Search results for: input constraints

Authors: Moustafa Osman Mohammed

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The resilient and sustainable of construction ecology is essential to world’s socio-economic development. Environmental resilience is crucial in relating construction ecology to topology of spatial-economic model. Sustainability of spatial-economic model gives attention to green business to comply with Earth’s System for naturally exchange patterns of ecosystems. The systems ecology has consistent and periodic cycles to preserve energy and materials flow in Earth’s System. When model structure is influencing communication of internal and external features in system networks, it postulated the valence of the first-level spatial outcomes (i.e., project compatibility success). These instrumentalities are dependent on second-level outcomes (i.e., participant security satisfaction). These outcomes of model are based on measuring database efficiency, from 2015 to 2025. The model topology has state-of-the-art in value-orientation impact and correspond complexity of sustainability issues (e.g., build a consistent database necessary to approach spatial structure; construct the spatial-economic model; develop a set of sustainability indicators associated with model; allow quantification of social, economic and environmental impact; use the value-orientation as a set of important sustainability policy measures), and demonstrate environmental resilience. The model is managing and developing schemes from perspective of multiple sources pollutants through the input–output criteria. These criteria are evaluated the external insertions effects to conduct Monte Carlo simulations and analysis for using matrices in a unique spatial structure. The balance “equilibrium patterns” such as collective biosphere features, has a composite index of the distributed feedback flows. These feedback flows have a dynamic structure with physical and chemical properties for gradual prolong of incremental patterns. While these structures argue from system ecology, static loads are not decisive from an artistic/architectural perspective. The popularity of system resilience, in the systems structure related to ecology has not been achieved without the generation of confusion and vagueness. However, this topic is relevant to forecast future scenarios where industrial regions will need to keep on dealing with the impact of relative environmental deviations. The model attempts to unify analytic and analogical structure of urban environments using database software to integrate sustainability outcomes where the process based on systems topology of construction ecology.

Keywords: system ecology, construction ecology, industrial ecology, spatial-economic model, systems topology

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Jilbert Novelero, Oliver Mariano

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In a world where technological evolution and competition to create innovative products are at its peak, problems on Electronic Waste (E-Waste) are now becoming a global concern. E-waste is said to be any electrical or electronic devices that have reached the terminal of its useful life. The major issue are the volume and the raw materials used in crafting E-waste which is non-biodegradable and contains hazardous substances that are toxic to human health and the environment. The objective of this study is to gather baseline data in terms of the composition of E-waste in the solid waste stream and to determine the top 5 E-waste categories in a highly urbanized city. Recommendations in managing these wastes for its reduction were provided which may serve as a guide for acceptance and implementation in the locality. Pasig City was the chosen beneficiary of the research output and through the collaboration of the City Government of Pasig and its Solid Waste Management Office (SWMO); the researcher successfully conducted the Electronic Waste Analysis and Characterization Study (E-WACS) to achieve the objectives. E-WACS that was conducted on April 2019 showed that E-waste ranked 4th which comprises the 10.39% of the overall solid waste volume. Out of 345, 127.24kg which is the total daily domestic waste generation in the city, E-waste covers 35,858.72kg. Moreover, an average of 40 grams was determined to be the E-waste generation per person per day. The top 5 E-waste categories were then classified after the analysis. The category which ranked first is the office and telecommunications equipment that contained the 63.18% of the total generated E-waste. Second in ranking was the household appliances category with 21.13% composition. Third was the lighting devices category with 8.17%. Fourth on ranking was the consumer electronics and batteries category which was composed of 5.97% and fifth was the wires and cables category where it comprised the 1.41% of the average generated E-waste samples. One of the recommendations provided in this research is the implementation of the Pasig City Waste Advantage Card. The card can be used as a privilege card and earned points can be converted to avail of and enjoy services such as haircut, massage, dental services, medical check-up, and etc. Another recommendation raised is for the LGU to encourage a communication or dialogue with the technology and electronics manufacturers and distributors and international and local companies to plan the retrieval and disposal of the E-wastes in accordance with the Extended Producer Responsibility (EPR) policy where producers are given significant responsibilities for the treatment and disposal of post-consumer products.

Keywords: E-waste, E-WACS, E-waste characterization, electronic waste, electronic waste analysis

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Authors: Dhruvit S. Berawala, Jann R. Ursin, Obrad Slijepcevic

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Shale gas is one of the most rapidly growing forms of natural gas. Unconventional natural gas deposits are difficult to characterize overall, but in general are often lower in resource concentration and dispersed over large areas. Moreover, gas is densely packed into the matrix through adsorption which accounts for large volume of gas reserves. Gas production from tight shale deposits are made possible by extensive and deep well fracturing which contacts large fractions of the formation. The conventional reservoir modelling and production forecasting methods, which rely on fluid-flow processes dominated by viscous forces, have proved to be very pessimistic and inaccurate. This paper presents a new approach to forecast shale gas production by detailed modeling of gas desorption, diffusion and non-linear flow mechanisms in combination with statistical representation of these processes. The representation of the model involves a cube as a porous media where free gas is present and a sphere (SiC: Sphere in Cube model) inside it where gas is adsorbed on to the kerogen or organic matter. Further, the sphere is considered consisting of many layers of adsorbed gas in an onion-like structure. With pressure decline, the gas desorbs first from the outer most layer of sphere causing decrease in its molecular concentration. The new available surface area and change in concentration triggers the diffusion of gas from kerogen. The process continues until all the gas present internally diffuses out of the kerogen, gets adsorbs onto available surface area and then desorbs into the nanopores and micro-fractures in the cube. Each SiC idealizes a gas pathway and is characterized by sphere diameter and length of the cube. The diameter allows to model gas storage, diffusion and desorption; the cube length takes into account the pathway for flow in nanopores and micro-fractures. Many of these representative but general cells of the reservoir are put together and linked to a well or hydraulic fracture. The paper quantitatively describes these processes as well as clarifies the geological conditions under which a successful shale gas production could be expected. A numerical model has been derived which is then compiled on FORTRAN to develop a simulator for the production of shale gas by considering the spheres as a source term in each of the grid blocks. By applying SiC to field data, we demonstrate that the model provides an effective way to quickly access gas production rates from shale formations. We also examine the effect of model input properties on gas production.

Keywords: adsorption, diffusion, non-linear flow, shale gas production

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Authors: Jessica Scher Lisa, Eric Shyman

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Autism spectrum disorder (ASD) is a neuro-developmental disorder that is characterized by persistent deficits in social communication and social interaction (i.e. deficits in social-emotional reciprocity, nonverbal communicative behaviors, and establishing/maintaining social relationships), as well as by the presence of repetitive behaviors and perseverative areas of interest (i.e. stereotyped or receptive motor movements, use of objects, or speech, rigidity, restricted interests, and hypo or hyperactivity to sensory input or unusual interest in sensory aspects of the environment). Additionally, diagnoses of ASD require the presentation of symptoms in the early developmental period, marked impairments in adaptive functioning, and a lack of explanation by general intellectual impairment or global developmental delay (although these conditions may be co-occurring). Over the past several decades, many theories have been developed in an effort to explain the root cause of ASD in terms of atypical central cognitive processes. The field of neuroscience is increasingly finding structural and functional differences between autistic and neurotypical individuals using neuro-imaging technology. One main area this research has focused upon is in visuospatial processing, with specific attention to the notion of ‘weak central coherence’ (WCC). This paper offers an analysis of findings from selected studies in order to explore research that challenges the ‘deficit’ characterization of a weak central coherence theory as opposed to a ‘superiority’ characterization of strong local coherence. The weak central coherence theory has long been both supported and refuted in the ASD literature and has most recently been increasingly challenged by advances in neuroscience. The selected studies lend evidence to the notion of amplified localized perception rather than deficient global perception. In other words, WCC may represent superiority in ‘local processing’ rather than a deficit in global processing. Additionally, the right hemisphere and the specific area of the extrastriate appear to be key in both the visual and lexicosemantic process. Overactivity in the striate region seems to suggest inaccuracy in semantic language, which lends itself to support for the link between the striate region and the atypical organization of the lexicosemantic system in ASD.

Keywords: autism spectrum disorder, neurology, visual processing, weak coherence

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Authors: Thanduxolo Elford Fana

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Rationale and Purpose: Performance management and development system are central to effective and efficient service delivery, especially in highly labour intensive sectors such as South African public health. Performance management and development systems seek to ensure that good employee performance is rewarded accordingly, while those who underperform are developed so that they can reach their full potential. An effective and efficiently implemented performance management system motivates and improves employee engagement. The purpose of this study is to examine the implementation of the performance management and development system and the challenges that are encountered during its implementation in the Eastern Cape Provincial Department of Health. Methods: A qualitative research approach and a case study design was adopted in this study. The primary data were collected through observations, focus group discussions with employees, a group interview with shop stewards, and in-depth interviews with supervisors and managers, from April 2019 to September 2019. There were 45 study participants. In-depth interviews were held with 10 managers at facility level, which included chief executive officer, chief medical officer, assistant director’s in human resources management, patient admin, operations, finance, and two area manager and two operation managers nursing. A group interview was conducted with five shop stewards and an in-depth interview with one shop steward from the group. Five focus group discussions were conducted with clinical and non-clinical staff. The focus group discussions were supplemented with an in-depth interview with one person from each group in order to counter the group effect. Observations included moderation committee, contracting, and assessment meetings. Findings: The study shows that the performance management and development system was not properly implemented. There was non-compliance to performance management and development system policy guidelines in terms of time lines for contracting, evaluation, payment of incentives to good performers, and management of poor performance. The study revealed that the system is ineffective in raising the performance of employees and unable to assist employees to grow. The performance bonuses were no longer paid to qualifying employees. The study also revealed that lack of capacity and commitment, poor communication, constant policy changes, financial constraints, weak and highly bureaucratic management structures, union interference were challenges that were encountered during the implementation of the performance management and development system. Lastly, employees and supervisors were rating themselves three irrespective of how well or bad they performed. Conclusion: Performance management is regarded as vital to improved performance of the health workforce and healthcare service delivery among populations. Effective implementation of performance management and development system depends on well-capacitated and unbiased management at facility levels. Therefore, there is an urgent need to improve communication, link performance management to rewards, and capacitate staff on performance management and development system, as it is key to improved public health sector outcomes or performance.

Keywords: challenges, implementation, performance management and development system, public hospital

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Authors: M. A. Okezue, K. L. Clase, S. R. Byrn

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The requirement for maintaining data integrity in laboratory operations is critical for regulatory compliance. Automation of procedures reduces incidence of human errors. Quality control laboratories located in low-income economies may face some barriers in attempts to automate their processes. Since data from quality control tests on pharmaceutical products are used in making regulatory decisions, it is important that laboratory reports are accurate and reliable. Zinc Sulphate (ZnSO₄) tablets is used in treatment of diarrhea in pediatric population, and as an adjunct therapy for COVID-19 regimen. Unfortunately, zinc content in these formulations is determined titrimetrically; a manual analytical procedure. The assay for ZnSO4 tablets involves time-consuming steps that contain mathematical formulae prone to calculation errors. To achieve consistency, save costs, and improve data integrity, validated spreadsheets were developed to simplify the two critical steps in the analysis of ZnSO4 tablets: standardization of 0.1M Sodium Edetate (EDTA) solution, and the complexometric titration assay procedure. The assay method in the United States Pharmacopoeia was used to create a process flow for ZnSO₄ tablets. For each step in the process, different formulae were input into two spreadsheets to automate calculations. Further checks were created within the automated system to ensure validity of replicate analysis in titrimetric procedures. Validations were conducted using five data sets of manually computed assay results. The acceptance criteria set for the protocol were met. Significant p-values (p < 0.05, α = 0.05, at 95% Confidence Interval) were obtained from students’ t-test evaluation of the mean values for manual-calculated and spreadsheet results at all levels of the analysis flow. Right-first-time analysis and principles of data integrity were enhanced by use of the validated spreadsheet calculators in titrimetric evaluations of ZnSO₄ tablets. Human errors were minimized in calculations when procedures were automated in quality control laboratories. The assay procedure for the formulation was achieved in a time-efficient manner with greater level of accuracy. This project is expected to promote cost savings for laboratory business models.

Keywords: data integrity, spreadsheets, titrimetry, validation, zinc sulphate tablets

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Authors: Phillip Kearney, Constantina Lekakou, Stephen Belcher, Alessandro Sordon

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The focus in the automotive industry is to reduce human operator and machine interaction, so manufacturing becomes more automated and safer. The aim is to lower part cost and construction time as well as defects in the parts, sometimes occurring due to the physical limitations of human operators. A move to automate the layup of reinforcement material in composites manufacturing has resulted in the use of tapes that are placed in position by a robotic deposition head, also described as Automated Fibre Placement (AFP). The process of AFP is limited with respect to the finite amount of material that can be loaded into the machine at any one time. Joining two batches of tape material together involves a splice to secure the ends of the finishing tape to the starting edge of the new tape. The splicing method of choice for the majority of prepreg applications is a hand stich method, and as the name suggests requires human input to achieve. This investigation explores three methods for automated splicing, namely, adhesive, binding and stitching. The adhesive technique uses an additional adhesive placed on the tape ends to be joined. Binding uses the binding agent that is already impregnated onto the tape through the application of heat. The stitching method is used as a baseline to compare the new splicing methods to the traditional technique currently in use. As the methods will be used within a High Speed Automated Fibre Placement (HSAFP) process, this meant the parameters of the splices have to meet certain specifications: (a) the splice must be able to endure a load of 50 N in tension applied at a rate of 1 mm/s; (b) the splice must be created in less than 6 seconds, dictated by the capacity of the tape accumulator within the system. The samples for experimentation were manufactured with controlled overlaps, alignment and splicing parameters, these were then tested in tension using a tensile testing machine. Initial analysis explored the use of the impregnated binding agent present on the tape, as in the binding splicing technique. It analysed the effect of temperature and overlap on the strength of the splice. It was found that the optimum splicing temperature was at the higher end of the activation range of the binding agent, 100 °C. The optimum overlap was found to be 25 mm; it was found that there was no improvement in bond strength from 25 mm to 30 mm overlap. The final analysis compared the different splicing methods to the baseline of a stitched bond. It was found that the addition of an adhesive was the best splicing method, achieving a maximum load of over 500 N compared to the 26 N load achieved by a stitching splice and 94 N by the binding method.

Keywords: analysis, automated fibre placement, high speed, splicing

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Authors: Sridhar A. Malkaram, Tamer E. Fandy

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Purpose: 5-Azacytidine (5AC) and decitabine (DC) are DNA hypomethylating agents. We recently demonstrated that both drugs increase the enzymatic activity of the histone deacetylase enzyme SIRT6. Accordingly, we are comparing the changes H3K9 acetylation changes in the whole genome induced by both drugs using leukemia cells. Description of Methods & Materials: Mononuclear cells from the bone marrow of six de-identified naive acute myeloid leukemia (AML) patients were cultured with either 500 nM of DC or 5AC for 72 h followed by ChIP-Seq analysis using a ChIP-validated acetylated-H3K9 (H3K9ac) antibody. Chip-Seq libraries were prepared from treated and untreated cells using SMARTer ThruPLEX DNA- seq kit (Takara Bio, USA) according to the manufacturer’s instructions. Libraries were purified and size-selected with AMPure XP beads at 1:1 (v/v) ratio. All libraries were pooled prior to sequencing on an Illumina HiSeq 1500. The dual-indexed single-read Rapid Run was performed with 1x120 cycles at 5 pM final concentration of the library pool. Sequence reads with average Phred quality < 20, with length < 35bp, PCR duplicates, and those aligning to blacklisted regions of the genome were filtered out using Trim Galore v0.4.4 and cutadapt v1.18. Reads were aligned to the reference human genome (hg38) using Bowtie v2.3.4.1 in end-to-end alignment mode. H3K9ac enriched (peak) regions were identified using diffReps v1.55.4 software using input samples for background correction. The statistical significance of differential peak counts was assessed using a negative binomial test using all individuals as replicates. Data & Results: The data from the six patients showed significant (Padj<0.05) acetylation changes at 925 loci after 5AC treatment versus 182 loci after DC treatment. Both drugs induced H3K9 acetylation changes at different chromosomal regions, including promoters, coding exons, introns, and distal intergenic regions. Ten common genes showed H3K9 acetylation changes by both drugs. Approximately 84% of the genes showed an H3K9 acetylation decrease by 5AC versus 54% only by DC. Figures 1 and 2 show the heatmaps for the top 100 genes and the 99 genes showing H3K9 acetylation decrease after 5AC treatment and DC treatment, respectively. Conclusion: Despite the similarity in hypomethylating activity and chemical structure, the effect of both drugs on H3K9 acetylation change was significantly different. More changes in H3K9 acetylation were observed after 5 AC treatments compared to DC. The impact of these changes on gene expression and the clinical efficacy of these drugs requires further investigation.

Keywords: DNA methylation, leukemia, decitabine, 5-Azacytidine, epigenetics

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Authors: Khachen Kongpakwattana, Nathorn Chaiyakunapruk

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Background: Decision-analytic models for Alzheimer’s disease (AD) have been advanced to discrete-event simulation (DES), in which individual-level modelling of disease progression across continuous severity spectra and incorporation of key parameters such as treatment persistence into the model become feasible. This study aimed to apply the DES to perform a cost-effectiveness analysis of treatment for AD in Thailand. Methods: A dataset of Thai patients with AD, representing unique demographic and clinical characteristics, was bootstrapped to generate a baseline cohort of patients. Each patient was cloned and assigned to donepezil, galantamine, rivastigmine, memantine or no treatment. Throughout the simulation period, the model randomly assigned each patient to discrete events including hospital visits, treatment discontinuation and death. Correlated changes in cognitive and behavioral status over time were developed using patient-level data. Treatment effects were obtained from the most recent network meta-analysis. Treatment persistence, mortality and predictive equations for functional status, costs (Thai baht (THB) in 2017) and quality-adjusted life year (QALY) were derived from country-specific real-world data. The time horizon was 10 years, with a discount rate of 3% per annum. Cost-effectiveness was evaluated based on the willingness-to-pay (WTP) threshold of 160,000 THB/QALY gained (4,994 US$/QALY gained) in Thailand. Results: Under a societal perspective, only was the prescription of donepezil to AD patients with all disease-severity levels found to be cost-effective. Compared to untreated patients, although the patients receiving donepezil incurred a discounted additional costs of 2,161 THB, they experienced a discounted gain in QALY of 0.021, resulting in an incremental cost-effectiveness ratio (ICER) of 138,524 THB/QALY (4,062 US$/QALY). Besides, providing early treatment with donepezil to mild AD patients further reduced the ICER to 61,652 THB/QALY (1,808 US$/QALY). However, the dominance of donepezil appeared to wane when delayed treatment was given to a subgroup of moderate and severe AD patients [ICER: 284,388 THB/QALY (8,340 US$/QALY)]. Introduction of a treatment stopping rule when the Mini-Mental State Exam (MMSE) score goes below 10 to a mild AD cohort did not deteriorate the cost-effectiveness of donepezil at the current treatment persistence level. On the other hand, none of the AD medications was cost-effective when being considered under a healthcare perspective. Conclusions: The DES greatly enhances real-world representativeness of decision-analytic models for AD. Under a societal perspective, treatment with donepezil improves patient’s quality of life and is considered cost-effective when used to treat AD patients with all disease-severity levels in Thailand. The optimal treatment benefits are observed when donepezil is prescribed since the early course of AD. With healthcare budget constraints in Thailand, the implementation of donepezil coverage may be most likely possible when being considered starting with mild AD patients, along with the stopping rule introduced.

Keywords: Alzheimer's disease, cost-effectiveness analysis, discrete event simulation, health technology assessment

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Authors: Zsolt János Viharos, Krisztián Balázs Kis, Imre Paniti, Gábor Belső, Péter Németh, János Farkas

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The paper presents the results and industrial applications in the production setup period estimation based on industrial data inherited from the field of polymer cutting. The literature of polymer cutting is very limited considering the number of publications. The first polymer cutting machine is known since the second half of the 20th century; however, the production of polymer parts with this kind of technology is still a challenging research topic. The products of the applying industrial partner must met high technical requirements, as they are used in medical, measurement instrumentation and painting industry branches. Typically, 20% of these parts are new work, which means every five years almost the entire product portfolio is replaced in their low series manufacturing environment. Consequently, it requires a flexible production system, where the estimation of the frequent setup periods' lengths is one of the key success factors. In the investigation, several (input) parameters have been studied and grouped to create an adequate training information set for an artificial neural network as a base for the estimation of the individual setup periods. In the first group, product information is collected such as the product name and number of items. The second group contains material data like material type and colour. In the third group, surface quality and tolerance information are collected including the finest surface and tightest (or narrowest) tolerance. The fourth group contains the setup data like machine type and work shift. One source of these parameters is the Manufacturing Execution System (MES) but some data were also collected from Computer Aided Design (CAD) drawings. The number of the applied tools is one of the key factors on which the industrial partners’ estimations were based previously. The artificial neural network model was trained on several thousands of real industrial data. The mean estimation accuracy of the setup periods' lengths was improved by 30%, and in the same time the deviation of the prognosis was also improved by 50%. Furthermore, an investigation on the mentioned parameter groups considering the manufacturing order was also researched. The paper also highlights the manufacturing introduction experiences and further improvements of the proposed methods, both on the shop floor and on the quotation preparation fields. Every week more than 100 real industrial setup events are given and the related data are collected.

Keywords: artificial neural network, low series manufacturing, polymer cutting, setup period estimation

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Authors: Irfan Anjum Manarvi, Fawzi Aljassir

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Human bones have been a keen area of research over a long time in the field of biomechanical engineering. Medical professionals, as well as engineering academics and researchers, have investigated various bones by using medical, mechanical, and materials approaches to discover the available body of knowledge. Their major focus has been to establish properties of these and ultimately develop processes and tools either to prevent fracture or recover its damage. Literature shows that mechanical professionals conducted a variety of tests for hardness, deformation, and strain field measurement to arrive at their findings. However, they considered these results accuracy to be insufficient due to various limitations of tools, test equipment, difficulties in the availability of human bones. They proposed the need for further studies to first overcome inaccuracies in measurement methods, testing machines, and experimental errors and then carry out experimental or theoretical studies. Finite Element analysis is a technique which was developed for the aerospace industry due to the complexity of design and materials. But over a period of time, it has found its applications in many other industries due to accuracy and flexibility in selection of materials and types of loading that could be theoretically applied to an object under study. In the past few decades, the field of biomechanical engineering has also started to see its applicability. However, the work done in the area of Tarsals, metatarsals and phalanges using this technique is very limited. Therefore, present research has been focused on using this technique for analysis of these critical bones of the human body. This technique requires a 3-dimensional geometric computer model of the object to be analyzed. In the present research, a 3d laser scanner was used for accurate geometric scans of individual tarsals, metatarsals, and phalanges from a typical human foot to make these computer geometric models. These were then imported into a Finite Element Analysis software and a length refining process was carried out prior to analysis to ensure the computer models were true representatives of actual bone. This was followed by analysis of each bone individually. A number of constraints and load conditions were applied to observe the stress and strain distributions in these bones under the conditions of compression and tensile loads or their combination. Results were collected for deformations in various axis, and stress and strain distributions were observed to identify critical locations where fracture could occur. A comparative analysis of failure properties of all the three types of bones was carried out to establish which of these could fail earlier which is presented in this research. Results of this investigation could be used for further experimental studies by the academics and researchers, as well as industrial engineers, for development of various foot protection devices or tools for surgical operations and recovery treatment of these bones. Researchers could build up on these models to carryout analysis of a complete human foot through Finite Element analysis under various loading conditions such as walking, marching, running, and landing after a jump etc.

Keywords: tarsals, metatarsals, phalanges, 3D scanning, finite element analysis

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Authors: Hsiao-Ting Huang, Chang Hsueh-Sheng

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Taiwan is an island country which is affected by the monsoon deeply. Under the climate change, the frequency of extreme rainstorm by typhoon becomes more and more often Since 2000. When the extreme rainstorm comes, it will cause serious damage in Taiwan, especially in urban area. It is suffered by the flooding and the government take it as the urgent issue. On the past, the land use of urban planning does not take flood-detention into consideration. With the development of the city, the impermeable surface increase and most of the people live in urban area. It means there is the highly vulnerability in the urban area, but it cannot deal with the surface runoff and the flooding. However, building the detention pond in hydraulic engineering way to solve the problem is not feasible in urban area. The land expropriation is the most expensive construction of the detention pond in the urban area, and the government cannot afford it. Therefore, the management strategy of flooding in urban area should use the existing resource, public facilities land. It can archive the performance of flood-detention through providing the public facilities land with the detention function. As multi-use public facilities land, it also can show the combination of the land use and water agency. To this purpose, this research generalizes the factors of multi-use for public facilities land as flood-detention space with literature review. The factors can be divided into two categories: environmental factors and conditions of public facilities. Environmental factors including three factors: the terrain elevation, the inundation potential and the distance from the drainage system. In the other hand, there are six factors for conditions of public facilities, including area, building rate, the maximum of available ratio etc. Each of them will be according to it characteristic to given the weight for the land use suitability analysis. This research selects the rules of combination from the logical combination. After this process, it can be classified into three suitability levels. Then, three suitability levels will input to the physiographic inundation model for simulating the evaluation of flood-detention respectively. This study tries to respond the urgent issue in urban area and establishes a model of multi-use for public facilities land as flood-detention through the systematic research process of this study. The result of this study can tell which combination of the suitability level is more efficacious. Besides, The model is not only standing on the side of urban planners but also add in the point of view from water agency. Those findings may serve as basis for land use indicators and decision-making references for concerned government agencies.

Keywords: flooding management strategy, land use suitability analysis, multi-use for public facilities land, physiographic inundation model

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Authors: Sajjad Hassanpour, Vicente A. González, Yang Zou, Jiamou Liu

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Earthquakes pose a significant threat to both structural and non-structural elements in buildings, putting human lives at risk. Effective post-earthquake evacuation is critical for ensuring the safety of building occupants. However, current design practices often neglect the integration of post-earthquake evacuation considerations into the early-stage architectural design process. To address this gap, this paper presents a novel automated internal architectural layout generation tool that optimizes post-earthquake evacuation performance. The tool takes an initial plain floor plan as input, along with specific requirements from the user/architect, such as minimum room dimensions, corridor width, and exit lengths. Based on these inputs, firstly, the tool randomly generates different architectural layouts. Secondly, the human post-earthquake evacuation behaviour will be thoroughly assessed for each generated layout using the advanced Agent-Based Building Earthquake Evacuation Simulation (AB2E2S) model. The AB2E2S prototype is a post-earthquake evacuation simulation tool that incorporates variables related to earthquake intensity, architectural layout, and human factors. It leverages a hierarchical agent-based simulation approach, incorporating reinforcement learning to mimic human behaviour during evacuation. The model evaluates different layout options and provides feedback on evacuation flow, time, and possible casualties due to earthquake non-structural damage. By integrating the AB2E2S model into the automated layout generation tool, architects and designers can obtain optimized architectural layouts that prioritize post-earthquake evacuation performance. Through the use of the tool, architects and designers can explore various design alternatives, considering different minimum room requirements, corridor widths, and exit lengths. This approach ensures that evacuation considerations are embedded in the early stages of the design process. In conclusion, this research presents an innovative automated internal architectural layout generation tool that integrates post-earthquake evacuation simulation. By incorporating evacuation considerations into the early-stage design process, architects and designers can optimize building layouts for improved post-earthquake evacuation performance. This tool empowers professionals to create resilient designs that prioritize the safety of building occupants in the face of seismic events.

Keywords: agent-based simulation, automation in design, architectural layout, post-earthquake evacuation behavior

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Authors: Temitayo Abayomi Ewemoje, Oluwamayowa Oluwafemi Oluwaniyi

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Agriculture and food processing, industry are among the largest industrial sectors that uses large amount of energy. Thus, a larger amount of gases from their fuel combustion technologies is being released into the environment. The choice of input energy supply not only directly having affects the environment, but also poses a threat to human health. The study was therefore designed to assess each unit production processes in order to identify hotspots using life cycle assessments (LCA) approach in South-western Nigeria. Data such as machine power rating, operation duration, inputs and outputs of shea butter materials for unit processes obtained at site were used to modelled Life Cycle Impact Analysis on GaBi6 (Holistic Balancing) software. Four scenarios were drawn for the impact assessments. Material sourcing from Kaiama, Scenarios 1, 3 and Minna Scenarios 2, 4 but different heat supply sources (Liquefied Petroleum Gas ‘LPG’ Scenarios 1, 2 and 10.8 kW Diesel Heater, scenarios 3, 4). Modelling of shea butter production on GaBi6 was for 1kg functional unit of shea butter produced and the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI) midpoint assessment was tool used to was analyse the life cycle inventories of the four scenarios. Eight categories in all four Scenarios were observed out of which three impact categories; Global Warming Potential (GWP) (0.613, 0.751, 0.661, 0.799) kg CO2¬-Equiv., Acidification Potential (AP) (0.112, 0.132, 0.129, 0.149) kg H+ moles-Equiv., and Smog (0.044, 0.059, 0.049, 0.063) kg O3-Equiv., categories had the greater impacts on the environment in Scenarios 1-4 respectively. Impacts from transportation activities was also seen to contribute more to these environmental impact categories due to large volume of petrol combusted leading to releases of gases such as CO2, CH4, N2O, SO2, and NOx into the environment during the transportation of raw shea kernel purchased. The ratio of transportation distance from Minna and Kaiama to production site was approximately 3.5. Shea butter unit processes with greater impacts in all categories was the packaging, milling and with the churning processes in ascending order of magnitude was identified as hotspots that may require attention. From the 1kg shea butter functional unit, it was inferred that locating production site at the shortest travelling distance to raw material sourcing and combustion of LPG for heating would reduce all the impact categories assessed on the environment.

Keywords: GaBi6, Life cycle assessment, shea butter production, TRACI

Procedia PDF Downloads 324

Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

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Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

Procedia PDF Downloads 288

Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

Procedia PDF Downloads 39

Authors: Sittampalam Manoharan, Gary Chai, Sanaul Chowdhury, Andrew Golding

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The primary objective of this paper is to present a simplified approach to develop the structural deterioration model using traffic speed deflectometer data for flexible pavements. Maintaining assets to meet functional performance is not economical or sustainable in the long terms, and it would end up needing much more investments for road agencies and extra costs for road users. Performance models have to be included for structural and functional predicting capabilities, in order to assess the needs, and the time frame of those needs. As such structural modelling plays a vital role in the prediction of pavement performance. A structural condition is important for the prediction of remaining life and overall health of a road network and also major influence on the valuation of road pavement. Therefore, the structural deterioration model is a critical input into pavement management system for predicting pavement rehabilitation needs accurately. The Traffic Speed Deflectometer (TSD) is a vehicle-mounted Doppler laser system that is capable of continuously measuring the structural bearing capacity of a pavement whilst moving at traffic speeds. The device’s high accuracy, high speed, and continuous deflection profiles are useful for network-level applications such as predicting road rehabilitations needs and remaining structural service life. The methodology adopted in this model by utilizing time series TSD maximum deflection (D0) data in conjunction with rutting, rutting progression, pavement age, subgrade strength and equivalent standard axle (ESA) data. Then, regression analyses were undertaken to establish a correlation equation of structural deterioration as a function of rutting, pavement age, seal age and equivalent standard axle (ESA). This study developed a simple structural deterioration model which will enable to incorporate available TSD structural data in pavement management system for developing network-level pavement investment strategies. Therefore, the available funding can be used effectively to minimize the whole –of- life cost of the road asset and also improve pavement performance. This study will contribute to narrowing the knowledge gap in structural data usage in network level investment analysis and provide a simple methodology to use structural data effectively in investment decision-making process for road agencies to manage aging road assets.

Keywords: adjusted structural number (SNP), maximum deflection (D0), equant standard axle (ESA), traffic speed deflectometer (TSD)

Procedia PDF Downloads 151

Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

Procedia PDF Downloads 134

Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

Procedia PDF Downloads 103

Authors: Ana Teixeira, Joao Orvalho

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This paper describes experiments using Scratch games, to check the feasibility of employing cerebral palsy users gestures as an alternative of interaction with a computer carried out by students of Master Human Computer Interaction (HCI) of IPC Coimbra. The main focus of this work is to study the usability of a Web Camera as a motion tracking device to achieve a virtual human-computer interaction used by individuals with CP. An approach for Human-computer Interaction (HCI) is present, where individuals with cerebral palsy react and interact with a scratch game through the use of a webcam as an external interaction device. Motion tracking interaction is an emerging technology that is becoming more useful, effective and affordable. However, it raises new questions from the HCI viewpoint, for example, which environments are most suitable for interaction by users with disabilities. In our case, we put emphasis on the accessibility and usability aspects of such interaction devices to meet the special needs of people with disabilities, and specifically people with CP. Despite the fact that our work has just started, preliminary results show that, in general, computer vision interaction systems are very useful; in some cases, these systems are the only way by which some people can interact with a computer. The purpose of the experiments was to verify two hypothesis: 1) people with cerebral palsy can interact with a computer using their natural gestures, 2) scratch games can be a research tool in experiments with disabled young people. A game in Scratch with three levels is created to be played through the use of a webcam. This device permits the detection of certain key points of the user’s body, which allows to assume the head, arms and specially the hands as the most important aspects of recognition. Tests with 5 individuals of different age and gender were made throughout 3 days through periods of 30 minutes with each participant. For a more extensive and reliable statistical analysis, the number of both participants and repetitions in further investigations should be increased. However, already at this stage of research, it is possible to draw some conclusions. First, and the most important, is that simple scratch games on the computer can be a research tool that allows investigating the interaction with computer performed by young persons with CP using intentional gestures. Measurements performed with the assistance of games are attractive for young disabled users. The second important conclusion is that they are able to play scratch games using their gestures. Therefore, the proposed interaction method is promising for them as a human-computer interface. In the future, we plan to include the development of multimodal interfaces that combine various computer vision devices with other input devices improvements in the existing systems to accommodate more the special needs of individuals, in addition, to perform experiments on a larger number of participants.

Keywords: motion tracking, cerebral palsy, rehabilitation, HCI

Procedia PDF Downloads 235

Authors: Andy Alubaidy

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In this work, a robotic additive manufacturing system (RAMS) that is capable of three-dimensional (3D) printing in six degrees of freedom (DOF) with very high accuracy and virtually on any surface has been designed and built. One of the major shortcomings in existing 3D printer technology is the limitation to three DOF, which results in prolonged fabrication time. Depending on the techniques used, it usually takes at least two hours to print small objects and several hours for larger objects. Another drawback is the size of the printed objects, which is constrained by the physical dimensions of most low-cost 3D printers, which are typically small. In such cases, large objects are produced by dividing them into smaller components that fit the printer’s workable area. They are then glued, bonded or otherwise attached to create the required object. Another shortcoming is material constraints and the need to fabricate a single part using different materials. With the flexibility of a six-DOF robot, the RAMS has been designed to overcome these problems. A feeding mechanism using an adaptive Proportional-Integral-Derivative (PID) controller is utilized along with a national instrument compactRIO (NI cRIO), an ABB robot, and off-the-shelf sensors. The RAMS have the ability to 3D print virtually anywhere in six degrees of freedom with very high accuracy. It is equipped with an ABB IRB 120 robot to achieve this level of accuracy. In order to convert computer-aided design (CAD) files to digital format that is acceptable to the robot, Hypertherm Robotic Software Inc.’s state-of-the-art slicing software called “ADDMAN” is used. ADDMAN is capable of converting any CAD file into RAPID code (the programing language for ABB robots). The robot uses the generated code to perform the 3D printing. To control the entire process, National Instrument (NI) compactRIO (cRio 9074), is connected and communicated with the robot and a feeding mechanism that is designed and fabricated. The feeding mechanism consists of two major parts, cold-end and hot-end. The cold-end consists of what is conventionally known as an extruder. Typically, a stepper-motor is used to control the push on the material, however, for optimum control, a DC motor is used instead. The hot-end consists of a melt-zone, nozzle, and heat-brake. The melt zone ensures a thorough melting effect and consistent output from the nozzle. Nozzles are made of brass for thermo-conductivity while the melt-zone is comprised of a heating block and a ceramic heating cartridge to transfer heat to the block. The heat-brake ensures that there is no heat creep-up effect as this would swell the material and prevent consistent extrusion. A control system embedded in the cRio is developed using NI Labview which utilizes adaptive PID to govern the heating cartridge in conjunction with a thermistor. The thermistor sends temperature feedback to the cRio, which will issue heat increase or decrease based on the system output. Since different materials have different melting points, our system will allow us to adjust the temperature and vary the material.

Keywords: robotic, additive manufacturing, PID controller, cRIO, 3D printing

Procedia PDF Downloads 217

Authors: Priyanka Chaudhary, M. Rizwan

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This paper presents a grid synchronization technique based on adaptive notch filter for SPV (Solar Photovoltaic) system along with MPPT (Maximum Power Point Tracking) techniques. An efficient grid synchronization technique offers proficient detection of various components of grid signal like phase and frequency. It also acts as a barrier for harmonics and other disturbances in grid signal. A reference phase signal synchronized with the grid voltage is provided by the grid synchronization technique to standardize the system with grid codes and power quality standards. Hence, grid synchronization unit plays important role for grid connected SPV systems. As the output of the PV array is fluctuating in nature with the meteorological parameters like irradiance, temperature, wind etc. In order to maintain a constant DC voltage at VSC (Voltage Source Converter) input, MPPT control is required to track the maximum power point from PV array. In this work, a variable step size P & O (Perturb and Observe) MPPT technique with DC/DC boost converter has been used at first stage of the system. This algorithm divides the dPpv/dVpv curve of PV panel into three separate zones i.e. zone 0, zone 1 and zone 2. A fine value of tracking step size is used in zone 0 while zone 1 and zone 2 requires a large value of step size in order to obtain a high tracking speed. Further, adaptive notch filter based control technique is proposed for VSC in PV generation system. Adaptive notch filter (ANF) approach is used to synchronize the interfaced PV system with grid to maintain the amplitude, phase and frequency parameters as well as power quality improvement. This technique offers the compensation of harmonics current and reactive power with both linear and nonlinear loads. To maintain constant DC link voltage a PI controller is also implemented and presented in this paper. The complete system has been designed, developed and simulated using SimPower System and Simulink toolbox of MATLAB. The performance analysis of three phase grid connected solar photovoltaic system has been carried out on the basis of various parameters like PV output power, PV voltage, PV current, DC link voltage, PCC (Point of Common Coupling) voltage, grid voltage, grid current, voltage source converter current, power supplied by the voltage source converter etc. The results obtained from the proposed system are found satisfactory.

Keywords: solar photovoltaic systems, MPPT, voltage source converter, grid synchronization technique

Procedia PDF Downloads 594

Authors: Dmitry V. Pasynkov, Ivan A. Egoshin, Alexey A. Kolchev, Ivan V. Kliouchkin

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In most cases, typical cysts are easily recognized at ultrasonography. The specificity of this method for typical cysts reaches 98%, and it is usually considered as gold standard for typical cyst diagnosis. However, it is necessary to have all the following features to conclude the typical cyst: clear margin, the absence of internal echoes and dorsal acoustic enhancement. At the same time, not every breast cyst is typical. It is especially characteristic for protein-contained cysts that may have significant internal echoes. On the other hand, some solid lesions (predominantly malignant) may have cystic appearance and may be falsely accepted as cysts. Therefore we tried to develop the automatic method of cystic and solid breast lesions differentiation. Materials and methods. The input data were the ultrasonography digital images with the 256-gradations of gray color (Medison SA8000SE, Siemens X150, Esaote MyLab C). Identification of the lesion on these images was performed in two steps. On the first one, the region of interest (or contour of lesion) was searched and selected. Selection of such region is carried out using the sigmoid filter where the threshold is calculated according to the empirical distribution function of the image brightness and, if necessary, it was corrected according to the average brightness of the image points which have the highest gradient of brightness. At the second step, the identification of the selected region to one of lesion groups by its statistical characteristics of brightness distribution was made. The following characteristics were used: entropy, coefficients of the linear and polynomial regression, quantiles of different orders, an average gradient of brightness, etc. For determination of decisive criterion of belonging to one of lesion groups (cystic or solid) the training set of these characteristics of brightness distribution separately for benign and malignant lesions were received. To test our approach we used a set of 217 ultrasonic images of 107 cystic (including 53 atypical, difficult for bare eye differentiation) and 110 solid lesions. All lesions were cytologically and/or histologically confirmed. Visual identification was performed by trained specialist in breast ultrasonography. Results. Our system correctly distinguished all (107, 100%) typical cysts, 107 of 110 (97.3%) solid lesions and 50 of 53 (94.3%) atypical cysts. On the contrary, with the bare eye it was possible to identify correctly all (107, 100%) typical cysts, 96 of 110 (87.3%) solid lesions and 32 of 53 (60.4%) atypical cysts. Conclusion. Automatic approach significantly surpasses the visual assessment performed by trained specialist. The difference is especially large for atypical cysts and hypoechoic solid lesions with the clear margin. This data may have a clinical significance.

Keywords: breast cyst, breast solid lesion, differentiation, ultrasonography

Procedia PDF Downloads 270

Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

Procedia PDF Downloads 389

Authors: Ioannis Souris

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In this paper, we would like to present how we taught German through playwriting and analyze the usefulness of this method for teaching languages and cultivating a sense of political agency in students and teachers alike. Last academic year, we worked at the German Saturday School in Greenwich, London. This school offers Saturday German lessons to children whose parents are German, living in London. The lessons are two hours long, and the children’s level of German varies according to how often or how much German is spoken at home or how often the families visit Germany (as well as other factors which will be discussed in more detail in the paper). The directors of the school provide teachers with learning material and course books, but they strongly encourage individual input on lesson structure and methods of teaching German. The class we taught consisted of six eight-to-nine-year-olds. Midway into the academic year, we ran out of teaching material, and we, therefore, decided to write a play. In the paper, we would like to explore the process we followed in creating or writing this play and how this encouraged the children to collaborate and exercise their skills in writing, storytelling, speaking, and opinion-sharing. We want to examine the impact this project had on the children who wrote and performed the play, the wider community of the Saturday school, and the development of our language teaching practice. We found, for instance, that some students, who were quiet or shy, became very open and outspoken in the process of writing and performing the play. They took the initiative and led the process, putting us, their teachers, in the role of simple observers or facilitators. When we showed the play in front of the school, the other children and teachers, as audience members, also became part of the process as they commented on the plot, language, and characters and gave feedback on further development. In the paper, we will discuss how this teaching project fits into recent developments in the research of creativity and the teaching of languages and how engagement with creative approaches to teaching has the potential to question and subvert traditional notions of ‘lesson’, ‘teacher’, and ‘student’. From the moment a questioning of norms takes place, we inadvertently raise questions about politics, agency, and resistance. We will conclude the paper with a definition of what we mean by ‘political agency’ within the context of our teaching project and education, in general, and why inspiring creativity and imagination within teaching can be considered a political act. Finally, our aim in this paper will be to propose the possibility of analyzing teaching languages through creativity and political agency theories.

Keywords: innovation in language teaching and learning, language acquisition and learning, language curriculum development, language education

Procedia PDF Downloads 84

Authors: Rachel S. Chang, Samuel P. Massion, Alan Z. Grusky, Heather A. Ridinger

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Introduction Health advocacy is defined as activities that improve access to care, utilize resources, address health disparities, and influence health policy. Advocacy is increasingly being recognized as a critical component of a physician’s role, as understanding social determinants of health and improving patient care are important aspects within the American Medical Association’s Health Systems Science framework. However, despite this growing prominence, educational interventions that address advocacy topics are limited and variable across medical school curricula. Furthermore, few recent studies have evaluated attitudes toward health advocacy among physicians-in-training in the United States. This study examines medical student attitudes towards health advocacy, along with perceived knowledge, ability, and current level of engagement with health advocacy during their clerkships. Methods This study employed a cross-sectional survey design using a single anonymous, self-report questionnaire to all second-year medical students at Vanderbilt University School of Medicine (n=96) in December 2020 during clerkship rotations. The survey had 27 items with 5-point Likert scale (15), multiple choice (11), and free response questions (1). Descriptive statistics and thematic analysis were utilized to analyze responses. The study was approved by the Vanderbilt University Institutional Review Board. Results There was an 88% response rate among second-year clerkship medical students. A majority (83%) agreed that formal training in health advocacy should be a mandatory part of the medical student curriculum Likewise, 83% of respondents felt that acting as a health advocate or patients should be part of their role as a clerkship student. However, a minority (25%) felt adequately prepared. While 72% of respondents felt able to identify a psychosocial need, 18% felt confident navigating the healthcare system and only 9% felt able to connect a patient to a psychosocial resource to fill that gap. 44% of respondents regularly contributed to conversations with their medical teams when discussing patients’ social needs, such as housing insecurity, financial insecurity, or legal needs. On average, respondents reported successfully connecting patients to psychosocial resources 1-2 times per 8-week clerkship block. Barriers to participating in health advocacy included perceived time constraints, lack of awareness of resources, lower emphasis among medical teams, and scarce involvement with social work teams. Conclusions In this single-institutional study, second-year medical students on clerkships recognize the importance of advocating for patients and support advocacy training within their medical school curriculum. However, their perceived lack of ability to navigate the healthcare system and connect patients to psychosocial resources, result in students feeling unprepared to advocate as effectively as they hoped during their clerkship rotations. Our results support the ongoing need to equip medical students with training and resources necessary for them to effectively act as advocates for patients.

Keywords: clerkships, medical students, patient advocacy, social medicine

Procedia PDF Downloads 130

Authors: Riofrio Jonathan, Fernandez Guillermo

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Renewable energy represented around 80% of the energy matrix for power generation in Ecuador during 2020, so the deployment of current public policies is focused on taking advantage of the high presence of renewable sources to carry out several electrification projects. These projects are part of the portfolio sent to the United Nations Framework on Climate Change (UNFCCC) as a commitment to reduce greenhouse gas emissions (GHG) in the established national determined contribution (NDC). In this sense, the Ecuadorian Organic Energy Efficiency Law (LOEE) published in 2019 promotes E-mobility as one of the main milestones. In fact, it states that the new vehicles for urban and interurban usage must be E-buses since 2025. As a result, and for a successful implementation of this technological change in a national context, it is important to deploy land surveys focused on technical and geographical areas to keep the quality of services in both the electricity and transport sectors. Therefore, this research presents a technological and energy comparison between a conventional diesel bus and its equivalent E-bus. Both vehicles fulfill all the technical requirements to ride in the study-case city, which is Ambato in the province of Tungurahua-Ecuador. In addition, the analysis includes the development of a model for the energy estimation of both technologies that are especially applied in a highland city such as Ambato. The altimetry of the most important bus routes in the city varies from 2557 to 3200 m.a.s.l., respectively, for the lowest and highest points. These operation conditions provide a grade of novelty to this paper. Complementary, the technical specifications of diesel buses are defined following the common features of buses registered in Ambato. On the other hand, the specifications for E-buses come from the most common units introduced in Latin America because there is not enough evidence in similar cities at the moment. The achieved results will be good input data for decision-makers since electric demand forecast, energy savings, costs, and greenhouse gases emissions are computed. Indeed, GHG is important because it allows reporting the transparency framework that it is part of the Paris Agreement. Finally, the presented results correspond to stage I of the called project “Analysis and Prospective of Electromobility in Ecuador and Energy Mix towards 2030” supported by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).

Keywords: high altitude cities, energy planning, NDC, e-buses, e-mobility

Procedia PDF Downloads 151

Authors: Chun-Yu Chen, Kua-Rong Wu, Yu-Cheng Chen, Perng-Jy Tsai

Abstract:

Cooking fumes are known containing polycyclic aromatic hydrocarbons (PAHs) and aldehydes, and some of them have been proven carcinogenic or possibly carcinogenic to humans. Considering their chronic health effects, long-term exposure data is required for assessing cooking workers’ lifetime health risks. Previous exposure assessment studies, due to both time and cost constraints, mostly were based on the cross-sectional data. Therefore, establishing a long-term exposure data has become an important issue for conducting health risk assessment for cooking workers. An approach was proposed in this study. Here, the generation rates of both PAHs and aldehydes from a cooking process were determined by placing a sampling train exactly under the under the exhaust fan under the both the total enclosure condition and normal operating condition, respectively. Subtracting the concentration collected by the former (representing the total emitted concentration) from that of the latter (representing the hood collected concentration), the fugitive emitted concentration was determined. The above data was further converted to determine the generation rates based on the flow rates specified for the exhaust fan. The determinations of the above generation rates were conducted in a testing chamber with a selected cooking process (deep-frying chicken nuggets under 3 L peanut oil at 200°C). The sampling train installed under the exhaust fan consisted respectively an IOM inhalable sampler with a glass fiber filter for collecting particle-phase PAHs, followed by a XAD-2 tube for gas-phase PAHs. The above was also used to sample aldehydes, however, installed with a filter pre-coated with DNPH, and followed by a 2,4-DNPH-cartridge for collecting particle-phase and gas-phase aldehydes, respectively. PAHs and aldehydes samples were analyzed by GC/MS-MS (Agilent 7890B), and HPLC-UV (HITACHI L-7100), respectively. The obtained generation rates of both PAHs and aldehydes were applied to the near-field/ far-field exposure model to estimate the exposures of cooks (the estimated near-field concentration), and helpers (the estimated far-field concentration). For validating purposes, both PAHs and aldehydes samplings were conducted simultaneously using the same sampling train at both near-field and far-field sites of the testing chamber. The sampling results, together with the use of the mixed-effect model, were used to calibrate the estimated near-field/ far-field exposures. In the present study, the obtained emission rates were further converted to emission factor of both PAHs and aldehydes according to the amount of food oil consumed. Applying the long-term food oil consumption records, the emission rates for both PAHs and aldehydes were determined, and the long-term exposure databanks for cooks (the estimated near-field concentration), and helpers (the estimated far-field concentration) were then determined. Results show that the proposed approach was adequate to determine the generation rates of both PAHs and aldehydes under various fan exhaust flow rate conditions. The estimated near-field/ far-field exposures, though were significantly different from that obtained from the field, can be calibrated using the mixed effect model. Finally, the established long-term data bank could provide a useful basis for conducting long-term exposure assessments for cooking workers exposed to PAHs and aldehydes.

Keywords: aldehydes, cooking oil fumes, long-term exposure assessment, modeling, polycyclic aromatic hydrocarbons (PAHs)

Procedia PDF Downloads 142

Authors: Vijay Kumar, G. K. Grewal, Prasad S. Burange

Abstract:

India is one of the largest cultivators of cotton in the world. Among the various constraints, insect pests are posing a major hurdle to the success of cotton cultivation. Various bollworms, including the pink bollworm, Pectinophora gossypiella (Saunders), cause serious losses in India, China, Pakistan, Egypt, Brazil, tropical America, and Africa, etc. Bt cotton cultivars having Cry genes were introduced in India in 2002 (Cry1Ac) and 2006 (Cry1Ac+ Cry2Ab) for control of American, spotted, and pink bollworms. Pink bollworm (PBW) larvae infest flowers, squares, and bolls. Larva burrows into flowers and bolls to feed on pollen and seeds, respectively. It has a shorter lifecycle and more generations per year, so it develops resistance more quickly than other bollworms. Further, it has cryptic feeding sites, i.e., flowers and bolls/seeds, so it is not exposed to harsh environmental fluctuations and insecticidal applications. The cry toxin concentration is low in its feeding sites, i.e., seeds and flowers of cotton. The use of insecticide and Bt cotton is the primary control measure that has been successful in limiting the damage of PBW. But with the passage of time, it has developed resistance against insecticides and Bt cotton. However, the use of insecticides increases chemical control costs while causing secondary pest problems and environmental pollution. Extensive research has indicated that monitoring and control measures such as biological, cultural, chemical, and host plant resistance methods can be integrated for effective PBW management. The potential of various biological control organisms needs to be explored. The impact of transgenic cotton on non-target organisms, particularly natural enemies, which play an important role in pest control, is still being debated. According to some authors, Bt crops have a negative impact on natural enemies, particularly parasitoids. An experiment was carried out in the Integrated Pest Management Laboratory of the Department of Entomology, Punjab Agricultural University, Ludhiana, Punjab, India, to study the natural parasitization of PBW on Bt cotton in 2022. A large population of larvae of PBW were kept individually in plastic containers and fed with cotton bolls until the emergence of a parasitoid cocoon. The first cocoon of the parasitoid was observed on October 25, 2022. Symptoms of parasitization were never seen on larvae. Larvae stopped feeding and became inactive before the emergence of parasitoids for pupation. Grub makes its way out of larvae by making a hole in the integument, and immediately after coming out, it spins the cocoon. The adult parasitoid emerged from the cocoon after eight days. The parasitoids that emerged from the cocoon were identified as Cotesia (Braconidae: Hymenoptera) based on the features of the adult. Out of 475 larvae of PBW, 87 were parasitized, with 18.31% of parasitization. Out of these, 6.73% were first instar, 10.52% were second instar, and 1.05% were third instar larvae of PBW. No parasitization was observed in fourth instar larvae. Parasitoids were observed during the fag end of cropping season and mostly on the earlier instars. It is concluded that the potential of Cotesia may be explored as a biological control agent against PBW, which is safer to human beings, environment and non-taraltoget organisms.

Keywords: biocontrol, Bt cotton, Cotesia, Pectinophora gossypiella

Procedia PDF Downloads 81