Search results for: floor determination

Authors: Daniel Martin Fellows, Sean P. Walton, Jennifer Thompson, Oubay Hassan, Ella Quigley, Kevin Tinkham

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Fire performance is a crucial aspect to consider when designing cladding products, and testing this performance is extremely expensive. Appropriate use of numerical simulation of fire performance has the potential to reduce the total number of fire tests required when designing a product by eliminating poor-performing design ideas early in the design phase. Due to the complexity of fire and the large spectrum of failures it can cause, multi-disciplinary models are needed to capture the complex fire behavior and its structural effects on its surroundings. Working alongside Tata Steel U.K., the authors have focused on completing a coupled CFD-FEA simulation model suited to test Polyisocyanurate (PIR) based sandwich panel products to gain confidence before costly experimental standards testing. The sandwich panels are part of a thermally insulating façade system primarily for large non-domestic buildings. The work presented in this paper compares two coupling methodologies of a replicated physical experimental standards test LPS 1181-1, carried out by Tata Steel U.K. The two coupling methodologies that are considered within this research are; one-way and two-way. A one-way coupled analysis consists of importing thermal data from the CFD solver into the FEA solver. A two-way coupling analysis consists of continuously importing the updated changes in thermal data, due to the fire's behavior, to the FEA solver throughout the simulation. Likewise, the mechanical changes will also be updated back to the CFD solver to include geometric changes within the solution. For CFD calculations, a solver called Fire Dynamic Simulator (FDS) has been chosen due to its adapted numerical scheme to focus solely on fire problems. Validation of FDS applicability has been achieved in past benchmark cases. In addition, an FEA solver called ABAQUS has been chosen to model the structural response to the fire due to its crushable foam plasticity model, which can accurately model the compressibility of PIR foam. An open-source code called FDS-2-ABAQUS is used to couple the two solvers together, using several python modules to complete the process, including failure checks. The coupling methodologies and experimental data acquired from Tata Steel U.K are compared using several variables. The comparison data includes; gas temperatures, surface temperatures, and mechanical deformation of the panels. Conclusions are drawn, noting improvements to be made on the current coupling open-source code FDS-2-ABAQUS to make it more applicable to Tata Steel U.K sandwich panel products. Future directions for reducing the computational cost of the simulation are also considered.

Keywords: fire engineering, numerical coupling, sandwich panels, thermo fluids

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Authors: David Hough

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Indigenous peoples can generally be described as the original or first peoples of a land prior to colonization. While there is no single definition of indigenous peoples, the United Nations has developed a general understanding based on self-identification and historical continuity with pre-colonial societies. Indigenous peoples are often traditional holders of unique languages, knowledge systems and beliefs who possess valuable knowledge and practices which support sustainable management of natural resources. They often have social, economic, political systems, languages and cultures, which are distinct from dominant groups in the society or state where they live. They generally resist attempts by the dominant culture at assimilation and endeavour to maintain and reproduce their ancestral environments and systems as distinctive peoples and communities. In 2007, the United Nations General Assembly passed a declaration on the rights of indigenous peoples, known as UNDRIP. It (in addition to other international instruments such as ILO 169), sets out far-reaching guidelines, which – among other things – attempt to protect and promote indigenous languages and cultures. Paragraphs 13 and 14 of the declaration state the following regarding language, culture and education: Article 13, Paragraph 1: Indigenous peoples have the right to revitalize, use, develop and transmit for future generations their histories, languages, oral traditions, philosophies, writing systems, and literatures, and to designate and retain their own names for communities, places and persons. Article 14, Paragraph I: Indigenous peoples have the right to establish and control their educational systems and institutions providing education in their own languages, in a manner appropriate to their cultural methods of teaching and learning. These two paragraphs call for the right of self-determination in education. Paragraph 13 gives indigenous peoples the right to control the content of their teaching, while Paragraph 14 states that the teaching of this content should be based on methods of teaching and learning which are appropriate to indigenous peoples. This paper reviews an approach to furthering linguistic and cultural human rights for indigenous peoples in education, which supports UNDRIP. It has been employed in countries in Asia and the Pacific, including the Republic of the Marshall Islands, the Federated States of Micronesia, Far East Russia and Nepal. It is based on bottom-up community-based initiatives where students, teachers and local knowledge holders come together to produce classroom materials in their own languages that reflect their traditional beliefs and value systems. They may include such things as knowledge about herbal medicines and traditional healing practices, local history, numerical systems, weights and measures, astronomy and navigation, canoe building, weaving and mat making, life rituals, feasts, festivals, songs, poems, etc. Many of these materials can then be mainstreamed into math, science language arts and social studies classes.

Keywords: Indigenous peoples, linguistic and cultural human rights, materials development, teacher training, traditional knowledge

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Authors: Maciej Szeląg, Stanisław Fic

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The impact of elevated temperatures on the construction materials manifests in change of their physical and mechanical characteristics. Stresses and thermal deformations that occur inside the volume of the material cause its progressive degradation as temperature increase. Finally, the reactions and transformations of multiphase structure of cementitious composite cause its complete destruction. A particularly dangerous phenomenon is the impact of thermal shock – a sudden high temperature load. The thermal shock leads to a high value of the temperature gradient between the outer surface and the interior of the element in a relatively short time. The result of mentioned above process is the formation of the cracks and scratches on the material’s surface and inside the material. The article describes the use of computer image analysis techniques to identify and assess the structure of the cluster cracks on the surfaces of modified cement pastes, caused by thermal shock. Four series of specimens were tested. Two Portland cements were used (CEM I 42.5R and CEM I 52,5R). In addition, two of the series contained metakaolinite as a replacement for 10% of the cement content. Samples in each series were made in combination of three w/b (water/binder) indicators of respectively 0.4; 0.5; 0.6. Surface cracks of the samples were created by a sudden temperature load at 200°C for 4 hours. Images of the cracked surfaces were obtained via scanning at 1200 DPI; digital processing and measurements were performed using ImageJ v. 1.46r software. In order to examine the cracked surface of the cement paste as a system of closed clusters – the dispersal systems theory was used to describe the structure of cement paste. Water is used as the dispersing phase, and the binder is used as the dispersed phase – which is the initial stage of cement paste structure creation. A cluster itself is considered to be the area on the specimen surface that is limited by cracks (created by sudden temperature loading) or by the edge of the sample. To describe the structure of cracks two stereological parameters were proposed: A ̅ – the cluster average area, L ̅ – the cluster average perimeter. The goal of this study was to compare the investigated stereological parameters with the mechanical properties of the tested specimens. Compressive and tensile strength testes were carried out according to EN standards. The method used in the study allowed the quantitative determination of defects occurring in the examined modified cement pastes surfaces. Based on the results, it was found that the nature of the cracks depends mainly on the physical parameters of the cement and the intermolecular interactions on the dispersal environment. Additionally, it was noted that the A ̅/L ̅ relation of created clusters can be described as one function for all tested samples. This fact testifies about the constant geometry of the thermal cracks regardless of the presence of metakaolinite, the type of cement and the w/b ratio.

Keywords: cement paste, cluster cracks, elevated temperature, image analysis, metakaolinite, stereological parameters

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Authors: Léa Caban, Lucile Soudani, Julien Berger, Armelle Nouviaire, Emilio Bastidas-Arteaga

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Improvement of the thermal performance of buildings is a high concern for the construction industry. With the increase in environmental issues, new types of construction materials are being developed. These include bio-based insulation materials. They capture carbon dioxide, can be produced locally, and have good thermal performance. However, their behavior with respect to moisture transfer is still facing some issues. With a high porosity, the mass transfer is more important in those materials than in mineral insulation ones. Therefore, they can be more sensitive to moisture disorders such as mold growth, condensation risks or decrease of the wall energy efficiency. For this reason, the initial moisture content on the construction site is a piece of crucial knowledge. Measuring moisture content in a laboratory is a mastered task. Diverse methods exist but the easiest and the reference one is gravimetric. A material is weighed dry and wet, and its moisture content is mathematically deduced. Non-destructive methods (NDT) are promising tools to determine in an easy and fast way the moisture content in a laboratory or on construction sites. However, the quality and reliability of the measures are influenced by several factors. Classical NDT portable devices usable on-site measure the capacity or the resistivity of materials. Water’s electrical properties are very different from those of construction materials, which is why the water content can be deduced from these measurements. However, most moisture meters are made to measure wooden materials, and some of them can be adapted for construction materials with calibration curves. Anyway, these devices are almost never calibrated for insulation materials. The main objective of this study is to determine the reliability of moisture meters in the measurement of biobased insulation materials. The determination of which one of the capacitive or resistive methods is the most accurate and which device gives the best result is made. Several biobased insulation materials are tested. Recycled cotton, two types of wood fibers of different densities (53 and 158 kg/m3) and a mix of linen, cotton, and hemp. It seems important to assess the behavior of a mineral material, so glass wool is also measured. An experimental campaign is performed in a laboratory. A gravimetric measurement of the materials is carried out for every level of moisture content. These levels are set using a climatic chamber and by setting the relative humidity level for a constant temperature. The mass-based moisture contents measured are considered as references values, and the results given by moisture meters are compared to them. A complete analysis of the uncertainty measurement is also done. These results are used to analyze the reliability of moisture meters depending on the materials and their water content. This makes it possible to determine whether the moisture meters are reliable, and which one is the most accurate. It will then be used for future measurements on construction sites to assess the initial hygrothermal state of insulation materials, on both new-build and renovation projects.

Keywords: capacitance method, electrical resistance method, insulation materials, moisture transfer, non-destructive testing

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Authors: Michelle S. Pereira, Analice C. Azevedo, Julliane D. Medeiros, Ana Claudia S. Martins, Didier S. Castellano-Filho, Claudio G. Diniz, Vania L. Silva

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Vaginal microbiota is a complex ecosystem, composed by aerobic and anaerobic bacteria, living in a dynamic equilibrium. Lactobacillus spp. are predominant in vaginal ecosystem, and factors such as immunity and hormonal variations may lead to disruptions, resulting in proliferation of opportunistic pathogens. Bacterial vaginosis (BV) is a polymicrobial syndrome, caused by an increasing of anaerobic bacteria replacing Lactobacillus spp. Microorganisms such as Gardnerella vaginalis, Mycoplasma hominis, Mobiluncus spp., and Atopobium vaginae can be found in BV, which may also be associated to other infections such as by Human Papillomavirus (HPV). HPV is highly prevalent in sexually active women, and is considered a risk factor for development of cervical cancer. As long as few data is available on vaginal microbiota of women with HPV-associated cervical lesions, our objectives were to evaluate the diversity in vaginal ecosystem in these women. To all patients, clinical and socio-demographic data were collected after gynecological examination. This study was approved by the Ethics Committee from Federal University of Juiz de Fora, Minas Gerais, Brazil. Vaginal secretion and cervical scraping were collected. Gram-stained smears were evaluated to establish Nugent score for BV determination. Viral and bacterial DNA obtained was used as template for HPV genotyping (PCR) and bacterial fingerprint (REP-PCR). In total 31 patients were included (mean age 35 and 93.6% sexually active). The Nugent score showed that 38.7% were BV. From the medical records, Pap smear tests showed that 32.3% had low grade squamous epithelial lesion (LSIL), 29% had high grade squamous epithelial lesion (HSIL), 25.8% had atypical squamous cells of undetermined significance (ASC-US) and 12.9% with atypical squamous cells that would not exclude high-grade lesion (ASC-H). All participants were HPV+. HPV-16 was the most frequent (87.1%), followed by HPV-18 (61.3%). HPV-31, HPV-52 and HPV-58 were also detected. Coinfection HPV-16/HPV-18 was observed in 75%. In the 18-30 age group, HPV-16 was detected in 40%, and HPV-16/HPV-18 coinfection in 35%. HPV-16 was associated to 30% of ASC-H and 20% of HSIL patients. BV was observed in 50% of HPV-16+ participants and in 45% of HPV-16/HPV-18+. Fingerprints of bacterial communities showed clusters with low similarity suggesting high heterogeneity in vaginal microbiota within the sampled group. Overall, the data is worrisome once cervical-cancer highly risk-associated HPV-types were identified. The high microbial diversity observed may be related to the different levels of cellular lesions, and different physiological conditions of the participants (age, social behavior, education). Further prospective studies are needed to better address correlations and BV and microbial imbalance in vaginal ecosystems which would be related to the different cellular lesions in women with HPV infections. Supported by FAPEMIG, CNPq, CAPES, PPGCBIO/UFJF.

Keywords: human papillomavirus, bacterial vaginosis, bacterial diversity, cervical cancer

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Authors: Fernanda A. P. Peres, Thiago N. Peres, Flavio S. Fogliatto, Michel J. Anzanello

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Batch processes are widely used in food industry and have an important role in the production of high added value products, such as chocolate. Process performance is usually described by variables that are monitored as the batch progresses. Data arising from these processes are likely to display a strong correlation-autocorrelation structure, and are usually monitored using control charts based on multiway principal components analysis (MPCA). Process control of a new batch is carried out comparing the trajectories of its relevant process variables with those in a reference set of batches that yielded products within specifications; it is clear that proper determination of the reference set is key for the success of a correct signalization of non-conforming batches in such quality control schemes. In chocolate manufacturing, misclassifications of non-conforming batches in the conching phase may lead to significant financial losses. In such context, the accuracy of process control grows in relevance. In addition to that, the main assumption in MPCA-based monitoring strategies is that all batches are synchronized in duration, both the new batch being monitored and those in the reference set. Such assumption is often not satisfied in chocolate manufacturing process. As a consequence, traditional techniques as MPCA-based charts are not suitable for process control and monitoring. To address that issue, the objective of this work is to compare the performance of three dynamic time warping (DTW) methods in the alignment and synchronization of chocolate conching process variables’ trajectories, aimed at properly determining the reference distribution for multivariate statistical process control. The power of classification of batches in two categories (conforming and non-conforming) was evaluated using the k-nearest neighbor (KNN) algorithm. Real data from a milk chocolate conching process was collected and the following variables were monitored over time: frequency of soybean lecithin dosage, rotation speed of the shovels, current of the main motor of the conche, and chocolate temperature. A set of 62 batches with durations between 495 and 1,170 minutes was considered; 53% of the batches were known to be conforming based on lab test results and experts’ evaluations. Results showed that all three DTW methods tested were able to align and synchronize the conching dataset. However, synchronized datasets obtained from these methods performed differently when inputted in the KNN classification algorithm. Kassidas, MacGregor and Taylor’s (named KMT) method was deemed the best DTW method for aligning and synchronizing a milk chocolate conching dataset, presenting 93.7% accuracy, 97.2% sensitivity and 90.3% specificity in batch classification, being considered the best option to determine the reference set for the milk chocolate dataset. Such method was recommended due to the lowest number of iterations required to achieve convergence and highest average accuracy in the testing portion using the KNN classification technique.

Keywords: batch process monitoring, chocolate conching, dynamic time warping, reference set distribution, variable duration

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Authors: Paola Montenegro, Maria de los Angeles Balaguera Villa

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Contraceptive methods play a fundamental role in preventing unwanted pregnancies and protecting users from sexually transmitted infections (STIs). Despite being known to almost the entire population of reproductive age living in Colombia, there are barriers, practices and complex notions about contraceptives that affect their desired mass use and effectiveness. This work aims to analyse some of the perceptions and practices discussed with young people (13-28 years old) living in Colombia regarding the use of contraceptives in their daily lives, preferences, needs and perceived side effects. This research also examines the perceived paradox in autonomy that young people experience regarding contraceptive use: in one hand, its use (or lack of it) is interpreted as an act of self-determination and primary example of reproductive agency, on the other hand, it was frequently associated with coercion and limited autonomy derived from the gaps in reliable information available for young people, the difficulty of accessing certain preferred methods, and sometimes the experienced coercion exercise by doctors, partners and/or family members. The data and analysis discussed in this work stems from a research project whose objective was to provide information about needs and preferences in sexual and reproductive health of young people living in Colombia in relation to a possible telehealth service that could close the gap in access to quality care and safe information. Through a mixed methods approach, this study collected 5.736 responses to a virtual survey disseminated nationwide in Colombia and 47 inperson interviews (24 of them with people who were assigned female at birth and 21 with local key stakeholders in the abortion ecosystem). Quantitative data was analyzed using Stata SE Version 16.0 and qualitative analysis was completed through NVivo using thematic analysis. Key findings on contraception use in young people living in Colombia reveal that 85,8% of participants had used a contraceptive method in the last two years, and that the most commonly used methods were condoms, contraceptive pills, the morning-after pill and the method of interruption. The remaining 14,2% of respondents who declared to not have used contraceptives in the last two years expressed that the main four barriers to access were: "Lack of knowledge about contraceptive methods and where to obtain information and/or access them (13.9%)", "Have had sex with people who have vaginas (10.2%)", "Cost of contraceptive method (8.4%)" and "Difficulties in obtaining medical authorisations (7.6%)". These barriers coincided with the ones used to explain the non-use of contraceptives in young people, which reveals that limitations in information, cost, and quality care represent structural issues that need to be address in programmes, services, and public policy. Finally, interviews showed that young people perceive contraceptive use and non-use as an example of reaffirming reproductive agency and limitations to this can be explained through the widespread incomplete knowledge about how methods work and the prevalence of other social representations of contraception associated with trust, fidelity, and partner preferences, that in the end create limitations to young people’s autonomy.

Keywords: contraception, family planning, premarital fertility, unplanned pregnancy

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Authors: Amirhossein Dodankeh, Hadi Dabiryan, Saeed Hamze

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Using fabrics to reinforce composites considerably leads to improved mechanical properties, including resistance to the impact load and the energy absorption of composites. Warp-knitted spacer fabrics (WKSF) are fabrics consisting of two layers of warp-knitted fabric connected by pile yarns. These connections create a space between the layers filled by pile yarns and give the fabric a three-dimensional shape. Today because of the unique properties of spacer fabrics, they are widely used in the transportation, construction, and sports industries. Polyurethane (PU) foams are commonly used as energy absorbers, but WKSF has much better properties in moisture transfer, compressive properties, and lower heat resistance than PU foam. It seems that the use of warp-knitted spacer fabric reinforced PU foam (WKSFRF) can lead to the production and use of composite, which has better properties in terms of energy absorption from the foam, its mold formation is enhanced, and its mechanical properties have been improved. In this paper, the energy absorption efficiency (EAE) of WKSFRF under low-velocity impact is investigated experimentally. The contribution of the effect of each of the structural parameters of the WKSF on the absorption of impact energy has also been investigated. For this purpose, WKSF with different structures such as two different thicknesses, small and large mesh sizes, and position of the meshes facing each other and not facing each other were produced. Then 6 types of composite samples with different structural parameters were fabricated. The physical properties of samples like weight per unit area and fiber volume fraction of composite were measured for 3 samples of any type of composites. Low-velocity impact with an initial energy of 5 J was carried out on 3 samples of any type of composite. The output of the low-velocity impact test is acceleration-time (A-T) graph with a lot deviation point, in order to achieve the appropriate results, these points were removed using the FILTFILT function of MATLAB R2018a. Using Newtonian laws of physics force-displacement (F-D) graph was drawn from an A-T graph. We know that the amount of energy absorbed is equal to the area under the F-D curve. Determination shows the maximum energy absorption is 2.858 J which is related to the samples reinforced with fabric with large mesh, high thickness, and not facing of the meshes relative to each other. An index called energy absorption efficiency was defined, which means absorption energy of any kind of our composite divided by its fiber volume fraction. With using this index, the best EAE between the samples is 21.6 that occurs in the sample with large mesh, high thickness, and meshes facing each other. Also, the EAE of this sample is 15.6% better than the average EAE of other composite samples. Generally, the energy absorption on average has been increased 21.2% by increasing the thickness, 9.5% by increasing the size of the meshes from small to big, and 47.3% by changing the position of the meshes from facing to non-facing.

Keywords: composites, energy absorption efficiency, foam, geometrical parameters, low-velocity impact, warp-knitted spacer fabric

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Authors: Benedict Ofori, Kwabena Sarpong, Stephen Antwi

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Background: Medicinal plants are utilized all around the world and are becoming increasingly important economically. The WHO notes that ‘inappropriate use of traditional medicines or practices can have negative or dangerous effects and that future research is needed to ascertain the efficacy and safety of such practices and medicinal plants used by traditional medicine systems. The poor around the world have limited access to palliative care or pain relief. Pharmacologists have been focused on developing safe and effective anti-inflammatory drugs. Most of the issues related to their use have been linked to the fact that numerous traditional and herbal treatments are classified in different nations as meals or dietary supplements. As a result, there is no need for evidence of the quality, efficacy, or safety of these herbal formulations before they are marketed. The fact that access to drugs meant for pain relief is limited in low-income countries means advanced studies should be done on home drugs meant for inflammation to close the gap. Methods: The ethanolic extracts of the plant were screened for the presence of 10 phytochemicals. The Pierce BCA Protein Assay Kit was used for the determination of the protein concentration of the egg white. The rats were randomly selected and put in 6 groups. The egg white was sub-plantar injected into the right-hand paws of the rats to induce inflammation. The animals were treated with the three plant extracts obtained from the root bark, stem, and leaves of the plant. The control groups were treated with normal saline, while the standard groups were treated with standard drugs indomethacin and celecoxib. Plethysmometer was used to measure the change in paw volume of the animals over the course of the experiment. Results: The results of the phytochemical screening revealed the presence of reducing sugars and saponins. Alkaloids were present in only R.L.S (1:1:1), and phytosterols were found in R.L(1:1) and R.L.S (1:1:1). The estimated protein concentration was found to be 103.75 mg/ml. The control group had an observable increase in paw volume, which indicated that inflammation was induced during the 5 hours. The increase in paw volume for the control group peaked at the 1st hour and decreased gradually throughout the experiment, with minimal changes in the paw volumes. The 2nd and 3rd groups were treated with 20 mg/kg of indomethacin and celecoxib. The anti-inflammatory activities of indomethacin and celecoxib were calculated to be 21.4% and 4.28%, respectively. The remaining 3 groups were treated with 2 dose levels of 200mg/kg plant extracts. R.L.S, R.L, and S.R.L had anti-inflammatory activities of 22.3%, 8.2%, and 12.07%, respectively. Conclusions: Egg albumin-induced paw model in rats can be used to evaluate the anti-inflammatory activity of herbs that might have potential anti-inflammatory activity. Herbal medications have potential anti-inflammatory activities and can be used to manage various inflammatory conditions if their efficacy and side effects are well studied. The three extracts all possessed anti-inflammatory activity, with R.L.S having the highest anti-inflammatory activity.

Keywords: inflammation, capparis erythrocarpos, anti-inflammatory activity, herbal medicine, paw volume, egg albumin

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Authors: Yuriko Takayama, Norihiro Kato

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Quorum sensing (QS) is a cell-to-cell communication system in bacteria to regulate expression of target genes. In gram-negative bacteria, activation on QS is controlled by a concentration increase of N-acylhomoserine lactone (AHL), which can diffuse in and out of the cell. Effective control of QS is expected to avoid virulence factor production in infectious pathogens, biofilm formation, and antibiotic production because various cell functions in gram-negative bacteria are controlled by AHL-mediated QS. In this research, we applied cyclodextrins (CDs) as artificial hosts for the AHL signal to reduce the AHL concentration in the culture broth below its threshold for QS activation. The AHL-receptor complex induced under the high AHL concentration activates transcription of the QS-target gene. Accordingly, artificial reduction of the AHL concentration is one of the effective strategies to inhibit the QS. A hydrophobic cavity of the CD can interact with the acyl-chain of the AHL due to hydrophobic interaction in aqueous media. We studied N-hexanoylhomoserine lactone (C6HSL)-mediated QS in Serratia marcescens; accumulation of C6HSL is responsible for regulation of the expression of pig cluster. Inhibitory effects of added CDs on QS were demonstrated by determination of prodigiosin amount inside cells after reaching stationary phase, because production of prodigiosin depends on the C6HSL-mediated QS. By adding approximately 6 wt% hydroxypropyl-β-CD (HP-β-CD) in Luria-Bertani (LB) medium prior to inoculation of S. maecescens AS-1, the intracellularly accumulated prodigiosin was drastically reduced to 7-10%, which was determined after the extraction of prodigiosin in acidified ethanol. The AHL retention ability of HP-β-CD was also demonstrated by Chromobacterium violacuem CV026 bioassay. The CV026 strain is an AHL-synthase defective mutant that activates QS solely by adding AHLs from outside of cells. A purple pigment violacein is induced by activation of the AHL-mediated QS. We demonstrated that the violacein production was effectively suppressed when the C6HSL standard solution was spotted on a LB agar plate dispersing CV026 cells and HP-β-CD. Physico-chemical analysis was performed to study the affinity between the immobilized CD and added C6HSL using a quartz crystal microbalance (QCM) sensor. The COOH-terminated self-assembled monolayer was prepared on a gold electrode of 27-MHz AT-cut quartz crystal. Mono(6-deoxy-6-N, N-diethylamino)-β-CD was immobilized on the electrode using water-soluble carbodiimide. The C6HSL interaction with the β-CD cavity was studied by injecting the C6HSL solution to a cup-type sensor cell filled with buffer solution. A decrement of resonant frequency (ΔFs) clearly showed the effective C6HSL complexation with immobilized β-CD and its stability constant for MBP-SpnR-C6HSL complex was on the order of 102 M-1. The CD has high potential for engineered control of QS because it is safe for human use.

Keywords: acylhomoserine lactone, cyclodextrin, intracellular signaling, quorum sensing

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Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

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Authors: Silvia Grassi, Stefano Schiavon, Ernestina Casiraghi, Cristina Alamprese

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Dry-cured ham is an uncooked meat product particularly appreciated for its peculiar sensory traits among which lipid component plays a key role in defining quality and, consequently, consumers’ acceptability. Usually, fat content and distribution are chemically determined by expensive, time-consuming, and destructive analyses. Moreover, different sensory techniques are applied to assess product conformity to desired standards. In this context, visual systems are getting a foothold in the meat market envisioning more reliable and time-saving assessment of food quality traits. The present work aims at developing a simple but systematic and objective visual methodology to assess the fat amount of dry-cured ham slices, in terms of total, intermuscular and intramuscular fractions. To the aim, 160 slices from 80 PDO dry-cured hams were evaluated by digital image analysis and Soxhlet extraction. RGB images were captured by a flatbed scanner, converted in grey-scale images, and segmented based on intensity histograms as well as on a multi-stage algorithm aimed at edge enhancement. The latter was performed applying the Canny algorithm, which consists of image noise reduction, calculation of the intensity gradient for each image, spurious response removal, actual thresholding on corrected images, and confirmation of strong edge boundaries. The approach allowed for the automatic calculation of total, intermuscular and intramuscular fat fractions as percentages of the total slice area. Linear regression models were run to estimate the relationships between the image analysis results and the chemical data, thus allowing for the prediction of the total, intermuscular and intramuscular fat content by the dry-cured ham images. The goodness of fit of the obtained models was confirmed in terms of coefficient of determination (R²), hypothesis testing and pattern of residuals. Good regression models have been found being 0.73, 0.82, and 0.73 the R2 values for the total fat, the sum of intermuscular and intramuscular fat and the intermuscular fraction, respectively. In conclusion, the edge enhancement visual procedure brought to a good fat segmentation making the simple visual approach for the quantification of the different fat fractions in dry-cured ham slices sufficiently simple, accurate and precise. The presented image analysis approach steers towards the development of instruments that can overcome destructive, tedious and time-consuming chemical determinations. As future perspectives, the results of the proposed image analysis methodology will be compared with those of sensory tests in order to develop a fast grading method of dry-cured hams based on fat distribution. Therefore, the system will be able not only to predict the actual fat content but it will also reflect the visual appearance of samples as perceived by consumers.

Keywords: dry-cured ham, edge detection algorithm, fat content, image analysis

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Authors: Ahlam Said Al Azkawi, Nallusamy Sivakumar, Saif Nasser Al Bahri

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Diverse approaches and pathways are under development with the determination to develop cellulosic biofuels and other bio-products eventually at commercial scale in “bio-refineries”; however, the key challenge is mainly the high level of complexity in processing the feedstock which is complicated and energy consuming. To overcome the complications in utilizing the naturally occurring lignocellulose biomass, using waste paper as a feedstock for bio-production may solve the problem. Besides being abundant and cheap, bioprocessing of waste paper has evolved in response to the public concern from rising landfill cost from shrinking landfill capacity. Cardboard (CB) is one of the major components of municipal solid waste and one of the most important items to recycle. Although 50-70% of cardboard constitute is known to be cellulose and hemicellulose, the presence of lignin around them cause hydrophobic cross-link which physically obstructs the hydrolysis by rendering it resistant to enzymatic cleavage. Therefore, pretreatment is required to disrupt this resistance and to enhance the exposure of the targeted carbohydrates to the hydrolytic enzymes. Several pretreatment approaches have been explored, and the best ones would be those can influence cellulose conversion rates and hydrolytic enzyme performance with minimal or less cost and downstream processes. One of the promising strategies in this field is the application of surfactants, especially non-ionic surfactants. In this study, triton-X 100 was used as surfactants to treat cardboard prior enzymatic hydrolysis and compare it with acid treatment using 0.1% H2SO4. The effect of the surfactant enhancement was evaluated through its effect on hydrolysis rate in respect to time in addition to evaluating the structural changes and modification by scanning electron microscope (SEM) and X-ray diffraction (XRD) and through compositional analysis. Further work was performed to produce ethanol from CB treated with triton-X 100 via separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). The hydrolysis studies have demonstrated enhancement in saccharification by 35%. After 72 h of hydrolysis, a saccharification rate of 98% was achieved from CB enhanced with triton-X 100, while only 89 of saccharification achieved from acid pre-treated CB. At 120 h, the saccharification % exceeded 100 as reducing sugars continued to increase with time. This enhancement was not supported by any significant changes in the cardboard content as the cellulose, hemicellulose and lignin content remained same after treatment, but obvious structural changes were observed through SEM images. The cellulose fibers were clearly exposed with very less debris and deposits compared to cardboard without triton-X 100. The XRD pattern has also revealed the ability of the surfactant in removing calcium carbonate, a filler found in waste paper known to have negative effect on enzymatic hydrolysis. The cellulose crystallinity without surfactant was 73.18% and reduced to 66.68% rendering it more amorphous and susceptible to enzymatic attack. Triton-X 100 has proved to effectively enhance CB hydrolysis and eventually had positive effect on the ethanol yield via SSF. Treating cardboard with only triton-X 100 was a sufficient treatment to enhance the enzymatic hydrolysis and ethanol production.

Keywords: cardboard, enhancement, ethanol, hydrolysis, treatment, Triton-X 100

Procedia PDF Downloads 152

Authors: Mindaugas Liaudanskas, Darius Kviklys, Kristina Zymonė, Raimondas Raudonis, Jonas Viškelis, Norbertas Uselis, Pranas Viškelis, Valdimaras Janulis

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Apples are among the most commonly consumed fruits in the world. Based on data from the year 2014, approximately 84.63 million tons of apples are grown per annum. Apples are widely used in food industry to produce various products and drinks (juice, wine, and cider); they are also used unprocessed. Apples in human diet are an important source of different groups of biological active compounds that can positively contribute to the prevention of various diseases. They are a source of various biologically active substances – especially vitamins, organic acids, micro- and macro-elements, pectins, and phenolic, triterpenic, and other compounds. Triterpenic compounds, which are characterized by versatile biological activity, are the biologically active compounds found in apples that are among the most promising and most significant for human health. A specific analytical procedure including sample preparation and High Performance Liquid Chromatography (HPLC) analysis was developed, optimized, and validated for the detection of triterpenic compounds in the samples of different apples, their peels, and flesh from widespread apple cultivars 'Aldas', 'Auksis', 'Connel Red', 'Ligol', 'Lodel', and 'Rajka' grown in Lithuanian climatic conditions. The conditions for triterpenic compound extraction were optimized: the solvent of the extraction was 100% (v/v) acetone, and the extraction was performed in an ultrasound bath for 10 min. Isocratic elution (the eluents ratio being 88% (solvent A) and 12% (solvent B)) for a rapid separation of triterpenic compounds was performed. The validation of the methodology was performed on the basis of the ICH recommendations. The following characteristics of validation were evaluated: the selectivity of the method (specificity), precision, the detection and quantitation limits of the analytes, and linearity. The obtained parameters values confirm suitability of methodology to perform analysis of triterpenic compounds. Using the optimised and validated HPLC technique, four triterpenic compounds were separated and identified, and their specificity was confirmed. These compounds were corosolic acid, betulinic acid, oleanolic acid, and ursolic acid. Ursolic acid was the dominant compound in all the tested apple samples. The detected amount of betulinic acid was the lowest of all the identified triterpenic compounds. The greatest amounts of triterpenic compounds were detected in whole apple and apple peel samples of the 'Lodel' cultivar, and thus apples and apple extracts of this cultivar are potentially valuable for use in medical practice, for the prevention of various diseases, for adjunct therapy, for the isolation of individual compounds with a specific biological effect, and for the development and production of dietary supplements and functional food enriched in biologically active compounds. Acknowledgements. This work was supported by a grant from the Research Council of Lithuania, project No. MIP-17-8.

Keywords: apples, HPLC, triterpenic compounds, validation

Procedia PDF Downloads 173

Authors: Oscar Nieto-Garzón, Angélica Lozano

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In urban areas, on-road freight transportation creates several social and environmental externalities. Then, it is crucial that freight transport considers not only economic aspects, like retailer distribution cost reduction and service improvement, but also environmental effects such as global CO2 and local emissions (e.g. Particulate Matter, NOX, CO) and noise. Inadequate infrastructure development, high rate of urbanization, the increase of motorization, and the lack of transportation planning are characteristics that urban areas from developing countries share. The Metropolitan Area of Mexico City (MAMC), the Metropolitan Area of São Paulo (MASP), and Bogota are three of the largest urban areas in Latin America where air pollution is often a problem associated with emissions from mobile sources. The effect of the lockdown due to COVID-19 was analyzedfor these urban areas, comparing the same period (January to August) of years 2016 – 2019 with 2020. A strong reduction in the concentration of primary criteria pollutants emitted by road traffic were observed at the beginning of 2020 and after the lockdown measures.Daily mean concentration of NOx decreased 40% in the MAMC, 34% in the MASP, and 62% in Bogota. Daily mean ozone levels increased after the lockdown measures in the three urban areas, 25% in MAMC, 30% in the MASP and 60% in Bogota. These changes in emission patterns from mobile sources drastically changed the ambient atmospheric concentrations of CO and NOX. The CO/NOX ratioat the morning hours is often used as an indicator of mobile sources emissions. In 2020, traffic from cars and light vehicles was significantly reduced due to the first lockdown, but buses and trucks had not restrictions. In theory, it implies a decrease in CO and NOX from cars or light vehicles, maintaining the levels of NOX by trucks(or lower levels due to the congestion reduction). At rush hours, traffic was reduced between 50% and 75%, so trucks could get higher speeds, which would reduce their emissions. By means an emission model, it was found that an increase in the average speed (75%) would reduce the emissions (CO, NOX, and PM) from diesel trucks by up to 30%. It was expected that the value of CO/NOXratio could change due to thelockdownrestrictions. However, although there was asignificant reduction of traffic, CO/NOX kept its trend, decreasing to 8-9 in 2020. Hence, traffic restrictions had no impact on the CO/NOX ratio, although they did reduce vehicle emissions of CO and NOX. Therefore, these emissions may not adequately represent the change in the vehicle emission patterns, or this ratio may not be a good indicator of emissions generated by vehicles. From the comparison of the theoretical data and those observed during the lockdown, results that the real NOX reduction was lower than the theoretical reduction. The reasons could be that there are other sources of NOX emissions, so there would be an over-representation of NOX emissions generated by diesel vehicles, or there is an underestimation of CO emissions. Further analysis needs to consider this ratioto evaluate the emission inventories and then to extend these results forthe determination of emission control policies to non-mobile sources.

Keywords: COVID-19, emissions, freight transport, latin American metropolis

Procedia PDF Downloads 136

Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

Procedia PDF Downloads 137

Authors: Lina Paola Orozco Marin, Yuliet Montoya Osorio, John Bustamante Osorno

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Ischemic events can culminate in acute myocardial infarction by irreversible cardiac lesions that cannot be restored due to the limited regenerative capacity of the heart. Cell therapy seeks to replace these injured or necrotic cells by transplanting healthy and functional cells. The therapeutic alternatives proposed by tissue engineering and cardiovascular regenerative medicine are the use of biomaterials to mimic the native extracellular medium, which is full of proteins, proteoglycans, and glycoproteins. The selected biomaterials must provide structural support to the encapsulated cells to avoid their migration and death in the host tissue. In this context, the present research work focused on developing a natural thermosensitive hydrogel, its physical and chemical characterization, and the determination of its biocompatibility in vitro. The hydrogel was developed by mixing hydrolyzed bovine and porcine collagen at 2% w/v, chitosan at 2.5% w/v, and beta-glycerolphosphate at 8.5% w/w and 10.5% w/w in magnetic stirring at 4°C. Once obtained, the thermosensitivity and gelation time were determined, incubating the samples at 37°C and evaluating them through the inverted tube method. The morphological characterization of the hydrogels was carried out through scanning electron microscopy. Chemical characterization was carried out employing infrared spectroscopy. The biocompatibility was determined using the MTT cytotoxicity test according to the ISO 10993-5 standard for the hydrogel’s precursors using the fetal human ventricular cardiomyocytes cell line RL-14. The RL-14 cells were also seeded on the top of the hydrogels, and the supernatants were subculture at different periods to their observation under a bright field microscope. Four types of thermosensitive hydrogels were obtained, which differ in their composition and concentration, called A1 (chitosan/bovine collagen/beta-glycerolphosphate 8.5%w/w), A2 (chitosan/porcine collagen/beta-glycerolphosphate 8.5%), B1 (chitosan/bovine collagen/beta-glycerolphosphate 10.5%) and B2 (chitosan/porcine collagen/beta-glycerolphosphate 10.5%). A1 and A2 had a gelation time of 40 minutes, and B1 and B2 had a gelation time of 30 minutes at 37°C. Electron micrographs revealed a three-dimensional internal structure with interconnected pores for the four types of hydrogels. This facilitates the exchange of nutrients, oxygen, and the exit of metabolites, allowing to preserve a microenvironment suitable for cell proliferation. In the infrared spectra, it was possible to observe the interaction that occurs between the amides of polymeric compounds with the phosphate groups of beta-glycerolphosphate. Finally, the biocompatibility tests indicated that cells in contact with the hydrogel or with each of its precursors are not affected in their proliferation capacity for a period of 16 days. These results show the potential of the hydrogel to increase the cell survival rate in the cardiac cell therapies under investigation. Moreover, the results lay the foundations for its characterization and biological evaluation in both in vitro and in vivo models.

Keywords: cardiac cell therapy, cardiac ischemia, natural polymers, thermosensitive hydrogel

Procedia PDF Downloads 191

Authors: H. M. Wilhelm, P. O. Fernandes, L. P. Dill, C. Steffens, K. G. Moscon, S. M. Peres, V. Bender, T. Marchesan, J. B. Ferreira Neto

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Deterioration of insulating oil is a natural process that occurs during transformers operation. However, this process can be accelerated by some factors, such as oxygen, high temperatures, metals and, moisture, which rapidly reduce oil insulating capacity and favor transformer faults. Parts of building materials of a transformer can be degraded and yield soluble compounds and insoluble particles that shorten the equipment life. Physicochemical tests, dissolved gas analysis (including propane, propylene and, butane), volatile and furanic compounds determination, besides quantitative and morphological analyses of particulate are proposed in this study in order to correlate transformers building materials degradation with insulating oil characteristics. The present investigation involves tests of medium temperature overheating simulation by means of an electric resistance wrapped with the following materials immersed in mineral insulating oil: test I) copper, tin, lead and, paper (heated at 350-400 °C for 8 h); test II) only copper (at 250 °C for 11 h); and test III) only paper (at 250 °C for 8 h and at 350 °C for 8 h). A different experiment is the simulation of electric arc involving copper, using an electric welding machine at two distinct energy sets (low and high). Analysis results showed that dielectric loss was higher in the sample of test I, higher neutralization index and higher values of hydrogen and hydrocarbons, including propane and butane, were also observed. Test III oil presented higher particle count, in addition, ferrographic analysis revealed contamination with fibers and carbonized paper. However, these particles had little influence on the oil physicochemical parameters (dielectric loss and neutralization index) and on the gas production, which was very low. Test II oil showed high levels of methane, ethane, and propylene, indicating the effect of metal on oil degradation. CO₂ and CO gases were formed in the highest concentration in test III, as expected. Regarding volatile compounds, in test I acetone, benzene and toluene were detected, which are oil oxidation products. Regarding test III, methanol was identified due to cellulose degradation, as expected. Electric arc simulation test showed the highest oil oxidation in presence of copper and at high temperature, since these samples had huge concentration of hydrogen, ethylene, and acetylene. Particle count was also very high, showing the highest release of copper in such conditions. When comparing high and low energy, the first presented more hydrogen, ethylene, and acetylene. This sample had more similar results to test I, pointing out that the generation of different particles can be the cause for faults such as electric arc. Ferrography showed more evident copper and exfoliation particles than in other samples. Therefore, in this study, by using different combined analytical techniques, it was possible to correlate insulating oil characteristics with possible contaminants, which can lead to transformers failure.

Keywords: Ferrography, gas analysis, insulating mineral oil, particle contamination, transformer failures

Procedia PDF Downloads 225

Authors: Ieva Jokubauskaite, Alvyra Slepetiene, Danute Karcauskiene, Inga Liaudanskiene, Kristina Amaleviciute

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Soil organic carbon (SOC) is the key soil quality and ecological stability indicator, therefore, carbon accumulation in stable forms not only supports and increases the organic matter content in the soil, but also has a positive effect on the quality of soil and the whole ecosystem. Soil liming is one of the most common ways to improve the carbon sequestration in the soil. Determination of the optimum intensity and combinations of liming in order to ensure the optimal carbon quantitative and qualitative parameters is one of the most important tasks of this work. The field experiments were carried out at the Vezaiciai Branch of Lithuanian Research Centre for Agriculture and Forestry (LRCAF) during the 2011–2013 period. The effect of liming with different intensity (at a rate 0.5 every 7 years and 2.0 every 3-4 years) was investigated in the topsoil of acid moraine loam Bathygleyic Dystric Glossic Retisol. Chemical analyses were carried out at the Chemical Research Laboratory of Institute of Agriculture, LRCAF. Soil samples for chemical analyses were taken from the topsoil after harvesting. SOC was determined by the Tyurin method modified by Nikitin, measuring with spectrometer Cary 50 (VARIAN) at 590 nm wavelength using glucose standards. SOC fractional composition was determined by Ponomareva and Plotnikova version of classical Tyurin method. Dissolved organic carbon (DOC) was analyzed using an ion chromatograph SKALAR in water extract at soil-water ratio 1:5. Spectral properties (E4/E6 ratio) of humic acids were determined by measuring the absorbance of humic and fulvic acids solutions at 465 and 665 nm. Our study showed a negative statistically significant effect of periodical liming (at 0.5 and 2.0 liming rates) on SOC content in the soil. The content of SOC was 1.45% in the unlimed treatment, while in periodically limed at 2.0 liming rate every 3–4 years it was approximately by 0.18 percentage points lower. It was revealed that liming significantly decreased the DOC concentration in the soil. The lowest concentration of DOC (0.156 g kg-1) was established in the most intensively limed (2.0 liming rate every 3–4 years) treatment. Soil liming exerted an increase of all humic acids and fulvic acid bounded with calcium fractions content in the topsoil. Soil liming resulted in the accumulation of valuable humic acids. Due to the applied liming, the HR/FR ratio, indicating the quality of humus increased to 1.08 compared with that in unlimed soil (0.81). Intensive soil liming promoted the formation of humic acids in which groups of carboxylic and phenolic compounds predominated. These humic acids are characterized by a higher degree of condensation of aromatic compounds and in this way determine the intensive organic matter humification processes in the soil. The results of this research provide us with the clear information on the characteristics of SOC change, which could be very useful to guide the climate policy and sustainable soil management.

Keywords: acid soil, carbon sequestration, long–term liming, soil organic carbon

Procedia PDF Downloads 229

Authors: Nicholas Anderson, Steven Petersen, Tom Bates, Val Anderson

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Under the multiple-use management regime established in the United States for federally owned lands, government agencies have come under pressure from commercial apiaries to grant permits for the summer pasturing of honeybees on government lands. Federal agencies have struggled to integrate honeybees into their management plans and have little information to make regulations that resolve how many colonies should be allowed in a single location and at what distance sets of hives should be placed. Many conservation groups have voiced their concerns regarding the introduction of honeybees to these natural lands, as they may outcompete and displace native pollinating species. Assessing the quality of an area in regard to its floral resources, pollen, and nectar can be important when attempting to create regulations for the integration of commercial honeybee operations into a native ecosystem. Areas with greater floral resources may be able to support larger numbers of honeybee colonies, while poorer resource areas may be less resilient to introduced disturbances. Attempts are made in this study to determine flower cover using high resolution drone imagery to help assess the floral resource availability to pollinators in high elevation, tall forb communities. This knowledge will help in determining the potential that different areas may have for honeybee pasturing and honey production. Roughly 700 images were captured at 23m above ground level using a drone equipped with a Sony QX1 RGB 20-megapixel camera. These images were stitched together using Pix4D, resulting in a 60m diameter high-resolution mosaic of a tall forb meadow. Using the program ENVI, a supervised maximum likelihood classification was conducted to calculate the percentage of total flower cover and flower cover by color (blue, white, and yellow). A complete vegetation inventory was taken on site, and the major flowers contributing to each color class were noted. An accuracy assessment was performed on the classification yielding an 89% overall accuracy and a Kappa Statistic of 0.855. With this level of accuracy, drones provide an affordable and time efficient method for the assessment of floral cover in large areas. The proximal step of this project will now be to determine the average pollen and nectar loads carried by each flower species. The addition of this knowledge will result in a quantifiable method of measuring pollen and nectar resources of entire landscapes. This information will not only help land managers determine stocking rates for honeybees on public lands but also has applications in the agricultural setting, aiding producers in the determination of the number of honeybee colonies necessary for proper pollination of fruit and nut crops.

Keywords: honeybee, flower, pollinator, remote sensing

Procedia PDF Downloads 141

Authors: Mamunur Rashid, Jialin Yan

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The current years have witnessed a rapid evolution and integration of artificial intelligence (AI) in various fields, prominently influencing the education industry. Acknowledging this transformative wave, AI tools like ChatGPT and Grammarly have undeniably introduced perspectives and skills, enriching the educational experiences of higher education students. The prevalence of AI utilization in higher education also drives an increasing number of researchers' attention in various dimensions. Departments, offices, and professors in universities also designed and released a set of policies and guidelines on using AI effectively. In regard to this, the study targets exploring and analyzing graduate students' perspectives regarding AI guidelines set by teachers. A mixed-methods study will be mainly conducted in this study, employing in-depth interviews and focus groups to investigate and collect students' perspectives. Relevant materials, such as syllabi and course instructions, will also be analyzed through the documentary analysis to facilitate understanding of the study. Surveys will also be used for data collection and students' background statistics. The integration of both interviews and surveys will provide a comprehensive array of student perspectives across various academic disciplines. The study is anchored in the theoretical framework of self-determination theory (SDT), which emphasizes and explains the students' perspective under the AI guidelines through three core needs: autonomy, competence, and relatedness. This framework is instrumental in understanding how AI guidelines influence students' intrinsic motivation and sense of empowerment in their learning environments. Through qualitative analysis, the study reveals a sense of confusion and uncertainty among students regarding the appropriate application and ethical considerations of AI tools, indicating potential challenges in meeting their needs for competence and autonomy. The quantitative data further elucidates these findings, highlighting a significant communication gap between students and educators in the formulation and implementation of AI guidelines. The critical findings of this study mainly come from two aspects: First, the majority of graduate students are uncertain and confused about relevant AI guidelines given by teachers. Second, this study also demonstrates that the design and effectiveness of course materials, such as the syllabi and instructions, also need to adapt in regard to AI policies. It indicates that certain of the existing guidelines provided by teachers lack consideration of students' perspectives, leading to a misalignment with students' needs for autonomy, competence, and relatedness. More emphasize and efforts need to be dedicated to both teacher and student training on AI policies and ethical considerations. To conclude, in this study, graduate students' perspectives on teacher-provided AI guidelines are explored and reflected upon, calling for additional training and strategies to improve how these guidelines can be better disseminated for their effective integration and adoption. Although AI guidelines provided by teachers may be helpful and provide new insights for students, educational institutions should take a more anchoring role to foster a motivating, empowering, and student-centered learning environment. The study also provides some relevant recommendations, including guidance for students on the ethical use of AI and AI policy training for teachers in higher education.

Keywords: higher education policy, graduate students’ perspectives, higher education teacher, AI guidelines, AI in education

Procedia PDF Downloads 74

Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have played an indispensable role in controlling illness and death associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global healthcare problem. Many antibiotics had lost their effectiveness since the beginning of the antibiotic era because many bacteria have adapted defenses against these antibiotics. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing require the isolation of the pathogen from a clinical specimen by culturing on the appropriate media (this culturing stage lasts 24 h-first culturing). Then, chosen colonies are grown on media containing antibiotic(s), using micro-diffusion discs (second culturing time is also 24 h) in order to determine its bacterial susceptibility. Other methods, genotyping methods, E-test and automated methods were also developed for testing antimicrobial susceptibility. Most of these methods are expensive and time-consuming. Fourier transform infrared (FTIR) microscopy is rapid, safe, effective and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria; nonetheless, its true potential in routine clinical diagnosis has not yet been established. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The UTI E.coli bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 700 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 90% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E.coli, FTIR, multivariate analysis, susceptibility, UTI

Procedia PDF Downloads 171

Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

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Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

Procedia PDF Downloads 264

Authors: Bangkit Adhi Nugraha, Bambang Suripto

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On September 2016 Bank Indonesia (BI) release regulation no.18/18/PBI/2016 that permit bank clients for using the FX option call spread USD/IDR. Basically, this product is a combination between clients buy FX call option (pay premium) and sell FX call option (receive premium) to protect against currency depreciation while also capping the potential upside with cheap premium cost. BI classifies this product as a structured product. The structured product is combination at least two financial instruments, either derivative or non-derivative instruments. The call spread is the first structured product against IDR permitted by BI since 2009 as response the demand increase from Indonesia firms on FX hedging through derivative for protecting market risk their foreign currency asset or liability. The composition of hedging products on Indonesian FX market increase from 35% on 2015 to 40% on 2016, the majority on swap product (FX forward, FX swap, cross currency swap). Swap is formulated by interest rate difference of the two currency pairs. The cost of swap product is 7% for USD/IDR with one year USD/IDR volatility 13%. That cost level makes swap products seem expensive for hedging buyers. Because call spread cost (around 1.5-3%) cheaper than swap, the most Indonesian firms are using NDF FX call spread USD/IDR on offshore with outstanding amount around 10 billion USD. The cheaper cost of call spread is the main advantage for hedging buyers. The problem arises because BI regulation requires the call spread buyer doing the dynamic hedging. That means, if call spread buyer choose strike price 1 and strike price 2 and volatility USD/IDR exchange rate surpass strike price 2, then the call spread buyer must buy another call spread with strike price 1’ (strike price 1’ = strike price 2) and strike price 2’ (strike price 2’ > strike price 1‘). It could make the premium cost of call spread doubled or even more and dismiss the purpose of hedging buyer to find the cheapest hedging cost. It is very crucial for the buyer to choose best optimum strike price before entering into the transaction. To help hedging buyer find the optimum strike price and avoid expensive multiple premium cost, we observe ten years 2005-2015 historical data of USD/IDR volatility to be compared with the price movement of the call spread USD/IDR using Garman–Kohlhagen Model (as a common formula on FX option pricing). We use statistical tools to analysis data correlation, understand nature of call spread price movement over ten years, and determine factors affecting price movement. We select some range of strike price and tenor and calculate the probability of dynamic hedging to occur and how much it’s cost. We found USD/IDR currency pairs is too uncertain and make dynamic hedging riskier and more expensive. We validated this result using one year data and shown small RMS. The study result could be used to understand nature of FX call spread and determine optimum strike price for hedging plan.

Keywords: FX call spread USD/IDR, USD/IDR volatility statistical analysis, Garman–Kohlhagen Model on FX Option USD/IDR, Bank Indonesia Regulation no.18/18/PBI/2016

Procedia PDF Downloads 379

Authors: Francesca Marsili

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The reliability assessment of existing structures with probabilistic methods is becoming an increasingly important and frequent engineering task. However probabilistic reliability methods are based on an exhaustive knowledge of the stochastic modeling of the variables involved in the assessment; at the moment standards for the modeling of variables are absent, representing an obstacle to the dissemination of probabilistic methods. The framework according to probability distribution functions (PDFs) are established is represented by the Bayesian statistics, which uses Bayes Theorem: a prior PDF for the considered parameter is established based on information derived from the design stage and qualitative judgments based on the engineer past experience; then, the prior model is updated with the results of investigation carried out on the considered structure, such as material testing, determination of action and structural properties. The application of Bayesian statistics arises two different kind of problems: 1. The results of the updating depend on the engineer previous experience; 2. The updating of the prior PDF can be performed only if the structure has been tested, and quantitative data that can be statistically manipulated have been collected; performing tests is always an expensive and time consuming operation; furthermore, if the considered structure is an ancient building, destructive tests could compromise its cultural value and therefore should be avoided. In order to solve those problems, an interesting research path is represented by investigating Artificial Intelligence (AI) techniques that can be useful for the automation of the modeling of variables and for the updating of material parameters without performing destructive tests. Among the others, one that raises particular attention in relation to the object of this study is constituted by Case-Based Reasoning (CBR). In this application, cases will be represented by existing buildings where material tests have already been carried out and an updated PDFs for the material mechanical parameters has been computed through a Bayesian analysis. Then each case will be composed by a qualitative description of the material under assessment and the posterior PDFs that describe its material properties. The problem that will be solved is the definition of PDFs for material parameters involved in the reliability assessment of the considered structure. A CBR system represent a good candi¬date in automating the modelling of variables because: 1. Engineers already draw an estimation of the material properties based on the experience collected during the assessment of similar structures, or based on similar cases collected in literature or in data-bases; 2. Material tests carried out on structure can be easily collected from laboratory database or from literature; 3. The system will provide the user of a reliable probabilistic description of the variables involved in the assessment that will also serve as a tool in support of the engineer’s qualitative judgments. Automated modeling of variables can help in spreading probabilistic reliability assessment of existing buildings in the common engineering practice, and target at the best intervention and further tests on the structure; CBR represents a technique which may help to achieve this.

Keywords: reliability assessment of existing buildings, Bayesian analysis, case-based reasoning, historical structures

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Authors: Serdar Yaman, Hanzade Haykiri-Acma

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Biomass is a CO₂-neutral fuel that is renewable and sustainable along with having very huge global potential. Efficient use of biomass in power generation and production of biomass-based biofuels can mitigate the greenhouse gasses (GHG) and reduce dependency on fossil fuels. There are also other beneficial effects of biomass energy use such as employment creation and pollutant reduction. However, most of the biomass materials are not capable of competing with fossil fuels in terms of energy content. High moisture content and high volatile matter yields of biomass make it low calorific fuel, and it is very significant concern over fossil fuels. Besides, the density of biomass is generally low, and it brings difficulty in transportation and storage. These negative aspects of biomass can be overcome by thermal pretreatments that upgrade the fuel property of biomass. That is, torrefaction is such a thermal process in which biomass is heated up to 300ºC under non-oxidizing conditions to avoid burning of the material. The treated biomass is called as biochar that has considerably lower contents of moisture, volatile matter, and oxygen compared to the parent biomass. Accordingly, carbon content and the calorific value of biochar increase to the level which is comparable with that of coal. Moreover, hydrophilic nature of untreated biomass that leads decay in the structure is mostly eliminated, and the surface properties of biochar turn into hydrophobic character upon torrefaction. In order to investigate the effectiveness of torrefaction process on biomass properties, several biomass species such as olive milling residue (OMR), Rhododendron (small shrubby tree with bell-shaped flowers), and ash tree (timber tree) were chosen. The fuel properties of these biomasses were analyzed through proximate and ultimate analyses as well as higher heating value (HHV) determination. For this, samples were first chopped and ground to a particle size lower than 250 µm. Then, samples were subjected to torrefaction in a horizontal tube furnace by heating from ambient up to temperatures of 200, 250, and 300ºC at a heating rate of 10ºC/min. The biochars obtained from this process were also tested by the methods applied to the parent biomass species. Improvement in the fuel properties was interpreted. That is, increasing torrefaction temperature led to regular increases in the HHV in OMR, and the highest HHV (6065 kcal/kg) was gained at 300ºC. Whereas, torrefaction at 250ºC was seen optimum for Rhododendron and ash tree since torrefaction at 300ºC had a detrimental effect on HHV. On the other hand, the increase in carbon contents and reduction in oxygen contents were determined. Burning characteristics of the biochars were also studied using thermal analysis technique. For this purpose, TA Instruments SDT Q600 model thermal analyzer was used and the thermogravimetric analysis (TGA), derivative thermogravimetry (DTG), differential scanning calorimetry (DSC), and differential thermal analysis (DTA) curves were compared and interpreted. It was concluded that torrefaction is an efficient method to upgrade the fuel properties of biomass and the biochars from which have superior characteristics compared to the parent biomasses.

Keywords: biochar, biomass, fuel upgrade, torrefaction

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Authors: Tuğrul Varol, Halil Barış Özel

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In this study which has been conducted in Akçasu Forest Range District of Devrek Forest Directorate; 3 methods (cover removal with human force, cover removal with Hitachi F20 Excavator, and cover removal with agricultural equipment mounted on a Ferguson 240S agriculture tractor) utilized in weed control efforts in regeneration of degraded oriental beech forests have been compared. In this respect, 3 methods have been compared by determining certain work hours and standard durations of unit areas (1 hectare). For this purpose, evaluating the tasks made with human and machine force from the aspects of duration, productivity and costs, it has been aimed to determine the most productive method in accordance with the actual ecological conditions of research field. Within the scope of the study, the time studies have been conducted for 3 methods used in weed control efforts. While carrying out those studies, the performed implementations have been evaluated by dividing them into business stages. Also, the actual data have been used while calculating the cost accounts. In those calculations, the latest formulas and equations which are also used in developed countries have been utilized. The variance of analysis (ANOVA) was used in order to determine whether there is any statistically significant difference among obtained results, and the Duncan test was used for grouping if there is significant difference. According to the measurements and findings carried out within the scope of this study, it has been found during living cover removal efforts in regeneration efforts in demolished oriental beech forests that the removal of weed layer in 1 hectare of field has taken 920 hours with human force, 15.1 hours with excavator and 60 hours with an equipment mounted on a tractor. On the other hand, it has been determined that the cost of removal of living cover in unit area (1 hectare) was 3220.00 TL for man power, 788.70 TL for excavator and 2227.20 TL for equipment mounted on a tractor. According to the obtained results, it has been found that the utilization of excavator in weed control effort in regeneration of degraded oriental beech regions under actual ecological conditions of research field has been found to be more productive from both of aspects of duration and costs. These determinations carried out should be repeated in weed control efforts in degraded forest fields with different ecological conditions, it is compulsory for finding the most efficient weed control method. These findings will light the way of technical staff of forestry directorate in determination of the most effective and economic weed contol method. Thus, the more actual data will be used while preparing the weed control budgets, and there will be significant contributions to national economy. Also the results of this and similar studies are very important for developing the policies for our forestry in short and long term.

Keywords: artificial regeneration, weed control, oriental beech, productivity, mechanization, man power, cost analysis

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Authors: Eszter Repasi, Akos Koller

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Our genetic information determines our look, physiology, sports performance and all our features. Maximizing the performances of athletes have adopted a science-based approach to the nutritional support. Nowadays genetics studies have blended with nutritional sciences, and a dynamically evolving, new research field have appeared. Nutritional genomics is needed to be used by nutritional experts. This is a recent field of nutritional science, which can provide a solution to reach the best sport performance using correlations between the athlete’s genome, nutritions, molecules, included human microbiome (links between food, microbiome and epigenetics), nutrigenomics and nutrigenetics. Nutritional genomics has a tremendous potential to change the future of dietary guidelines and personal recommendations. Experts need to use new technology to get information about the athletes, like nutritional genomics profile (included the determination of the oral and gut microbiome and DNA coded reaction for food components), which can modify the preparation term and sports performance. The influence of nutrients on the genes expression is called Nutrigenomics. The heterogeneous response of gene variants to nutrients, dietary components is called Nutrigenetics. The human microbiome plays a critical role in the state of health and well-being, and there are more links between food or nutrition and the human microbiome composition, which can develop diseases and epigenetic changes as well. A nutritional genomics-based profile of athletes can be the best technic for a dietitian to make a unique sports nutrition diet plan. Using functional food and the right food components can be effected on health state, thus sports performance. Scientists need to determine the best response, due to the effect of nutrients on health, through altering genome promote metabolites and result changes in physiology. Nutritional biochemistry explains why polymorphisms in genes for the absorption, circulation, or metabolism of essential nutrients (such as n-3 polyunsaturated fatty acids or epigallocatechin-3-gallate), would affect the efficacy of that nutrient. Controlled nutritional deficiencies and failures, prevented the change of health state or a newly discovered food intolerance are observed by a proper medical team, can support better sports performance. It is important that the dietetics profession informed on gene-diet interactions, that may be leading to optimal health, reduced risk of injury or disease. A special medical application for documentation and monitoring of data of health state and risk factors can uphold and warn the medical team for an early action and help to be able to do a proper health service in time. This model can set up a personalized nutrition advice from the status control, through the recovery, to the monitoring. But more studies are needed to understand the mechanisms and to be able to change the composition of the microbiome, environmental and genetic risk factors in cases of athletes.

Keywords: gene-diet interaction, multidisciplinary team, microbiome, diet plan

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

Abstract:

The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Ignitia Motjolopane

Abstract:

Small and medium enterprises (SMEs) play an important role in economies by contributing to economic growth and employment. In the fourth industrial revolution, the convergence of technologies and the changing nature of work created pressures on economies globally. Generative artificial intelligence (AI) may support SMEs in exploring, exploiting, and transforming business models to align with their growth aspirations. SMEs' growth aspirations fall into four categories: subsistence, income, growth, and speculative. Subsistence-oriented firms focus on meeting basic financial obligations and show less motivation for business model innovation. SMEs focused on income, growth, and speculation are more likely to pursue business model innovation to support growth strategies. SMEs' strategic goals link to distinct business model innovation paths depending on whether SMEs are starting a new business, pursuing growth, or seeking profitability. Integrating generative artificial intelligence in start-up SME business model innovation enhances value creation, user-oriented innovation, and SMEs' ability to adapt to dynamic changes in the business environment. The existing literature may lack comprehensive frameworks and guidelines for effectively integrating generative AI in start-up reiterative business model innovation paths. This paper examines start-up business model innovation path with generative artificial intelligence. A theoretical approach is used to examine start-up-focused SME reiterative business model innovation path with generative AI. Articulating how generative AI may be used to support SMEs to systematically and cyclically build the business model covering most or all business model components and analyse and test the BM's viability throughout the process. As such, the paper explores generative AI usage in market exploration. Moreover, market exploration poses unique challenges for start-ups compared to established companies due to a lack of extensive customer data, sales history, and market knowledge. Furthermore, the paper examines the use of generative AI in developing and testing viable value propositions and business models. In addition, the paper looks into identifying and selecting partners with generative AI support. Selecting the right partners is crucial for start-ups and may significantly impact success. The paper will examine generative AI usage in choosing the right information technology, funding process, revenue model determination, and stress testing business models. Stress testing business models validate strong and weak points by applying scenarios and evaluating the robustness of individual business model components and the interrelation between components. Thus, the stress testing business model may address these uncertainties, as misalignment between an organisation and its environment has been recognised as the leading cause of company failure. Generative AI may be used to generate business model stress-testing scenarios. The paper is expected to make a theoretical and practical contribution to theory and approaches in crafting a robust business model innovation path with generative artificial intelligence in start-up SMEs.

Keywords: business models, innovation, generative AI, small medium enterprises

Procedia PDF Downloads 71