Search results for: experimental evaluation

Authors: M. Turmanauli, T. Todua, O. Gvaberidze, R. Javakhadze, N. Chkhaidze, N. Khatiashvili

Abstract:

Background: Mining process has the significant influence on human health and quality of life. In recent years the events in Georgia were reflected on the industry working process, especially minimal requirements of labor safety, hygiene standards of workplace and the regime of work and rest are not observed. This situation is often caused by the lack of responsibility, awareness, and knowledge both of workers and employers. The control of working conditions and its protection has been worsened in many of industries. Materials and Methods: For evaluation of the current situation the prospective epidemiological study by face to face interview method was conducted at Georgian “Manganese Zestafoni Ferroalloy Plant” in 2011-2013. 65.7% of employees (1428 bulletin) were surveyed and the incidence rates of temporary disability days were studied. Results: The average length of a temporary disability single accident was studied taking into consideration as sex groups as well as the whole cohort. According to the classes of harmfulness the following results were received: Class 2.0-10.3%; 3.1-12.4%; 3.2-35.1%; 3.3-12.1%; 3.4-17.6%; 4.0-12.5%. Among the employees 47.5% and 83.1% were tobacco and alcohol consumers respectively. According to the age groups and years of work on the base of previous experience ≥50 ages and ≥21 years of work data prevalence respectively. The obtained data revealed increased morbidity rate according to age and years of work. It was found that the bone and articulate system and connective tissue diseases, aggravation of chronic respiratory diseases, ischemic heart diseases, hypertension and cerebral blood discirculation were the leading among the other diseases. High prevalence of morbidity observed in the workplace with not satisfactory labor conditions from the hygienic point of view. Conclusion: According to received data the causes of morbidity are the followings: unsafety labor conditions; incomplete of preventive medical examinations (preliminary and periodic); lack of access to appropriate health care services; derangement of gathering, recording, and analysis of morbidity data. This epidemiological study was conducted at the JSC “Manganese Ferro Alloy Plant” according to State program “ Prevention of Occupational Diseases” (Program code is 35 03 02 05).

Keywords: occupational health, mining process, morbidity level, cerebral blood discirculation

Procedia PDF Downloads 426

Authors: Fouzia Brihmat

Abstract:

Economic planning models, which are used to build microgrids and distributed energy resources, are the current norm for expressing such confidence (DER). These models often decide both short-term DER dispatch and long-term DER investments. This research investigates the most cost-effective hybrid (photovoltaic-diesel) renewable energy system (HRES) based on Total Net Present Cost (TNPC) in an Algerian Saharan area, which has a high potential for solar irradiation and has a production capacity of 1GW/h. Lead-acid batteries have been around much longer and are easier to understand, but have limited storage capacity. Lithium-ion batteries last longer, are lighter, but generally more expensive. By combining the advantages of each chemistry, we produce cost-effective high-capacity battery banks that operate solely on AC coupling. The financial implications of this research describe the corrosion process that occurs at the interface between the active material and grid material of the positive plate of a lead-acid battery. The best cost study for the HRES is completed with the assistance of the HOMER Pro MATLAB Link. Additionally, during the course of the project's 20 years, the system is simulated for each time step. In this model, which takes into consideration decline in solar efficiency, changes in battery storage levels over time, and rises in fuel prices above the rate of inflation. The trade-off is that the model is more accurate, but it took longer to compute. As a consequence, the model is more precise, but the computation takes longer. We initially utilized the Optimizer to run the model without MultiYear in order to discover the best system architecture. The optimal system for the single-year scenario is the Danvest generator, which has 760 kW, 200 kWh of the necessary quantity of lead-acid storage, and a somewhat lower COE of $0.309/kWh. Different scenarios that account for fluctuations in the gasified biomass generator's production of electricity have been simulated, and various strategies to guarantee the balance between generation and consumption have been investigated. The technological optimization of the same system has been finished and is being reviewed in a recent paper study.

Keywords: battery, corrosion, diesel, economic planning optimization, hybrid energy system, lead-acid battery, multi-year planning, microgrid, price forecast, PV, total net present cost

Procedia PDF Downloads 83

Authors: Esmaeil Abbaszadeh Molaei, Zongyan Zhou, Aibing Yu

Abstract:

The applications of liquid fluidizations have been increased in many parts of industries such as particle classification, backwashing of granular filters, crystal growth, leaching and washing, and bioreactors due to high-efficient liquid–solid contact, favorable mass and heat transfer, high operation flexibilities, and reduced back mixing of phases. In most of these multiphase operations the particles properties, i.e. size, density, and shape, may change during the process because of attrition, coalescence or chemical reactions. Previous studies, either experimentally or numerically, mainly have focused on studies of liquid-solid fluidized beds containing spherical particles; however, the role of particle shape on the hydrodynamics of liquid fluidized beds is still not well-known. A three-dimensional Discrete Element Model (DEM) and Computational Fluid Dynamics (CFD) are coupled to study the influence of particles shape on particles and liquid flow patterns in liquid-solid fluidized beds. In the simulations, ellipsoid particles are used to study the shape factor since they can represent a wide range of particles shape from oblate and sphere to prolate shape particles. Different particle shapes from oblate (disk shape) to elongated particles (rod shape) are selected to investigate the effect of aspect ratio on different flow characteristics such as general particles and liquid flow pattern, pressure drop, and particles orientation. First, the model is verified based on experimental observations, then further detail analyses are made. It was found that spherical particles showed a uniform particle distribution in the bed, which resulted in uniform pressure drop along the bed height. However for particles with aspect ratios less than one (disk-shape), some particles were carried into the freeboard region, and the interface between the bed and freeboard was not easy to be determined. A few particle also intended to leave the bed. On the other hand, prolate particles showed different behaviour in the bed. They caused unstable interface and some flow channeling was observed for low liquid velocities. Because of the non-uniform particles flow pattern for particles with aspect ratios lower (oblate) and more (prolate) than one, the pressure drop distribution in the bed was not observed as uniform as what was found for spherical particles.

Keywords: CFD, DEM, ellipsoid, fluidization, multiphase flow, non-spherical, simulation

Procedia PDF Downloads 306

Authors: Manal M. Shehata, Kawther M. Abdel-Hamid, Nashwa A. Mohamed, Marwa H. Bakr, Maged S. Mahmoud, Hala M. Elbadre

Abstract:

Introduction: The worldwide rapid increase in diabetes poses a significant challenge to current therapeutic approaches. Therapeutic utility of bone marrow transplantation in diabetes is an attractive approach. However, the oxidative stress generated by hyperglycemia may hinder β-cell regeneration. Curcumin, is a dietary spice with antioxidant activity. Aim of work: The present study was undertaken to investigate the therapeutic potential of curcumin, bone marrow transplantation, and their combined effects in the reversal of experimental diabetes. Material and Methods: Fifty adult male healthy albino rats were included in the present study.They were divided into two groups: Group І: (control group) included 10 rats. Group П: (diabetic group): included 40 rats. Diabetes was induced by single intraperitoneal injection of streptozotocin (STZ). Group II will be further subdivided into four groups (10 rats for each): Group II-a (diabetic control). Group II-b: rats were received single intraperitoneal injection of bone marrow suspension (un-fractionated bone marrow cells) prepared from rats of the same family. Group II-c: rats were treated with curcumin orally by gastric intubation for 6 weeks. Group II-d: rats were received a combination of single bone marrow transplantation and curcumin for 6 weeks. After 6 weeks, blood glucose, insulin levels were measured and the pancreas from all rats were processed for Histological, Immunohistochemical and morphometric examination. Results: Diabetic group, showed progressive histological changes in the pancreatic islets. Treatment with either curcumin or bone marrow transplantation improved the structure of the islets and reversed streptozotocin-induced hyperglycemia and hypoinsulinemia. Combination of curcumin and bone marrow transplantation elicited more profound alleviation of streptozotocin-induced changes including islet regeneration and insulin secretion. Conclusion: The use of natural antioxidants combined with bone marrow transplantation to induce pancreatic regeneration is a promising strategy in the management of diabetes.

Keywords: diabtes, panceatic islets, bone marrow transplantation, curcumin

Procedia PDF Downloads 380

Authors: Siti Noor Syahirah Mohd Sabri

Abstract:

The oil and gas industry is committed to net zero carbon emissions because the consequences of climate change could be catastrophic unless responded to very soon. One way of reducing CO₂ emissions is to inject it into a depleted reservoir buried underground. This greenhouse gas reduction technique significantly reduces CO₂ released into the atmosphere. In general, depleted oil and gas reservoirs provide readily available sites for the storage of CO₂ in offshore areas. This is mainly due to the hydrocarbons have been optimally produced and the existence of voids for effective CO₂ storage. Hence, make it a good candidate for a CO₂ well injector location. Geological storage sites are often evaluated in terms of capacity, injectivity and containment. Leakage through the cap rock or existing well is the main concern in the depleted fields. In order to develop these fields as CO₂ storage sites, the long-term integrity of wells drilled in these oil & gas fields must be ascertained to ensure good CO₂ containment. Well, integrity is often defined as the ability to contain fluids without significant leakage through the project lifecycle. Most plugged and abandoned (P & A) wells in Peninsular Malaysia have drilled 20 – 30 years ago and were not designed to withstand downhole conditions having >50%vol CO₂ and CO₂/H₂O mixture. In addition, Corrosive-Resistant Alloy (CRA) tubular and CO₂-resistant cement was not used during good construction. The reservoir pressure and temperature conditions may have further degraded the material strength and elevated the corrosion rate. Understanding all the uncertainties that may have affected cement-casing bonds, such as the quality of cement behind the casing, subsidence effect, corrosion rate, etc., is the first step toward well integrity evaluation. Secondly, proper quantification of all the uncertainties involved needs to be done to ensure long-term underground storage objectives of CO₂ are achieved. This paper will discuss challenges associated with estimating the performance of well barrier elements in existing P&A wells. Risk ranking of the existing P&A wells is to be carried out in order to ensure the integrity of the storage site is maintained for long-term CO₂ storage. High-risk existing P&A wells are to be re-entered to restore good integrity and to reduce future leakage that may happen. In addition, the requirement to design a fit-for-purpose monitoring and mitigation technology package for potential CO₂ leakage/seepage in the marine environment will be discussed accordingly. The holistic approach will ensure that the integrity is maintained, and CO₂ is contained underground for years to come.

Keywords: CCUS, well integrity, co₂ storage, offshore

Procedia PDF Downloads 87

Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa

Abstract:

The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: angle of internal friction, cone penetrating test, general regression neural network, soil modulus of elasticity

Procedia PDF Downloads 414

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 45

Authors: A. Mohammad Abdul Motalib Momin, B. Sheikh Mohammad Adil Uddin, C. Md Mamunur Rashid, D. Sheikh Arman Mahbub, E. Mohammad Sazzad Rahman, F. Abdullah Faruque

Abstract:

The present study was designed to investigate the antioxidant and cytotoxicity potential of methanol and pet ether extracts of the Lagenaria siceraria (LM, LP), Cucumis sativus (CSM, CSP), Cucurbita maxima (CMM, CMP) plants. For the phytochemical screening, crude extract was tested for the presence of different chemical groups. In Lagenaria siceraria the following groups were identified: alkaloids, steroids, glycosides and saponins for methanol extract and alkaloids, steroids, glycosides, tannins and saponins are for pet ether extract. Glycosides, steroids, alkaloids, saponins and tannins are present in the methanol extract of Cucumis sativus; the pet ether extract has the alkaloids, steroids and saponins. Glycosides, steroids, alkaloids, saponins and tannins are present in both the methanolic and pet ether extract of Cucurbita maxima. In vitro antioxidant activity of the extracts were performed using DPPH radical scavenging, nitric oxide (NO) scavenging, total antioxidant capacity, total phenol content, total flavonoid content, and Cupric Reducing Antioxidant Capacity assays. The most prominent antioxidant activity was observed with the CSM in the DPPH free radical scavenging test with an IC50 value of 1667.23±11.00271 μg/ml as opposed to that of standard ascorbic acid (IC50 value of 15.707± 1.181 μg/ml.) In total antioxidant capacity method, CMP showed the highest activity (427.81±11.4 mg ascorbic acid/g). The total phenolic and flavonoids content were determined by Folin-Ciocalteu Reagent and aluminium chloride colorimetric method, respectively. The highest total phenols and total flavonoids content were found in CMM and LP with the value of 79.06±16.06 mg gallic acid/g & 119.0±1.41 mg quercetin/g, respectively. In nitric oxide (NO) scavenging the most prominent antioxidant activity was observed in CMM with an IC50 value of 8.119± 0.0036 μg/ml. The Cupric reducing capacity of the extracts was strong and dose dependent manner and CSM showed lowest reducing capacity. The cytotoxicity was determined by Brine shrimp lethality test and among these extracts most potent cytotoxicity was shown by CMM with LC50 value 16.98 µg/ml. The obtained results indicate that the investigated plants could be potential sources of natural antioxidants and can be used for various types of diseases.

Keywords: antioxidant, cytotoxicity, methanol, petroleum ether

Procedia PDF Downloads 573

Authors: Rebecca-Lee Kuhnert, Ron Rapee

Abstract:

Up to 15% of Australian youth will experience an emotional disorder, yet relatively few get the help they need. Schools provide an ideal environment through which we can identify young people who are struggling and provide them with appropriate help. Universal mental health screening is a method by which all young people in school can be quickly assessed for emotional disorders, after which identified youth can be linked to appropriate health services. Despite the obvious logic of this process, universal mental health screening has received little scientific evaluation and even less application in Australian schools. This study will develop methods for Australian education systems to help identify young people (aged 9-17 years old) who are struggling with existing and emerging emotional disorders. Prior to testing, a series of focus groups will be run to get feedback and input from young people, parents, teachers, and mental health professionals. They will be asked about their thoughts on school-based screening methods and and how to best help students at risk of emotional distress. Schools (n=91) across New South Wales, Australia will be randomised to do either immediate screening (in May 2021) or delayed screening (in February 2022). Students in immediate screening schools will complete a long online mental health screener consisting of standard emotional health questionnaires. Ultimately, this large set of items will be reduced to a small number of items to form the final brief screener. Students who score in the “at-risk” range on any measure of emotional health problems will be identified to schools and offered pathways to relevant help according to the most accepted and approved processes identified by the focus groups. Nine months later, the same process will occur among delayed screening schools. At this same time, students in the immediate screening schools will complete screening for a second time. This will allow a direct comparison of the emotional health and help-seeking between youth whose schools had engaged in the screening and pathways to care process (immediate) and those whose schools had not engaged in the process (delayed). It is hypothesised that there will be a significant increase in students who receive help from mental health support services after screening, compared with baseline. It is also predicted that all students will show significantly less emotional distress after screening and access to pathways of care. This study will be an important contribution to Australian youth mental health prevention and early intervention by determining whether school screening leads to a greater number of young people with emotional disorders getting the help that they need and improving their mental health outcomes.

Keywords: children and young people, early intervention, mental health, mental health screening, prevention, school-based mental health

Procedia PDF Downloads 93

Authors: Fatema Akram, Mohammad G. Rasul, M. Masud K. Khan, M. Sharif I. I. Amir

Abstract:

Australia is a country of some 7,700 million square kilometres with a population of about 22.6 million. At present water security is a major challenge for Australia. In some areas the use of water resources is approaching and in some parts it is exceeding the limits of sustainability. A focal point of proposed national water conservation programs is the recycling of both urban storm-water and treated wastewater. But till now it is not widely practiced in Australia, and particularly storm-water is neglected. In Australia, only 4% of storm-water and rainwater is recycled, whereas less than 1% of reclaimed wastewater is reused within urban areas. Therefore, accurately monitoring, assessing and predicting the availability, quality and use of this precious resource are required for better management. As storm-water is usually of better quality than untreated sewage or industrial discharge, it has better public acceptance for recycling and reuse, particularly for non-potable use such as irrigation, watering lawns, gardens, etc. Existing storm-water recycling practice is far behind of research and no robust technologies developed for this purpose. Therefore, there is a clear need for using modern technologies for assessing feasibility of storm-water harvesting and reuse. Numerical modelling has, in recent times, become a popular tool for doing this job. It includes complex hydrological and hydraulic processes of the study area. The hydrologic model computes storm-water quantity to design the system components, and the hydraulic model helps to route the flow through storm-water infrastructures. Nowadays water quality module is incorporated with these models. Integration of Geographic Information System (GIS) with these models provides extra advantage of managing spatial information. However for the overall management of a storm-water harvesting project, Decision Support System (DSS) plays an important role incorporating database with model and GIS for the proper management of temporal information. Additionally DSS includes evaluation tools and Graphical user interface. This research aims to critically review and discuss all the aspects of storm-water harvesting and reuse such as available guidelines of storm-water harvesting and reuse, public acceptance of water reuse, the scopes and recommendation for future studies. In addition to these, this paper identifies, understand and address the importance of modern technologies capable of proper management of storm-water harvesting and reuse.

Keywords: storm-water management, storm-water harvesting and reuse, numerical modelling, geographic information system, decision support system, database

Procedia PDF Downloads 367

Authors: I. A. Yola

Abstract:

Impact of poultry dropping, cow dung and rumen content on utilization and proximate composition of Oreochromis niliticus, Clarias gariepinus and Cyprinus capio in a polyculture system were studied. The research was conducted over a period of 52 weeks. Poultry droppings (PD), cow dung (CD) and rumen content (RC) were applied at three levels 30g,60g and 120g/m2/week, 25g,50g and 100g/m2/week and 22g, 44g and 88g/m2/week treatment, respectively. The control only conventional feed with 40% CP without manure application was used. Physicochemical and biological properties measured were higher in manure pond than control. The difference was statistically significant (P < 0.05) between and within treatments with exception of temperature with a combined mean of 27.900C. The water was consistently alkaline with mean values for pH of 6.61, transparency 22.6cm, conductivity 35.00µhos/cm, dissolved oxygen 4.6 mg/l, biological oxygen demand 2.8mg/l, nitrate and phosphates 0.9mg/l and 0.35mg/l, respectively. The three fish species increase in weight with increased manure rate, with a higher value in PD treatment on C. capio record 340g, O. niloticus weighed 310g and C. gariepinus 280g over the experimental period. Fishes fed supplementary diet (control) grew bigger with highest value on C. capio (685g) O. niloticus (620g) and then C. gariepinus (526g). The differences were statistically significant (P < 0.05). The result of whole body proximate analysis indicated that various manures and rates had an irregular pattern on the protein and ash gain per 100g of fish body weight gain. The combined means for whole fish carcass protein, lipids, moisture, ash and gross energy were 11.84, 2.43, 74.63, 3.00 and 109.9 respectively. The notable exceptions were significant (p < 0.05) increases in body fat and gross energy gains in all fish species accompanied by decreases in percentages of moisture as manure rates increased. Survival percentage decreases from 80% to 70%. It is recommended to use poultry dropping as manure/feeds at the rate of 120kg/ha/week for good performances in polyculture.

Keywords: organic manure, Nile tilapia, African mud fish, common carp, proximate composition

Procedia PDF Downloads 552

Authors: Songul Aktas, Turkan Pasinlioglu, Kiymet Yesilcicek Calik

Abstract:

Introduction: Emphatic approach during labor increases both quality of care and birth satisfaction of mothers. Besides; maternal satisfaction statements and expressions about midwives who assist labor contribute to a positive birth perception and wish to give vaginal delivery again. Aim: The study aimed at investigating the effect of empathy training given to midwives on mothers’ satisfaction with midwives and their birth perception. Material/Method: This experimental study was undertaken between February 2013 and January 2014 at a public hospital in Trabzon Province. The population of the study was composed of mothers who gave vaginal delivery and the sample was composed of 222 mothers determined with power analyzes. Ethical approval and written informed consents were obtained. Mothers who were assisted by midwives during 1st, 2nd and 3rd phases of delivery and first two postpartum hours were included. Empathy training given to midwives included didactic narration, creative drama, psychodrama techniques and lasted 32 hours. The data were collected before the empathy training (BET), right after empathy training (RAET) and 8 weeks later after birth (8WLAB). Mothers were homogenous in terms of socio-demographic, obstetric characteristics. Data were collected with a questionnaire and were analyzed with Chi-square tests. Findings: Rate of mother’s satisfaction with midwives was 36.5% in BET, 81.1% in RAET and 75.7% in 8WLAB. Key mother’s satisfaction with midwives were as follows: 27.6% of mothers told that midwives were “smiling-kind” in BET, 39.6% of them in RAET and 33.7% of them in 8WLAB; 31% of mothers told that midwives were “understanding” in BET, 38.2% of them in RAET and 33.7% of them in 8WLAB; 15.7% of mothers told that midwives were “reassuring” in BET, 44.9% of them in RAET and 39.3% of them in 8WLAB;19.5% of mothers told that midwives were “encouraging and motivating” in BET, 39.8% of them in RAET and 19.8% of mothers told that midwives were “informative” in BET, 45.6% of them in RAET and 35.1% of them in 8WLAB (p<0.05). Key mother’s dissatisfaction with midwives were as follows: 55.3% of mothers told that midwives were “poorly-informed” in BET, 17% of them in RAET and 27.7% of them in 8WLAB; 56.9% of mothers told that midwives were “poorly-listening” in BET, 17.6% of them in RAET and 25.5% of them in 8WLAB; 53.2% of mothers told that midwives were “judgmental-embarrassing” in BET, 17% of them in RAET and 29.8% of them in 8WLAB; 56.2% of mothers told that midwives had “fierce facial expressions” in BET, 15.6% of them in RAET and 28.1% of them in 8WLAB. Rates of mothers’ perception that labor was “easy” were 8.1% in BET, 21.6% in RAET and 13.5% in 8WLAB and rates of mothers’ perception that labor was “very difficult and tiring” were 41.9% in BET, 5.4% in RAET and 13.5% in 8WLAB (p<0.05). Conclusion: The effect of empathy training given to midwives upon statements that described mothers’ satisfaction with midwives and their birth perception was positive. Note: This study was financially funded by TUBİTAK project with number 113S672.

Keywords: empathy training, labor perception, mother’s satisfaction with midwife, vaginal delivery

Procedia PDF Downloads 367

Authors: James Boylan, Denny Meyer, Won Sun Chen

Abstract:

Background: Globally, there is an increasing trend of road traffic deaths, reaching 1.35 million in 2016 in comparison to 1.3 million a decade ago, and overall, road traffic injuries are ranked as the eighth leading cause of death for all age groups. The reported death rate for younger drivers aged 16-19 years is almost twice the rate reported for older drivers aged 25 and above, with a rate of 3.5 road traffic fatalities per annum for every 10,000 licenses held. Telematics refers to a system with the ability to capture real-time data about vehicle usage. The data collected from telematics can be used to better assess a driver's risk. It is typically used to measure acceleration, turn, braking, and speed, as well as to provide locational information. With the Australian government creating the National Telematics Framework, there has been an increase in the government's focus on using telematics data to improve road safety outcomes. The purpose of this study is to test the hypothesis that improvements in telematics measured driving behaviour to relate to improvements in road safety attitudes measured by the Driving Behaviour Questionnaire (DBQ). Methodology: 28 participants were recruited and given a telematics device to insert into their vehicles for the duration of the study. The participant's driving behaviour over the course of the first month will be compared to their driving behaviour in the second month to determine whether feedback from telematics devices improves driving behaviour. Participants completed the DBQ, evaluated using a 6-point Likert scale (0 = never, 5 = nearly all the time) at the beginning, after the first month, and after the second month of the study. This is a well-established instrument used worldwide. Trends in the telematics data will be captured and correlated with the changes in the DBQ using regression models in SAS. Results: The DBQ has provided a reliable measure (alpha = .823) of driving behaviour based on a sample of 23 participants, with an average of 50.5 and a standard deviation of 11.36, and a range of 29 to 76, with higher scores, indicating worse driving behaviours. This initial sample is well stratified in terms of gender and age (range 19-27). It is expected that in the next six weeks, a larger sample of around 40 will have completed the DBQ after experiencing in-vehicle telematics for 30 days, allowing a comparison with baseline levels. The trends in the telematics data over the first 30 days will be compared with the changes observed in the DBQ. Conclusions: It is expected that there will be a significant relationship between the improvements in the DBQ and the trends in reduced telematics measured aggressive driving behaviours supporting the hypothesis.

Keywords: telematics, driving behavior, young drivers, driving behaviour questionnaire

Procedia PDF Downloads 102

Authors: A. Y. Purnama, N. Yasufuku

Abstract:

The bridge structure is a building that has to be maintained, especially for the elastomeric bearing. The girder of the bridge needs to be lifted upward to maintain this elastomeric bearing, that needs high cost. Nowadays, integral abutment bridges are becoming popular. The integral abutment bridge is less costly because the elastomeric bearings are eliminated, which reduces the construction cost and maintenance costs. However, when this elastomeric bearing removed, the girder movement due to environmental thermal forces directly support by pile foundation, and it needs to be considered in the design. In case of pile foundation in a stiff soil, in the top area of the pile cannot move freely due to the fixed condition by soil stiffness. Pre-bored pile system can be used to increase the flexibility of pile foundation using a pre-bored hole that filled with elastic materials, but the behavior of soil-pile interaction and soil response due to this system is still rarely explained. In this paper, an experimental study using small-scale laboratory model test conducted in a half size model. Single flexible pile model embedded in sandy soil with the pre-bored ring, which filled with the filler material. The testing box made from an acrylic glass panel as observation area of the pile shaft to monitor the displacement of the pile during the lateral loading. The failure behavior of the soil inside the pre-bored ring and around the pile shaft was investigated to determine the point of pile rotation and the movement of this point due to the pre-bored ring system along the pile shaft. Digital images were used to capture the deformations of the soil and pile foundation during the loading from the acrylic glass on the side of the testing box. The results were presented in the form of lateral load resistance charts against the pile shaft displacement. The failure pattern result also established due to the cyclic lateral loading. The movement of the rotational point was measured due to the pre-bored system filled with appropriate filler material. Based on the findings, design considerations for pre-bored pile system due to cyclic lateral loading can be introduced.

Keywords: failure behavior, pre-bored pile system, cyclic lateral loading, sandy soil

Procedia PDF Downloads 228

Authors: Hana Barak, Alex Sivan, Ariel Kushmaro

Abstract:

Microorganisms are the most diverse and abundant life forms on Earth and account for a large portion of the Earth’s biomass and biodiversity. To date though, our knowledge regarding microbial life is lacking, as it is based mainly on information from cultivated organisms. Indeed, microbiologists have borrowed from astrophysics and termed the ‘uncultured microbial majority’ as ‘microbial dark matter’. The realization of how diverse and unexplored microorganisms are, actually stems from recent advances in molecular biology, and in particular from novel methods for sequencing microbial small subunit ribosomal RNA genes directly from environmental samples termed next-generation sequencing (NGS). This has led us to use NGS that generates several gigabases of sequencing data in a single experimental run, to identify and classify environmental samples of microorganisms. In metagenomics sequencing analysis (both 16S and shotgun), sequences are compared to reference databases that contain only small part of the existing microorganisms and therefore their taxonomy assignment may reveal groups of unknown microorganisms or origins. These unknowns, or the ‘microbial sequences dark matter’, are usually ignored in spite of their great importance. The goal of this work was to develop an improved bioinformatics method that enables more complete analyses of the microbial communities in numerous environments. Therefore, NGS was used to identify previously unknown microorganisms from three different environments (industrials wastewater, Negev Desert’s rocks and water wells at the Arava valley). 16S rRNA gene metagenome analysis of the microorganisms from those three environments produce about ~4 million reads for 75 samples. Between 0.1-12% of the sequences in each sample were tagged as ‘Unassigned’. Employing relatively simple methodology for resequencing of original gDNA samples through Sanger or MiSeq Illumina with specific primers, this study demonstrates that the mysterious ‘Unassigned’ group apparently contains sequences of candidate phyla. Those unknown sequences can be located on a phylogenetic tree and thus provide a better understanding of the ‘sequences dark matter’ and its role in the research of microbial communities and diversity. Studying this ‘dark matter’ will extend the existing databases and could reveal the hidden potential of the ‘microbial dark matter’.

Keywords: bacteria, bioinformatics, dark matter, Next Generation Sequencing, unknown

Procedia PDF Downloads 252

Authors: Noreen Pulte

Abstract:

Prior to the 'Back to Sleep' Campaign in 1992, 1 of every 300 infants seen by Advanced Practice Providers had plagiocephaly. Insufficient attention is given to plagiocephaly and brachycephaly diagnoses in practice and pediatric education. In this talk, Nurse Practitioners and Pediatric Providers will be able to: (1) identify red flags associated with head shape abnormalities, (2) learn techniques they can teach parents to prevent head shape abnormalities, and (3) differentiate between plagiocephaly, brachycephaly, and craniosynostosis. The presenter is a Primary Care Pediatric Nurse Practitioner at Ann & Robert H. Lurie Children's Hospital of Chicago and the primary provider for its head shape abnormality clinics. She will help participants translate key information obtained from birth history, review of systems, and developmental history to understand risk factors for head shape abnormalities and progression of deformities. Synostotic and non-synostotic head shapes will be explained to help participants differentiate plagiocephaly and brachycephaly from synostotic head shapes. This knowledge is critical for the prompt referral of infants with craniosynostosis for surgical evaluation and correction. Rapid referral for craniosynostosis can possibly direct the patient to a minimally invasive surgical procedure versus a craniectomy. As for plagiocephaly and brachycephaly, this timely referral can also aid in a physical therapy referral if necessitated, which treats torticollis and aids in improving head shape. A well-timed referral to a head shape clinic can possibly eliminate the need for a helmet and/or minimize the time in a helmet. Practitioners will learn the importance of obtaining head measurements using calipers. The presenter will explain head calculations and how the calculations are interpreted to determine the severity of the head shape abnormalities. Severity defines the treatment plan. Participants will learn when to refer patients to a head shape abnormality clinic and techniques they should teach parents to perform while waiting for the referral appointment. The purpose, mechanics, and logistics of helmet therapy, including optimal time to initiate helmet therapy, recommended helmet wear-time, and tips for helmet therapy compliance, will be described. Case scenarios will be incorporated into the presenter's presentation to support learning. The salient points of the case studies will be explained and discussed. Practitioners will be able to immediately translate the knowledge and skills gained in this presentation into their clinical practice.

Keywords: plagiocephaly, brachycephaly, craniosynostosis, red flags

Procedia PDF Downloads 93

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

Abstract:

Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 234

Authors: Alvaro Figueira, Bruno Cabral

Abstract:

Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

Procedia PDF Downloads 120

Authors: Franco Primo Soffietti, Diego Fernando Turello, Federico Pinto

Abstract:

Shear waves that propagate through the ground impose deformations that must be taken into account in the design and assessment of buried longitudinal structures such as tunnels, pipelines, and piles. Conventional engineering approaches for seismic evaluation often rely on a Euler-Bernoulli beam models supported by a Winkler foundation. This approach, however, falls short in capturing the distortions induced when the structure is subjected to shear waves. To overcome these limitations, in the present work an analytical solution is proposed considering a Timoshenko beam and including transverse and rotational springs. The present research proposes ground springs derived as closed-form analytical solutions of the equations of elasticity including the seismic wavelength. These proposed springs extend the applicability of previous plane-strain models. By considering variations in displacements along the longitudinal direction, the presented approach ensures the springs do not approach zero at low frequencies. This characteristic makes them suitable for assessing pseudo-static cases, which typically govern structural forces in kinematic interaction analyses. The results obtained, validated against existing literature and a 3D Finite Element model, reveal several key insights: i) the cutoff frequency significantly influences transverse and rotational springs; ii) neglecting displacement variations along the structure axis (i.e., assuming plane-strain deformation) results in unrealistically low transverse springs, particularly for wavelengths shorter than the structure length; iii) disregarding lateral displacement components in rotational springs and neglecting variations along the structure axis leads to inaccurately low spring values, misrepresenting interaction phenomena; iv) transverse springs exhibit a notable drop in resonance frequency, followed by increasing damping as frequency rises; v) rotational springs show minor frequency-dependent variations, with radiation damping occurring beyond resonance frequencies, starting from negative values. This comprehensive analysis sheds light on the complex behavior of embedded longitudinal structures when subjected to shear waves and provides valuable insights for the seismic assessment.

Keywords: shear waves, Timoshenko beams, Winkler springs, sol-structure interaction

Procedia PDF Downloads 59

Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

Abstract:

Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

Procedia PDF Downloads 216

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 32

Authors: María Magdalena Méndez-González, Miguel García Rocha, Carlos Manuel Yermo De la Cruz

Abstract:

Calcium phosphate (Ca5(PO4)3(X)) is widely used in orthopedic applications and is widely used as powder and granules. However, their presence in bone is in the form of nanometric needles 60 nm in length with a non-stoichiometric phase of apatite contains CO3-2, Na+, OH-, F-, and other ions in a matrix of collagen fibers. The crystal size, morphology control and interaction with cells are essential for the development of nanotechnology. The structural results of calcium phosphate, synthesized by chemical precipitation with crystal size of 22.85 nm are presented in this paper. The calcium phosphate powders were analyzed by X-ray diffraction, energy dispersive spectroscopy (EDS), infrared spectroscopy and FT-IR transmission electron microscopy. Network parameters, atomic positions, the indexing of the planes and the calculation of FWHM (full width at half maximum) were obtained. The crystal size was also calculated using the Scherer equation d (hkl) = cλ/βcosѲ. Where c is a constant related to the shape of the crystal, the wavelength of the radiation used for a copper anode is 1.54060Å, Ѳ is the Bragg diffraction angle, and β is the width average peak height of greater intensity. Diffraction pattern corresponding to the calcium phosphate called hydroxyapatite phase of a hexagonal crystal system was obtained. It belongs to the space group P63m with lattice parameters a = 9.4394 Å and c = 6.8861 Å. The most intense peak is obtained 2Ѳ = 31.55 (FWHM = 0.4798), with a preferred orientation in 121. The intensity difference between the experimental data and the calculated values is attributable to the temperature at which the sintering was performed. The intensity of the highest peak is at angle 2Ѳ = 32.11. The structure of calcium phosphate obtained was a hexagonal configuration. The intensity changes in the peaks of the diffraction pattern, in the lattice parameters at the corners, indicating the possible presence of a dopant. That each calcium atom is surrounded by a tetrahedron of oxygen and hydrogen was observed by infrared spectra. The unit cell pattern corresponds to hydroxyapatite and transmission electron microscopic crystal morphology corresponding to the hexagonal phase with a preferential growth along the c-plane was obtained.

Keywords: structure, nanoparticles, calcium phosphate, metallurgical and materials engineering

Procedia PDF Downloads 499

Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

Abstract:

When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

Procedia PDF Downloads 37

Authors: Adnan Celik

Abstract:

Entrepreneurs are people who attempt to do a business and do not hesitate to do so. They are involved in the production of economic goods and services through factors of production. They also find the financial resources necessary for production and the markets where the production will be evaluated. After all, they create economic values. The main function of the entrepreneur in contemporary societies is to realize innovations. From this point, the power of the modern entrepreneur is based on her/his capacity to innovate and transform his innovations into tangible commercial products. In this context, the concept of an entrepreneur is used to mean the person or persons who constantly innovate. Successful entrepreneurs take on the role of the locomotive in the development of their countries. They support economic development with their activities. In addition to production and marketing activities, it also has important contributions to employment. Along with the development of the country, they also try to make the income distribution more balanced. Especially developed country entrepreneurs intensely perform the following functions; “to produce new goods and services or to increase the quality and quality of known goods and services; ability to develop and apply new production methods; establishing new organizations in the industry; reach new markets; to find new sources from which raw materials and similar materials can be obtained”. Entrepreneurs who fully implement business functions are easier to achieve economic efficiency. Thus, they provide great advantages to the business and the national economy. Successful entrepreneurs are people who make money by creating economic values. These revenues are; on the one hand, it is distributed to individuals in the business as wages, premiums, or dividends; It is also used in the growth of companies. Thus, employees, managers, entrepreneurs and the whole country can benefit greatly. In the least developed countries, the guiding effect of traditional value patterns on individuals' attitudes and behaviors varies depending on the socio-economic characteristics of individuals. It is normal for an entrepreneur with a low level of education, who was brought up in a traditional structure, to behave in accordance with traditional value patterns. In fact, this is the primary problem of all countries in the development effort. The solution to this problem will be possible by giving the necessary importance to the social dimension as well as the technical dimension of development. This study mainly focuses on the importance of entrepreneurship for the national economy. This issue has been handled separately in terms of developed and least developed countries. As a result of the study, entrepreneurship suggestions were made, especially to least developed countries, with the goal of national economy and development.

Keywords: entrepreneur, entrepreneurship, national economy, entrepreneurship in developed and least developed countries

Procedia PDF Downloads 135

Authors: Silvia A. Magalhaes, Vasco P. De Freitas, Jose L. Alexandre

Abstract:

Most of the European literature concerning energy efficiency and thermal comfort of dwellings assumes permanent heating and focuses on energy-saving measures. European National regulations are designed for those permanent comfort conditions. On the other hand, very few studies focus on the effect of the improvement measures in comfort reduction, for free-floating conditions or intermittent heating, in fuel poverty vulnerable countries. In Portugal, only 21% of the household energy consumptions (and 10% of the cost) are spent in space heating, while, on average European bills, this value rises to 67%. The mild climate, but mainly fuel poverty and cultural background, justifies these low heating practices. This study proposes a “passive discomfort” index definition, considering free-floating temperatures or with intermittent heating profiles (more realistic conditions), putting the focus on comfort rather than energy consumption (which is low for these countries). The aim is to compare both energy (regarding the legal framework of national regulation) and comfort (considering realistic conditions of use) to identify some correlation. It was developed an experimental campaign of indoor thermal conditions in a 19th building located in Porto with several apartments. One dwelling was chosen as a case study to carry out a sensitivity analysis. The results are discussed comparing both theoretical energy consumption (energy rates from national regulation) and discomfort (new index defined), for different insulation thicknesses, orientations, and intermittent heating profiles. The results show that the different passive options (walls insulation and glazing options) have a small impact on winter discomfort, which is always high for low heating profiles. Moreover, it was shown that the insulation thickness on walls has no influence, and the minimum insulation thickness considered is enough to achieve the same impact on discomfort reduction. Plus, for these low heating profiles, other conditions are critical, as the orientation. Finally, there isn’t an unequivocal relation between the energy label and the discomfort index. These and other results are surprising when compared with the most usual approaches, which assume permanent heating.

Keywords: dwellings in historical buildings, low-heating countries, mild climates, thermal comfort

Procedia PDF Downloads 141

Authors: Nurhajati Hakim, Kiki Amalia, A. Agustian, H. Hermansah, Y. Yulnafatmawita

Abstract:

Ultisols require greater amounts of fertilizer application compared to other soils and susceptible to erosion. Unfortunately, the price of synthetic fertilizers has increased over time during the years, making them unaffordable for most Indonesian farmers. While terrace technique to control erosion very costly.Over the last century, efforts to reduce reliance on synthetic agro-chemicals fertilizers and erosion control have recently focused on Tithonia diversifolia as a fertilizer alternative, and as hedgerow plant to control erosion. Generally known by its common name of tree marigold or Mexican sunflower, this plant has attracted considerable attention for its prolific production of green biomass, rich in nitrogen, phosphorous and potassium (NPK). In pot experiments has founded some microbial such as Mycorrhizal, Azotobacter, Azospirillum, phosphate solubilizing bacterial (PSB) and fungi (PSF) are expected to play an important role in biomass production and high nutrient uptake of this plant. This issue of importance was pursued further in the following investigation in field condition. The aim of this study was to determine the type of microbial combination suitable for Tithonia cultivation as hedgerow plants in Ultisols which have higher biomass production and nutrient content, and decline soil erosion. The field experiment was conducted with 6 treatments in a randomized block design (RBD) using 3 replications. The treatments were: Tithonia rhizosphere without microbial inoculated (A); Inokulanted by Mycorrhizal + Azotobacter + Azospirillium (B); Mycorrhizal + PSF (C); Mycorrhizal + PSB(D); Mycorrhizal + PSB + PSF(E);and without hedgerow Tithonia (F).The microbial substrates were inoculated into the Tithonia rhizosphere in the nursery. The young Tithonia plants were then planted as hedgerow on Ultisols in the experimental field for 8 months, and pruned once every 2 months. Soil erosion were collected every rainy time. The differences between treatments were statistically significant by HSD test at the 95% level of probability. The result showed that treatment C (mycorrhizal + PSB) was the most effective, and followed by treatment D (mycorrhizal + PSF) in producing higher Tithonia biomass about 8 t dry matter 2000 m-2 ha-1 y-1 and declined soil erosion 71-75%.

Keywords: hedgerow tithonia, microbial inoculants, organic fertilizer, soil erosion control

Procedia PDF Downloads 353

Authors: A. O. Omonona, T. A. Jarikre

Abstract:

Increase specialization in agriculture and use of pesticides may inadvertently cause ecosystem degradation and eventually loss of biodiversity. The populations of numerous wildlife species have undergone a precipitous decline. Many of these problems have been attributed directly to habitat loss and over exploitation resulting from unregulated pesticide uses. Carbendazim a broad spectrum benzimidazole fungicide and a metabolite of benomyl, is used to control plant disease in cereals and fruit. The effect of carbendazim exposure and the ameliorative effect of tocopherol (vitamin E) were assessed on African giant rat AGR. Hematological, biochemical and histological changes were used to determine the health condition of the animals exposed to pesticide. Sixteen AGR were stabilized, weighed and then divided into four experimental groups (A to D). Two groups were pretreated with vitamin. Group A was exposed to carbendazim only, B- carbendazim + vitamin, C- vitamin only, and D- blank (control). Packed cell volume PCV was estimated by the microhematocrit method, Leucocyte and Platelet counts were determined using the hemocytometric method. Cholinesterase (AchE) and markers of oxidative stress were quantified, and tissue changes examined microscopically. There were no behavioral changes observed in the animals, but there was a decrease in body weight and abortion after 23 days of exposure to carbendazim. There was significant differences in the packed cell volume, the hemoglobin concentration and the red blood cell counts (p < 0.05). The increases in malonyl aldehyde MDA was significant (p < 0.05) in the pesticide intoxicated rats compared to control. Vitamin E supplementation reduced MDA level significantly (p < 0.05). There was a sharp remarkable decrease in acetylcholinesterase levels in the pesticide intoxicated rats (p < 0.05). Vitamin E supplementation normalise the AchE levels comparable to that in control. Grossly, the vital organs appeared normal in the pesticide exposed and control groups except moderate pulmonary congestion. Microscopically, there was severe diffuse hepatocellular swelling in carbendazim exposed group. The severity of hepatocellular injury was reduced in the rats with vitamin E. This study ascertained the toxic effect of carbendazim and antioxidative properties of vitamins in the Africa giant rat.

Keywords: African giant rat, antioxidant, carbendazim, pesticides, toxicity

Procedia PDF Downloads 358

Authors: Jaspreet Kaur, Parminder Nain, Vipin Saini, Sumitra Dahiya

Abstract:

Elaeocarpus sphaericus has been traditionally used in the Indian traditional medicine system for the treatment of stress, anxiety, depression, palpitation, epilepsy, migraine and lack of concentration. The study was investigated to evaluate the neurological potential such as anxiolytic, muscle relaxant and sedative activity of methanolic extract of Elaeocarpus sphaericus stem bark (MEESSB) in mice. Preliminary phytochemical screening and acute oral toxicity of MEESSB was carried out by using standard methods. The anxiety was induced by employing Elevated Plus-Maze (EPM), Light and Dark Test (LDT), Open Field Test (OFT) and Social Interaction test (SIT). The motor coordination and sedative effect was also observed by using actophotometer, rota-rod apparatus and ketamine-induced sleeping time, respectively. Animals were treated with different doses of MEESSB (i.e.100, 200, 400 and 800 mg/kg orally) and diazepam (2 mg/kg i.p) for 21 days. Brain neurotransmitters like dopamine, serotonin and nor-epinephrine level were estimated by validated methods. Preliminary phytochemical analysis of the extract revealed the presence of tannins, phytosterols, steroids and alkaloids. In the acute toxicity studies, MEESSB was found to be non-toxic and with no mortality. In anxiolytic studies, the different doses of MEESSB showed a significant (p<0.05) effect on EPM and LDT. In OFT and SIT, a significant (p<0.05) increase in ambulation, rearing and social interaction time was observed. In the case of motor coordination activity, the MEESSB does not cause any significant effect on the latency to fall off from the rotarod bar as compared to the control group. Moreover, no significant effects on ketamine-induced sleep latency and total sleeping time induced by ketamine were observed. Results of neurotransmitter estimation revealed the increased concentration of dopamine, whereas the level of serotonin and nor-epinephrine was found to be decreased in the mice brain, with MEESSB at dose 800 mg/kg only. The study has validated the folkloric use of the plant as an anxiolytic in Indian traditional medicine while also suggesting potential usefulness in the treatment of stress and anxiety without causing sedation.

Keywords: anxiolytic, behavior experiments, brain neurotransmitters, elaeocarpus sphaericus

Procedia PDF Downloads 173

Authors: Joanna Estkowska

Abstract:

Sensory integration methods are very useful and improve daily functioning autistic and mentally disabled children. Autism is a neurobiological disorder that impairs one's ability to communicate with and relate to others as well as their sensory system. Children with autism, even highly functioning kids, can find it difficult to process language with surrounding noise or smells. They are hypersensitive to things we can ignore such as sight, sounds and touch. Adolescents with highly functioning autism or Asperger Syndrome can study Science and Math but the social aspect is difficult for them. Nature science is an area of study that attracts many of these kids. It is a systematic field in which the children can focus on a small aspect. If you follow these rules you can come up with an expected result. Sensory integration program and systematic classroom observation are quantitative methods of measuring classroom functioning and behaviors from direct observations. These methods specify both the events and behaviors that are to be observed and how they are to be recorded. Our students with and without autism attended the lessons in the classroom of nature science in the school and in the laboratory of University of Science and Technology in Bydgoszcz. The aim of this study is investigation the effects of sensory integration methods in teaching to students with autism. They were observed during experimental lessons in the classroom and in the laboratory. Their physical characteristics, sensory dysfunction, and behavior in class were taken into consideration by comparing their similarities and differences. In the chemistry classroom, every autistic student is paired with a mentor from their school. In the laboratory, the children are expected to wear goggles, gloves and a lab coat. The chemistry classes in the laboratory were held for four hours with a lunch break, and according to the assistants, the children were engaged the whole time. In classroom of nature science, the students are encouraged to use the interactive exhibition of chemical, physical and mathematical models constructed by the author of this paper. Our students with and without autism attended the lessons in those laboratories. The teacher's goals are: to assist the child in inhibiting and modulating sensory information and support the child in processing a response to sensory stimulation.

Keywords: autism spectrum disorder, science education, sensory integration techniques, student with special educational needs

Procedia PDF Downloads 190

Authors: Dina Magnaye

Abstract:

In the Philippines, the performance of the agricultural sector is gauged through crop productivity and returns from farm production rather than the biodiversity in the agricultural ecosystem. Agricultural development hinges on the overall goal of increasing productivity through intensive agriculture, monoculture system, utilization of high yielding varieties in plants, and genetic upgrading in animals. This merits an analysis of the role of agro-biodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. These enterprises conserve biodiversity while equitably sharing production income in the utilization of biological resources. The study aims to determine how community-based pro-biodiversity enterprises become instrumental in local climate change adaptation and agro-biodiversity conservation as input to local green economic development planning. It also involves an assessment of the role of agrobiodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. The perceptions of the local community members both in urban and upland rural areas on community-based pro-biodiversity enterprises were evaluated. These served as a basis in developing a planning modality that can be mainstreamed in the management of local green economic enterprises to benefit the environment, provide local income opportunities, conserve species diversity, and sustain environment-friendly farming systems and practices. The interviews conducted with organic farmer-owners, entrepreneur-organic farmers, and organic farm workers revealed that pro-biodiversity enterprise such as organic farming involved the cyclic use of natural resources within the carrying capacity of a farm; recognition of the value of tradition and culture especially in the upland rural area; enhancement of socio-economic capacity; conservation of ecosystems in harmony with nature; and climate change mitigation. The suggested planning modality for community-based pro-biodiversity enterprises for a green economy encompasses four (4) phases to include community resource or capital asset profiling; stakeholder vision development; strategy formulation for sustained enterprises; and monitoring and evaluation.

Keywords: agro-biodiversity, agro-biodiversity conservation, local green economy, organic farming, pro-biodiversity enterprise

Procedia PDF Downloads 359