Search results for: measurement accuracy

Authors: Ojas Chaudhari, Ali Nejad Ghafar, Giedrius Zirgulis, Marjan Mousavi, Tommy Ellison, Sandra Pousette, Patrick Fontana

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In Swedish tunnel construction, a critical issue that has been repeatedly acknowledged is corrosion and, consequently, failure of the rock bolts in rock support systems. The defective installation of rock bolts results in the formation of cavities in the cement mortar that is regularly used to fill the area under the dome plates. These voids allow for water-ingress to the rock bolt assembly, which results in corrosion of rock bolt components and eventually failure. In addition, the current installation technique consists of several manual steps with intense labor works that are usually done in uncomfortable and exhausting conditions, e.g., under the roof of the tunnels. Such intense tasks also lead to a considerable waste of materials and execution errors. Moreover, adequate quality control of the execution is hardly possible with the current technique. To overcome these issues, a non-shrinking/expansive cement-based mortar filled in the paper packaging has been developed in this study which properly fills the area under the dome plates without or with the least remaining cavities, ultimately that diminishes the potential of corrosion. This article summarizes the development process and the experimental evaluation of this technique for the installation of rock bolts. In the development process, the cementitious mortar was first developed using specific cement and shrinkage reducing/expansive additives. The mechanical and flow properties of the mortar were then evaluated using compressive strength, density, and slump flow measurement methods. In addition, isothermal calorimetry and shrinkage/expansion measurements were used to elucidate the hydration and durability attributes of the mortar. After obtaining the desired properties in both fresh and hardened conditions, the developed dry mortar was filled in specific permeable paper packaging and then submerged in water bath for specific intervals before the installation. The tests were enhanced progressively by optimizing different parameters such as shape and size of the packaging, characteristics of the paper used, immersion time in water and even some minor characteristics of the mortar. Finally, the developed prototype was tested in a lab-scale rock bolt assembly with various angles to analyze the efficiency of the method in real life scenario. The results showed that the new technique improves the performance of the rock bolts by reducing the material wastage, improving environmental performance, facilitating and accelerating the labor works, and finally enhancing the durability of the whole system. Accordingly, this approach provides an efficient alternative for the traditional way of tunnel bolt installation with considerable advantages for the Swedish tunneling industry.

Keywords: corrosion, durability, mortar, rock bolt

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Authors: Bernard Polednik, Adam Piotrowicz, Lukasz Guz, Marzenna Dudzinska

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Transportation is considered to be responsible for increased exposure of road users – i.e., drivers, car passengers, and pedestrians as well as inhabitants of houses located near roads - to pollutants emitted from vehicles. Accurate estimates are, however, difficult as exposure depends on many factors such as traffic intensity or type of fuel as well as the topography and the built-up area around the individual routes. The season and weather conditions are also of importance. In the case of inhabitants of houses located near roads, their exposure depends on the distance from the road, window tightness and other factors that decrease pollutant infiltration. This work reports the variations of particle concentrations along a selected road in Lublin, Poland. Their impact on the exposure for road users as well as for inhabitants of houses located near the road is also presented. Mobile and fixed-site measurements were carried out in peak (around 8 a.m. and 4 p.m.) and off-peak (12 a.m., 4 a.m., and 12 p.m.) traffic times in all 4 seasons. Fixed-site measurements were performed in 12 measurement points along the route. The number and mass concentration of particles was determined with the use of P-Trak model 8525, OPS 3330, DustTrak DRX model 8533 (TSI Inc. USA) and Grimm Aerosol Spectrometer 1.109 with Nano Sizer 1.321 (Grimm Aerosol Germany). The obtained results indicated that the highest concentrations of traffic-related pollution were measured near 4-way traffic intersections during peak hours in the autumn and winter. The highest average number concentration of ultrafine particles (PN0.1), and mass concentration of fine particles (PM2.5) in fixed-site measurements were obtained in the autumn and amounted to 23.6 ± 9.2×10³ pt/cm³ and 135.1 ± 11.3 µg/m³, respectively. The highest average number concentration of submicrometer particles (PN1) was measured in the winter and amounted to 68 ± 26.8×10³ pt/cm³. The estimated doses of particles deposited in the commuters’ and pedestrians’ lungs within an hour near 4-way TIs in peak hours in the summer amounted to 4.3 ± 3.3×10⁹ pt/h (PN0.1) and 2.9 ± 1.4 µg/h (PM2.5) and 3.9 ± 1.1×10⁹ pt/h (PN0.1) or 2.5 ± 0.4 µg/h (PM2.5), respectively. While estimating the doses inhaled by the inhabitants of premises located near the road one should take into account different fractional penetration of particles from outdoors to indoors. Such doses assessed for the autumn and winter are up to twice as high as the doses inhaled by commuters and pedestrians in the summer. In the winter traffic-related ultrafine particles account for over 70% of all ultrafine particles deposited in the pedestrians’ lungs. The share of traffic-related PM10 particles was estimated at approximately 33.5%. Concluding, the results of the particle concentration measurements along a road in Lublin indicated that the concentration is mainly affected by the traffic intensity and weather conditions. Further detailed research should focus on how the season and the metrological conditions affect concentration levels of traffic-related pollutants and the exposure of commuters and pedestrians as well as the inhabitants of houses located near traffic routes.

Keywords: air quality, deposition dose, health effects, vehicle emissions

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Oshma Chakoory, Sophie Comtet-Marre, Pierre Peyret

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Metagenomic classifiers are widely used for the taxonomic profiling of metagenomic data and estimation of taxa relative abundance. Small subunit rRNA genes are nowadays a gold standard for the phylogenetic resolution of complex microbial communities, although the power of this marker comes down to its use as full-length. We benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then built a pipeline called RiboTaxa to generate a highly sensitive and specific metataxonomic approach. Using metagenomics data, RiboTaxa gave the best results compared to other tools (Kraken2, Centrifuge (1), METAXA2 (2), PhyloFlash (3)) with precise taxonomic identification and relative abundance description, giving no false positive detection. Using real datasets from various environments (ocean, soil, human gut) and from different approaches (metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not seen by current bioinformatics analysis opening new biological perspectives in human and environmental health. In a study focused on corals’ health involving 20 metagenomic samples (4), an affiliation of prokaryotes was limited to the family level with Endozoicomonadaceae characterising healthy octocoral tissue. RiboTaxa highlighted 2 species of uncultured Endozoicomonas which were dominant in the healthy tissue. Both species belonged to a genus not yet described, opening new research perspectives on corals’ health. Applied to metagenomics data from a study on human gut and extreme longevity (5), RiboTaxa detected the presence of an uncultured archaeon in semi-supercentenarians (aged 105 to 109 years) highlighting an archaeal genus, not yet described, and 3 uncultured species belonging to the Enorma genus that could be species of interest participating in the longevity process. RiboTaxa is user-friendly, rapid, allowing microbiota structure description from any environment and the results can be easily interpreted. This software is freely available at https://github.com/oschakoory/RiboTaxa under the GNU Affero General Public License 3.0.

Keywords: metagenomics profiling, microbial diversity, SSU rRNA genes, full-length phylogenetic marker

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Authors: Hideaki Endo, Mika Goto

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The global outbreak of the COVID-19 pandemic has seriously affected railway businesses. The number of railway passengers decreased due to the decline in the number of commuters and business travelers to avoid crowded trains and a sharp drop in inbound tourists visiting Japan. This has affected not only railway businesses but also related businesses, including hotels, leisure businesses, and retail businesses at station buildings. In 2021, the companies were divided into profitable and loss-making companies. This division suggests that railway companies, particularly loss-making companies, needed to decrease operational inefficiency. To measure the impact of COVID-19 and discuss the sustainable management strategies of railway companies, we examine the cost inefficiency of Japanese listed railway companies by applying stochastic frontier analysis (SFA) to their operational and financial data. First, we employ the stochastic frontier cost function approach to measure inefficiency. The cost frontier function is formulated as a Cobb–Douglas type, and we estimated parameters and variables for inefficiency. This study uses panel data comprising 26 Japanese-listed railway companies from 2005 to 2020. This period includes several events deteriorating the business environment, such as the financial crisis from 2007 to 2008 and the Great East Japan Earthquake of 2011, and we compare those impacts with those of the COVID-19 pandemic after 2020. Second, we identify the characteristics of the best-practice railway companies and examine the drivers of cost inefficiencies. Third, we analyze the factors influencing cost inefficiency by comparing the profiles of the top 10 railway companies and others before and during the pandemic. Finally, we examine the relationship between cost inefficiency and the implementation of efficiency measures for each railway company. We obtained the following four findings. First, most Japanese railway companies showed the lowest cost inefficiency (most efficient) in 2014 and the highest in 2020 (least efficient) during the COVID-19 pandemic. The second worst occurred in 2009 when it was affected by the financial crisis. However, we did not observe a significant impact of the 2011 Great East Japan Earthquake. This is because no railway company was influenced by the earthquake in this operating area, except for JR-EAST. Second, the best-practice railway companies are KEIO and TOKYU. The main reason for their good performance is that both operate in and near the Tokyo metropolitan area, which is densely populated. Third, we found that non-best-practice companies had a larger decrease in passenger kilometers than best-practice companies. This indicates that passengers made fewer long-distance trips because they refrained from inter-prefectural travel during the pandemic. Finally, we found that companies that implement more efficiency improvement measures had higher cost efficiency and they effectively used their customer databases through proactive DX investments in marketing and asset management.

Keywords: COVID-19 pandemic, stochastic frontier analysis, railway sector, cost efficiency

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Authors: Jochen Nelles, Susanne Kohns, Julia Spies, Friederike Schmitz-Buhl, Roland Thietje, Christopher Brandl, Alexander Mertens, Christopher M. Schlick

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Industrial robots as part of highly automated manufacturing are recently developed to cooperative (light-weight) robots. This offers the opportunity of using them as assistance robots and to improve the participation in professional life of disabled or handicapped people such as tetraplegics. Robots under development are located within a cooperation area together with the working person at the same workplace. This cooperation area is an area where the robot and the working person can perform tasks at the same time. Thus, working people and robots are operating in the immediate proximity. Considering the physical restrictions and the limited mobility of tetraplegics, a hands-free robot control could be an appropriate approach for a cooperative assistance robot. To meet these requirements, the research project MeRoSy (human-robot synergy) develops methods for cooperative assistance robots based on the measurement of head movements of the working person. One research objective is to improve the participation in professional life of people with disabilities and, in particular, mobility impaired persons (e.g. wheelchair users or tetraplegics), whose participation in a self-determined working life is denied. This raises the research question, how a human-robot cooperation workplace can be designed for hands-free robot control. Here, the example of a library scenario is demonstrated. In this paper, an empirical study that focuses on the impact of head movement related stress is presented. 12 test subjects with tetraplegia participated in the study. Tetraplegia also known as quadriplegia is the worst type of spinal cord injury. In the experiment, three various basic head movements were examined. Data of the head posture were collected by a motion capture system; muscle activity was measured via surface electromyography and the subjective mental stress was assessed via a mental effort questionnaire. The muscle activity was measured for the sternocleidomastoid (SCM), the upper trapezius (UT) or trapezius pars descendens, and the splenius capitis (SPL) muscle. For this purpose, six non-invasive surface electromyography sensors were mounted on the head and neck area. An analysis of variance shows differentiated muscular strains depending on the type of head movement. Systematically investigating the influence of different basic head movements on the resulting strain is an important issue to relate the research results to other scenarios. At the end of this paper, a conclusion will be drawn and an outlook of future work will be presented.

Keywords: assistance robot, human-robot interaction, motion capture, stress-strain-concept, surface electromyography, tetraplegia

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Authors: Shin Woo Kim, Eui Ju Lee

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The recent development of biofuel production technology facilitates the use of bioethanol and biodiesel on automobile. Bioethanol, especially, can be used as a fuel for gasoline vehicles because the addition of ethanol has been known to increase octane number and reduce soot emissions. However, the wide application of biofuel has been still limited because of lack of detailed combustion properties such as auto-ignition temperature and pollutant emissions such as NOx and soot, which has been concerned mainly on the vehicle fire safety and environmental safety. In this study, the combustion characteristics of gasoline/ethanol fuel were investigated both numerically and experimentally. For auto-ignition temperature and NOx emission, the numerical simulation was performed on the well-stirred reactor (WSR) to simulate the homogeneous gasoline engine and to clarify the effect of ethanol addition in the gasoline fuel. Also, the response surface method (RSM) was introduced as a design of experiment (DOE), which enables the various combustion properties to be predicted and optimized systematically with respect to three independent variables, i.e., ethanol mole fraction, equivalence ratio and residence time. The results of stoichiometric gasoline surrogate show that the auto-ignition temperature increases but NOx yields decrease with increasing ethanol mole fraction. This implies that the bioethanol added gasoline is an eco-friendly fuel on engine running condition. However, unburned hydrocarbon is increased dramatically with increasing ethanol content, which results from the incomplete combustion and hence needs to adjust combustion itself rather than an after-treatment system. RSM results analyzed with three independent variables predict the auto-ignition temperature accurately. However, NOx emission had a big difference between the calculated values and the predicted values using conventional RSM because NOx emission varies very steeply and hence the obtained second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, NOx emission is taken as common logarithms and worked again with RSM. NOx emission predicted through logarithm transformation is in a fairly good agreement with the experimental results. For more tangible understanding of gasoline/ethanol fuel on pollutant emissions, experimental measurements of combustion products were performed in gasoline/ethanol pool fires, which is widely used as a fire source of laboratory scale experiments. Three measurement methods were introduced to clarify the pollutant emissions, i.e., various gas concentrations including NOx, gravimetric soot filter sampling for elements analysis and pyrolysis, thermophoretic soot sampling with transmission electron microscopy (TEM). Soot yield by gravimetric sampling was decreased dramatically as ethanol was added, but NOx emission was almost comparable regardless of ethanol mole fraction. The morphology of the soot particle was investigated to address the degree of soot maturing. The incipient soot such as a liquid like PAHs was observed clearly on the soot of higher ethanol containing gasoline, and the soot might be matured under the undiluted gasoline fuel.

Keywords: gasoline/ethanol fuel, NOx, pool fire, soot, well-stirred reactor (WSR)

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Authors: Alexandra Emmanuelle Membangbi, Jacky Njiki Bikoï, Esther Del-florence Moni Ndedi, Marie Joseph Nkodo Mindimi, Donatien Serge Mbaga, Elsa Nguiffo Makue, André Chris Mikangue Mbongue, Martha Mesembe, George Ikomey Mondinde, Eric Walter Perfura-yone, Sara Honorine Riwom Essama

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Background: Tuberculosis (TB) is one of the top lethal infectious diseases worldwide. In recent years, interferon-γ (INF-γ) release assays (IGRAs) have been established as routine tests for diagnosing TB infection. However, produced INF-γ assessment failed to distinguish active TB (ATB) from latent TB infection (LTBI), especially in TB epidemic areas. In addition to IFN-γ, interleukin-2 (IL-2), another cytokine secreted by activated T cells, is also involved in immune response against Mycobacterium tuberculosis. The aim of the study was to assess the capacity of IFN-γ and IL2 to evaluate the therapeutic response of patients on anti-tuberculosis treatment. Material and Methods: We conducted a cross-sectional study in the Pneumonology Departments of the Jamot Hospital in Yaoundé between May and August 2021. After signed the informed consent, the sociodemographic data, as well as 5 mL of blood, were collected in the crook of the elbow of each participant. Sixty-one subjects were selected (n= 61) and divided into 4 groups as followed: group 1: resistant tuberculosis (n=13), group 2: active tuberculosis (n=19), group 3 cured tuberculosis (n=16), and group 4: presumed healthy persons (n=13). The cytokines of interest were determined using an indirect Enzyme-linked Immuno-Sorbent Assay (ELISA) according to the manufacturer's recommendations. P-values < 0.05 were interpreted as statistically significant. All statistical calculations were performed using SPSS version 22.0 Results: The results showed that men were more 14/61 infected (31,8%) with a high presence in active and resistant TB groups. The mean age was 41.3±13.1 years with a 95% CI = [38.2-44.7], the age group with the highest infection rate was ranged between 31 and 40 years. The IL-2 and INF-γ means were respectively 327.6±160.6 pg/mL and 26.6±13.0 pg/mL in active tuberculosis patients, 251.1±30.9 pg/mL and 21.4±9.2 pg/mL in patients with resistant tuberculosis, while it was 149.3±93.3 pg/mL and 17.9±9.4 pg/mL in cured patients, 15.1±8.4 pg/mL and 5.3±2.6 pg/mL in participants presumed healthy (p <0.0001). Significant differences in IFN-γ and IL-2 rates were observed between the different groups. Conclusion: Monitoring the serum levels of INF-γ and IL-2 would be useful to evaluate the therapeutic response of anti-tuberculosis patients, particularly in the both cytokines association case, that could improve the accuracy of routine examinations.

Keywords: antibiotic therapy, interferon gamma, interleukin 2, tuberculosis

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Authors: Anna Krukowska, Tomasz Klimczuk, Adriana Zaleska-Medynska

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Photoactive materials have attracted attention due to their potential application in the degradation of environmental pollutants to non-hazardous compounds in an eco-friendly route. Among semiconductor photocatalysts, tantalates such as potassium tantalate (KTaO3) is one of the excellent functional photomaterial. However, tantalates-based materials are less active under visible-light irradiation, the enhancement in photoactivity could be improved with the modification of opto-eletronic properties of KTaO3 by doping rare earth metal (Er) and further photodeposition of noble metal nanoparticles (Pt). Inclusion of rare earth element in orthorhombic structure of tantalate can generate one high-energy photon by absorbing two or more incident low-energy photons, which convert visible-light and infrared-light into the ultraviolet-light to satisfy the requirement of KTaO3 photocatalysts. On the other hand, depositions of noble metal nanoparticles on the surface of semiconductor strongly absorb visible-light due to their surface plasmon resonance, in which their conducting electrons undergo a collective oscillation induced by electric field of visible-light. Furthermore, the high dispersion of Pt nanoparticles, which will be obtained by photodeposition process is additional important factor to improve the photocatalytic activity. The present work is aimed to study the effect of photocatalytic process of the prepared Er-doped KTaO3 and further incorporation of Pt nanoparticles by photodeposition. Moreover, the research is also studied correlations between photocatalytic activity and physico-chemical properties of obtained Pt/Er-KTaO3 samples. The Er-doped KTaO3 microcomposites were synthesized by a hydrothermal method. Then photodeposition method was used for Pt loading over Er-KTaO3. The structural and optical properties of Pt/Er-KTaO3 photocatalytic were characterized using scanning electron microscope (SEM), X-ray diffraction (XRD), volumetric adsorption method (BET), UV-Vis absorption measurement, Raman spectroscopy and luminescence spectroscopy. The photocatalytic properties of Pt/Er-KTaO3 microcomposites were investigated by degradation of phenol in aqueous phase as model pollutant under visible and ultraviolet-light irradiation. Results of this work show that all the prepared photocatalysis exhibit low BET surface area, although doping of the bare KTaO3 with rare earth element (Er) presents a slight increase in this value. The crystalline structure of Pt/Er-KTaO3 powders exhibited nearly identical positions for the main peak at about 22,8o and the XRD pattern could be assigned to an orthorhombic distorted perovskite structure. The Raman spectra of obtained semiconductors confirmed demonstrating perovskite-like structure. The optical absorption spectra of Pt nanoparticles exhibited plasmon absorption band for main peaks at about 216 and 264 nm. The addition of Pt nanoparticles increased photoactivity compared to Er-KTaO3 and pure KTaO3. Summary optical properties of KTaO3 change with its doping Er-element and further photodeposition of Pt nanoparticles.

Keywords: heterogeneous photocatalytic, KTaO3 photocatalysts, Er3+ ion doping, Pt photodeposition

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Authors: László L. Lippai

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In addition to the fact that mental health bears great significance to a particular individual, it can also be regarded as an organizational, community and societal resource. Within the Szeged Health Promotion Research Group, we conducted mental health surveys on two levels: The inhabitants of a medium-sized Hungarian town and students of a Hungarian university with a relatively big headcount were requested to participate in surveys whose goals were to define local government priorities and organization-level health promotion programmes, respectively. To facilitate professional decision-making, we defined three, pragmatically relevant, groups of the target population: the mentally healthy, the vulnerable and the endangered. In order to determine which group a person actually belongs to, we designed a simple and quick measurement tool, which could even be utilised as a smoothing method, the Mental State Questionnaire validity of the above three categories was verified by analysis of variance against psychological quality of life variables. We demonstrate the pragmatic significance of our method via the analyses of the scores of our two mental health surveys. On town level, during our representative survey in Hódmezővásárhely (N=1839), we found that 38.7% of the participants was mentally healthy, 35.3% was vulnerable, while 16.3% was considered as endangered. We were able to identify groups that were in a dramatic state in terms of mental health. For example, such a group consisted of men aged 45 to 64 with only primary education qualification and the ratios of the mentally healthy, vulnerable and endangered were 4.5, 45.5 and 50%, respectively. It was also astonishing to see to what a little extent qualification prevailed as a protective factor in the case of women. Based on our data, the female group aged 18 to 44 with primary education—of whom 20.3% was mentally healthy, 42.4% vulnerable and 37.3% was endangered—as well as the female group aged 45 to 64 with university or college degree—of whom 25% was mentally healthy, 51.3 vulnerable and 23.8% endangered—are to be handled as priority intervention target groups in a similarly difficult position. On organizational level, our survey involving the students of the University of Szeged, N=1565, provided data to prepare a strategy of mental health promotion for a university with a headcount exceeding 20,000. When developing an organizational strategy, it was important to gather information to estimate the proportions of target groups in which mental health promotion methods; for example, life management skills development, detection, psychological consultancy, psychotherapy, would be applied. Our scores show that 46.8% of the student participants were mentally healthy, 42.1% were vulnerable and 11.1% were endangered. These data convey relevant information as to the allocation of organizational resources within a university with a considerable headcount. In conclusion, The Mental State Questionnaire, as a valid smoothing method, is adequate to describe a community in a plain and informative way in the terms of mental health. The application of the method can promote the preparation, design and implementation of mental health promotion interventions. 

Keywords: health promotion, mental health promotion, mental state questionnaire, psychological well-being

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Authors: Majid Hejazian, Nam-Trung Nguyen

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Over the past few years, microfluidic devices have generated significant attention from industry and academia due to advantages such as small sample volume, low cost and high efficiency. Microfluidic devices have applications in chemical, biological and industry analysis and can facilitate assay of bio-materials and chemical reactions, separation, and sensing. Micromixers are one of the important microfluidic concepts. Micromixers can work as stand-alone devices or be integrated in a more complex microfluidic system such as a lab on a chip (LOC). Micromixers are categorized as passive and active types. Passive micromixers rely only on the arrangement of the phases to be mixed and contain no moving parts and require no energy. Active micromixers require external fields such as pressure, temperature, electric and acoustic fields. Rapid and efficient mixing is important for many applications such as biological, chemical and biochemical analysis. Achieving fast and homogenous mixing of multiple samples in the microfluidic devices has been studied and discussed in the literature recently. Improvement in mixing rely on effective mass transport in microscale, but are currently limited to molecular diffusion due to the predominant laminar flow in this size scale. Using magnetic field to elevate mass transport is an effective solution for mixing enhancement in microfluidics. The use of a non-uniform magnetic field to improve mass transfer performance in a microfluidic device is demonstrated in this work. The phenomenon of mixing ferrofluid and DI-water streams has been reported before, but mass transfer enhancement for other non-magnetic species through magnetic field have not been studied and evaluated extensively. In the present work, permanent magnets were used in a simple microfluidic device to create a non-uniform magnetic field. Two streams are introduced into the microchannel: one contains fluorescent dye mixed with diluted ferrofluid to induce enhanced mass transport of the dye, and the other one is a non-magnetic DI-water stream. Mass transport enhancement of fluorescent dye is evaluated using fluorescent measurement techniques. The concentration field is measured for different flow rates. Due to effect of magnetic field, a body force is exerted on the paramagnetic stream and expands the ferrofluid stream into non-magnetic DI-water flow. The experimental results demonstrate that without a magnetic field, both magnetic nanoparticles of the ferrofluid and the fluorescent dye solely rely on molecular diffusion to spread. The non-uniform magnetic field, created by the permanent magnets around the microchannel, and diluted ferrofluid can improve mass transport of non-magnetic solutes in a microfluidic device. The susceptibility mismatch between the fluids results in a magnetoconvective secondary flow towards the magnets and subsequently the mass transport of the non-magnetic fluorescent dye. A significant enhancement in mass transport of the fluorescent dye was observed. The platform presented here could be used as a microfluidics-based micromixer for chemical and biological applications.

Keywords: ferrofluid, mass transfer, micromixer, microfluidics, magnetic

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Authors: Alicia Ettlin

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Amid the rise of neoliberalism to the leading economic policy model in Western societies in the 1980s, people have started to internalise a neoliberal way of thinking, whereby the human body has become an entity that can and needs to be precisely managed through free yet rational decision-making processes. The neoliberal citizen has consequently become an entrepreneur of the self who is free, independent, rational, productive and responsible for themselves, their health and wellbeing as well as their appearance. The focus on individuals as entrepreneurs who manage their bodies through the rationally thinking mind has, however, become increasingly criticised for viewing the social actor as ‘disembodied’, as a detached, social actor whose powerful mind governs over the passive body. On the other hand, the discourse around embodiment seeks to connect rational decision-making processes to the dominant neoliberal discourse which creates an embodied understanding that the body, just as other areas of people’s lives, can and should be shaped, monitored and managed through cognitive and rational thinking. This perspective offers an understanding of the body regarding its connections with the social environment that reaches beyond the debates around mind-body binary thinking. Hence, following this argument, body management should not be thought of as either solely guided by embodied discourses nor as merely falling into a mind-body dualism, but rather, simultaneously and inseparably as both at once. The descriptive, qualitative analysis of semi-structured in-depth interviews conducted with young Australian amateur athletes between the age of 18 and 24 has shown that most participants are interested in measuring and managing their body to create self-knowledge and self-improvement. The participants thereby connected self-improvement to weight loss, muscle gain or simply staying fit and healthy. Self-knowledge refers to body measurements including weight, BMI or body fat percentage. Self-management and self-knowledge that are reliant on one another to take rational and well-thought-out decisions, are both characteristic values of the neoliberal doctrine. A neoliberal way of thinking and looking after the body has also by many been connected to rewarding themselves for their discipline, hard work or achievement of specific body management goals (e.g. eating chocolate for reaching the daily step count goal). A few participants, however, have shown resistance against these neoliberal values, and in particular, against the precise monitoring and management of the body with the help of self-tracking devices. Ultimately, however, it seems that most participants have internalised the dominant discourses around self-responsibility, and by association, a sense of duty to discipline their body in normative ways. Even those who have indicated their resistance against body work and body management practices that follow neoliberal thinking and measurement systems, are aware and have internalised the concept of the rational operating mind that needs or should decide how to look after the body in terms of health but also appearance ideals. The discussion around the collected data thereby shows that embodiment and the mind/body dualism constitute two connected, rather than two separate or opposing concepts.

Keywords: dualism, embodiment, mind, neoliberalism

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Authors: Maria Reznakova, Michala Strnadova, Lukas Reznak

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Successful achievement of strategic goals requires an effective performance management system, i.e. determining the appropriate indicators measuring the rate of goal achievement. Assuming that the goal of the owners is to grow the assets they invested in, it is vital to identify the key performance indicators, which contribute to value creation. These indicators are known as value drivers. Based on the undertaken literature search, a value driver is defined as any factor that affects the value of an enterprise. The important factors are then monitored by both financial and non-financial indicators. Financial performance indicators are most useful in strategic management, since they indicate whether a company's strategy implementation and execution are contributing to bottom line improvement. Non-financial indicators are mainly used for short-term decisions. The identification of value drivers, however, is problematic for companies which are not publicly traded. Therefore financial ratios continue to be used to measure the performance of companies, despite their considerable criticism. The main drawback of such indicators is the fact that they are calculated based on accounting data, while accounting rules may differ considerably across different environments. For successful enterprise performance management it is vital to avoid factors that may reduce (or even destroy) its value. Among the known factors reducing the enterprise value are the lack of capital, lack of strategic management system and poor quality of production. In order to gain further insight into the topic, the paper presents results of the research identifying factors that adversely affect the performance of mechanical engineering enterprises in the Czech Republic. The research methodology focuses on both the qualitative and the quantitative aspect of the topic. The qualitative data were obtained from a questionnaire survey of the enterprises senior management, while the quantitative financial data were obtained from the Analysis Major Database for European Sources (AMADEUS). The questionnaire prompted managers to list factors which negatively affect business performance of their enterprises. The range of potential factors was based on a secondary research – analysis of previously undertaken questionnaire surveys and research of studies published in the scientific literature. The results of the survey were evaluated both in general, by average scores, and by detailed sub-analyses of additional criteria. These include the company specific characteristics, such as its size and ownership structure. The evaluation also included a comparison of the managers’ opinions and the performance of their enterprises – measured by return on equity and return on assets ratios. The comparisons were tested by a series of non-parametric tests of statistical significance. The results of the analyses show that the factors most detrimental to the enterprise performance include the incompetence of responsible employees and the disregard to the customers‘ requirements.

Keywords: business value, financial ratios, performance measurement, value drivers

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Authors: Neveen AlQasas, Daniel Johnson

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Membranes for water treatment are an established technology that attracts great attention due to its simplicity and cost effectiveness. However, membranes in operation suffer from the adverse effect of membrane fouling. Bio-fouling is a phenomenon that occurs at the water-membrane interface, and is a dynamic process that is initiated by the adsorption of dissolved organic material, including biomacromolecules, on the membrane surface. After initiation, attachment of microorganisms occurs, followed by biofilm growth. The biofilm blocks the pores of the membrane and consequently results in reducing the water flux. Moreover, the presence of a fouling layer can have a substantial impact on the membrane separation properties. Understanding the mechanism of the initiation phase of biofouling is a key point in eliminating the biofouling on membrane surfaces. The adhesion and attachment of different fouling materials is affected by the surface properties of the membrane materials. Therefore, surface properties of different polymeric materials had been studied in terms of their surface energies and Hansen solubility parameters (HSP). The difference between the combined HSP parameters (HSP distance) allows prediction of the affinity of two materials to each other. The possibilities of measuring the HSP of different polymer films via surface measurements, such as contact angle has been thoroughly investigated. Knowing the HSP of a membrane material and the HSP of a specific foulant, facilitate the estimation of the HSP distance between the two, and therefore the strength of attachment to the surface. Contact angle measurements using fourteen different solvents on five different polymeric films were carried out using the sessile drop method. Solvents were ranked as good or bad solvents using different ranking method and ranking was used to calculate the HSP of each polymeric film. Results clearly indicate the absence of a direct relation between contact angle values of each film and the HSP distance between each polymer film and the solvents used. Therefore, estimating HSP via contact angle alone is not sufficient. However, it was found if the surface tensions and viscosities of the used solvents are taken in to the account in the analysis of the contact angle values, a prediction of the HSP from contact angle measurements is possible. This was carried out via training of a neural network model. The trained neural network model has three inputs, contact angle value, surface tension and viscosity of solvent used. The model is able to predict the HSP distance between the used solvent and the tested polymer (material). The HSP distance prediction is further used to estimate the total and individual HSP parameters of each tested material. The results showed an accuracy of about 90% for all the five studied films

Keywords: surface characterization, hansen solubility parameter estimation, contact angle measurements, artificial neural network model, surface measurements

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Authors: Rui Teixeira, Alan O’Connor, Maria Nogal

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The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events.

Keywords: extreme events, offshore structures, peak-over-threshold, significant wave data

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Authors: Bagireanu Astrid, Bros-Williamson Julio, Duncheva Mila, Currie John

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The Architecture, Engineering, and Construction (AEC) sector plays a critical role in the global transition toward sustainable and net-zero built environments. However, the industry faces unique challenges in planning for net-zero while struggling with low productivity, cost overruns and overall resistance to change. Traditional practices fall short due to their inability to meet the requirements for systemic change, especially as governments increasingly demand transformative approaches. Working in silos and rigid hierarchies and a short-term, client-centric approach prioritising immediate gains over long-term benefit stands in stark contrast to the fundamental requirements for the realisation of net-zero objectives. These practices have limited capacity to effectively integrate AEC stakeholders and promote the essential knowledge sharing required to address the multifaceted challenges of achieving net-zero. In the context of built environment, procurement may be described as the method by which a project proceeds from inception to completion. Collaborative procurement methods under the Integrated Practices (IP) umbrella have the potential to align more closely with net-zero objectives. This paper explores the synergies between collaborative procurement principles and the pursuit of net zero in the AEC sector, drawing upon the shared values of cross-disciplinary collaboration, Early Supply Chain involvement (ESI), use of standards and frameworks, digital information management, strategic performance measurement, integrated decision-making principles and contractual alliancing. To investigate the role of collaborative procurement in advancing net-zero objectives, a structured research methodology was employed. First, the study focuses on a systematic review on the application of collaborative procurement principles in the AEC sphere. Next, a comprehensive analysis is conducted to identify common clusters of these principles across multiple procurement methods. An evaluative comparison between traditional procurement methods and collaborative procurement for achieving net-zero objectives is presented. Then, the study identifies the intersection between collaborative procurement principles and the net-zero requirements. Lastly, an exploration of key insights for AEC stakeholders focusing on the implications and practical applications of these findings is made. Directions for future development of this research are recommended. Adopting collaborative procurement principles can serve as a strategic framework for guiding the AEC sector towards realising net-zero. Synergising these approaches overcomes fragmentation, fosters knowledge sharing, and establishes a net-zero-centered ecosystem. In the context of the ongoing efforts to amplify project efficiency within the built environment, a critical realisation of their central role becomes imperative for AEC stakeholders. When effectively leveraged, collaborative procurement emerges as a powerful tool to surmount existing challenges in attaining net-zero objectives.

Keywords: collaborative procurement, net-zero, knowledge sharing, architecture, built environment

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

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The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Diabetic nephropathy (DN) is a major complication of diabetes, with fibrosis and propionate metabolism playing critical roles in its progression. Identifying biomarkers linked to these pathways may provide novel insights into DN diagnosis and treatment. This study aims to identify biomarkers associated with fibrosis and propionate metabolism in DN. Analyze the biological pathways and regulatory mechanisms of these biomarkers. Develop a machine learning model to predict DN-related biomarkers and validate their functional roles. Publicly available transcriptome datasets related to DN (GSE96804 and GSE104948) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds), and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were identified. The analysis began with the extraction of DN-differentially expressed genes (DN-DEGs) and propionate metabolism-related DEGs (PM-DEGs), followed by the intersection of these with fibrosis-related genes to identify key intersected genes. Instead of relying on traditional models, we employed a combination of deep neural networks (DNNs) and ensemble methods such as Gradient Boosting Machines (GBM) and XGBoost to enhance feature selection and biomarker discovery. Recursive feature elimination (RFE) was coupled with these advanced algorithms to refine the selection of the most critical biomarkers. Functional validation was conducted using convolutional neural networks (CNN) for gene set enrichment and immunoinfiltration analysis, revealing seven significant biomarkers—SLC37A4, ACOX2, GPD1, ACE2, SLC9A3, AGT, and PLG. These biomarkers are involved in critical biological processes such as fatty acid metabolism and glomerular development, providing a mechanistic link to DN progression. Furthermore, a TF–miRNA–mRNA regulatory network was constructed using natural language processing models to identify 8 transcription factors and 60 miRNAs that regulate these biomarkers, while a drug–gene interaction network revealed potential therapeutic targets such as UROKINASE–PLG and ATENOLOL–AGT. This integrative approach, leveraging deep learning and ensemble models, not only enhances the accuracy of biomarker discovery but also offers new perspectives on DN diagnosis and treatment, specifically targeting fibrosis and propionate metabolism pathways.

Keywords: diabetic nephropathy, deep neural networks, gradient boosting machines (GBM), XGBoost

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Authors: Priya Saha, Sourav Dey Roy Jr., Debotosh Bhattacharjee, Mita Nasipuri, Barin Kumar De, Mrinal Kanti Bhowmik

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Biometric face recognition technology (FRT) has gained a lot of attention due to its extensive variety of applications in both security and non-security perspectives. It has come into view to provide a secure solution in identification and verification of person identity. Although other biometric based methods like fingerprint scans, iris scans are available, FRT is verified as an efficient technology for its user-friendliness and contact freeness. Accurate facial feature localization plays an important role for many facial analysis applications including biometrics and emotion recognition. But, there are certain factors, which make facial feature localization a challenging task. On human face, expressions can be seen from the subtle movements of facial muscles and influenced by internal emotional states. These non-rigid facial movements cause noticeable alterations in locations of facial landmarks, their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper proposes a new hybrid based technique for automatic landmark detection in both neutral and expressive frontal and near frontal face images. The method uses the concept of thresholding, sequential searching and other image processing techniques for locating the landmark points on the face. Also, a Graphical User Interface (GUI) based software is designed that could automatically detect 16 landmark points around eyes, nose and mouth that are mostly affected by the changes in facial muscles. The proposed system has been tested on widely used JAFFE and Cohn Kanade database. Also, the system is tested on DeitY-TU face database which is created in the Biometrics Laboratory of Tripura University under the research project funded by Department of Electronics & Information Technology, Govt. of India. The performance of the proposed method has been done in terms of error measure and accuracy. The method has detection rate of 98.82% on JAFFE database, 91.27% on Cohn Kanade database and 93.05% on DeitY-TU database. Also, we have done comparative study of our proposed method with other techniques developed by other researchers. This paper will put into focus emotion-oriented systems through AU detection in future based on the located features.

Keywords: biometrics, face recognition, facial landmarks, image processing

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Authors: Mohammad Khoshini, Arman Khoshghalb, Meghdad Payan, Nasser Khalili

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Most of the Earth’s crust surface rocks are technically categorized as weak rocks or weakly bonded geomaterials. Deeply weathered, weakly cemented, friable and easily erodible, they demonstrate complex material behaviour and understanding the overlooked mechanical behaviour of such materials is of particular importance in geotechnical engineering practice. Weakly bonded geomaterials are so susceptible to surface shear and moisture that conventional methods of core drilling fail to extract high-quality undisturbed samples out of them. Moreover, most of these geomaterials are of high heterogeneity rendering less reliable and feasible material characterization. In order to compensate for the unpredictability of the material response, either numerous experiments are needed to be conducted or large factors of safety must be implemented in the design process. However, none of these approaches is sustainable. In this study, a method for dry core drilling of such materials is introduced to take high-quality undisturbed core samples. By freezing the material at certain moisture content, a secondary structure is developed throughout the material which helps the whole structure to remain intact during the core drilling process. Moreover, to address the heterogeneity issue, the natural material was reconstructed artificially to obtain a homogeneous material with very high similarity to the natural one in both micro and macro-mechanical perspectives. The method is verified for both micro and macro scale. In terms of micro-scale analysis, using Scanning Electron Microscopy (SEM), pore spaces and inter-particle bonds were investigated and compared between natural and artificial materials. X-Ray Diffraction, XRD, analyses are also performed to control the chemical composition. At the macro scale, several uniaxial compressive strength tests, as well as triaxial tests, were performed to verify the similar mechanical response of the materials. A high level of agreement is observed between micro and macro results of natural and artificially bonded geomaterials. The proposed methods can play an important role to cut down the costs of experimental programs for material characterization and also to promote the accuracy of the numerical modellings based on the experimental results.

Keywords: Artificial geomaterial, core drilling, macro-mechanical behavior, micro-scale, sample preparation, SEM photography, weakly bonded geomaterials

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Authors: Md. Soebur Rahman, Mahbuba Begum, Raquib Ahsan

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Composite column is a structural member that uses a combination of structural steel shapes, pipes or tubes with or without reinforcing steel bars and reinforced concrete to provide adequate load carrying capacity to sustain either axial compressive loads alone or a combination of axial loads and bending moments. Composite construction takes the advantages of the speed of construction, light weight and strength of steel, and the higher mass, stiffness, damping properties and economy of reinforced concrete. The most usual types of composite columns are the concrete filled steel tubes and the partially or fully encased steel profiles. Fully encased composite column (FEC) provides compressive strength, stability, stiffness, improved fire proofing and better corrosion protection. This paper reports experimental and numerical investigations of the behaviour of concrete encased steel composite columns subjected to short-term axial load. In this study, eleven short FEC columns with square shaped cross section were constructed and tested to examine the load-deflection behavior. The main variables in the test were considered as concrete compressive strength, cross sectional size and percentage of structural steel. A nonlinear 3-D finite element (FE) model has been developed to analyse the inelastic behaviour of steel, concrete, and longitudinal reinforcement as well as the effect of concrete confinement of the FEC columns. FE models have been validated against the current experimental study conduct in the laboratory and published experimental results under concentric load. It has been observed that FE model is able to predict the experimental behaviour of FEC columns under concentric gravity loads with good accuracy. Good agreement has been achieved between the complete experimental and the numerical load-deflection behaviour in this study. The capacities of each constituent of FEC columns such as structural steel, concrete and rebar's were also determined from the numerical study. Concrete is observed to provide around 57% of the total axial capacity of the column whereas the steel I-sections contributes to the rest of the capacity as well as ductility of the overall system. The nonlinear FE model developed in this study is also used to explore the effect of concrete strength and percentage of structural steel on the behaviour of FEC columns under concentric loads. The axial capacity of FEC columns has been found to increase significantly by increasing the strength of concrete.

Keywords: composite, columns, experimental, finite element, fully encased, strength

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Authors: Suleyman Ogul Ertugrul, Mustafa Turgut, Serkan Inandı, Mustafa Gorkem Coban, Mustafa Kıgılı, Ali Mert, Oguzhan Kesmez, Murat Ozancı, Caglar Uyulan

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In high-performance automotive engineering, the realistic simulation of suspension systems is crucial for enhancing vehicle dynamics and handling. This study focuses on the double wishbone suspension system, prevalent in racing vehicles due to its superior control and stability characteristics. Utilizing MATLAB and Adams Car simulation software, we conduct a comprehensive analysis of displacement behaviors and damper sizing under various dynamic conditions. The initial phase involves using MATLAB to simulate the entire suspension system, allowing for the preliminary determination of damper size based on the system's response under simulated conditions. Following this, manual calculations of wheel loads are performed to assess the forces acting on the front and rear suspensions during scenarios such as braking, cornering, maximum vertical loads, and acceleration. Further dynamic force analysis is carried out using MATLAB Simulink, focusing on the interactions between suspension components during key movements such as bumps and rebounds. This simulation helps in formulating precise force equations and in calculating the stiffness of the suspension springs. To enhance the accuracy of our findings, we focus on a detailed kinematic and dynamic analysis. This includes the creation of kinematic loops, derivation of relevant equations, and computation of Jacobian matrices to accurately determine damper travel and compression metrics. The calculated spring stiffness is crucial in selecting appropriate springs to ensure optimal suspension performance. To validate and refine our results, we replicate the analyses using the Adams Car software, renowned for its detailed handling of vehicular dynamics. The goal is to achieve a robust, reliable suspension setup that maximizes performance under the extreme conditions encountered in racing scenarios. This study exemplifies the integration of theoretical mechanics with advanced simulation tools to achieve a high-performance suspension setup that can significantly improve race car performance, providing a methodology that can be adapted for different types of racing vehicles.

Keywords: FSAE, suspension system, Adams Car, kinematic

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Authors: Erva Akin

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– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: Marina Shargorodsky

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Aim: Gestational weight gain (GWG) has been related to altering future weight-gain curves and increased risks of obesity later in life. Obesity may contribute to vascular atherosclerotic changes as well as excess cardiovascular morbidity and mortality observed in these patients. Noninvasive arterial testing, such as ultrasonographic measurement of carotid IMT, is considered a surrogate for systemic atherosclerotic disease burden and is predictive of cardiovascular events in asymptomatic individuals as well as recurrent events in patients with known cardiovascular disease. Currently, there is no consistent evidence regarding the vascular impact of excessive GWG. The present study was designed to investigate the impact of GWG on early atherosclerotic changes during late pregnancy, using intima-media thickness, as well as placental vascular circulation and inflammatory lesions and pregnancy outcomes. Methods: The study group consisted of 59 pregnant women who gave birth and underwent a placental histopathological examination at the Department of Obstetrics and Gynecology, Edith Wolfson Medical Center, Israel, in 2019. According to the IOM guidelines the study group has been divided into two groups: Group 1 included 32 women with pregnancy weight gain within recommended range; Group 2 included 27 women with excessive weight gain during pregnancy. The IMT was measured from non-diseased intimal and medial wall layers of the carotid artery on both sides, visualized by high-resolution 7.5 MHz ultrasound (Apogee CX Color, ATL). Placental histology subdivided placental findings to lesions consistent with maternal vascular and fetal vascular malperfusion according to the criteria of the Society for Pediatric Pathology, subdividing placental findings to lesions consistent with maternal vascular and fetal vascular malperfusion, as well as the inflammatory response of maternal and fetal origin. Results: IMT levels differed between groups and were significantly higher in Group 1 compared to Group 2 (0.7+/-0.1 vs 0.6+/-0/1, p=0.028). Multiple linear regression analysis of IMT included variables based on their associations in univariate analyses with a backward approach. Included in the model were pre-gestational BMI, HDL cholesterol and fasting glucose. The model was significant (p=0.001) and correctly classified 64.7% of study patients. In this model, pre-pregnancy BMI remained a significant independent predictor of subclinical atherosclerosis assessed by IMT (OR 4.314, 95% CI 0.0599-0.674, p=0.044). Among placental lesions related to fetal vascular malperfusion, villous changes consistent with fetal thrombo-occlusive disease (FTOD) were significantly higher in Group 1 than in Group 2, p=0.034). In Conclusion, the present study demonstrated that excessive weight gain during pregnancy is associated with an adverse effect on early stages of subclinical atherosclerosis, placental vascular circulation and neonatal complications. The precise mechanism for these vascular changes, as well as the overall clinical impact of weight control during pregnancy on IMT, placental vascular circulation as well as pregnancy outcomes, deserves further investigation.

Keywords: obesity, pregnancy, complications, weight gain

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Authors: Iddy Iddy, Qin Jiang, Changkuan Zhang

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This paper addresses the hydraulic performance of curtain wall breakwaters as a coastal structure protection based on the particles method modelling. The hydraulic functions of curtain wall as wave barriers by reflecting large parts of incident waves through the vertical wall, a part transmitted and a particular part was dissipating the wave energies through the eddy flows formed beneath the lower end of the plate. As a Lagrangian particle, the Moving Particle Semi-implicit (MPS) method which has a robust capability for numerical representation has proven useful for design of structures application that concern free-surface hydrodynamic flow, such as wave breaking and overtopping. In this study, a vertical two-dimensional numerical model for the simulation of violent flow associated with the interaction between the curtain-wall breakwaters and progressive water waves is developed by MPS method in which a higher precision pressure gradient model and free surface particle recognition model were proposed. The wave transmission, reflection, and energy dissipation of the vertical wall were experimentally and theoretically examined. With the numerical wave flume by particle method, very detailed velocity and pressure fields around the curtain-walls under the action of waves can be computed in each calculation steps, and the effect of different wave and structural parameters on the hydrodynamic characteristics was investigated. Also, the simulated results of temporal profiles and distributions of velocity and pressure in the vicinity of curtain-wall breakwaters are compared with the experimental data. Herein, the numerical investigation of hydraulic performance of curtain wall breakwaters indicated that the incident wave is largely reflected from the structure, while the large eddies or turbulent flows occur beneath the curtain-wall resulting in big energy losses. The improved MPS method shows a good agreement between numerical results and analytical/experimental data which are compared to related researches. It is thus verified that the improved pressure gradient model and free surface particle recognition methods are useful for enhancement of stability and accuracy of MPS model for water waves and marine structures. Therefore, it is possible for particle method (MPS method) to achieve an appropriate level of correctness to be applied in engineering fields through further study.

Keywords: curtain wall breakwaters, free surface flow, hydraulic performance, improved MPS method

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Authors: Belyihun Muchie

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This study investigates the low English oral communication skills of 5 second-year English major students at Debark University. It aims to identify the key factors contributing to their weaknesses and propose effective interventions to improve their spoken English proficiency. Mixed-methods research will be employed, utilizing observations, questionnaires, and semi-structured interviews to gather data from the participants. To clearly identify these factors, structured and informal observations will be employed; the former will be used to identify their fluency, pronunciation, vocabulary use, and grammar accuracy, and the later will be suited to observe the natural interactions and communication patterns of learners in the classroom setting. The questionnaires will assess their self-perceptions of their skills, perceived barriers to fluency, and preferred learning styles. Interviews will also delve deeper into their experiences and explore specific obstacles faced in oral communication. Data analysis will involve both quantitative and qualitative responses. The structured observation and questionnaire will be analyzed quantitatively, whereas the informal observation and interview transcripts will be analyzed thematically. Findings will be used to identify the major causes of low oral communication skills, such as limited vocabulary, grammatical errors, pronunciation difficulties, or lack of confidence. They are also helpful to develop targeted solutions addressing these causes, such as intensive pronunciation practice, conversation simulations, personalized feedback, or anxiety-reduction techniques. Finally, the findings will guide designing an intervention plan for implementation during the action research phase. The study's outcomes are expected to provide valuable insights into the challenges faced by English major students in developing oral communication skills, contribute to the development of evidence-based interventions for improving spoken English proficiency in similar contexts, and offer practical recommendations for English language instructors and curriculum developers to enhance student learning outcomes. By addressing the specific needs of these students and implementing tailored interventions, this research aims to bridge the gap between theoretical knowledge and practical speaking ability, equipping them with the confidence and skills to flourish in English communication settings.

Keywords: oral communication skills, mixed-methods, evidence-based interventions, spoken English proficiency

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Authors: Justo Rodriguez, Daming Chen, Amador M. Guzman

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The detection, treatment, and control of rapidly propagating, deadly viruses such as COVID-19, require the development of inexpensive, fast, and accurate devices to address the urgent needs of the population. Microfluidics-based sensors are amongst the different methods and techniques for detection that are easy to use. A micro analyzer is defined as a microfluidics-based sensor, composed of a network of microchannels with varying functions. Given their size, portability, and accuracy, they are proving to be more effective and convenient than other solutions. A micro analyzer based on the concept of “Lab on a Chip” presents advantages concerning other non-micro devices due to its smaller size, and it is having a better ratio between useful area and volume. The integration of multiple processes in a single microdevice reduces both the number of necessary samples and the analysis time, leading the next generation of analyzers for the health-sciences. In some applications, the flow of solution within the microchannels is originated by a pressure gradient, which can produce adverse effects on biological samples. A more efficient and less dangerous way of controlling the flow in a microchannel-based analyzer is applying an electric field to induce the fluid motion and either enhance or suppress the mixing process. Electrokinetic flows are characterized by no less than two non-dimensional parameters: the electric Rayleigh number and its geometrical aspect ratio. In this research, stable and unstable flows have been studied numerically (and when possible, will be experimental) in a T-shaped microchannel. Additionally, unstable electrokinetic flows for Rayleigh numbers higher than critical have been characterized. The flow mixing enhancement was quantified in relation to the stretching and folding that fluid particles undergo when they are subjected to supercritical electrokinetic flows. Computational simulations were carried out using a finite element-based program while working with the flow mixing concepts developed by Gollub and collaborators. Hundreds of seeded massless particles were tracked along the microchannel from the entrance to exit for both stable and unstable flows. After post-processing, their trajectories, the folding and stretching values for the different flows were found. Numerical results show that for supercritical electrokinetic flows, the enhancement effects of the folding and stretching processes become more apparent. Consequently, there is an improvement in the mixing process, ultimately leading to a more homogenous mixture.

Keywords: microchannel, stretching and folding, electro kinetic flow mixing, micro-analyzer

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Authors: Umut Yıldız, Berkin Kurtuluş, Yunus Emre Muslubaş

Abstract:

Thrust vectoring, especially in military aviation, is a concept that sees much use to improve maneuverability in already agile aircraft. As this concept is fairly new and cost intensive to design and test, computational methods are useful in easing the preliminary design process. Computational Fluid Dynamics (CFD) can be utilized in many forms to simulate nozzle flow, and there exist various CFD studies in both 2D mechanical and 3D injection based thrust vectoring, and yet, 3D mechanical thrust vectoring analyses, at this point in time, are lacking variety. Additionally, the freely available test data is constrained to limited pitch angles and geometries. In this study, based on a test case provided by NASA, both steady and unsteady 3D CFD simulations are conducted to examine the aerodynamic performance of a mechanical thrust vectoring nozzle model and to validate the utilized numerical model. Steady analyses are performed to verify the flow characteristics of the nozzle at pitch angles of 0, 10 and 20 degrees, and the results are compared with experimental data. It is observed that the pressure data obtained on the inner surface of the nozzle at each specified pitch angle and under different flow conditions with pressure ratios of 1.5, 2 and 4, as well as at azimuthal angle of 0, 45, 90, 135, and 180 degrees exhibited a high level of agreement with the corresponding experimental results. To validate the CFD model, the insights from the steady analyses are utilized, followed by unsteady analyses covering a wide range of pitch angles from 0 to 20 degrees. Throughout the simulations, a mesh morphing method using a carefully calculated mathematical shape deformation model that simulates the vectored nozzle shape exactly at each point of its travel is employed to dynamically alter the divergent part of the nozzle over time within this pitch angle range. The mesh morphing based vectored nozzle shapes were compared with the drawings provided by NASA, ensuring a complete match was achieved. This computational approach allowed for the creation of a comprehensive database of results without the need to generate separate solution domains. The database contains results at every 0.01° increment of nozzle pitch angle. The unsteady analyses, generated using the morphing method, are found to be in excellent agreement with experimental data, further confirming the accuracy of the CFD model.

Keywords: thrust vectoring, computational fluid dynamics, 3d mesh morphing, mathematical shape deformation model

Procedia PDF Downloads 89

Authors: Zhihao Peng, Qiong Zhang, Xiyao Li, Yongzhen Peng

Abstract:

Nowadays, conventional nitrogen removal process (nitrification and denitrification) was adopted in most wastewater treatment plants, but many problems have occurred, such as: high aeration energy consumption, extra carbon sources dosage and high sludge treatment costs. The emergence of anammox has bring about the great revolution to the nitrogen removal technology, and only the ammonia and nitrite were required to remove nitrogen autotrophically, no demand for aeration and sludge treatment. However, there existed many challenges in anammox applications: difficulty of biomass retention, insufficiency of nitrite substrate, damage from complex organic etc. Much effort was put into the research in overcoming the above challenges, and the payment was rewarded. It was also imperative to establish an innovative process that can settle the above problems synchronously, after all any obstacle above mentioned can cause the collapse of anammox system. Therefore, in this study, a two-stage process was established that the sequencing batch reactor (SBR) and upflow anaerobic sludge blanket (UASB) were used in the pre-stage and post-stage, respectively. The domestic wastewater entered into the SBR first and went through anaerobic/aerobic/anoxic (An/O/A) mode, and the draining at the aerobic end of SBR was mixed with domestic wastewater, the mixture then entering to the UASB. In the long term, organic and nitrogen removal performance was evaluated. All along the operation, most COD was removed in pre-stage (COD removal efficiency > 64.1%), including some macromolecular organic matter, like: tryptophan, tyrosinase and fulvic acid, which could weaken the damage of organic matter to anammox. And the An/O/A operating mode of SBR was beneficial to the achievement and maintenance of partial nitrification (PN). Hence, sufficient and steady nitrite supply was another favorable condition to anammox enhancement. Besides, the flexible mixing ratio helped to gain a substrate ratio appropriate to anammox (1.32-1.46), which further enhance the anammox. Further, the UASB was used and gas recirculation strategy was adopted in the post-stage, aiming to achieve granulation by the selection pressure. As expected, the granules formed rapidly during 38 days, which increased from 153.3 to 354.3 μm. Based on bioactivity and gene measurement, the anammox metabolism and abundance level rose evidently, by 2.35 mgN/gVss·h and 5.3 x109. The anammox bacteria mainly distributed in the large granules (>1000 μm), while the biomass in the flocs (<200 μm) and microgranules (200-500 μm) barely displayed anammox bioactivity. Enhanced anammox promoted the advanced autotrophic nitrogen removal, which increased from 71.9% to 93.4%, even when the temperature was only 12.9 ℃. Therefore, it was feasible to enhance anammox in the multiple favorable conditions created, and the strategy extended the application of anammox to the full-scale mainstream, enhanced the understanding of anammox in the aspects of culturing conditions.

Keywords: anammox, granules, nitrite accumulation, nitrogen removal efficiency

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