Search results for: generalized likelihood uncertainty estimation

Authors: Brooklyn Rushton, Michelle Rutty, Natalie Knowles, Daniel Scott

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As people are increasingly on the search for extraordinary experiences and connections with nature, adventure tourism is experiencing significant growth and providing tourists with life-changing experiences. Unlike built attraction-based tourism, adventure tourism relies entirely on natural heritage, which leaves communities dependent on adventure tourism extremely vulnerable to environmental and climatic changes. A growing body of evidence suggests that global climate change will influence the future of adventure tourism and mountain outdoor recreation opportunities on a global scale. Across Canada, more specifically, climate change is broadly anticipated to present risks for winter-snow sports, while opportunities are anticipated to arise for green season activities. These broad seasonal shifts do not account for the indirect impacts of climate change on adventure tourism, such as the cost of adaptation or the increase of natural hazards and the associated likelihood of accidents. While some research has examined the impact of climate change on natural environments that adventure tourism relies on, a very small body of research has specifically focused on guides’ perspectives or included hard adventure tourism activities. The guiding industry is unique, as guides are trained through an elegant blend of art and science to make decisions based on experience, observation, and intuition. While quantitative research can monitor change in natural environments, guides local knowledge can provide eye-witness accounts and outline what environmental changes mean for the future sustainability of adventure tourism. This research will capture the extensive knowledge of mountain guides to better understand the implications of climate change for mountain adventure and potential adaptive responses for the adventure tourism industry. This study uses a structured online survey with open and close-ended questions that will be administered using Qualtrics (an online survey platform). This survey is disseminated to current members of the Association of Canadian Mountain Guides (ACMG). Participation in this study will be exclusive to members of the ACMG operating in the outdoor guiding streams. The 25 survey questions are organized into four sections: demographic and professional operation (9 questions), physical change (4 questions), climate change perception (6 questions), and climate change adaptation (6 questions). How mountain guides perceive and respond to climate change is important knowledge for the future of the expanding adventure tourism industry. Results from this study are expected to provide important information to mountain destinations on climate change vulnerability and adaptive capacity. Expected results of this study include guides insight into: (1) experience-safety relevant observed physical changes in guided regions (i.e. glacial coverage, permafrost coverage, precipitation, temperature, and slope instability) (2) changes in hazards within the guiding environment (i.e. avalanches, rockfall, icefall, forest fires, flooding, and extreme weather events), (3) existing and potential adaptation strategies, and (4) key information and other barriers for adaptation. By gaining insight from the knowledge of mountain guides, this research can help the tourism industry at large understand climate risk and create adaptation strategies to ensure the resiliency of the adventure tourism industry.

Keywords: adventure tourism, climate change, environmental change, mountain hazards

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Authors: Priyanka Sharma, Manoj Jais, Poonam Gupta, Suraiya K. Ansari, Ravinder Kaur

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Introduction: As much as the Health Care is necessary for the population, so is the management of the Biomedical waste produced. Biomedical waste is a wide terminology used for the waste material produced during the diagnosis, treatment or immunization of human beings and animals, in research or in the production or testing of biological products. Biomedical waste management is a chain of processes from the point of generation of Biomedical waste to its final disposal in the correct and proper way, assigned for that particular type of waste. Any deviation from the said processes leads to improper disposal of Biomedical waste which itself is a major health hazard. Proper segregation of Biomedical waste is the key for Biomedical Waste management. Improper disposal of BMW can cause sharp injuries which may lead to HIV, Hepatitis-B virus, Hepatitis-C virus infections. Therefore, proper disposal of BMW is of upmost importance. Health care establishments segregate the Biomedical waste and dispose it as per the Biomedical waste management rules in India. Objectives: This study was done to observe the current trends of Biomedical waste generated in a tertiary care Hospital in Delhi. Methodology: Biomedical waste management rounds were conducted in the hospital wards. Relevant details were collected and analysed and sites with maximum Biomedical waste generation were identified. All the data was cross checked with the commons collection site. Results: The total amount of waste generated in the hospital during January 2014 till December 2014 was 6,39,547 kg, of which 70.5% was General (non-hazardous) waste and the rest 29.5% was BMW which consisted highly infectious waste (12.2%), disposable plastic waste (16.3%) and sharps (1%). The maximum quantity of Biomedical waste producing sites were Obstetrics and Gynaecology wards with a total Biomedical waste production of 45.8%, followed by Paediatrics, Surgery and Medicine wards with 21.2 %, 4.6% and 4.3% respectively. The maximum average Biomedical waste generated was by Obstetrics and Gynaecology ward with 0.7 kg/bed/day, followed by Paediatrics, Surgery and Medicine wards with 0.29, 0.28 and 0.18 kg/bed/day respectively. Conclusions: Hospitals should pay attention to the sites which produce a large amount of BMW to avoid improper segregation of Biomedical waste. Also, induction and refresher training Program of Biomedical waste management should be conducted to avoid improper management of Biomedical waste. Healthcare workers should be made aware of risks of poor Biomedical waste management.

Keywords: biomedical waste, biomedical waste management, hospital-tertiary care, New Delhi

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Authors: Erick R. Bandala, Kebret Kebede, Nicole Johnson, Rebecca Murray, Destiny Green, John Mejia, Polioptro Martinez-Austria

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Summertemperature data from Clark County was collected and used to estimate two different heat-related indexes: the heat index (HI) and excess heat factor (EHF). These two indexes were used jointly with data of health-related deaths in Clark County to assess the effect of extreme heat on the exposed population. The trends of the heat indexes were then analyzed for the 2007-2016 decadeandthe correlation between heat wave episodes and the number of heat-related deaths in the area was estimated. The HI showed that this value has increased significantly in June, July, and August over the last ten years. The same trend was found for the EHF, which showed a clear increase in the severity and number of these events per year. The number of heat wave episodes increased from 1.4 per year during the 1980-2016 period to 1.66 per yearduring the 2007-2016 period. However, a different trend was found for heat-wave-event duration, which decreasedfrom an average of 20.4 days during the trans-decadal period (1980-2016) to 18.1 days during the most recent decade(2007-2016). The number of heat-related deaths was also found to increase from 2007 to 2016, with 2016 with the highest number of heat-related deaths. Both HI and the number of deaths showeda normal-like distribution for June, July, and August, with the peak values reached in late July and early August. The average maximum HI values better correlated with the number of deaths registered in Clark County than the EHF, probably because HI uses the maximum temperature and humidity in its estimation,whereas EHF uses the average medium temperature. However, it is worth testing the EHF of the study zone because it was reported to fit properly in the case of heat-related morbidity. For the overall period, 437 heat-related deaths were registered in Clark County, with 20% of the deaths occurring in June, 52% occurring in July, 18% occurring in August,and the remaining 10% occurring in the other months of the year. The most vulnerable subpopulation was people over 50 years old, for which 76% of the heat-related deaths were registered.Most of the cases were associated with heart disease preconditions. The second most vulnerable subpopulation was young adults (20-50), which accounted for 23% of the heat-related deaths. These deathswere associated with alcoholic/illegal drug intoxication.

Keywords: heat, health, hazards, workforce

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Authors: Alexandre Daliberto Frugoli, Pedro Jose Gabriel Ferreira, Pedro Americo Frugoli, Lucio Leonardo, Thais Cavalheri Santos

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Laboratory classes in Engineering courses are essential for students to be able to integrate theory with practical reality, by handling equipment and observing experiments. In the researches of physical phenomena, students can learn about the complexities of science. Over the past years, universities in developing countries have been reducing the course load of engineering courses, in accordance with cutting cost agendas. Quality education is the object of study for researchers and requires educators and educational administrators able to demonstrate that the institutions are able to provide great learning opportunities at reasonable costs. Didactic test benches are indispensable equipment in educational activities related to turbo hydraulic pumps and pumping facilities study, which have a high cost and require long class time due to measurements and equipment adjustment time. In order to overcome the aforementioned obstacles, aligned with the professional objectives of an engineer, GruPEFE - UNIP (Research Group in Physics Education for Engineering - Universidade Paulista) has developed a multi-purpose stand for the discipline of fluid mechanics which allows the study of velocity and flow meters, loads losses and pump association. In this work, results obtained by the association in series and in parallel of hydraulic pumps will be presented and discussed, mainly analyzing the repeatability of experimental procedures and their agreement with the theory. For the association in series two identical pumps were used, consisting of the connection of the discharge of a pump to the suction of the next one, allowing the fluid to receive the power of all machines in the association. The characteristic curve of the set is obtained from the curves of each of the pumps, by adding the heads corresponding to the same flow rates. The same pumps were associated in parallel. In this association, the discharge piping is common to the two machines together. The characteristic curve of the set was obtained by adding to each value of H (head height), the flow rates of each pump. For the tests, the input and output pressure of each pump were measured. For each set there were three sets of measurements, varying the flow rate in range from 6.0 to 8.5 m 3 / h. For the two associations, the results showed an excellent repeatability with variations of less than 10% between sets of measurements and also a good agreement with the theory. This variation agrees with the instrumental uncertainty. Thus, the results validate the use of the fluids bench designed for didactic purposes. As a future work, a digital acquisition system is being developed, using differential sensors of extremely low pressures (2 to 2000 Pa approximately) for the microcontroller Arduino.

Keywords: engineering education, fluid mechanics, hydrostatic pumps association, multi-purpose stand

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Authors: Mohd Harun, Cini Varghese, Eldho Varghese, Seema Jaggi

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Hybridization crosses find a vital role in breeding experiments to evaluate the combining abilities of individual parental lines or crosses for creation of lines with desirable qualities. There are various ways of obtaining progenies and further studying the combining ability effects of the lines taken in a breeding programme. Some of the most common methods are diallel or two-way cross, triallel or three-way cross, tetra-allele or four-way cross. These techniques help the breeders to improve the quantitative traits which are of economical as well as nutritional importance in crops and animals. Amongst these methods, tetra-allele cross provides extra information in terms of the higher specific combining ability (sca) effects and the hybrids thus produced exhibit individual as well as population buffering mechanism because of the broad genetic base. Most of the common commercial hybrids in corn are either three-way or four-way cross hybrids. Tetra-allele cross came out as the most practical and acceptable scheme for the production of slaughter pigs having fast growth rate, good feed efficiency, and carcass quality. Tetra-allele crosses are mostly used for exploitation of heterosis in case of commercial silkworm production. Experimental designs involving tetra-allele crosses have been studied extensively in literature. Optimality of designs has also been considered as a researchable issue. In practical situations, it is advisable to include sca effects in the model as this information is needed by the breeder to improve economically and nutritionally important quantitative traits. Thus, a model that provides information regarding the specific traits by utilizing sca effects along with general combining ability (gca) effects may help the breeders to deal with the problem of various stresses. In this paper, a model for experimental designs involving tetra-allele crosses that incorporates both gca and sca has been defined. Optimality aspects of such designs have been discussed incorporating sca effects in the model. Orthogonality conditions have been derived for block designs ensuring estimation of contrasts among the gca effects, after eliminating the nuisance factors, independently from sca effects. User friendly SAS macro and web solution (webPTC) have been developed for the generation and analysis of such designs.

Keywords: general combining ability, optimality, specific combining ability, tetra-allele cross, webPTC

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Authors: Domenico Carmelo Mongelli

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The context is that of great uncertainty in the 27 countries belonging to the European Union which has adopted an epochal measure: the elimination of internal combustion engines for the traction of road vehicles starting from 2035 with complete replacement with electric vehicles. If on the one hand there is great concern at various levels for the unpreparedness for this change, on the other the Scientific Community is not preparing accurate studies on the problem, as the scientific literature deals with single aspects of the issue, moreover addressing the issue at the level of individual countries, losing sight of the global implications of the issue for the entire EU. The aim of the research is to fill these gaps: the technological, plant engineering, environmental, economic and employment aspects of the energy transition in question are addressed and connected to each other, comparing the current situation with the different scenarios that could exist in 2035 and in the following years until total disposal of the internal combustion engine vehicle fleet for the entire EU. The methodologies adopted by the research consist in the analysis of the entire life cycle of electric vehicles and batteries, through the use of specific databases, and in the dynamic simulation, using specific calculation codes, of the application of the results of this analysis to the entire EU electric vehicle fleet from 2035 onwards. Energy balance sheets will be drawn up (to evaluate the net energy saved), plant balance sheets (to determine the surplus demand for power and electrical energy required and the sizing of new plants from renewable sources to cover electricity needs), economic balance sheets (to determine the investment costs for this transition, the savings during the operation phase and the payback times of the initial investments), the environmental balances (with the different energy mix scenarios in anticipation of 2035, the reductions in CO2eq and the environmental effects are determined resulting from the increase in the production of lithium for batteries), the employment balances (it is estimated how many jobs will be lost and recovered in the reconversion of the automotive industry, related industries and in the refining, distribution and sale of petroleum products and how many will be products for technological innovation, the increase in demand for electricity, the construction and management of street electric columns). New algorithms for forecast optimization are developed, tested and validated. Compared to other published material, the research adds an overall picture of the energy transition, capturing the advantages and disadvantages of the different aspects, evaluating the entities and improvement solutions in an organic overall picture of the topic. The results achieved allow us to identify the strengths and weaknesses of the energy transition, to determine the possible solutions to mitigate these weaknesses and to simulate and then evaluate their effects, establishing the most suitable solutions to make this transition feasible.

Keywords: engines, Europe, mobility, transition

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Authors: S. Koppikar, P. Kulkarni, D. Ingale , N. Wagh, S. Deshpande, A. Mahajan, A. Harsulkar

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Crucial role of reactive oxygen species (ROS) in the progression Osteoarthritis (OA) pathogenesis has been endorsed several times though its exact mechanism remains unclear. Oxidative stress is known to instigate classical stress factors such as cytokines, chemokines and ROS, which hampers cartilage remodelling process and ultimately results in worsening the disease. Synovial fluid (SF) is a biological communicator between cartilage and synovium that accumulates redox and biochemical signalling mediators. The present work attempts to measure several oxidative stress markers in the synovial fluid obtained from knee OA patients with varying degree of disease severity. Thirty OA and five Meniscal-tear (MT) patients were graded using Kellgren-Lawrence scale and assessed for Nitric oxide (NO), Nitrate-Nitrite (NN), 2,2-diphenyl-1-picrylhydrazyl (DPPH), Ferric Reducing Antioxidant Potential (FRAP), Catalase (CAT), Superoxide dismutase (SOD) and Malondialdehyde (MDA) levels for comparison. Out of various oxidative markers studied, NO and SOD showed significant difference between moderate and severe OA (p= 0.007 and p= 0.08, respectively), whereas CAT demonstrated significant difference between MT and mild group (p= 0.07). Interestingly, NN revealed statistically positive correlation with OA severity (p= 0.001 and p= 0.003). MDA, a lipid peroxidation by-product was estimated maximum in early OA when compared to MT (p= 0.06). However, FRAP did not show any correlation with OA severity or MT control. NO is an essential bio-regulatory molecule essential for several physiological processes, and inflammatory conditions. However, due to its short life, exact estimation of NO becomes difficult. NO and its measurable stable products are still it is considered as one of the important biomarker of oxidative damage. Levels of NO and nitrite-nitrate in SF of patients with OA indicated its involvement in the disease progression. When SF groups were compared, a significant correlation among moderate, mild and MT groups was established. To summarize, present data illustrated higher levels of NO, SOD, CAT, DPPH and MDA in early OA in comparison with MT, as a control group. NN had emerged as a prognostic bio marker in knee OA patients, which may act as futuristic targets in OA treatment.

Keywords: antioxidant, knee osteoarthritis, oxidative stress, synovial fluid

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Authors: Aldona Kluczek

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Energy efficient process have become a pressing research field in manufacturing. The arguments for having an effective industrial energy efficiency processes are interacted with factors: economic and environmental impact, and energy security. Improvements in energy efficiency are most often achieved by implementation of more efficient technology or manufacturing process. Current processes of electricity production represents the biggest consumption of energy and the greatest amount of emissions to the environment. The goal of this study is to improve the potential energy-savings and reduce greenhouse emissions related to improvement scenarios for the treatment of hardwood lumber produced by an industrial plant operating in the U.S. through the application of green balancing procedure, in order to find the preferable efficient technology. The green procedure for energy is based on analysis of energy efficiency data. Three alternative scenarios of the cogeneration systems plant (CHP) construction are considered: generation of fresh steam, the purchase of a new boiler with the operating pressure 300 pounds per square inch gauge (PSIG), an installation of a new boiler with a 600 PSIG pressure. In this paper, the application of a bottom-down modelling for energy flow to devise a streamlined Energy and Emission Flow Analyze method for the technology of producing electricity is illustrated. It will identify efficiency or technology of a given process to be reached, through the effective use of energy, or energy management. Results have shown that the third scenario seem to be the efficient alternative scenario considered from the environmental and economic concerns for treating hardwood lumber. The energy conservation evaluation options could save an estimated 6,215.78 MMBtu/yr in each year, which represents 9.5% of the total annual energy usage. The total annual potential cost savings from all recommendations is $143,523/yr, which represents 30.1% of the total annual energy costs. Estimation have presented that energy cost savings are possible up to 43% (US$ 143,337.85), representing 18.6% of the total annual energy costs.

Keywords: alternative scenario improvements, cogeneration system, energy and emission flow analyze, energy balancing, green procedure, hardwood lumber manufacturing process

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Authors: Elias Jemal Abdella

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The Blue Nile River is an important shared resource of Ethiopia, Sudan and also, because it is the major contributor of water to the main Nile River, Egypt. Despite the potential benefits of regional cooperation and integrated joint basin management, all three countries continue to pursue unilateral plans for development. Besides, there is great uncertainty about the likely impacts of climate change in water availability for existing as well as proposed irrigation and hydropower projects in the Blue Nile Basin. The main objective of this study is to quantitatively assess the impact of climate change on the hydrological regime of the upper Blue Nile basin, western Ethiopia. Three models were combined, a dynamic Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate model (RCM) that is used to determine climate projections for the Upper Blue Nile basin for Representative Concentration Pathways (RCPs) 4.5 and 8.5 greenhouse gas emissions scenarios for the period 2021-2050. The outputs generated from multimodel ensemble of four (4) CORDEX-RCMs (i.e., rainfall and temperature) were used as input to a Soil and Water Assessment Tool (SWAT) hydrological model which was setup, calibrated and validated with observed climate and hydrological data. The outputs from the SWAT model (i.e., projections in river flow) were used as input to a Water Evaluation and Planning (WEAP) water resources model which was used to determine the water resources implications of the changes in climate. The WEAP model was set-up to simulate three development scenarios. Current Development scenario was the existing water resource development situation, Medium-term Development scenario was planned water resource development that is expected to be commissioned (i.e. before 2025) and Long-term full Development scenario were all planned water resource development likely to be commissioned (i.e. before 2050). The projected change of mean annual temperature for period (2021 – 2050) in most of the basin are warmer than the baseline (1982 -2005) average in the range of 1 to 1.4oC, implying that an increase in evapotranspiration loss. Subbasins which already distressed from drought may endure to face even greater challenges in the future. Projected mean annual precipitation varies from subbasin to subbasin; in the Eastern, North Eastern and South western highland of the basin a likely increase of mean annual precipitation up to 7% whereas in the western lowland part of the basin mean annual precipitation projected to decrease by 3%. The water use simulation indicates that currently irrigation demand in the basin is 1.29 Bm3y-1 for 122,765 ha of irrigation area. By 2025, with new schemes being developed, irrigation demand is estimated to increase to 2.5 Bm3y-1 for 277,779 ha. By 2050, irrigation demand in the basin is estimated to increase to 3.4 Bm3y-1 for 372,779 ha. The hydropower generation simulation indicates that 98 % of hydroelectricity potential could be produced if all planned dams are constructed.

Keywords: Blue Nile River, climate change, hydropower, SWAT, WEAP

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: D. Mulligan, D. Casey

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Introductory Statement: Delegation is when a registered nurse (RN) transfers a task or activity that is normally within their scope of practice to another person (delegatee). RN delegation is common practice with unregistered staff, e.g., student nurses and health care assistants (HCAs). As the role of the HCA is increasingly embedded as a direct care and support role, especially in long-term residential care for older adults, there is RN uncertainty as to their role as a delegator. The assignment is when a task is transferred to a person that is within the role specification of the delegatee. RNs in long-term care (LTC) for older people are increasingly working in teams where there are less RNs and more HCAs providing direct care to the residents. The RN is responsible and accountable for their decision to delegate and assign tasks to HCAs. In an interpretive, multiple case studies to explore how delegation of tasks by RNs to HCAs occurred in long-term care settings in Ireland the importance of the RN understanding their scope of practice emerged. Methodology: Focus group interviews and individual interviews were undertaken as part of a multiple case study. Both cases, anonymized as Case A and Case B, were within the public health service in Ireland. The case study sites were long-term care settings for older adults located in different social care divisions, and in different geographical areas. Four focus group interviews with staff nurses and three individual interviews with CNMs were undertaken. The interactive data analysis approach was the analytical framework used, with within-case and cross-case analysis. The theoretical lens of organizational role theory, applying the role episode model (REM), was used to understand, interpret, and explain the findings. Study Findings: RNs and CNMs understood the role of the nurse regulator and the scope of practice. RNs understood that the RN was accountable for the care and support provided to residents. However, RNs and CNM2s could not describe delegation in the context of their scope of practice. In both cases, the RNs did not have a standardized process for assessing HCA competence to undertake nursing tasks or interventions. RNs did not routinely supervise HCAs. Tasks were assigned and not delegated. There were differences between the cases in relation to understanding which nursing tasks required delegation. HCAs in Case A undertook clinical vital sign assessments and documentation. HCAs in Case B did not routinely undertake these activities. Delegation and assignment were influenced by the organizational factors, e.g., model of care, absence of delegation policies, inadequate RN education on delegation, and a lack of RN and HCA role clarity. Concluding Statement: Nurse staffing levels and skill mix in long-term care settings continue to change with more HCAs providing more direct care and support. With decreasing RN staffing levels RNs will be required to delegate and assign more direct care to HCAs. There is a requirement to distinguish between RN assignment and delegation at policy, regulation, and organizational levels.

Keywords: assignment, delegation, registered nurse, scope of practice

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Authors: Eiji Nakamachi, Tsuyoshi Eguchi, Sayo Yamamoto, Yusuke Morita, H. Sakamoto

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In this study, we aim to reveal the relationship between the meniscal tearing and the articular cartilage injury of knee joint by using the dynamic explicit finite element (FE) method. Meniscal injuries reduce its functional ability and consequently increase the load on the articular cartilage of knee joint. In order to prevent the induction of osteoarthritis (OA) caused by meniscal injuries, many medical treatment techniques, such as artificial meniscus replacement and meniscal regeneration, have been developed. However, it is reported that these treatments are not the comprehensive methods. In order to reveal the fundamental mechanism of OA induction, the mechanical characterization of meniscus under the condition of normal and injured states is carried out by using FE analyses. At first, a FE model of the human knee joint in the case of normal state – ‘intact’ - was constructed by using the magnetron resonance (MR) tomography images and the image construction code, Materialize Mimics. Next, two types of meniscal injury models with the radial tears of medial and lateral menisci were constructed. In FE analyses, the linear elastic constitutive law was adopted for the femur and tibia bones, the visco-hyperelastic constitutive law for the articular cartilage, and the visco-anisotropic hyperelastic constitutive law for the meniscus, respectively. Material properties of articular cartilage and meniscus were identified using the stress-strain curves obtained by our compressive and the tensile tests. The numerical results under the normal walking condition revealed how and where the maximum compressive stress occurred on the articular cartilage. The maximum compressive stress and its occurrence point were varied in the intact and two meniscal tear models. These compressive stress values can be used to establish the threshold value to cause the pathological change for the diagnosis. In this study, FE analyses of knee joint were carried out to reveal the influence of meniscal injuries on the cartilage injury. The following conclusions are obtained. 1. 3D FE model, which consists femur, tibia, articular cartilage and meniscus was constructed based on MR images of human knee joint. The image processing code, Materialize Mimics was used by using the tetrahedral FE elements. 2. Visco-anisotropic hyperelastic constitutive equation was formulated by adopting the generalized Kelvin model. The material properties of meniscus and articular cartilage were determined by curve fitting with experimental results. 3. Stresses on the articular cartilage and menisci were obtained in cases of the intact and two radial tears of medial and lateral menisci. Through comparison with the case of intact knee joint, two tear models show almost same stress value and higher value than the intact one. It was shown that both meniscal tears induce the stress localization in both medial and lateral regions. It is confirmed that our newly developed FE analysis code has a potential to be a new diagnostic system to evaluate the meniscal damage on the articular cartilage through the mechanical functional assessment.

Keywords: finite element analysis, hyperelastic constitutive law, knee joint injury, meniscal tear, stress concentration

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Authors: Bernice Jeanne Lottering, Yi-Wen Lin

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Introduction: Chronic pain has a definitive lack of objective parameters in the measurement and treatment efficacy of diseases such as Fibromyalgia (FM). Persistent widespread pain and generalized tenderness are the characteristic symptoms affecting a large majority of the global population, particularly females. This disease has indicated a refractory tendency to conventional treatment ventures, largely resultant from a lack of etiological and pathogenic understanding of the disease development. Emerging evidence indicates that the central nervous system (CNS) plays a critical role in the amplification of pain signals and the neurotransmitters associated therewith. Various stimuli have been found to activate the channels existent on nociceptor terminals, thereby actuating nociceptive impulses along the pain pathways. The transient receptor potential vanalloid 1 (TRPV1) channel functions as a molecular integrator for numerous sensory inputs, such as nociception, and was explored in the current study. Current intervention approaches face a multitude challenges, ranging from effective therapeutic interventions to the limitation of pathognomonic criteria resultant from incomplete understanding and partial evidence on the mechanisms of action of FM. It remains unclear whether electroacupuncture (EA) plays an integral role in the functioning of the TRPV1 pathway, and whether or not it can reduce the chronic pain induced by FM. Aims: The aim of this study was to explore the mechanisms underlying the activation and modulation of the TRPV1 channel pathway in a cold stress model of FM applied to a murine model. Furthermore, the effect of EA in the treatment of mechanical and thermal pain, as expressed in FM was also to be investigated. Methods: 18 C57BL/6 wild type and 6 TRPV1 knockout (KO) mice, aged 8-12 weeks, were exposed to an intermittent cold stress-induced fibromyalgia-like pain model, with or without EA treatment at ZusanLi ST36 (2Hz/20min) on day 3 to 5. Von Frey and Hargreaves behaviour tests were implemented in order to analyze the mechanical and thermal pain thresholds on day 0, 3 and 5 in control group (C), FM group (FM), FM mice with EA treated group (FM + EA) and FM in KO group. Results: An increase in mechanical and thermal hyperalgesia was observed in the FM, EA and KO groups when compared to the control group. This initial increase was reduced in the EA group, which directs focus at the treatment efficacy of EA in nociceptive sensitization, and the analgesic effect EA has attenuating FM associated pain. Discussion: An increase in the nociceptive sensitization was observed through higher withdrawal thresholds in the von Frey mechanical test and the Hargreaves thermal test. TRPV1 function in mice has been scientifically associated with these nociceptive conduits, and the increased behaviour test results suggest that TRPV1 upregulation is central to the FM induced hyperalgesia. This data was supported by the decrease in sensitivity observed in results of the TRPV1 KO group. Moreover, the treatment of EA showed a decrease in this FM induced nociceptive sensitization, suggesting TRPV1 upregulation and overexpression can be attenuated by EA at bilateral ST36. This evidence compellingly implies that the analgesic effect of EA is associated with TRPV1 downregulation.

Keywords: fibromyalgia, electroacupuncture, TRPV1, nociception

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Authors: Sunday B. Alabi, Edikan P. Felix, Aniediong M. Umo

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Crystal size distribution is of great importance in the sugar factories. It determines the market value of granulated sugar and also influences the cost of production of sugar crystals. Typically, sugar is produced using fed-batch vacuum evaporative crystallizer. The crystallization quality is examined by crystal size distribution at the end of the process which is quantified by two parameters: the average crystal size of the distribution in the mean aperture (MA) and the width of the distribution of the coefficient of variation (CV). Lack of real-time measurement of the sugar crystal size hinders its feedback control and eventual optimisation of the crystallization process. An attractive alternative is to use a soft sensor (model-based method) for online estimation of the sugar crystal size. Unfortunately, the available models for sugar crystallization process are not suitable as they do not contain variables that can be measured easily online. The main contribution of this paper is the development of a regression model for estimating the sugar crystal size as a function of input variables which are easy to measure online. This has the potential to provide real-time estimates of crystal size for its effective feedback control. Using 7 input variables namely: initial crystal size (Lo), temperature (T), vacuum pressure (P), feed flowrate (Ff), steam flowrate (Fs), initial super-saturation (S0) and crystallization time (t), preliminary studies were carried out using Minitab 14 statistical software. Based on the existing sugar crystallizer models, and the typical ranges of these 7 input variables, 128 datasets were obtained from a 2-level factorial experimental design. These datasets were used to obtain a simple but online-implementable 6-input crystal size model. It seems the initial crystal size (Lₒ) does not play a significant role. The goodness of the resulting regression model was evaluated. The coefficient of determination, R² was obtained as 0.994, and the maximum absolute relative error (MARE) was obtained as 4.6%. The high R² (~1.0) and the reasonably low MARE values are an indication that the model is able to predict sugar crystal size accurately as a function of the 6 easy-to-measure online variables. Thus, the model can be used as a soft sensor to provide real-time estimates of sugar crystal size during sugar crystallization process in a fed-batch vacuum evaporative crystallizer.

Keywords: crystal size, regression model, soft sensor, sugar, vacuum evaporative crystallizer

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Authors: Chakkrit Poonpakdee, Jing-Hua Tzen, Ya-Zhen Huang, Yao-Tung Lin

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Potassium (K) is a known macro nutrient and essential element for plant growth. Single leaching and modified sequential extraction schemes have been developed to estimate the relative phase associations of soil samples. The sequential extraction process is a step in analyzing the partitioning of metals affected by environmental conditions, but it is not a tool for estimation of K bioavailability. While, traditional single leaching method has been used to classify K speciation for a long time, it depend on its availability to the plants and use for potash fertilizer recommendation rate. Clay mineral in soil is a factor for controlling soil fertility. The change of the micro-structure of clay minerals during various environment (i.e. swelling or shrinking) is characterized using Transmission X-Ray Microscopy (TXM). The objective of this study are to 1) compare the distribution of K speciation between single leaching and sequential extraction process 2) determined clay particle flocculation structure before/after suspension with K+ using TXM. Four tropical soil samples: farming without K fertilizer (10 years), long term applied K fertilizer (10 years; 168-240 kg K2O ha-1 year-1), red soil (450-500 kg K2O ha-1 year-1) and forest soil were selected. The results showed that the amount of K speciation by single leaching method were high in mineral K, HNO3 K, Non-exchangeable K, NH4OAc K, exchangeable K and water soluble K respectively. Sequential extraction process indicated that most K speciations in soil were associated with residual, organic matter, Fe or Mn oxide and exchangeable fractions and K associate fraction with carbonate was not detected in tropical soil samples. In farming long term applied K fertilizer and red soil were higher exchangeable K than farming long term without K fertilizer and forest soil. The results indicated that one way to increase the available K (water soluble K and exchangeable K) should apply K fertilizer and organic fertilizer for providing available K. The two-dimension of TXM image of clay particles suspension with K+ shows that the aggregation structure of clay mineral closed-void cellular networks. The porous cellular structure of soil aggregates in 1 M KCl solution had large and very larger empty voids than in 0.025 M KCl and deionized water respectively. TXM nanotomography is a new technique can be useful in the field as a tool for better understanding of clay mineral micro-structure.

Keywords: potassium, sequential extraction process, clay mineral, TXM

Procedia PDF Downloads 283

Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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The increase in the demand for precision optics, coupled with the absence of much research output in the ultra high precision grinding of precision optics as compared to the ultrahigh precision diamond turning of optical metals has fostered the need for more research in the ultra high precision grinding of an optical lens. Furthermore, the increase in the stringent demands for nanometric surface finishes through lapping, polishing and grinding processes necessary for the use of borosilicate-crown glass in the automotive and optics industries has created the demand to effectively monitor the surface roughness during the production process. Acoustic emission phenomenon has been proven as useful monitoring technique in several manufacturing processes ranging from monitoring of bearing production to tool wear estimation. This paper introduces a rare and unique approach with the application of acoustic emission technique to monitor the surface roughness of borosilicate-crown glass during an ultra high precision grinding process. This research was carried out on a 4-axes Nanoform 250 ultrahigh precision lathe machine using an ultra high precision grinding spindle to machine the flat surface of the borosilicate-crown glass with the tip of the grinding wheel. A careful selection of parameters and design of experiment was implemented using Box-Behnken method to vary the wheel speed, feed rate and depth of cut at three levels with a 3-center point design. Furthermore, the average surface roughness was measured using Taylor Hobson PGI Dimension XL optical profilometer, and an acoustic emission data acquisition device from National Instruments was utilized to acquire the signals while the data acquisition codes were designed with National Instrument LabVIEW software for acquisition at a sampling rate of 2 million samples per second. The results show that the raw and root mean square amplitude values of the acoustic signals increased with a corresponding increase in the measured average surface roughness values for the different parameter combinations. Therefore, this research concludes that acoustic emission monitoring technique is a potential technique for monitoring the surface roughness in the ultra high precision grinding of borosilicate-crown glass.

Keywords: acoustic emission, borosilicate-crown glass, surface roughness, ultra high precision grinding

Procedia PDF Downloads 287

Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Aditya Purohit, Neha Bansal

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Unplanned urbanization and multidirectional sprawl of the cities have resulted in increased motorization and deteriorating transport conditions like traffic congestion, longer commuting, pollution, increased carbon footprint, and above all increased fatalities. In order to overcome these problems, various practices have been adopted including– promoting and implementing mass transport; traffic junction channelization; smart transport etc. However, these methods are found to be primarily focusing on vehicular mobility rather than people accessibility. With this research gap, this paper tries to resolve the mobility issues for Ahmedabad city in India, which being the economic capital Gujarat state has a huge commuter and visitor inflow. This research aims to resolve the traffic congestion and urban mobility issues focusing on Gujarat State Regional Transport Corporation (GSRTC) for the city of Ahmadabad by analyzing the existing operations and network structure of GSRTC followed by finding possibilities of integrating it with other modes of urban transport. The network restructuring (NR) methodology is used with appropriate variations, based on commuter demand and growth pattern of the city. To do these ‘scenarios’ based on priority issues (using 12 parameters) and their best possible solution, are established after route network analysis for 2700 population sample of 20 traffic junctions/nodes across the city. Approximately 5% sample (of passenger inflow) at each node is considered using random stratified sampling technique two scenarios are – Scenario 1: Resolving mobility issues by use of Special Purpose Vehicle (SPV) in joint venture to GSRTC and Private Operators for establishing feeder service, which shall provide a transfer service for passenger for movement from inner city area to identified peripheral terminals; and Scenario 2: Augmenting existing mass transport services such as BRTS and AMTS for using them as feeder service to the identified peripheral terminals. Each of these has now been analyzed for the best suitability/feasibility in network restructuring. A desire-line diagram is constructed using this analysis which indicated that on an average 62% of designated GSRTC routes are overlapping with mass transportation service routes of BRTS and AMTS in the city. This has resulted in duplication of bus services causing traffic congestion especially in the Central Bus Station (CBS). Terminating GSRTC services on the periphery of the city is found to be the best restructuring network proposal. This limits the GSRTC buses at city fringe area and prevents them from entering into the city core areas. These end-terminals of GSRTC are integrated with BRTS and AMTS services which help in segregating intra-state and inter-state bus services. The research concludes that absence of integrated multimodal transport network resulted in complexity of transport access to the commuters. As a further scope of research comparing and understanding of value of access time in total travel time and its implication on generalized cost on trip and how it varies city wise may be taken up.

Keywords: mass transportation, multi-modal integration, network restructuring, travel behavior, urban transport

Procedia PDF Downloads 193

Authors: Sivakumar Kottapalli, Abdesselam Abdelouas, Christoph Hartnack

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Spent nuclear fuel generates a mixed field of ionizing radiation to the water. This radiation field is generally dominated by gamma rays and a limited flux of fast neutrons. The fuel cladding effectively attenuates beta and alpha particle radiation. Small fraction of the spent nuclear fuel exhibits some degree of fuel cladding penetration due to pitting corrosion and mechanical failure. Breaches in the fuel cladding allow the exposure of small volumes of water in the cask to alpha and beta ionizing radiation. The safety of the transport of radioactive material is assured by the package complying with the IAEA Requirements for the Safe Transport of Radioactive Material SSR-6. It is of high interest to avoid generation of hydrogen inside the cavity which may to an explosive mixture. The risk of hydrogen production along with other radiation gases should be analyzed for a typical spent fuel for safety issues. This work aims to perform a realistic study of the production of hydrogen by radiolysis assuming most penalizing initial conditions. It consists in the calculation of the radionuclide inventory of a pellet taking into account the burn up and decays. Westinghouse 17X17 PWR fuel has been chosen and data has been analyzed for different sets of enrichment, burnup, cycles of irradiation and storage conditions. The inventory is calculated as the entry point for the simulation studies of hydrogen production by radiolysis kinetic models by MAKSIMA-CHEMIST. Dose rates decrease strongly within ~45 μm from the fuel surface towards the solution(water) in case of alpha radiation, while the dose rate decrease is lower in case of beta and even slower in case of gamma radiation. Calculations are carried out to obtain spectra as a function of time. Radiation dose rate profiles are taken as the input data for the iterative calculations. Hydrogen yield has been found to be around 0.02 mol/L. Calculations have been performed for a realistic scenario considering a capsule containing the spent fuel rod. Thus, hydrogen yield has been debated. Experiments are under progress to validate the hydrogen production rate using cyclotron at > 5MeV (at ARRONAX, Nantes).

Keywords: radiolysis, spent fuel, hydrogen, cyclotron

Procedia PDF Downloads 518

Authors: Dor Oppenheim, Yael Edan, Guy Shani

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This paper presents an image analysis algorithm to detect and count yellow tomato flowers in a greenhouse with uneven illumination conditions, complex growth conditions and different flower sizes. The algorithm is designed to be employed on a drone that flies in greenhouses to accomplish several tasks such as pollination and yield estimation. Detecting the flowers can provide useful information for the farmer, such as the number of flowers in a row, and the number of flowers that were pollinated since the last visit to the row. The developed algorithm is designed to handle the real world difficulties in a greenhouse which include varying lighting conditions, shadowing, and occlusion, while considering the computational limitations of the simple processor in the drone. The algorithm identifies flowers using an adaptive global threshold, segmentation over the HSV color space, and morphological cues. The adaptive threshold divides the images into darker and lighter images. Then, segmentation on the hue, saturation and volume is performed accordingly, and classification is done according to size and location of the flowers. 1069 images of greenhouse tomato flowers were acquired in a commercial greenhouse in Israel, using two different RGB Cameras – an LG G4 smartphone and a Canon PowerShot A590. The images were acquired from multiple angles and distances and were sampled manually at various periods along the day to obtain varying lighting conditions. Ground truth was created by manually tagging approximately 25,000 individual flowers in the images. Sensitivity analyses on the acquisition angle of the images, periods throughout the day, different cameras and thresholding types were performed. Precision, recall and their derived F1 score were calculated. Results indicate better performance for the view angle facing the flowers than any other angle. Acquiring images in the afternoon resulted with the best precision and recall results. Applying a global adaptive threshold improved the median F1 score by 3%. Results showed no difference between the two cameras used. Using hue values of 0.12-0.18 in the segmentation process provided the best results in precision and recall, and the best F1 score. The precision and recall average for all the images when using these values was 74% and 75% respectively with an F1 score of 0.73. Further analysis showed a 5% increase in precision and recall when analyzing images acquired in the afternoon and from the front viewpoint.

Keywords: agricultural engineering, image processing, computer vision, flower detection

Procedia PDF Downloads 322

Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

Procedia PDF Downloads 248

Authors: Elena Collino, Dario A. Ronzio, Goffredo Decimi, Maurizio Riva

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The rapid increase of renewable energy in Italy is led by wind and solar installations. The 2017 Italian energy strategy foresees a further development of these sustainable technologies, especially solar. This fact has resulted in new opportunities, challenges, and different problems to deal with. The growth of renewables allows to meet the European requirements regarding energy and environmental policy, but these types of sources are difficult to manage because they are intermittent and non-programmable. Operationally, these characteristics can lead to instability on the voltage profile and increasing uncertainty on energy reserve scheduling. The increasing renewable production must be considered with more and more attention especially by the Transmission System Operator (TSO). The TSO, in fact, every day provides orders on energy dispatch, once the market outcome has been determined, on extended areas, defined mainly on the basis of power transmission limitations. In Italy, six market zone are defined: Northern-Italy, Central-Northern Italy, Central-Southern Italy, Southern Italy, Sardinia, and Sicily. An accurate hourly renewable power forecasting for the day-ahead on these extended areas brings an improvement both in terms of dispatching and reserve management. In this study, an operational forecasting tool of the hourly solar output for the six Italian market zones is presented, and the performance is analysed. The implementation is carried out by means of a numerical weather prediction model, coupled with a statistical post-processing in order to derive the power forecast on the basis of the meteorological projection. The weather forecast is obtained from the limited area model RAMS on the Italian territory, initialized with IFS-ECMWF boundary conditions. The post-processing calculates the solar power production with the Analog Ensemble technique (AN). This statistical approach forecasts the production using a probability distribution of the measured production registered in the past when the weather scenario looked very similar to the forecasted one. The similarity is evaluated for the components of the solar radiation: global (GHI), diffuse (DIF) and direct normal (DNI) irradiation, together with the corresponding azimuth and zenith solar angles. These are, in fact, the main factors that affect the solar production. Considering that the AN performance is strictly related to the length and quality of the historical data a training period of more than one year has been used. The training set is made by historical Numerical Weather Prediction (NWP) forecasts at 12 UTC for the GHI, DIF and DNI variables over the Italian territory together with corresponding hourly measured production for each of the six zones. The AN technique makes it possible to estimate the aggregate solar production in the area, without information about the technologic characteristics of the all solar parks present in each area. Besides, this information is often only partially available. Every day, the hourly solar power forecast for the six Italian market zones is made publicly available through a website.

Keywords: analog ensemble, electricity market, PV forecast, solar energy

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Authors: B. Neethu, Diptesh Das

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The present study investigates the performance of a semi-active controller using magneto-rheological dampers (MR) for seismic response reduction of a multi-span bridge. The application of structural control to the structures during earthquake excitation involves numerous challenges such as proper formulation and selection of the control strategy, mathematical modeling of the system, uncertainty in system parameters and noisy measurements. These problems, however, need to be tackled in order to design and develop controllers which will efficiently perform in such complex systems. A control algorithm, which can accommodate un-certainty and imprecision compared to all the other algorithms mentioned so far, due to its inherent robustness and ability to cope with the parameter uncertainties and imprecisions, is the sliding mode algorithm. A sliding mode control algorithm is adopted in the present study due to its inherent stability and distinguished robustness to system parameter variation and external disturbances. In general a semi-active control scheme using an MR damper requires two nested controllers: (i) an overall system controller, which derives the control force required to be applied to the structure and (ii) an MR damper voltage controller which determines the voltage required to be supplied to the damper in order to generate the desired control force. In the present study a sliding mode algorithm is used to determine the desired optimal force. The function of the voltage controller is to command the damper to produce the desired force. The clipped optimal algorithm is used to find the command voltage supplied to the MR damper which is regulated by a semi active control law based on sliding mode algorithm. The main objective of the study is to propose a robust semi active control which can effectively control the responses of the bridge under real earthquake ground motions. Lumped mass model of the bridge is developed and time history analysis is carried out by solving the governing equations of motion in the state space form. The effectiveness of MR dampers is studied by analytical simulations by subjecting the bridge to real earthquake records. In this regard, it may also be noted that the performance of controllers depends, to a great extent, on the characteristics of the input ground motions. Therefore, in order to study the robustness of the controller in the present study, the performance of the controllers have been investigated for fourteen different earthquake ground motion records. The earthquakes are chosen in such a way that all possible characteristic variations can be accommodated. Out of these fourteen earthquakes, seven are near-field and seven are far-field. Also, these earthquakes are divided into different frequency contents, viz, low-frequency, medium-frequency, and high-frequency earthquakes. The responses of the controlled bridge are compared with the responses of the corresponding uncontrolled bridge (i.e., the bridge without any control devices). The results of the numerical study show that the sliding mode based semi-active control strategy can substantially reduce the seismic responses of the bridge showing a stable and robust performance for all the earthquakes.

Keywords: bridge, semi active control, sliding mode control, MR damper

Procedia PDF Downloads 123

Authors: Patrick Weber, Daniel Gredig

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Background and Purpose: Research has well documented the impact of discrimination and micro-aggressions on the wellbeing of gay men and, especially, adolescents. For the prevention of homophobic behavior against gay adolescents, however, the focus has to shift on those who discriminate: For the design and tailoring of prevention and intervention, it is important to understand the factors responsible for homophobic behavior such as, for example, verbal abuse. Against this background, the present study aimed to assess homophobic – in terms of verbally abusive – behavior against gay people among high school students. Furthermore, it aimed to establish the predictors of the reported behavior by testing an explanatory model. This model posits that homophobic behavior is determined by negative attitudes and knowledge. These variables are supposed to be predicted by the acceptance of traditional gender roles, religiosity, orientation toward social dominance, contact with gay men, and by the perceived expectations of parents, friends and teachers. These social-cognitive variables in turn are assumed to be determined by students’ gender, age, immigration background, formal school level, and the discussion of gay issues in class. Method: From August to October 2016, we visited 58 high school classes in 22 public schools in a county in Switzerland, and asked the 8th and 9th year students on three formal school levels to participate in survey about gender and gay issues. For data collection, we used an anonymous self-administered questionnaire filled in during class. Data were analyzed using descriptive statistics and structural equation modelling (Generalized Least Square Estimates method). The sample included 897 students, 334 in the 8th and 563 in the 9th year, aged 12–17, 51.2% being female, 48.8% male, 50.3% with immigration background. Results: A proportion of 85.4% participants reported having made homophobic statements in the 12 month before survey, 4.7% often and very often. Analysis showed that respondents’ homophobic behavior was predicted directly by negative attitudes (β=0.20), as well as by the acceptance of traditional gender roles (β=0.06), religiosity (β=–0.07), contact with gay people (β=0.10), expectations of parents (β=–0.14) and friends (β=–0.19), gender (β=–0.22) and having a South-East-European or Western- and Middle-Asian immigration background (β=0.09). These variables were predicted, in turn, by gender, age, immigration background, formal school level, and discussion of gay issues in class (GFI=0.995, AGFI=0.979, SRMR=0.0169, CMIN/df=1.199, p>0.213, adj. R2 =0.384). Conclusion: Findings evidence a high prevalence of homophobic behavior in the responding high school students. The tested explanatory model explained 38.4% of the assessed homophobic behavior. However, data did not found full support of the model. Knowledge did not turn out to be a predictor of behavior. Except for the perceived expectation of teachers and orientation toward social dominance, the social-cognitive variables were not fully mediated by attitudes. Equally, gender and immigration background predicted homophobic behavior directly. These findings demonstrate the importance of prevention and provide also leverage points for interventions against anti-gay bias in adolescents – also in social work settings as, for example, in school social work, open youth work or foster care.

Keywords: discrimination, high school students, gay men, predictors, Switzerland

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Tigist Mekonnen Melesse

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Since rural households are basically entitled to food through own production, improving productivity might lead to enhance the welfare of rural population through higher food availability at the household level and lowering the price of agricultural products. Increasing agricultural productivity through the use of improved technology is one of the desired outcomes from sensible food security and agricultural policy. The ultimate objective of this study was to evaluate the potential impact of improved agricultural technology adoption on smallholders’ crop productivity and welfare. The study is conducted in Ethiopia covering 1500 rural households drawn from four regions and 15 rural villages based on data collected by Ethiopian Rural Household Survey. Endogenous treatment effect model is employed in order to account for the selection bias on adoption decision that is expected from the self-selection of households in technology adoption. The treatment indicator, technology adoption is a binary variable indicating whether the household used improved seeds and chemical fertilizer or not. The outcome variables were cereal crop productivity, measured in real value of production and welfare of households, measured in real per capita consumption expenditure. Results of the analysis indicate that there is positive and significant effect of improved technology use on rural households’ crop productivity and welfare in Ethiopia. Adoption of improved seeds and chemical fertilizer alone will increase the crop productivity by 7.38 and 6.32 percent per year of each. Adoption of such technologies is also found to improve households’ welfare by 1.17 and 0.25 percent per month of each. The combined effect of both technologies when adopted jointly is increasing crop productivity by 5.82 percent and improving welfare by 0.42 percent. Besides, educational level of household head, farm size, labor use, participation in extension program, expenditure for input and number of oxen positively affect crop productivity and household welfare, while large household size negatively affect welfare of households. In our estimation, the average treatment effect of technology adoption (average treatment effect on the treated, ATET) is the same as the average treatment effect (ATE). This implies that the average predicted outcome for the treatment group is similar to the average predicted outcome for the whole population.

Keywords: Endogenous treatment effect, technologies, productivity, welfare, Ethiopia

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Authors: Kuang Sheng Liu, Yu Lin Shih*, Chun Ta Tzeng, Cheng Chen Chen

Abstract:

Buildings in the 21st century are facing issues such as an extreme climate and low-carbon/energy-saving requirements. Many countries in the world are of the opinion that a building during its medium- and long-term life cycle is an energy-consuming entity. As for the use of architectural resources, including the United Nations-implemented "Global Green Policy" and "Sustainable building and construction initiative", all are working towards "zero-energy building" and "zero-carbon building" policies. Because of this, countries are cooperating with industry development using policies such as "mandatory design criteria", "green procurement policy" and "incentive grants and rebates programme". The results of this study can provide a reference for sustainable building renovation design criteria. Aimed at townhouses in Kaohsiung City, this study uses different levels of solar blocking depth to carry out evaluation of design and energy-saving renovation of the outer shell of existing buildings by using data collection and the selection of representative cases. Using building resources from a building information model (BIM), simulation and efficiency evaluation are carried out and proven with simulation estimation. This leads into the ECO-efficiency model (EEM) for the life cycle cost efficiency (LCCE) evalution. The buildings selected by this research sit in a north-south direction set with different solar blocking depths. The indoor air-conditioning consumption rates are compared. The current balcony depth of 1 metre as the simulated EUI value acts as a reference value of 100%. The solar blocking of the balcony is increased to 1.5, 2, 2.5 and 3 metres for a total of 5 different solar-blocking balcony depths, for comparison of the air-conditioning improvement efficacy. This research uses different solar-blocking balcony depths to carry out air-conditioning efficiency analysis. 1.5m saves 3.08%, 2m saves 6.74%, 2.5m saves 9.80% and 3m saves 12.72% from the air-conditioning EUI value. This shows that solar-blocking balconies have an efficiency-increasing potential for indoor air-conditioning.

Keywords: building information model, eco-efficiency model, energy-saving in the external shell, solar blocking depth.

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Authors: Egemen Sert, Leila Hedayatifar, Rachel A. Rigg, Amir Akhavan, Olha Buchel, Dominic Elias Saadi, Aabir Abubaker Kar, Alfredo J. Morales, Yaneer Bar-Yam

Abstract:

The complexity of providing timely and cost-effective distribution of finished goods from industrial facilities to customers makes effective operational coordination difficult, yet effectiveness is crucial for maintaining customer service levels and sustaining a business. Logistics planning becomes increasingly complex with growing numbers of customers, varied geographical locations, the uncertainty of future orders, and sometimes extreme competitive pressure to reduce inventory costs. Linear optimization methods become cumbersome or intractable due to a large number of variables and nonlinear dependencies involved. Here we develop a complex systems approach to optimizing logistics networks based upon dimensional reduction methods and apply our approach to a case study of a manufacturing company. In order to characterize the complexity in customer behavior, we define a “customer space” in which individual customer behavior is described by only the two most relevant dimensions: the distance to production facilities over current transportation routes and the customer's demand frequency. These dimensions provide essential insight into the domain of effective strategies for customers; direct and indirect strategies. In the direct strategy, goods are sent to the customer directly from a production facility using box or bulk trucks. In the indirect strategy, in advance of an order by the customer, goods are shipped to an external warehouse near a customer using trains and then "last-mile" shipped by trucks when orders are placed. Each strategy applies to an area of the customer space with an indeterminate boundary between them. Specific company policies determine the location of the boundary generally. We then identify the optimal delivery strategy for each customer by constructing a detailed model of costs of transportation and temporary storage in a set of specified external warehouses. Customer spaces help give an aggregate view of customer behaviors and characteristics. They allow policymakers to compare customers and develop strategies based on the aggregate behavior of the system as a whole. In addition to optimization over existing facilities, using customer logistics and the k-means algorithm, we propose additional warehouse locations. We apply these methods to a medium-sized American manufacturing company with a particular logistics network, consisting of multiple production facilities, external warehouses, and customers along with three types of shipment methods (box truck, bulk truck and train). For the case study, our method forecasts 10.5% savings on yearly transportation costs and an additional 4.6% savings with three new warehouses.

Keywords: logistics network optimization, direct and indirect strategies, K-means algorithm, dimensional reduction

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Authors: S. D. Kadir

Abstract:

Background: Antibiotics have always been at the centrum of the revolution of modern microbiology. Micro-organisms and its pathogenicity, resistant organisms, inappropriate or over usage of various types of antibiotic agents are fuelled multidrug-resistant pathogenic organisms. Our present time review report mainly focuses on the therapeutic condition of antibiotic resistance and the possible roots behind the development of antibiotic resistance in Bangladesh in 2019. Methodology: The systemic review has progressed through a series of research analyses on various manuscripts published on Google Scholar, PubMed, Research Gate, and collected relevant information from established popular healthcare and diagnostic center and its subdivisions all over Bangladesh. Our research analysis on the possible assurance of antibiotic resistance been ensured by the selective medical reports and on random assay on the extent of individual antibiotic in 2019. Results: 5 research articles, 50 medical report summary, and around 5 patients have been interviewed while going through the estimation process. We have prioritized research articles where the research analysis been performed by the appropriate use of the Kirby-Bauer method. Kirby-Bauer technique is preferred as it provides greater efficiency, ensures lower performance expenditure, and supplies greater convenience and simplification in the application. In most of the reviews, clinical and laboratory standards institute guidelines were strictly followed. Most of our reports indicate significant resistance shown by the Beta-lactam drugs. Specifically by the derivatives of Penicillin's, Cephalosporin's (rare use of the first generation Cephalosporin and overuse of the second and third generation of Cephalosporin and misuse of the fourth generation of Cephalosporin), which are responsible for almost 67 percent of the bacterial resistance. Moreover, approximately 20 percent of the resistance was due to the fact of drug pumping from the bacterial cell by tetracycline and sulphonamides and their derivatives. Conclusion: 90 percent of the approximate antibiotic resistance is due to the usage of relative and true broad-spectrum antibiotics. The environment has been created by the following circumstances where; the excessive usage of broad-spectrum antibiotics had led to a condition where the disruption of native bacteria and a series of anti-microbial resistance causing a disturbance of the surrounding environments in medium, leading to a state of super-infection.

Keywords: antibiotics, antibiotic resistance, Kirby Bauer method, microbiology

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