unigene- and de novo-mapped transcripts (5100) were utilized to generate expression clusters across time of salt stress treatment. Two-dimensional hierarchical clustering classified differential expression profiles into nine expression clusters, four of which were selected for further analysis. Differentially expressed transcripts were assigned to the main functional categories. The most important groups were ‘response to external stimulus’ and ‘electron-carrier activity’. Highly expressed transcripts are involved in several biological processes, including electron transport and exchanger mechanisms, flavonoid biosynthesis, reactive oxygen species (ROS) scavenging, ethylene production, signaling network and protein refolding. The comparisons demonstrated that mRNA-Seq is an efficient method for the analysis of differentially expressed genes and biological processes under salt stress.

Keywords: electron transport, flavonoid biosynthesis, reactive oxygen species, rnaseq

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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Barbara Farinon, Maurizio E. Picarella, Lorenzo Mancini, Andrea Mazzucato

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Italy is notoriously a secondary center of diversification for cultivated tomatoes (Solanum lycopersicum L.). The study of phenotypic and genetic diversity in landrace collections is important for germplasm conservation and biodiversity protection. Here, we set up to study the germplasm collected in the region of Lazio in Central Italy with a focus on the distinctiveness among landraces and the attribution of membership to unnamed accessions. Our regional collection included 30 accessions belonging to six different locally recognized landraces and 21 unnamed accessions. All accessions were gathered in Lazio and belonged to the collection held at the Regional Agency for the Development and Innovation of Agriculture in Lazio (ARSIAL, in the application of the Regional Act n. 15/2000, funded by Lazio Rural Development Plan 2014 – 2020 Agro-environmental Measure, Action 10.2.1) and at the University of Tuscia. We included 13 control genotypes as references. The collection showed wide phenotypic variability for several traits, such as fruit weight (range 14-277 g), locule number (2-12), shape index (0.54-2.65), yield (0.24-3.08 kg/plant), and soluble solids (3.4-7.5 °B). A few landraces showed uncommon phenotypes, such as potato leaf, colorless fruit epidermis, or delayed ripening. Multivariate analysis of 25 cardinal phenotypic variables grouped the named varieties and allowed to assign of some of the unnamed to recognized groups. A case study for distinctiveness is presented for the flattened-ribbed types that presented overlapping distribution according to the phenotypic data. Molecular markers retrieved by previous studies revealed differences compared to the phenotyping clustering, indicating that the named varieties “Scatolone di Bolsena” and “Pantano Romanesco” belong to the Marmande group, together with the reference landrace from Tuscany “Costoluto Fiorentino”. Differently, the landrace “Spagnoletta di Formia e Gaeta” was clearly distinct from the former at the molecular level. Therefore, a genotypic analysis of the analyzed collection appears needed to better define the molecular distinctiveness among the flattened-ribbed accessions, as well as to properly attribute the membership group of the unnamed accessions.

Keywords: distinctiveness, flattened-ribbed fruits, regional landraces, tomato

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Authors: Christine Y. Yeh, Brian D. Piening, Sarah M. Totten, Kimberly Kukurba, Wenyu Zhou, Kevin P. F. Contrepois, Gucci J. Gu, Sharon Pitteri, Michael Snyder

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Three million deaths worldwide are attributed to obesity. However, the biomolecular mechanisms that describe the link between adiposity and subsequent disease states are poorly understood. Insulin resistance characterizes approximately half of obese individuals and is a major cause of obesity-mediated diseases such as Type II diabetes, hypertension and other cardiovascular diseases. This study makes use of longitudinal quantitative and high-throughput multi-omics (genomics, epigenomics, transcriptomics, glycoproteomics etc.) methodologies on blood samples to develop multigenic and multi-analyte signatures associated with weight gain and insulin resistance. Participants of this study underwent a 30-day period of weight gain via excessive caloric intake followed by a 60-day period of restricted dieting and return to baseline weight. Blood samples were taken at three different time points per patient: baseline, peak-weight and post weight loss. Patients were characterized as either insulin resistant (IR) or insulin sensitive (IS) before having their samples processed via longitudinal multi-omic technologies. This comparative study revealed a wealth of biomolecular changes associated with weight gain after using methods in machine learning, clustering, network analysis etc. Pathways of interest included those involved in lipid remodeling, acute inflammatory response and glucose metabolism. Some of these biomolecules returned to baseline levels as the patient returned to normal weight whilst some remained elevated. IR patients exhibited key differences in inflammatory response regulation in comparison to IS patients at all time points. These signatures suggest differential metabolism and inflammatory pathways between IR and IS patients. Biomolecular differences associated with weight gain and insulin resistance were identified on various levels: in gene expression, epigenetic change, transcriptional regulation and glycosylation. This study was not only able to contribute to new biology that could be of use in preventing or predicting obesity-mediated diseases, but also matured novel biomedical informatics technologies to produce and process data on many comprehensive omics levels.

Keywords: insulin resistance, multi-omics, next generation sequencing, proteogenomics, type ii diabetes

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Authors: Ankit Khurana

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The sufficient heat storage capacity or ability to dissipate heat is the most decisive parameter to have an effective and efficient functioning of Friction-based Brake Disc systems. The primary aim of the research was to analyse the effect of multiple coatings on lightweight disk rotors surface which not only alleviates the mass of vehicle & also, augments heat transfer. This research is projected to aid the automobile fraternity with an enunciated view over the thermal aspects in a braking system. The results of the project indicate that with the advent of modern coating technologies a brake system’s thermal curtailments can be removed and together with forced convection, heat transfer processes can see a drastic improvement leading to increased lifetime of the brake rotor. Other advantages of modifying the surface of a lightweight rotor substrate will be to reduce the overall weight of the vehicle, decrease the risk of thermal brake failure (brake fade and fluid vaporization), longer component life, as well as lower noise and vibration characteristics. A mathematical model was constructed in MATLAB which encompassing the various thermal characteristics of the proposed coatings and substrate materials required to approximate the heat flux values in a free and forced convection environment; resembling to a real-time braking phenomenon which could easily be modelled into a full cum scaled version of the alloy brake rotor part in ABAQUS. The finite element of a brake rotor was modelled in a constrained environment such that the nodal temperature between the contact surfaces of the coatings and substrate (Wrought Aluminum alloy) resemble an amalgamated solid brake rotor element. The initial results obtained were for a Plasma Electrolytic Oxidized (PEO) substrate wherein the Aluminum alloy gets a hard ceramic oxide layer grown on its transitional phase. The rotor was modelled and then evaluated in real-time for a constant ‘g’ braking event (based upon the mathematical heat flux input and convective surroundings), which reflected the necessity to deposit a conducting coat (sacrificial) above the PEO layer in order to inhibit thermal degradation of the barrier coating prematurely. Taguchi study was then used to bring out certain critical factors which may influence the maximum operating temperature of a multi-coated brake disc by simulating brake tests: a) an Alpine descent lasting 50 seconds; b) an Autobahn stop lasting 3.53 seconds; c) a Six–high speed repeated stop in accordance to FMVSS 135 lasting 46.25 seconds. Thermal Barrier coating thickness and Vane heat transfer coefficient were the two most influential factors and owing to their design and manufacturing constraints a final optimized model was obtained which survived the 6-high speed stop test as per the FMVSS -135 specifications. The simulation data highlighted the merits for preferring Wrought Aluminum alloy 7068 over Grey Cast Iron and Aluminum Metal Matrix Composite in coherence with the multiple coating depositions.

Keywords: lightweight brakes, surface modification, simulated braking, PEO, aluminum

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Authors: Caroline Smith, Professor Debi Bhattacharya, Sion Scott

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Introduction: Oropharyngeal Dysphagia (OD) effects around 15% of older people, however it is often unrecognised and under diagnosed until they are hospitalised. There is a need for primary care healthcare practitioners (HCPs) to assume a proactive role in identifying and managing OD to prevent adverse outcomes such as aspiration pneumonia. Understanding the determinants of primary care HCPs undertaking this new behaviour provides the intervention targets for addressing. This realist review, underpinned by the Theoretical Domains Framework (TDF), aims to synthesise relevant literature and develop programme theories to understand what interventions work, how they work and under what circumstances to facilitate HCPs to prevent harm from OD. Combining realist methodology with behavioural science will permit conceptualisation of intervention components as theoretical behavioural constructs, thus informing the design of a future behaviour change intervention. Furthermore, through the TDF’s linkage to a taxonomy of behaviour change techniques, we will identify corresponding behaviour change techniques to include in this intervention. Methods & analysis: We are following the five steps for undertaking a realist review: 1) clarify the scope 2) Literature search 3) appraise and extract data 4) evidence synthesis 5) evaluation. We have searched Medline, Google scholar, PubMed, EMBASE, CINAHL, AMED, Scopus and PsycINFO databases. We are obtaining additional evidence through grey literature, snowball sampling, lateral searching and consulting the stakeholder group. Literature is being screened, evaluated and synthesised in Excel and Nvivo. We will appraise evidence in relation to its relevance and rigour. Data will be extracted and synthesised according to its relation to Initial programme theories (IPTs). IPTs were constructed after the preliminary literature search, informed by the TDF and with input from a stakeholder group of patient and public involvement advisors, general practitioners, speech and language therapists, geriatricians and pharmacists. We will follow the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and publication standards to report study results. Results: In this ongoing review our search has identified 1417 manuscripts with approximately 20% progressing to full text screening. We inductively generated 10 IPTs that hypothesise practitioners require: the knowledge to spot the signs and symptoms of OD; the skills to provide initial advice and support; and access to resources in their working environment to support them conducting these new behaviours. We mapped the 10 IPTs to 8 TDF domains and then generated a further 12 IPTs deductively using domain definitions to fulfil the remaining 6 TDF domains. Deductively generated IPTs broadened our thinking to consider domains such as ‘Emotion,’ ‘Optimism’ and ‘Social Influence’, e.g. If practitioners perceive that patients, carers and relatives expect initial advice and support, then they will be more likely to provide this, because they will feel obligated to do so. After prioritisation with stakeholders using a modified nominal group technique approach, a maximum of 10 IPTs will progress to test against the literature.

Keywords: behaviour change, deglutition disorders, primary healthcare, realist review

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: cluster analysis, construction management, earned value, schedule

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Authors: Farwa Rizvi, Joanne Williams, Humaira Maheen, Elizabeth Hoban

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There is an increase in risky sexual behavior and unsafe sex in adolescent girls and young women aged 15 to 24 years in Cambodia, which negatively affects their reproductive health by increasing the risk of contracting sexually transmitted infections and unintended pregnancies. Risky sexual behavior includes ‘having sex at an early age, having multiple sexual partners, having sex while under the influence of alcohol or drugs, and unprotected sexual behaviors’. A systematic review of quantitative research conducted in Cambodia was undertaken, using the theoretical framework of the Social Ecological Model to identify the personal, social and cultural factors associated with risky sexual behavior and unsafe sex in young Cambodian women. PRISMA guidelines were used to search databases including Medline Complete, PsycINFO, CINAHL Complete, Academic Search Complete, Global Health, and Social Work Abstracts. Additional searches were conducted in Science Direct, Google Scholar and in the grey literature sources. A risk-of-bias tool developed explicitly for the systematic review of cross-sectional studies was used. Summary item on the overall risk of study bias after the inter-rater response showed that the risk-of-bias was high in two studies, moderate in one study and low in one study. The search strategy included a combination of subject terms and free text terms. The medical subject headings (MeSH) terms included were; contracept* or ‘birth control’ or ‘family planning’ or pregnan* or ‘safe sex’ or ‘protected intercourse’ or ‘unprotected intercourse’ or ‘protected sex’ or ‘unprotected sex’ or ‘risky sexual behaviour*’ or ‘abort*’ or ‘planned parenthood’ or ‘unplanned pregnancy’ AND ( barrier* or obstacle* or challenge* or knowledge or attitude* or factor* or determinant* or choic* or uptake or discontinu* or acceptance or satisfaction or ‘needs assessment’ or ‘non-use’ or ‘unmet need’ or ‘decision making’ ) AND Cambodia*. Initially, 300 studies were identified by using key words and finally, four quantitative studies were selected based on the inclusion criteria. The four studies were published between 2010 and 2016. The study participants ranged in age from 10-24 years, single or married, with 3 to 10 completed years of education. The mean age at sexual debut was reported to be 18 years. Using the perspective of the Social Ecological Model, risky sexual behavior was associated with individual-level factors including young age at sexual debut, low education, unsafe sex under the influence of alcohol and substance abuse, multiple sexual partners or transactional sex. Family level factors included living away from parents, orphan status and low levels of family support. Peer and partner level factors included peer delinquency and lack of condom use. Low socioeconomic status at the society level was also associated with risky sexual behaviour. There is scant research on sexual and reproductive health of adolescent girls and young women in Cambodia. Individual, family and social factors were significantly associated with risky sexual behaviour. More research is required to inform potential preventive strategies and policies that address young women’s sexual and reproductive health.

Keywords: adolescents, high-risk sex, sexual activity, unplanned pregnancies

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Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

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The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Dadash Huseynov, Elmira Aliyeva, Robert Hoogendoorn, Salomon Kroonenberg

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The study of organic material in bottom sediments together with lithologic and biostratigraphic data improves our understanding of the evolution of fluvial and deltaic systems. The modern Kura River delta is located in the Southwest Caspian Sea and is fluvial-dominated. The river distributes its sediment load through three channels oriented North-East, South-East, and South-West. The offshore modern delta consists of thinly bedded or laminated silty clays and dark grey clays. Locally sand and shell-rich horizons occur. Onshore delta is composed of channel-levee sands and floodplain silts and clays. Overall sedimentation rates in the delta determined by the 210Pb method range between 1.5-3.0 cm/yr. We investigated the distribution of organic material in the deltaic sediments in 300 samples selected from 3m deep piston cores. The studies of transparent sections demonstrate that deltaic sediments are enriched in terrestrial debris. It is non-transparent and has an irregular, isometric, or elongated shape, angular edges, black or dark-brown colour, and a clearly expressed fabric. Partially it is dissolved at the edges and is replaced by iron sulphides. Fragments of marine algae have more smooth edges, brown colour. They are transparent; the fabric is rarely preserved. The evidences of dissolution and gelification are well observed. Iron sulphides are common. The recorded third type of organic material has a round, drop-like, or oval shape and belongs to planktonic organisms. Their initial organic material is strongly transformed or replaced by dark organic compounds, probably, neoplasms. The particles are red-brown and transparent. The iron sulphides are not observed. The amount of Corg in the uppermost portion of sediments accumulated in the offshore Kura River delta varies from 0.2 to 1.22%, with median values of 0.6-0.8%. In poorly sorted sediments Corg content changes from 0.24 to 0.97% (average 0.69%), silty-sandy clay - 0.45 to 1.22% (average 0.77%), sandy-silty clay - 0.5 to 0.97% (average 0.67%), silty clay - 0.52 to 0.95% (average 0.70%). The data demonstrate that in sediments deposited during Caspian Sea high stand in 1929, the minimum of Corg content is localised near the mouth of the main south-eastern distributary channel and coincides with the minimum of the clay fraction. At the same time, the maximum of organic matter content locates near the mouth of the eastern channel, which was inactive at that time. In sediments accumulated during the last Caspian Sea low stand in 1977, the area of Corg minimum is attached to the north-eastern distributary’s mouth. It indicates the high activity of this distributary during the Caspian Sea fall. The area of Corg minimum is also recorded around the mouth of the main channel and eastern part of the delta. Maximums of Corg and clay fraction shift towards the basin. During the Caspian high stand in 1995, the minimum of Corg content is again observed in the mouth of the main south-eastern channel. The distribution of organic matter in the modern sediments of the Kura river delta displays the strong time dependence and reflects progradational-retrogradational cycles of evolution of this fluvial-deltaic system.

Keywords: high and low stands, Kura River delta, South Caspian Sea, organic matter

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Authors: Ebtehal Qulisy, Geoffrey Dougherty, Kholoud Hothan, Mylene Dandavino

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Background: For more than 200 years, journal clubs (JCs) have been used to teach the fundamentals of critical appraisal and evidence-based practice (EBP). However, JCs curricula face important challenges, including poor sustainability, insufficient time to prepare for and conduct the activities, and lack of trainee skills and self-efficacy with critical appraisal. Andragogy principles and modern technology could help EBP be taught in more relevant, modern, and interactive ways. Method: We propose a fresh educational activity to teach EBP. Educational sessions are designed to encourage collaborative and experiential learning and do not require advanced preparation by the participants. Each session lasts 60 minutes and is adaptable to in-person, virtual, or hybrid contexts. Sessions are structured around a worksheet and include three educational objectives: “1. Identify a Clinical Conundrum”, “2. Compare and Contrast Current Guidelines”, and “3. Choose a Recent Journal Article”. Sessions begin with a short presentation by a facilitator of a clinical scenario highlighting a “grey-zone” in pediatrics. Trainees are placed in groups of two to four (based on the participants’ number) of varied training levels. The first task requires the identification of a clinical conundrum (a situation where there is no clear answer but only a reasonable solution) related to the scenario. For the second task, trainees must identify two or three clinical guidelines. The last task requires trainees to find a journal article published in the last year that reports an update regarding the scenario’s topic. Participants are allowed to use their electronic devices throughout the session. Our university provides full-text access to major journals, which facilitated this exercise. Results: Participants were a convenience sample of trainees in the inpatient services at the Montréal Children’s Hospital, McGill University. Sessions were conducted as a part of an existing weekly academic activity and facilitated by pediatricians with experience in critical appraisal. There were 28 participants in 4 sessions held during Spring 2022. Time was allocated at the end of each session to collect participants’ feedback via a self-administered online survey. There were 22 responses, were 41%(n=9) pediatric residents, 22.7%(n=5) family medicine residents, 31.8%(n=7) medical students, and 4.5%(n=1) nurse practitioner. Four respondents participated in more than one session. The “Satisfied” rates were 94.7% for session format, 100% for topic selection, 89.5% for time allocation, and 84.3% for worksheet structure. 60% of participants felt that including the sessions during the clinical ward rotation was “Feasible.” As per self-efficacy, participants reported being “Confident” for the tasks as follows: 89.5% for the ability to identify a relevant conundrum, 94.8% for the compare and contrast task, and 84.2% for the identification of a published update. The perceived effectiveness to learn EBP was reported as “Agreed” by all participants. All participants would recommend this session for further teaching. Conclusion: We developed a modern approach to teach EBP, enjoyed by all levels of participants, who also felt it was a useful learning experience. Our approach addresses known JCs challenges by being relevant to clinical care, fostering active engagement but not requiring any preparation, using available technology, and being adaptable to hybrid contexts.

Keywords: medical education, journal clubs, post-graduate teaching, andragogy, experiential learning, evidence-based practice

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Authors: Abigail Bentley, Audrey Prost, Nayreen Daruwalla, Apoorwa Gupta, David Osrin

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BACKGROUND: Intimate partner violence (IPV) can impact a woman’s physical, reproductive and mental health, including common mental disorders such as anxiety and depression. However, people other than an intimate partner may also perpetrate violence against women in the family, particularly in India. This study aims to investigate the relationship between experiences of violence perpetrated by the husband and other members of the wider household and symptoms of common mental disorders in women residing in informal settlement (slum) areas of Mumbai. METHODS: Experiences of violence were assessed through a detailed cross-sectional survey of 598 women, including questions about specific acts of emotional, economic, physical and sexual violence across different time points in the woman’s life and the main perpetrator of each act. Symptoms of common mental disorders were assessed using the 12-item General Health Questionnaire (GHQ-12). The GHQ-12 scores were divided into four groups and the relationship between experiences of each type of violence in the last 12 months and GHQ-12 score group was analyzed using ordinal logistic regression, adjusted for the woman’s age and clustering. RESULTS: 482 (81%) women consented to interview. On average, they were 28.5 years old, had completed 7 years of education and had been married 9 years. 88% were Muslim and 47% lived in joint and 53% in nuclear families. 44% of women had experienced at least one act of violence in their lifetime (33% emotional, 22% economic, 23% physical, 12% sexual). 7% had a high GHQ-12 score (6 or above). For violence experiences in the last 12 months, the odds of being in the highest GHQ-12 score group versus the lower groups combined were 13.1 for emotional violence, 6.5 for economic, 5.7 for physical and 6.3 for sexual (p<0.001 for all outcomes). DISCUSSION: The high level of violence reported across the lifetime could be due to the detailed assessment of violent acts at multiple time points and the inclusion of perpetrators within the family other than the husband. Each type of violence was associated with greater odds of a higher GHQ-12 score and therefore more symptoms of common mental disorders. Emotional violence was far more strongly associated with symptoms of common mental disorders than physical or sexual violence. However, it is not possible to attribute causal directionality to the association. Further work to investigate the relationship between differing severity of violence experiences and women’s mental health and the components of emotional violence that make it so strongly associated with symptoms of common mental disorders would be beneficial.

Keywords: common mental disorders, family violence, India, informal settlements, mental health, violence against women

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Authors: M. Kharrat, G. Moreau, Z. Aboura

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The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

Procedia PDF Downloads 155

Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

Procedia PDF Downloads 79

Authors: Jae-Hyeon Kim, Michael Lee

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Intrinsic and acquired resistance limits the therapeutic benefits of oncogenic BRAF inhibitors in melanoma. MicroRNAs (miRNA) regulate the expression of target mRNAs by repressing their translation. Thus, we investigated miRNA expression patterns in melanoma cell lines to identify candidate biomarkers for acquired resistance to BRAF inhibitor. Here, we used Affymetrix miRNA V3.0 microarray profiling platform to compare miRNA expression levels in three cell lines containing BRAF inhibitor-sensitive A375P BRAF V600E cells, their BRAF inhibitor-resistant counterparts (A375P/Mdr), and SK-MEL-2 BRAF-WT cells with intrinsic resistance to BRAF inhibitor. The miRNAs with at least a two-fold change in expression between BRAF inhibitor-sensitive and –resistant cell lines, were identified as differentially expressed. Averaged intensity measurements identified 138 and 217 miRNAs that were differentially expressed by 2 fold or more between: 1) A375P and A375P/Mdr; 2) A375P and SK-MEL-2, respectively. The hierarchical clustering revealed differences in miRNA expression profiles between BRAF inhibitor-sensitive and –resistant cell lines for miRNAs involved in intrinsic and acquired resistance to BRAF inhibitor. In particular, 43 miRNAs were identified whose expression was consistently altered in two BRAF inhibitor-resistant cell lines, regardless of intrinsic and acquired resistance. Twenty five miRNAs were consistently upregulated and 18 downregulated more than 2-fold. Although some discrepancies were detected when miRNA microarray data were compared with qPCR-measured expression levels, qRT-PCR for five miRNAs (miR-3617, miR-92a1, miR-1246, miR-1936-3p, and miR-17-3p) results showed excellent agreement with microarray experiments. To further investigate cellular functions of miRNAs, we examined effects on cell proliferation. Synthetic oligonucleotide miRNA mimics were transfected into three cell lines, and proliferation was quantified using a colorimetric assay. Of the 5 miRNAs tested, only miR-1246 altered cell proliferation of A375P/Mdr cells. The transfection of miR-1246 mimic strongly conferred PLX-4720 resistance to A375P/Mdr cells, implying that miR-1246 upregulation confers acquired resistance to BRAF inhibition. We also found that PLX-4720 caused much greater G2/M arrest in A375P/Mdr cells transfected with miR-1246mimic than that seen in scrambled RNA-transfected cells. Additionally, miR-1246 mimic partially caused a resistance to autophagy induction by PLX-4720. These results indicate that autophagy does play an essential death-promoting role inPLX-4720-induced cell death. Taken together, these results suggest that miRNA expression profiling in melanoma cells can provide valuable information for a network of BRAF inhibitor resistance-associated miRNAs.

Keywords: microRNA, BRAF inhibitor, drug resistance, autophagy

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Authors: Mwehe Mathenge, Ben G. J. S. Sonneveld, Jacqueline E. W. Broerse

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Smallholder farmers are important drivers of agriculture productivity, food security, and poverty reduction in Sub-Saharan Africa. However, they are faced with myriad challenges in their efforts at participating in agribusiness markets. How the geographic explicit factors existing at the local level interact to impede smallholder farmers' decision to participates (or not) in agribusiness markets is not well understood. Deconstructing the spatial complexity of the local environment could provide a deeper insight into how geographically explicit determinants promote or impede resource-poor smallholder farmers from participating in agribusiness. This paper’s objective was to identify, map, and analyze local spatial autocorrelation in factors that impede poor smallholders from participating in agribusiness markets. Data were collected using geocoded researcher-administered survey questionnaires from 392 households in Western Kenya. Three spatial statistics methods in geographic information system (GIS) were used to analyze data -Global Moran’s I, Cluster and Outliers Analysis (Anselin Local Moran’s I), and geographically weighted regression. The results of Global Moran’s I reveal the presence of spatial patterns in the dataset that was not caused by spatial randomness of data. Subsequently, Anselin Local Moran’s I result identified spatially and statistically significant local spatial clustering (hot spots and cold spots) in factors hindering smallholder participation. Finally, the geographically weighted regression results unearthed those specific geographic explicit factors impeding market participation in the study area. The results confirm that geographically explicit factors are indispensable in influencing the smallholder farming decisions, and policymakers should take cognizance of them. Additionally, this research demonstrated how geospatial explicit analysis conducted at the local level, using geographically disaggregated data, could help in identifying households and localities where the most impoverished and resource-poor smallholder households reside. In designing spatially targeted interventions, policymakers could benefit from geospatial analysis methods in understanding complex geographic factors and processes that interact to influence smallholder farmers' decision-making processes and choices.

Keywords: agribusiness markets, GIS, smallholder farmers, spatial statistics, disaggregated spatial data

Procedia PDF Downloads 139

Authors: Kumar Divyendra

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Underwater Sensor Networks (UWSNs) have wide applications in areas such as water quality monitoring, marine wildlife management etc. A typical UWSN system consists of a set of sensors deployed randomly underwater which communicate with each other using acoustic links. RF communication doesn't work underwater, and GPS too isn't available underwater. Additionally Automated Underwater Vehicles (AUVs) are deployed to collect data from some special nodes called Cluster Heads (CHs). These CHs aggregate data from their neighboring nodes and forward them to the AUVs using optical links when an AUV is in range. This helps reduce the number of hops covered by data packets and helps conserve energy. We consider the three-dimensional model of the UWSN. Nodes are initially deployed randomly underwater. They attach themselves to the surface using a rod and can only move upwards or downwards using a pump and bladder mechanism. We use graph theory concepts to maximize the coverage volume while every node maintaining connectivity with at least one surface node. We treat the surface nodes as landmarks and each node finds out its hop distance from every surface node. We treat these hop-distances as coordinates and use them for AUV navigation. An AUV intending to move closer to a node with given coordinates moves hop by hop through nodes that are closest to it in terms of these coordinates. In absence of GPS, multiple different approaches like Inertial Navigation System (INS), Doppler Velocity Log (DVL), computer vision-based navigation, etc., have been proposed. These systems have their own drawbacks. INS accumulates error with time, vision techniques require prior information about the environment. We propose a method that makes use of the earth's magnetic field values for navigation and combines it with other methods that simultaneously increase the coverage volume under the UWSN. The AUVs are fitted with magnetometers that measure the magnetic intensity (I), horizontal inclination (H), and Declination (D). The International Geomagnetic Reference Field (IGRF) is a mathematical model of the earth's magnetic field, which provides the field values for the geographical coordinateson earth. Researchers have developed an inverse deep learning model that takes the magnetic field values and predicts the location coordinates. We make use of this model within our work. We combine this with with the hop-by-hop movement described earlier so that the AUVs move in such a sequence that the deep learning predictor gets trained as quickly and precisely as possible We run simulations in MATLAB to prove the effectiveness of our model with respect to other methods described in the literature.

Keywords: clustering, deep learning, network backbone, parallel computing

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Authors: M. Bagavandas

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Introduction: South Indian stone sculptures are known for their elegance and history. They are available in large numbers in different monuments situated different parts of South India. These art pieces have been studied using iconography details, but this pioneering study introduces a novel method known as iconometry which is a quantitative study that deals with measurements of different parts of icons to find answers for important unanswered questions. The main aim of this paper is to compare iconometric measurements of the sculptures with canonical proportion to determine whether the sculptors of the past had followed any of the canonical proportions prescribed in the ancient text. If not, this study recovers the proportions used for carving sculptures which is not available to us now. Also, it will be interesting to see how these sculptural proportions of different monuments belonging to different dynasties differ from one another in terms these proportions. Methods and Materials: As Indian sculptures are depicted in different postures, one way of making measurements independent of size, is to decode on a suitable measurement and convert the other measurements as proportions with respect to the chosen measurement. Since in all canonical texts of Indian art, all different measurements are given in terms of face length, it is chosen as the required measurement for standardizing the measurements. In order to compare these facial measurements with measurements prescribed in Indian canons of Iconography, the ten facial measurements like face length, morphological face length, nose length, nose-to-chin length, eye length, lip length, face breadth, nose breadth, eye breadth and lip breadth were standardized using the face length and the number of measurements reduced to nine. Each measurement was divided by the corresponding face length and multiplied by twelve and given in angula unit used in the canonical texts. The reason for multiplying by twelve is that the face length is given as twelve angulas in the canonical texts for all figures. Clustering techniques were used to determine whether the sculptors of the past had followed any of the proportions prescribed in the canonical texts of the past to carve sculptures and also to compare the proportions of sculptures of different monuments. About one hundred twenty-seven stone sculptures from four monuments belonging to the Pallava, the Chola, the Pandya and the Vijayanagar dynasties were taken up for this study. These art pieces belong to a period ranging from the eighth to the sixteenth century A.D. and all of them adorning different monuments situated in different parts of Tamil Nadu State, South India. Anthropometric instruments were used for taking measurements and the author himself had measured all the sample pieces of this study. Result: Statistical analysis of sculptures of different centers of art from different dynasties shows a considerable difference in facial proportions and many of these proportions differ widely from the canonical proportions. The retrieved different facial proportions indicate that the definition of beauty has been changing from period to period and region to region.

Keywords: iconometry, proportions, sculptures, statistics

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Authors: Kanogkan Leerojanaprapa

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Textile and garment industry is used to a major exporting industry of Thailand. According to lacking of the nation's price-competitiveness by stopping the EU's GSP (Generalised Scheme of Preferences) and ‘Nationwide Minimum Wage Policy’ that Thailand’s employers must pay all employees at least 300 baht (about $10) a day, the supply chains of the Thai textile and garment industry is affected and need to be reformed. Therefore, either Thai textile or garment industry will be existed or not would be concerned. This is also challenged for the government to decide which industries should be promoted the future industries of Thailand. Recently Thai government launch The Cluster-based Special Economic Development Zones Policy for promoting business cluster (effect on September 16, 2015). They define a cluster as the concentration of interconnected businesses and related institutions that operate within the same geographic areas and textiles and garment is one of target industrial clusters and 9 provinces are targeted (Bangkok, Kanchanaburi, Nakhon Pathom, Ratchaburi, Samut Sakhon, Chonburi, Chachoengsao, Prachinburi, and Sa Kaeo). The cluster zone are defined to link west-east corridor connected to manufacturing source in Cambodia and Mynmar to Bangkok where are promoted to be design, sourcing, and trading hub. The Thai government will provide tax and non-tax incentives for targeted industries within the clusters and expects these businesses are scattered to where they can get the most benefit which will identify future industrial cluster. This research will show the difference between the current cluster and future cluster following the target provinces of the textile and garment. The current cluster is analysed from secondary data. The four characteristics of the numbers of plants in Spinning, weaving and finishing of textiles, Manufacture of made-up textile articles, except apparel, Manufacture of knitted and crocheted fabrics, and Manufacture of other textiles, not elsewhere classified in particular 77 provinces (in total) are clustered by K-means cluster analysis and Hierarchical Cluster Analysis. In addition, the cluster can be confirmed and showed which variables contribute the most to defined cluster solution with ANOVA test. The results of analysis can identify 22 provinces (which the textile or garment plants are located) into 3 clusters. Plants in cluster 1 tend to be large numbers of plants which is only Bangkok, Next plants in cluster 2 tend to be moderate numbers of plants which are Samut Prakan, Samut Sakhon and Nakhon Pathom. Finally plants in cluster 3 tend to be little numbers of plants which are other 18 provinces. The same methodology can be implemented in other industries for future study.

Keywords: ANOVA, hierarchical cluster analysis, industrial clusters, K -means cluster analysis, textile and garment industry

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Authors: Masilonyane Mokhele

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Across the world, different planning models of the so-called airport-led developments are becoming bandwagons hailed as key to the future of cities. However, in the existing knowledge, there is paucity of empirically informed description and explanation of the economic fundamentals driving the forces of attraction of airports. This void is arguably a result of the absence of an appropriate theoretical framework to guide the analyses. Given this paucity, the aim of the paper is to contribute towards a theoretical framework that could be used to describe and explain forces that drive the location and mix of airport-centric developments. Towards achieving this aim, the objectives of the paper are: one, to establish the type of economic activities that are located on and around O.R. Tambo International Airport (ORTIA), and analyse the reasons for locating there; two, to establish changes that have occurred over time in the form of the airport-centric development of ORTIA; three, to identify the propulsive economic qualities of ORTIA; four, to analyse the spatial, economic and structural linkages within the airport-centric development of ORTIA, between the airport-centric development and the airport, as well as the airport-centric development’s linkages with their metropolitan area and other regional, national and international airport-centric developments and locations. To address the objectives above, the study adopted a case study approach, centred on ORTIA in South Africa: Africa’s busiest airport in terms of passengers and airfreight handled. Using a lens of location theory, a survey was adopted as a main research method, wherein telephonic interviews were conducted with a representative number of firms on and around ORTIA. Other data collection methods encompassed in-depth qualitative interviews (to augment the information obtained through the survey) and analysis of secondary information, particularly as regards establishing changes that have occurred in the form of ORTIA and surrounds. From the empirical findings, ORTIA was discovered to have propulsive economic qualities that act as significant forces of attraction in the clustering of firms. Together with its airport-centric development, ORTIA was discovered to have growth pole properties because of the linkages that occur within the study area, and the linkages that exist between the airport-centric firms and the airport. It was noted that the transport-oriented firms (typified by couriers and freight carriers) act as anchors in some fellow airport-centric firms making use of elements of urbanisation economies, particularly as regards the use of the airport for airfreight services. The empirical findings presented in the paper (in conjunction with results from other airport-centric development case studies) could be used as contribution towards extending theory that describes and explains forces that drive the location and mix of airport-centric developments.

Keywords: airports, airport-centric development, O. R. Tambo international airport, South Africa

Procedia PDF Downloads 268

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

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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 721

Authors: Edward Shizha, Edward Makwarimba

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Newcomer and refugee youth feel excluded in the education system in Canada, and the formal education environment does not fully cater for their learning needs. The objective of this study was to build knowledge and understanding of the educational needs and experiences of these youth in Canada and how available afterschool programs can most effectively support their learning needs and academic outcomes. The Employment and Social Development Canada (ESDC), which funded this research, enables and empowers students to advance their educational experience through targeted investments in services that are delivered by youth-serving organizations outside the formal education system through afterschool initiatives. A literature review and a provincial/territorial internet scan were conducted to determine the availability of services and programs that serve the educational needs and academic outcomes of newcomer youth in 10 provinces and 3 territories in Canada. The goal was to identify intersectional factors (e.g., gender, sexuality, culture, social class, race, etc.) that influence educational outcomes of newcomer/refugee students and to recommend ways the ESDC could complement settlement services to enhance students’ educational success. First, data was collected through a literature search of various databases, including PubMed, Web of Science, Scopus, Google docs, ACADEMIA, and grey literature, including government documents, to inform our analysis. Second, a provincial/territorial internet scan was conducted using a template that was created by ESDC staff with the input of the researchers. The objective of the web-search scan was to identify afterschool programs, projects, and initiatives offered to newcomer/refugee youth by service provider organizations. The method for the scan included both qualitative and quantitative data gathering. Both the literature review and the provincial/territorial scan revealed that there are gender disparities in educational outcomes of newcomer and refugee youth. High school completion rates by gender show that boys are at higher risk of not graduating than girls and that girls are more likely than boys to have at least a high school diploma and more likely to proceed to postsecondary education. Findings from literature reveal that afterschool programs are required for refugee youth who experience mental health challenges and miss out on significant periods of schooling, which affect attendance, participation, and graduation from high school. However, some refugee youth use their resilience and ambition to succeed in their educational outcomes. Another finding showed that some immigrant/refugee students, through ethnic organizations and familial affiliation, maintain aspects of their cultural values, parental expectations and ambitious expectations for their own careers to succeed in both high school and postsecondary education. The study found a significant combination of afterschool programs that include academic support, scholarships, bursaries, homework support, career readiness, internships, mentorship, tutoring, non-clinical counselling, mental health and social well-being support, language skills, volunteering opportunities, community connections, peer networking, culturally relevant services etc. These programs assist newcomer youth to develop self-confidence and prepare for academic success and future career development. The study concluded that advantages of afterschool programs are greatest for youth at risk for poor educational outcomes, such as Latino and Black youth, including 2SLGBTQI+ immigrant youth.

Keywords: afterschool programs, educational outcomes, newcomer youth, refugee youth, youth-serving organizations

Procedia PDF Downloads 74

Authors: Felix Schumann, Giorgia Carratta, Tobias Dauth, Liv Jaeckel

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In the last few decades, the rapid growth of the use and disposal of plastic items has led to their overaccumulation in the environment. As a result, plastic pollution has become a subject of global concern. Today plastics are used as raw materials in almost every industry. While the recognition of the ecological, social, and economic impact of plastics in academic research is on the rise, the potential role of the ‘plastic industry’ in dealing with such issues is still largely underestimated. Therefore, the literature on sustainable plastic management is still nascent and fragmented. Working towards sustainability requires a fundamental shift in the way companies employ plastics in their day-to-day business. For that reason, the applicability of the business model concept has recently gained momentum in environmental research. Business model innovation is increasingly recognized as an important driver to re-conceptualize the purpose of the firm and to readily integrate sustainability in their business. It can serve as a starting point to investigate whether and how sustainability can be realized under industry- and firm-specific circumstances. Yet, there is no comprehensive view in the plastic industry on how firms start refining their business models to embed sustainability in their operations. Our study addresses this gap, looking primarily at the industrial sectors responsible for the production of the largest amount of plastic waste today: plastic packaging, consumer goods, construction, textile, and transport. Relying on the archetypes of sustainable business models and applying them to the aforementioned sectors, we try to identify companies’ current strategies to make their business models more sustainable. Based on the thematic clustering, we can develop an integrative framework for the plastic industry. The findings are underpinned and illustrated by a variety of relevant plastic management solutions that the authors have identified through a systematic literature review and analysis of existing, empirically grounded research in this field. Using the archetypes, we can promote options for business model innovations for the most important sectors in which plastics are used. Moreover, by linking the proposed business model archetypes to the plastic industry, our research approach guides firms in exploring sustainable business opportunities. Likewise, researchers and policymakers can utilize our classification to identify best practices. The authors believe that the study advances the current knowledge on sustainable plastic management through its broad empirical industry analyses. Hence, the application of business model archetypes in the plastic industry will be useful for shaping companies’ transformation to create and deliver more sustainability and provides avenues for future research endeavors.

Keywords: business models, environmental economics, plastic management, plastic pollution, sustainability

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Authors: A. Bentley, A. Prost, N. Daruwalla, D. Osrin

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Background: This study aims to investigate the relationship between experiences of violence against women in the family, and levels of self-esteem in women residing in informal settlement (slum) areas of Mumbai, India. The authors hypothesise that violence against women in Indian households extends beyond that of intimate partner violence (IPV), to include other members of the family and that experiences of violence are associated with lower levels of self-esteem. Methods: Experiences of violence were assessed through a cross-sectional survey of 598 women, including questions about specific acts of emotional, economic, physical and sexual violence across different time points, and the main perpetrator of each. Self-esteem was assessed using the Rosenberg self-esteem questionnaire. A global score for self-esteem was calculated and the relationship between violence in the past year and Rosenberg self-esteem score was assessed using multivariable linear regression models, adjusted for years of education completed, and clustering using robust standard errors. Results: 482 (81%) women consented to interview. On average, they were 28.5 years old, had completed 6 years of education and had been married 9.5 years. 88% were Muslim and 46% lived in joint families. 44% of women had experienced at least one act of violence in their lifetime (33% emotional, 22% economic, 24% physical, 12% sexual). Of the women who experienced violence after marriage, 70% cited a perpetrator other than the husband for at least one of the acts. 5% had low self-esteem (Rosenberg score < 15). For women who experienced emotional violence in the past year, the Rosenberg score was 2.6 points lower (p < 0.001). It was 1.2 points lower (p = 0.03) for women who experienced economic violence. For physical or sexual violence in the past year, no statistically significant relationship with Rosenberg score was seen. However, for a one-unit increase in the number of different acts of each type of violence experienced in the past year, a decrease in Rosenberg score was seen (-0.62 for emotional, -0.76 for economic, -0.53 for physical and -0.47 for sexual; p < 0.05 for all). Discussion: The high prevalence of violence experiences across the lifetime was likely due to the detailed assessment of violence and the inclusion of perpetrators within the family other than the husband. Experiences of emotional or economic violence in the past year were associated with lower Rosenberg scores and therefore lower self-esteem, but no relationship was seen between experiences of physical or sexual violence and Rosenberg score overall. For all types of violence in the past year, a greater number of different acts were associated with a decrease in Rosenberg score. Emotional violence showed the strongest relationship with self-esteem, but for all types of violence the more complex the pattern of perpetration with different methods used, the lower the levels of self-esteem. Due to the cross-sectional nature of the study causal directionality cannot be attributed. Further work to investigate the relationship between severity of violence and self-esteem and whether self-esteem mediates relationships between violence and poorer mental health would be beneficial.

Keywords: family violence, India, informal settlements, Rosenberg self-esteem scale, self-esteem, violence against women

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Authors: Shengnan Chen, Shuhua Wang

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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

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Ruchi B. Semwal, Deepak K. Semwal, Thobile A. N. Nkosi, Alvaro M. Viljoen

Abstract:

The use of Lawsonia inermis L. (Lythraeae), commonly known as henna, has many medicinal benefits and is used as a remedy for the treatment of diarrhoea, cancer, inflammation, headache, jaundice and skin diseases in folk medicine. Although widely used for hair dyeing and temporary tattooing, henna body art has popularized over the last 15 years and changed from being a traditional bridal and festival adornment to an exotic fashion accessory. The naphthoquinone, lawsone, is one of the main constituents of the plant and responsible for its dyeing property. Henna leaves typically contain 1.8–1.9% lawsone, which is used as a marker compound for the quality control of henna products. Adulteration of henna with various toxic chemicals such as p-phenylenediamine, p-methylaminophenol, p-aminobenzene and p-toluenodiamine to produce a variety of colours, is very common and has resulted in serious health problems, including allergic reactions. This study aims to assess the quality of henna products collected from different parts of the world by determining the lawsone content, as well as the concentrations of any adulterants present. Ultra high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to determine the lawsone concentrations in 172 henna products. Separation of the chemical constituents was achieved on an Acquity UPLC BEH C18 column using gradient elution (0.1% formic acid and acetonitrile). The results from UPLC-MS revealed that of 172 henna products, 11 contained 1.0-1.8% lawsone, 110 contained 0.1-0.9% lawsone, whereas 51 samples did not contain detectable levels of lawsone. High performance thin layer chromatography was investigated as a cheaper, more rapid technique for the quality control of henna in relation to the lawsone content. The samples were applied using an automatic TLC Sampler 4 (CAMAG) to pre-coated silica plates, which were subsequently developed with acetic acid, acetone and toluene (0.5: 1.0: 8.5 v/v). A Reprostar 3 digital system allowed the images to be captured. The results obtained corresponded to those from UPLC-MS analysis. Vibrational spectroscopy analysis (MIR or NIR) of the powdered henna, followed by chemometric modelling of the data, indicates that this technique shows promise as an alternative quality control method. Principal component analysis (PCA) was used to investigate the data by observing clustering and identifying outliers. Partial least squares (PLS) multivariate calibration models were constructed for the quantification of lawsone. In conclusion, only a few of the samples analysed contain lawsone in high concentrations, indicating that they are of poor quality. Currently, the presence of adulterants that may have been added to enhance the dyeing properties of the products, is being investigated.

Keywords: Lawsonia inermis, paraphenylenediamine, temporary tattooing, lawsone

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

Abstract:

Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

Abstract:

This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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