Search results for: models synthesis

Authors: Cyril O. Ehi-Eromosele, Ajayi Kayode

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The Li-rich layered metal oxides (LLO) are the most promising candidates for promising electrodes of high energy Li-ion battery (LIB). In literature, these electrode system has either been designed as a hetero-structure of the primary components (composite) or as a core-shell structure with improved electrochemistry reported for both configurations when compared with its primary components. With the on-going efforts to improve on the electrochemical performance of the LIB, it is important to investigate comparatively the structural and electrochemical characteristics of the core-shell like and ‘composite’ forms of these materials with the same compositions and synthesis conditions which could influence future engineering of these materials. Therefore, this study concerns the structural and electrochemical properties of the ‘composite’ and core-shell like LLO cathode materials with the same nominal composition of 0.5Li₂MnO₃-0.5LiNi₀.₅Mn₀.₃Co₀.₂O₂ (LiNi₀.₅Mn₀.₃Co₀.₂O₂ as core and Li₂MnO₃ as the shell). The results show that the core-shell sample (–CS) gave better electrochemical performance than the ‘composite’ sample (–C). Both samples gave the same initial charge capacity of ~300 mAh/g when cycled at 10 mA/g and comparable charge capacity (246 mAh/g for the –CS sample and 240 mAh/g for the –C sample) when cycled at 200 mA/g. However, the –CS sample gave a higher initial discharge capacity at both current densities. The discharge capacity of the –CS sample was 232 mAh/g and 164 mAh/g while the –C sample is 208 mAh/g and 143 mAh/g at the current densities of 10 mA/g and 200 mA/g, respectively. Electrochemical impedance spectroscopy (EIS) results show that the –CS sample generally exhibited a smaller resistance than the –C sample both for the uncycled and after 50th cycle. Detailed structural analysis is on-going, but preliminary results show that the –CS sample had bigger unit cell volume and a higher degree of cation mixing. The thermal stability of the –CS sample was higher than the –C sample. XPS investigation also showed that the pristine –C sample gave a more reactive surface (showing formation of carbonate species to a greater degree) which could result in the greater resistance seen in the EIS result. To reinforce the results obtained for the 0.5Li₂MnO₃-0.5LiNi₀.₅Mn₀.₃Co₀.₃O₂ composition, the same investigations were extended to another ‘composite’ and core-shell like LLO cathode materials also with the same nominal composition of 0.5Li₂MnO₃-0.5LiNi₀.₃Mn₀.₃Co₀.₃O₂. In this case, the aim was to determine the electrochemical performance of the material using a low Ni content (LiNi₀.₃Mn₀.₃Co₀.₃O₂) as the core to clarify the contributions of the core-shell configuration to the electrochemical performance of these materials. Ni-rich layered oxides show active catalytic surface leading to electrolyte oxidation resulting in poor thermal stability and cycle life. Here, the core-shell sample also gave better electrochemical performance than the ‘composite’ sample with 0.5Li₂MnO₃-0.5LiNi₀.₃Mn₀.₃Co₀.₃O₂ composition. Furthermore, superior electrochemical performance was also recorded for the core-shell like spinel modified LLO (0.5Li₂MnO₃-0.45LiNi₀.₅Mn₀.₃Co₀.₂O₂-0.05LiNi₀.₅Mn₁.₅O₄) when compared to the composite system. These results show that the core-shell configuration can generally be used to improve the structural and electrochemical properties of the LLO and spinel modified LLO materials.

Keywords: lithium-ion battery, lithium rich oxide cathode, core-shell structure, composite structure

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Authors: Nia McRae, Robert Wright, Ghalib Bello

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Introduction: Postpartum depression is a prevalent mood disorder that is detrimental to the mental and physical health of mothers and their newborns. Lead (Pb) is a toxic metal that is associated with hormonal imbalance and mental impairments. The hormone changes that accompany pregnancy and childbirth may be exacerbated by Pb and increase new mothers’ susceptibility to postpartum depression. To the best of the author’s knowledge, this is the only study that investigates the association between prenatal Pb exposure and postpartum depression. Identifying risk factors can contribute to improved prevention and treatment strategies for postpartum depression. Methods: Data was derived from the Programming Research in Obesity, Growth, Environment and Social Stress (PROGRESS) study which is an ongoing longitudinal birth cohort. Postpartum depression was identified by a score of 13 or above on the 10-Item Edinburg Postnatal Depression Scale (EPDS) 6-months and 12-months postpartum. Pb was measured in the blood (BPb) in the second and third trimester and in the tibia and patella 1-month postpartum. Quantile regression models were used to assess the relationship between BPb and postpartum depression. Results: BPb in the second trimester was negatively associated with the 80th percentile of depression 6-months postpartum (β: -0.26; 95% CI: -0.51, -0.01). No significant association was found between BPb in the third trimester and depression 6-months postpartum. BPb in the third trimester exhibited an inverse relationship with the 60th percentile (β: -0.23; 95% CI: -0.41, -0.06), 70th percentile (β: -0.31; 95% CI: -0.52, -0.10), and 90th percentile of depression 12-months postpartum (β: -0.36; 95% CI: -0.69, -0.03). There was no significant association between BPb in the second trimester and depression 12-months postpartum. Bone Pb concentrations were not significantly associated with postpartum depression. Conclusion: The negative association between BPb and postpartum depression may support research which demonstrates lead is a nontherapeutic stimulant. Further research is needed to verify these results and identify effect modifiers.

Keywords: depression, lead, postpartum, prenatal

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Authors: Vinicius Marzall, Jussanã Milograna

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The urban drainwatering remains a major challenge for most of the Brazilian cities. Not so different of the most part, Goiania, a state capital located in Midwest of the country has few legislations about the subject matter and only one registered solution of compensative techniques for drainwater. This paper clam to show some solutions which are adopted in other Brazilian cities with consolidated legislation, suggesting technics about detention tanks in a building sit. This study analyzed and compared the legislation of Curitiba, Porto Alegre e Sao Paulo, with the actual legislation and politics of Goiania. After this, were created models with adopted data for dimensioning the size of detention tanks using the envelope curve method considering synthetic series for intense precipitations and building sits between 250 m² and 600 m², with an impermeabilization tax of 50%. The results showed great differences between the legislation of Goiania and the documentation of the others cities analyzed, like the number of techniques for drainwatering applied to the reality of the cities, educational actions to awareness the population about care the water courses and political management by having a specified funds for drainwater subjects, for example. Besides, the use of detention tank showed itself practicable, have seen that the occupation of the tank is minor than 3% of the building sit, whatever the size of the terrain, granting the exit flow to pre-occupational taxes in extreme rainfall events. Also, was developed a linear equation to measure the detention tank based in the size of the building sit in Goiania, making simpler the calculation and implementation for non-specialized people.

Keywords: clean technology, legislation, rainwater management, urban drainwater

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Authors: Mahfoudh Hussein Mgammal

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The purpose of this paper is to investigate the moderating and mediating effect of corporate governance mechanisms proxy on the relationship of tax planning measured by effective tax rate components and tax disclosure. This paper tested the hypotheses by a 3-step hierarchical regression with 2010 to 2012 Malaysian-listed nonfinancial firms. We found companies positively value tax-planning activities. This indicates that tax planning is seen as a source of companies' wealth creation as the results show that there is an association between the tax disclosure and the extent of tax planning, and this relationship is highly significant. Examination of the implications of corporate governance mechanisms on the tax disclosure-tax planning association showed the lack of a significant coefficient related to any of the interactive variables. This makes it hard to understand the nature of the association. Finally, we further study the sensitivity of the results, the outcomes were also examined for the robustness and strength of the model specification utilizing OLS-effect estimators and the absence of tax planning related factors (GRTH, LEVE, and CAPNT). The findings of these tests display there is no effect on the tax planning-tax disclosure association. The outcomes of the annual regressions test show that the panel regressions results differ over time because there is a time difference impact on the associations, and the different models are not completely proportionate as a whole. Moreover, our paper lends some support to recent theory on the importance of taxes to corporate governance by demonstrating how the agency costs of tax planning allow certain shareholders to benefit from firm activities at the expense of others.

Keywords: tax disclosure, tax planning, corporate governance, effective tax rate

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Authors: Anuj Tyagi, Balwinder Singh, Naveen Kumar B. T., Niraj K. Singh

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Characterization of diverse microbial communities in specific environment plays a crucial role in the better understanding of their functional relationship with the ecosystem. It is now well established that gut microbiome of fish is not the simple replication of microbiota of surrounding local habitat, and extensive species, dietary, physiological and metabolic variations in fishes may have a significant impact on its composition. Moreover, overuse of antibiotics in human, veterinary and aquaculture medicine has led to rapid emergence and propagation of antibiotic resistance genes (ARGs) in the aquatic environment. Microbial communities harboring specific ARGs not only get a preferential edge during selective antibiotic exposure but also possess the significant risk of ARGs transfer to other non-resistance bacteria within the confined environments. This phenomenon may lead to the emergence of habitat-specific microbial resistomes and subsequent emergence of virulent antibiotic-resistant pathogens with severe fish and consumer health consequences. In this study, gut microbiota of freshwater carp (Labeo rohita) was investigated by shotgun metagenomics to understand its taxonomic composition and functional capabilities. Metagenomic DNA, extracted from the fish gut, was subjected to sequencing on Illumina NextSeq to generate paired-end (PE) 2 x 150 bp sequencing reads. After the QC of raw sequencing data by Trimmomatic, taxonomic analysis by Kraken2 taxonomic sequence classification system revealed the presence of 36 phyla, 326 families and 985 genera in the fish gut microbiome. At phylum level, Proteobacteria accounted for more than three-fourths of total bacterial populations followed by Actinobacteria (14%) and Cyanobacteria (3%). Commonly used probiotic bacteria (Bacillus, Lactobacillus, Streptococcus, and Lactococcus) were found to be very less prevalent in fish gut. After sequencing data assembly by MEGAHIT v1.1.2 assembler and PROKKA automated analysis pipeline, pathway analysis revealed the presence of 1,608 Metacyc pathways in the fish gut microbiome. Biosynthesis pathways were found to be the most dominant (51%) followed by degradation (39%), energy-metabolism (4%) and fermentation (2%). Almost one-third (33%) of biosynthesis pathways were involved in the synthesis of secondary metabolites. Metabolic pathways for the biosynthesis of 35 antibiotic types were also present, and these accounted for 5% of overall metabolic pathways in the fish gut microbiome. Fifty-one different types of antibiotic resistance genes (ARGs) belonging to 15 antimicrobial resistance (AMR) gene families and conferring resistance against 24 antibiotic types were detected in fish gut. More than 90% ARGs in fish gut microbiome were against beta-lactams (penicillins, cephalosporins, penems, and monobactams). Resistance against tetracycline, macrolides, fluoroquinolones, and phenicols ranged from 0.7% to 1.3%. Some of the ARGs for multi-drug resistance were also found to be located on sequences of plasmid origin. The presence of pathogenic bacteria and ARGs on plasmid sequences suggested the potential risk due to horizontal gene transfer in the confined gut environment.

Keywords: antibiotic resistance, fish gut, metabolic pathways, microbial diversity

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Authors: Behnaz Moradijamei, Michael Higgins

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Identifying community structure is a critical task in analyzing social media data sets often modeled by networks. Statistical models such as the stochastic block model have proven to explain the structure of communities in real-world network data. In this work, we develop a goodness-of-fit test to examine community structure's existence by using a distinguishing property in networks: cyclic structures are more prevalent within communities than across them. To better understand how communities are shaped by the cyclic structure of the network rather than just the number of edges, we introduce a novel method for deciding on the existence of communities. We utilize these structures by using renewal non-backtracking random walk (RNBRW) to the existing goodness-of-fit test. RNBRW is an important variant of random walk in which the walk is prohibited from returning back to a node in exactly two steps and terminates and restarts once it completes a cycle. We investigate the use of RNBRW to improve the performance of existing goodness-of-fit tests for community detection algorithms based on the spectral properties of the adjacency matrix. Our proposed test on community structure is based on the probability distribution of eigenvalues of the normalized retracing probability matrix derived by RNBRW. We attempt to make the best use of asymptotic results on such a distribution when there is no community structure, i.e., asymptotic distribution under the null hypothesis. Moreover, we provide a theoretical foundation for our statistic by obtaining the true mean and a tight lower bound for RNBRW edge weights variance.

Keywords: hypothesis testing, RNBRW, network inference, community structure

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Authors: Julio C. Ferreira, Maria T. A. Steiner

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The aim of this work is to present a solution procedure referred to here as the Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP) to provide solutions for a case study. The proposed methodology consists of three stages to resolve Scenario A. Stage 1 consists of the “treatment” of data; Stage 2 consists of applying mathematical models of the p-Median Capacitated Problem (with the objectives of minimization of distances and homogenization of demands between groups) and the Asymmetric Traveling Salesman Problem (with the objectives of minimizing distances and minimizing time). The weighted method was used as the multi-objective procedure. In Stage 3, an analysis of the results is conducted, taking into consideration the environmental aspects related to the case study, more specifically with regard to fuel consumption and air pollutant emission. This methodology was applied to a (partial) database that addresses newspaper distribution in the municipality of Curitiba, Paraná State, Brazil. The preliminary findings for Scenario A showed that it was possible to improve the distribution of the load, reduce the mileage and the greenhouse gas by 17.32% and the journey time by 22.58% in comparison with the current scenario. The intention for future works is to use other multi-objective techniques and an expanded version of the database and explore the triple bottom line of sustainability.

Keywords: Asymmetric Traveling Salesman Problem, Green Vehicle Routing Problem, Multi-objective Optimization, p-Median Capacitated Problem

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Authors: Meenu S. Babu, K. Jayasankara Reddy

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Depression is a serious mental health concern that leads to significant distress and dysfunction in an individual. Due to the high physical, psychological, social, and economic burden it causes, it is important to study various bio-psycho-social factors that influence the onset, course, duration, intensity of depressive symptoms. The study aims to explore relationship between autobiographical memory (AM) functions, perceived control over stressful events and depressive symptoms. AM functions and perceived control were both found to be protective factors for individuals against depression and were both modifiable to predict better behavioral and affective outcomes. An extensive review of literatur, with a systematic search on Google Scholar, JSTOR, Science Direct and Springer Journals database, was conducted for the purpose of this review paper. These were used for all the aforementioned databases. The time frame used for the search was 2010-2021. An additional search was conducted with no time bar to map the development of the theoretical concepts. The relevant studies with quantitative, qualitative, experimental, and quasi- experimental research designs were included for the review. Studies including a sample with a DSM- 5 or ICD-10 diagnosis of depressive disorders were excluded from the study to focus on the behavioral patterns in a non-clinical population. The synthesis of the findings that were obtained from the review indicates there is a significant relationship between cognitive variables of AM functions and perceived control and depressive symptoms. AM functions were found to be have significant effects on once sense of self, interpersonal relationships, decision making, self- continuity and were related to better emotion regulation and lower depressive symptoms. Not all the components of AM function were equally significant in their relationships with various depressive symptoms. While self and directive functions were more related to emotion regulation, anhedonia, motivation and hence mood and affect, the social function was related to perceived social support and social engagement. Perceived control was found to be another protective cognitive factor that provides individuals a sense of agency and control over one’s life outcomes which was found to be low in individuals with depression. This was also associated to the locus of control, competency beliefs, contingency beliefs and subjective well being in individuals and acted as protective factors against depressive symptoms. AM and perceived control over stressful events serve adaptive functions, hence it is imperative to study these variables more extensively. They can be imperative in planning and implementing therapeutic interventions to foster these cognitive protective factors to mitigate or alleviate depressive symptoms. Exploring AM as a determining factor in depressive symptoms along with perceived control over stress creates a bridge between biological and cognitive factors underlying depression and increases the scope of developing a more eclectic and effective treatment plan for individuals. As culture plays a crucial role in AM functions as well as certain aspects of control such as locus of control, it is necessary to study these variables keeping in mind the cultural context to tailor culture/community specific interventions for depression.

Keywords: autobiographical memories, autobiographical memory functions, perceived control, depressive symptoms, depression, young adults

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Authors: Thamer Kubat, Riadh Al-Mahaidi, Ahmad Shayan

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The alkali-aggregate reaction (AAR) in concrete has a negative influence on the mechanical properties and durability of concrete. Confinement by carbon fibre-reinforced polymer (CFRP) is an effective method of treatment for some AAR-affected elements. Eighteen reinforced columns affected by different levels of expansion due to AAR were confined using CFRP to evaluate the effect of expansion level on confinement efficiency. Strength and strain capacities (axial and circumferential) were measured using photogrammetry under uniaxial compressive loading to evaluate the efficiency of CFRP wrapping for the rehabilitation of affected columns. In relation to uniaxial compression capacity, the results indicated that the confinement of AAR-affected columns by one layer of CFRP is sufficient to reach and exceed the load capacity of unaffected sound columns. Parallel to the experimental study, finite element (FE) modeling using ATENA software was employed to predict the behavior of CFRP-confined damaged concrete and determine the possibility of using the model in a parametric study by simulating the number of CFRP layers. A comparison of the experimental results with the results of the theoretical models showed that FE modeling could be used for the prediction of the behavior of confined AAR-damaged concrete.

Keywords: carbon fiber reinforced polymer (CFRP), finite element (FE), ATENA, confinement efficiency

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Authors: Hassan M. H. Mustafa

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This piece of research addresses an interesting comparative analytical study. Which considers two concepts of diverse algorithmic computational intelligence approaches related tightly with Neural and Non-Neural Systems. The first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work. Besides a mouse’s trial during its movement inside figure of eight (8) maze, to reach an optimal solution for reconstruction problem. Conversely, second algorithmic intelligent approach originated from observed activities’ results for Non-Neural Ant Colony System (ACS). These results obtained after reaching an optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance shown to be similar for both introduced systems. Finally, performance of both intelligent learning paradigms shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.

Keywords: artificial neural network modeling, animal learning, ant colony system, traveling salesman problem, computational biology

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Authors: Muhammad Shahid, Muhammad Mansoor

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Increasing demands of contemporary applications for high strength and lightweight materials prompted the development of metal-matrix composites (MMCs). After the discovery of carbon nanotubes (CNTs) in 1991 (revealing an excellent set of mechanical properties) became one of the most promising strengthening materials for MMC applications. Additionally, the relatively low density of the nanotubes imparted high specific strengths, making them perfect strengthening material to reinforce MMCs. In the present study, aluminum-multiwalled carbon nanotubes (Al-MWCNTs) composite was prepared in an air induction furnace. The dispersion of the nanotubes in molten aluminum was assisted by inherent string action of induction heating at 790°C. During the fabrication process, multifunctional fluxes were used to avoid oxidation of the nanotubes and molten aluminum. Subsequently, the melt was cast in to a copper mold and cold rolled to 0.5 mm thickness. During metallographic examination using a scanning electron microscope, it was observed that the nanotubes were effectively dispersed in the matrix. The mechanical properties of the composite were significantly increased as compared to pure aluminum specimen i.e. the yield strength from 65 to 115 MPa, the tensile strength from 82 to 125 MPa and hardness from 27 to 30 HV for pure aluminum and Al-CNTs composite, respectively. To recognize the associated strengthening mechanisms in the nanocomposites, three foremost strengthening models i.e. shear lag model, Orowan looping and Hall-Petch have been critically analyzed; experimental data were found to be closely satisfying the shear lag model.

Keywords: carbon nanotubes, induction melting, strengthening mechanism, nanocomposite

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Authors: Kelvin Lo

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Nowadays, numerical modelling in geotechnical engineering is very common but sophisticated. Many advanced input settings and considerable computational efforts are required to optimize the design to reduce the construction cost. To optimize a design, it usually requires huge numerical models. If the optimization is conducted manually, there is a potentially dangerous consequence from human errors, and the time spent on the input and data extraction from output is significant. This paper presents an automation process introduced to numerical modelling (Plaxis 2D) of a trench excavation supported by a secant-pile retaining structure for a top-down tunnel project. Python code is adopted to control the process, and numerical modelling is conducted automatically in every 20m chainage along the 200m tunnel, with maximum retained height occurring in the middle chainage. Python code continuously changes the geological stratum and excavation depth under groundwater flow conditions in each 20m section. It automatically conducts trial and error to determine the required pile length and the use of props to achieve the required factor of safety and target displacement. Once the bending moment of the pile exceeds its capacity, it will increase in size. When the pile embedment reaches the default maximum length, it will turn on the prop system. Results showed that it saves time, increases efficiency, lowers design costs, and replaces human labor to minimize error.

Keywords: automation, numerical modelling, Python, retaining structures

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Authors: Florian Monetti, Brett Beamsley, Peter McComb, Simon Weppe

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Land reclamation from the ocean has considerably increased over past decades to support worldwide rapid urban growth. Reshaping the coastline, however, inevitably affects coastal systems. One of the main challenges for coastal oceanographers is to predict the physical and morphological responses for nearshore systems to man-made changes over multiple time-scales. Fully-coupled numerical models are powerful tools for simulating the wide range of interactions between flow field and bedform morphology. Restricted and inconsistent measurements, combined with limited computational resources, typically make this exercise complex and uncertain. In the present study, we investigate the impact of proposed land reclamation within a wave-dominated bay in New Zealand. For this purpose, we first calibrated our morphological model based on the long-term evolution of the bay resulting from land reclamation carried out in the 1950s. This included the application of sedimentological spin-up and reduction techniques based on historical bathymetry datasets. The updated bathymetry, including the proposed modifications of the bay, was then used to predict the effect of the proposed land reclamation on the wave climate and morphology of the bay after one decade. We show that reshaping the bay induces a distinct symmetrical response of the shoreline which likely will modify the nearshore wave patterns and consequently recreational activities in the area.

Keywords: coastal waves, impact of land reclamation, long-term coastal evolution, morphodynamic modeling

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Authors: Donatien Niragira

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The progress of technology creates new expectations for patients. The world of deafness is no exception. In recent years, there have been considerable advances in the field of technologies aimed at assisting failing hearing. According to the usual medical vocabulary, hearing aids are actually orthotics. They do not replace an organ but compensate for a functional impairment. The Amplifier Hearing amplification is useful for a large number of people with hearing loss. Hearing aids restore speech audibility. However, their benefits vary depending on the quality of residual hearing. The hearing aid is not a "cure" for deafness. It cannot correct all affected hearing abilities. It should be considered as an aid to communication. The urge to judge from the audiogram alone should be resisted here, as audiometry only indicates the ability to detect non-verbal sounds. To prevent hearing aids from ending up in the drawer, it is important to ensure that the patient's disability situations justify the use of this type of orthosis. If the problems of receptive Pre-fitting counseling are crucial: the person with hearing loss must be informed of the advantages and disadvantages of amplification in his or her case. Their expectations must be realistic. They also need to be aware that the adaptation process requires a good deal of patience and perseverance. They should be informed about the various models and types of hearing aids, including all the aesthetic, functional and financial considerations. If the person's motivation "survives" pre-fitting counseling, we are in the presence of a good candidate for amplification. In addition to its relevance, it shows that the results found in this study significantly improve the quality of audibility in the patient, from where this technology must be made accessible everywhere in the world.

Keywords: auditives protheses, hearing, aids, no medicaly treatable deafnes

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Authors: B.S. Roopa

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Objectives: To investigate the prevalence of concomitant use of GPDs in patients treated with NSAIDs and GPDs in recommended dose and frequency as prophylaxis. And also to know the association between risk factors and prescription of GPDs in patients treated with NSAIDs. Methods: Study was a prospective, observational, cross-sectional survey. Data from patients with prescription of NSAIDs at the out-patient departments of secondary care Hospital, Nilgiris, India were collected in a specially designed proforma for a period of 45 days. Analysis using χ2 tests for discrete variables. Factors that might be associated with prescription of GPD with NSIADs were assessed in multiple logistic regression models. Results: Three hundred and three patients were included in this study, and the rate of GPD prescription was 89.1%. Most of the patients received H2-receptor antagonist, and, to a lesser degree, antacid and proton pump inhibitor. Patients with history of GI ulcer/bleeding were much more likely to be co-prescribed GPD than those who had no history of GI disorders .Compared with patients who were managed in general outpatient clinic, those managed in Secondary care hospital in Nilgrisis, India were more likely to receive GPD. Conclusions: The prescription rate of GPD with NSAIDs is high. Patients were prescribed with H2RA with dose of 150mg twice daily, which are not effective in reducing the risk of NSAIDs induced gastric ulcer. Only the frequency of NSAIDs prescription was considered significant determinant for the co-prescription with GPAs in patients who are < 65 years and ≥ 65 years old.

Keywords: gastro protective agents, non steridol anti inlfammatory agents

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Authors: Zeineb Ben Salem, Wissem Frikha, Mounir Bouassida

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Liquefaction has been responsible for tremendous amounts of damage in historical earthquakes around the world. The installation of stone columns is widely adopted to prevent liquefaction. Stone columns provide a drainage path, and due to their high permeability, allow for the quick dissipation of earthquake generated excess pore water pressure. Several excess pore pressure generation models in silty sand have been developed and calibrated based on the results of shaking table and centrifuge tests focusing on the effect of silt content on liquefaction resistance. In this paper, the generation and dissipation of excess pore pressure variation of reinforced silty sand by stone columns during liquefaction are analyzedwith different silt content based on test results. In addition, the installation effect of stone columns is investigated. This effect is described by a decrease in horizontal permeability within a disturbed zone around the column. Obtained results show that reduced soil permeability and a larger disturbed zone around the stone column increases the generation of excess pore pressure during the cyclic loading and decreases the dissipation rate after cyclic loading. On the other hand, beneficial effects of silt content were observed in the form of a decrease in excess pore water pressure.

Keywords: stone column, liquefaction, excess pore pressure, silt content, disturbed zone, reduced permeability

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Authors: Mohsen Akhlaghi, Tahereh Ebrahimi

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Pricing strategy is a vital component in achieving the balance between customer value and business profitability. The aim of this study is to provide insights into the factors, techniques, and approaches involved in pricing decisions. The study utilizes a descriptive approach to discuss various aspects of pricing strategy in marketing, drawing on concepts from market research, consumer psychology, competitive analysis, and adaptability. This approach presents a comprehensive view of pricing decisions. The result of this exploration is a framework that highlights key factors influencing pricing decisions. The study examines how factors such as market positioning, product differentiation, and brand image shape pricing strategies. Additionally, it emphasizes the role of consumer psychology in understanding price elasticity, perceived value, and price-quality associations that influence consumer behavior. Various pricing techniques, including charm pricing, prestige pricing, and bundle pricing, are mentioned as methods to enhance sales by influencing consumer perceptions. The study also underscores the importance of adaptability in responding to market dynamics through regular price monitoring, dynamic pricing, and promotional strategies. It recognizes the role of digital platforms in enabling personalized pricing and dynamic pricing models. In conclusion, the study emphasizes that effective pricing strategies strike a balance between customer value and business profitability, ultimately driving sales, enhancing brand perception, and fostering lasting customer relationships.

Keywords: business, customer benefits, marketing, pricing

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Sina Keyhanian, Abbas Ahmadi, Behrooz Karimi

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We present a multi-objective binary programming model which considers surgical cases are scheduling among operating rooms and the configuration of surgical instruments in limited capacity hospital trays, simultaneously. Many mathematical models have been developed previously in the literature addressing different challenges in health-care logistics such as assigning operating rooms, leveling beds, etc. But what happens inside the operating rooms along with the inventory management of required instruments for various operations, and also their integration with surgical scheduling have been poorly discussed. Our model considers the minimization of movements between trays during a surgery which recalls the famous cell formation problem in group technology. This assumption can also provide a major potential contribution to robotic surgeries. The tray configuration problem which consumes surgical instruments requirement plan (SIRP) and sequence of surgical procedures based on required instruments (SIRO) is nested inside the bin packing problem. This modeling approach helps us understand that most of the same-output solutions will not be necessarily identical when it comes to the rearrangement of surgeries among rooms. A numerical example has been dealt with via a proposed nested simulated annealing (SA) optimization approach which provides insights about how various configurations inside a solution can alter the optimal condition.

Keywords: health-care logistics, hospital tray configuration, off-line bin packing, simulated annealing optimization, surgical case scheduling

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Ariane Plaisance, Holly O. Witteman, Patrick Michel Archambault

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Upon admission to an intensive care unit (ICU), all patients should discuss their wishes concerning life-sustaining interventions (e.g., cardiopulmonary resuscitation (CPR)). Without such discussions, interventions that prolong life at the cost of decreasing its quality may be used without appropriate guidance from patients. We employed user-centered design to adapt an existing decision aid (DA) about CPR to create a novel wiki-based DA adapted to the context of a single ICU and tailored to individual patient’s risk factors. During Phase 1, we conducted three weeks of ethnography of the decision-making context in our ICU to identify clinician and patient needs for a decision aid. During this time, we observed five dyads of intensivists and patients discussing their wishes concerning life-sustaining interventions. We also conducted semi-structured interviews with the attending intensivists in this ICU. During Phase 2, we conducted three rounds of rapid prototyping involving 15 patients and 11 other allied health professionals. We recorded discussions between intensivists and patients and used a standardized observation grid to collect patients’ comments and sociodemographic data. We applied content analysis to field notes, verbatim transcripts and the completed observation grids. Each round of observations and rapid prototyping iteratively informed the design of the next prototype. We also used the programming architecture of a wiki platform to embed the GO-FAR prediction rule programming code that we linked to a risk graphics software to better illustrate outcome risks calculated. During Phase I, we identified the need to add a section in our DA concerning invasive mechanical ventilation in addition to CPR because both life-sustaining interventions were often discussed together by physicians. During Phase II, we produced a context-adapted decision aid about CPR and mechanical ventilation that includes a values clarification section, questions about the patient’s functional autonomy prior to admission to the ICU and the functional decline that they would judge acceptable upon hospital discharge, risks and benefits of CPR and invasive mechanical ventilation, population-level statistics about CPR, a synthesis section to help patients come to a final decision and an online calculator based on the GO-FAR prediction rule. Even though the three rounds of rapid prototyping led to simplifying the information in our DA, 60% (n= 3/5) of the patients involved in the last cycle still did not understand the purpose of the DA. We also identified gaps in the discussion and documentation of patients’ preferences concerning life-sustaining interventions (e.g.,. CPR, invasive mechanical ventilation). The final version of our DA and our online wiki-based GO-FAR risk calculator using the IconArray.com risk graphics software are available online at www.wikidecision.org and are ready to be adapted to other contexts. Our results inform producers of decision aids on the use of wikis and user-centered design to develop DAs that are better adapted to users’ needs. Further work is needed on the creation of a video version of our DA. Physicians will also need the training to use our DA and to develop shared decision-making skills about goals of care.

Keywords: ethnography, intensive care units, life-sustaining therapies, user-centered design

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Authors: Etienne Provencal, David L. St-Pierre

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A facility exploiting only electronic gambling machines (EGMs) opened in 2007 in Quebec City, Canada under the name of Salons de Jeux du Québec (SdjQ). This facility is one of the first worldwide to rely on that business model. This paper models the performance of such EGMs. The interest from a managerial point of view is to identify the variables that can be controlled or influenced so that a comprehensive model can help improve the overall performance of the business. The EGM individual performance model contains eight different variables under study (Game Title, Progressive jackpot, Bonus Round, Minimum Coin-in, Maximum Coin-in, Denomination, Slant Top and Position). Using data from Quebec City’s SdjQ, a linear regression analysis explains 90.80% of the EGM performance. Moreover, results show a behavior slightly different than that of a casino. The addition of GameTitle as a factor to predict the EGM performance is one of the main contributions of this paper. The choice of the game (GameTitle) is very important. Games having better position do not have significantly better performance than games located elsewhere on the gaming floor. Progressive jackpots have a positive and significant effect on the individual performance of EGMs. The impact of BonusRound on the dependent variable is significant but negative. The effect of Denomination is significant but weakly negative. As expected, the Language of an EGMS does not impact its individual performance. This paper highlights some possible improvements by indicating which features are performing well. Recommendations are given to increase the performance of the EGMs performance.

Keywords: EGM, linear regression, model prediction, slot operations

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Authors: Sriram Kashyap Prasad, Ionut Florescu

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This study models the intraday asset prices as driven by Markov process. This work identifies the latent states of the Hidden Markov model, using limit order book data (trades and quotes) to continuously estimate the states throughout the day. This work builds a trading strategy using estimated states to generate signals. The strategy utilizes current state to recalibrate buy/ sell levels and the transition between states to trigger stop-loss when adverse price movements occur. The proposed trading strategy is tested on the Stevens High Frequency Trading (SHIFT) platform. SHIFT is a highly realistic market simulator with functionalities for creating an artificial market simulation by deploying agents, trading strategies, distributing initial wealth, etc. In the implementation several assets on the NASDAQ exchange are used for testing. In comparison to a strategy with static buy/ sell levels, this study shows that the number of limit orders that get matched and executed can be increased. Executing limit orders earns rebates on NASDAQ. The system can capture jumps in the limit order book prices, provide dynamic buy/sell levels and trigger stop loss signals to improve the PnL (Profit and Loss) performance of the strategy.

Keywords: algorithmic trading, Hidden Markov model, high frequency trading, limit order book learning

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Authors: Jin-Qu Chen, Jie Liu, Yong Yin, Zi-Qi Ju, Yu-Yao Wu

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Increase in travel fare and station’s failure will have huge impact on passengers’ travel. The Urban Rail Transit (URT) station’s accessibility reliability under increasing travel fare and station failure are analyzed in this paper. Firstly, the passenger’s travel path is resumed based on stochastic user equilibrium and Automatic Fare Collection (AFC) data. Secondly, calculating station’s importance by combining LeaderRank algorithm and Ratio of Station Affected Passenger Volume (RSAPV), and then the station’s accessibility evaluation indicators are proposed based on the analysis of passenger’s travel characteristic. Thirdly, station’s accessibility under different scenarios are measured and rate of accessibility change is proposed as station’s accessibility reliability indicator. Finally, the accessibility of Hangzhou metro stations is analyzed by the formulated models. The result shows that Jinjiang station and Liangzhu station are the most important and convenient station in the Hangzhou metro, respectively. Station failure and increase in travel fare and station failure have huge impact on station’s accessibility, except for increase in travel fare. Stations in Hangzhou metro Line 1 have relatively worse accessibility reliability and Fengqi Road station’s accessibility reliability is weakest. For Hangzhou metro operational department, constructing new metro line around Line 1 and protecting Line 1’s station preferentially can effective improve the accessibility reliability of Hangzhou metro.

Keywords: automatic fare collection data, AFC, station’s accessibility reliability, stochastic user equilibrium, urban rail transit, URT

Procedia PDF Downloads 133

Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Masica Jordan Alston, Angela N. Bullock, Angela S. Henderson, Stephanie Strianse, Sade Dunn, Joseph Hackett, Alaysia Black Hackett, Marcus Mason

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The Energy Model of Cumulative Grief was created in 2018. The Energy Model of Cumulative Grief utilizes historic models of grief stage theories. The innovative model is additionally unique due to its focus on cultural responsiveness. The Energy Model of Cumulative Grief helps to train practitioners who work with clients dealing with grief and loss. This paper assists in introducing the world to this innovative model and exploring how this model positively impacted a convenience sample of 140 practitioners and individuals experiencing grief and loss. Respondents participated in Webinars provided by the National Grief and Loss Center of America (NGLCA). Participants in this cross-sectional research design study completed one of three Grief and Loss Surveys created by the Grief and Loss Centers of America. Data analysis for this study was conducted via SPSS and Survey Hero to examine survey results for respondents. Results indicate that the Energy Model of Cumulative Grief was an effective resource for participants in addressing grief and loss. The majority of participants found the Webinars to be helpful and a conduit to providing them with higher levels of hope. The findings suggest that using The Energy Model of Cumulative Grief is effective in providing culturally responsive grief and loss resources to practitioners and clients. There are far reaching implications with the use of technology to provide hope to those suffering from grief and loss worldwide through The Energy Model of Cumulative Grief.

Keywords: grief, loss, grief energy, grieving brain

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Authors: Onur Kaya, Ilknur Tukenmez

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In this study, we consider vehicle routing problems on networks with alternative direct links between nodes, and we analyze a multi-objective problem considering the financial, environmental and social objectives in this context. In real life, there might exist several alternative direct roads between two nodes, and these roads might have differences in terms of their lengths and durations. For example, a road might be shorter than another but might require longer time due to traffic and speed limits. Similarly, some toll roads might be shorter or faster but require additional payment, leading to higher costs. We consider such alternative links in our problem and develop a mixed integer linear programming model that determines which alternative link to use between two nodes, in addition to determining the optimal routes for different vehicles, depending on the model objectives and constraints. We consider the minimum cost routing as the financial objective for the company, minimizing the CO2 emissions and gas usage as the environmental objectives, and optimizing the driver working conditions/working hours, and minimizing the risks of accidents as the social objectives. With these objective functions, we aim to determine which routes, and which alternative links should be used in addition to the speed choices on each link. We discuss the results of the developed vehicle routing models and compare their results depending on the system parameters.

Keywords: vehicle routing, alternative links between nodes, mixed integer linear programming, triple bottom line accounting

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Authors: Nana Katsitadze, Tamar Atanelishvili, Mariam Kutateladze, Alexandre Tushishvili

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In the modern world, tourism has emerged as one of the most powerful economic sectors, and due to its high economic performance, it is attractive to the countries with various levels of economic development. The purpose of the present paper, dedicated to discussing the current problems of tourism development, is to find ways which will contribute to bringing more benefits to the country from the sector. Georgia has been successfully developing leisure tourism for the last ten years, and at the next stage of development business, tourism gains particular importance for Georgia as a means of mitigating the negative socio-economic effects caused by the seasonality of tourism and as a high-cost tourism market. Therefore, the object of the paper is to study the factors that contribute to the development of business tourism. The paper uses the research methods such as system analysis, synthesis, analogy, as well as historical, comparative, economic, and statistical methods of analysis. The information base for the research is made up of the statistics on the functioning of the tourism market of Georgia and foreign countries as well as official data provided by international organizations in the field of tourism. Based on the experience of business tourism around the world and identifying the successful start of business tourism development in Georgia and its causing factors, a business tourism development model for Georgia has been developed. The model might be useful as a methodological material for developing a business tourism development concept for the countries with limited financial resources but rich in tourism resources like Georgia. On the initial stage of development (in absence of conventional centers), the suggested concept of business tourism development involves organizing small and medium-sized meetings both in large cities and in regions by using high-class hotel infrastructure and event management services. Relocation of small meetings to the regions encourages inclusive development of the sector based on increasing the awareness of these regions as tourist sites as well as the increase in employment and sales of other tourism or consumer products. Business tourism increases the number of hotel visitors in the non-seasonal period and improves hotel performance indicators, which enhances the attractiveness of investing in the hotel business. According to the present concept of business tourism development, at the initial stage, development of business tourism is based on the existing markets, including internal market, neighboring markets and the markets of geographically relatively near countries and at the next stage, the concept involves generating tourists from other relatively distant target markets. As a result, by gaining experience in business tourism, enhancing professionalism, increasing awareness and stimulating infrastructure development, the country will prepare the basis to move to a higher stage of tourism development. In addition, the experience showed that for attracting large customers, peculiarities of the field require activation of state policy and active use of marketing mechanisms and tools of the state.

Keywords: hotel industry development, MICE model, MICE strategy, MICE tourism in Georgia

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Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

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Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

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Authors: Elison Eduardo Jardim Bierhals, Claudineia Brazil, Francisco Pereira, Elton Rossini

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The use of alternative energy sources to meet energy demand reduces environmental damage. To diversify an energy matrix and to minimize global warming, a solar energy is gaining space, being an important source of renewable energy, and its potential depends on the climatic conditions of the region. Brazil presents a great solar potential for a generation of electric energy, so the knowledge of solar radiation and its characteristics are fundamental for the study of energy use. Due to the above reasons, this article aims to verify the climatic variability corresponding to the variations in solar radiation anomalies, in the face of climate change scenarios. The data used in this research are part of the Intercomparison of Interconnected Models, Phase 5 (CMIP5), which contributed to the preparation of the fifth IPCC-AR5 report. The solar radiation data were extracted from The Australian Community Climate and Earth System Simulator (ACCESS) model using the RCP 4.5 and RCP 8.5 scenarios that represent an intermediate structure and a pessimistic framework, the latter being the most worrisome in all cases. In order to allow the use of solar radiation as a source of energy in a given location and/or region, it is important, first, to determine its availability, thus justifying the importance of the study. The results pointed out, for the 75-year period (2026-2100), based on a pessimistic scenario, indicate a drop in solar radiation of the approximately 12% in the eastern region of Rio Grande do Sul. Factors that influence the pessimistic prospects of this scenario should be better observed by the responsible authorities, since they can affect the possibility to produce electricity from solar radiation.

Keywords: climate change, energy, IPCC, solar radiation

Procedia PDF Downloads 191