Search results for: evaluation questionnaire for parents

Authors: Fouzia Brihmat

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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

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Authors: Torsten Hermanns, Thoufik Al Khawli, Wolfgang Schulz

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In laser cutting as well as in long pulsed laser drilling of metals, it can be demonstrated that the ablation shape (the shape of cut faces respectively the hole shape) that is formed approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from the ultrashort pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in laser cutting and long pulse drilling of metals is identified, its underlying mechanism numerically implemented, tested and clearly confirmed by comparison with experimental data. In detail, there now is a model that allows the simulation of the temporal (pulse-resolved) evolution of the hole shape in laser drilling as well as the final (asymptotic) shape of the cut faces in laser cutting. This simulation especially requires much less in the way of resources, such that it can even run on common desktop PCs or laptops. Individual parameters can be adjusted using sliders – the simulation result appears in an adjacent window and changes in real time. This is made possible by an application-specific reduction of the underlying ablation model. Because this reduction dramatically decreases the complexity of calculation, it produces a result much more quickly. This means that the simulation can be carried out directly at the laser machine. Time-intensive experiments can be reduced and set-up processes can be completed much faster. The high speed of simulation also opens up a range of entirely different options, such as metamodeling. Suitable for complex applications with many parameters, metamodeling involves generating high-dimensional data sets with the parameters and several evaluation criteria for process and product quality. These sets can then be used to create individual process maps that show the dependency of individual parameter pairs. This advanced simulation makes it possible to find global and local extreme values through mathematical manipulation. Such simultaneous optimization of multiple parameters is scarcely possible by experimental means. This means that new methods in manufacturing such as self-optimization can be executed much faster. However, the software’s potential does not stop there; time-intensive calculations exist in many areas of industry. In laser welding or laser additive manufacturing, for example, the simulation of thermal induced residual stresses still uses up considerable computing capacity or is even not possible. Transferring the principle of reduced models promises substantial savings there, too.

Keywords: asymptotic ablation shape, interactive process simulation, laser drilling, laser cutting, metamodeling, reduced modeling

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Authors: Siti Noor Syahirah Mohd Sabri

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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

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Authors: Soheila Sadeghi

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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

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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

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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

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

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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

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Authors: Hailemariam Getachew, John Sandi, Isata Dumbuya, Patricia Efe.Azikiwe, Evaline Nginge, Moses Mugisha, Eseoghene Dase, Foday Mandaray, Grace Moore

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Enhanced recovery after cesarean section (ERAC) is a standardized peri- operative care program that comprises the multidisciplinary team's collective efforts working in collaboration throughout the peri-operative period with the principal goal to improve quality of surgical care, decrease surgical related complications, and increasing patient satisfaction. Objective: The main objective of this project is to improve the implementation of enhanced recovery after cesarean section at Koidu Government hospital. Identified gap: Even though the hospital is providing comprehensive maternal and child care service, there are gaps in the implementation of ERAC. According to our survey, we found that there is low (13.3%) utilization of WHO surgical safety checklist, only limited (15.9%) patients get opioid free analgesia, pain was not recorded as a vital sign, there is no standardized checklist for hand over to and from Post Anesthesia care Unit(PACU). Furthermore, there is inconsistent evidence based post-operative care and there is no local consensus protocol and guideline as well. Implementation plan: we aimed at designing standardized protocol, checklist and guideline, provide training, build staff capacity, document pain as vital sign, perform regional analgesia, and provide evidence based post-operative care, monitoring and evaluation. Result: Data from 389 cesarean mothers showed that, Utilization of the WHO surgical safety check list found to be 95%, and pain assessment and documentation was done for all surgical patients. Oral feeding, ambulation and catheter removal was performed as per the ERAC standard for all patients. Postoperative complications drastically decreased from 13.6% to 8.1%. While, the rate of readmission was kept below 1%. Furthermore, the duration of hospital stay decreased from 4.64 days to 3.12 days. Conclusion The successful implementation of ERAC protocols demonstrates through this Quality Improvement Project that, the effectiveness of the protocols in improving recovery and patient outcome following cesarean section.

Keywords: cesarean delivery, enhanced recovery, quality improvement, patient outcome

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Authors: Andréanne Gagné, Jo Anni Joncas, Éric Tendon

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Teaching in vocational training requires an excellent mastery of the trade being taught, but also solid professional skills in pedagogy. Teachers are typically recruited on the basis of their trade expertise, and they do not necessarily have training or experience in pedagogy. In order to counter this lack, the Ministry of Education (Québec, Canada) requires them to complete a 120-credit university program to obtain their teaching certificate. They must complete this training in addition to their teaching duties. This training was rarely planned in the teacher’s life course, and each teacher approaches it differently: some are enthusiastic, but many feel reluctant discouragement and even frustration at the idea of committing to a training program lasting an average of 10 years to completion. However, Quebec is experiencing an unprecedented shortage of teachers, and the perseverance of vocational teachers in their careers requires special attention because of the conditions of their specific integration conditions. Our research examines the perceptions that vocational teachers in training have of their academic experience in pre-service teaching. It differs from previous research in that it focuses on the influence of the academic experience on the teaching employment experience. The goal is that by better understanding the university experience of teachers in vocational education, we can identify support strategies to support their school experience and their teaching. To do this, the research is based on the theoretical framework of the sociology of experience, which allows us to study the way in which these “teachers-students” give meaning to their university program in articulation with their jobs according to three logics of action. The logic of integration is based on the process of socialization, where the action is preceded by the internalization of values, norms, and cultural models associated with the training context. The logic of strategy refers to the usefulness of this experience where the individual constructs a form of rationality according to his objectives, resources, social position, and situational constraints. The logic of subjectivation refers to reflexivity activities aimed at solving problems and making choices. These logics served as a framework for the development of an online questionnaire. Three hundred respondents, newly enrolled in an undergraduate teaching program (bachelor's degree in vocational education), expressed themselves about their academic experience. This paper relates qualitative data (open-ended questions) subjected to an interpretive repertory analysis approach to descriptive data (closed-ended questions) that emerged. The results shed light on how the respondents perceive themselves as teachers and students, their perceptions of university training and the support offered, and the place that training occupies in their professional path. Indeed, their professional and academic paths are inextricably linked, and it seems essential to take them into account simultaneously to better meet their needs and foster the development of their expertise in pedagogy. The discussion focuses on the strengths and limitations of university training from the perspective of the logic of action. The results also suggest support strategies that can be implemented to better support the integration and retention of student teachers in professional education.

Keywords: teacher, vocational training, pre-service training, academic experience

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Authors: Laura Pytel, Georg Baumann, Gregor Gstrein, Corina Klug

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Premium cars often coat the instrument panels with a bilaminate consisting of a soft foam and a plastic skin. The coating is torn open during the passenger airbag deployment under high strain rates. Characterizing and simulating the top coat layer is crucial for predicting the attenuation that delays the airbag deployment, effecting the design of the restrain system and to reduce the demand of simulation adjustments through expensive physical component testing.Up to now, bilaminates used within cars either have been modelled by using a two-dimensional shell formulation for the whole coating system as one which misses out the interaction of the two layers or by combining a three-dimensional formulation foam layer with a two-dimensional skin layer but omitting the foam in the significant parts like the expected tear line area and the hinge where high compression is expected. In both cases, the properties of the coating causing the attenuation are not considered. Further, at present, the availability of material information, as there are failure dependencies of the two layers, as well as the strain rate of up to 200 1/s, are insufficient. The velocity of the passenger airbag flap during an airbag shot has been measured with about 11.5 m/s during first ripping; the digital image correlation evaluation showed resulting strain rates of above 1500 1/s. This paper provides a high strain rate material characterization of a bilaminate consisting of a thin polypropylene foam and a thermoplasctic olefins (TPO) skin and the creation of validated material models. With the help of a Split Hopkinson tension bar, strain rates of 1500 1/s were within reach. The experimental data was used to calibrate and validate a more physical modelling approach of the forced ripping of the bilaminate. In the presented model, the three-dimensional foam layer is continuously tied to the two-dimensional skin layer, allowing failure in both layers at any possible position. The simulation results show a higher agreement in terms of the trajectory of the flaps and its velocity during ripping. The resulting attenuation of the airbag deployment measured by the contact force between airbag and flaps increases and serves usable data for dimensioning modules of an airbag system.

Keywords: bilaminate ripping behavior, High strain rate material characterization and modelling, induced material failure, TPO and foam

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Authors: Collen Tebogo Masilo, Erik Schmikl

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The demand for the DataMiner product by customers has presented a great challenge for the vendor in Skyline Communications in deploying its limited resources in the form of human resources, financial resources, and office space, to achieve high organisational performance in all its international operations. The rapid growth of the organisation has been unable to efficiently support its existing customers across the globe, and provide services to new customers, due to the limited number of approximately one hundred employees in its employ. The combined descriptive and explanatory case study research methods were selected as research design, making use of a survey questionnaire which was distributed to a sample of 100 respondents. A sample return of 89 respondents was achieved. The sampling method employed was non-probability sampling, using the convenient sampling method. Frequency analysis and correlation between the subscales (the four themes) were used for statistical analysis to interpret the data. The investigation was conducted into mechanisms that can be deployed to balance the high demand for products and the limited production capacity of the company’s Belgian operations across four aspects: demand management strategies, capacity management strategies, communication methods that can be used to align a sales management department, and reward systems in use to improve employee performance. The conclusions derived from the theme ‘demand management strategies’ are that the company is fully aware of the future market demand for its products. However, there seems to be no evidence that there is proper demand forecasting conducted within the organisation. The conclusions derived from the theme 'capacity management strategies' are that employees always have a lot of work to complete during office hours, and, also, employees seem to need help from colleagues with urgent tasks. This indicates that employees often work on unplanned tasks and multiple projects. Conclusions derived from the theme 'communication methods used to align sales management department with operations' are that communication is not good throughout the organisation. This means that information often stays with management, and does not reach non-management employees. This also means that there is a lack of smooth synergy as expected and a lack of good communication between the sales department and the projects office. This has a direct impact on the delivery of projects to customers by the operations department. The conclusions derived from the theme ‘employee reward systems’ are that employees are motivated, and feel that they add value in their current functions. There are currently no measures in place to identify unhappy employees, and there are also no proper reward systems in place which are linked to a performance management system. The research has made a contribution to the body of research by exploring the impact of the four sub-variables and their interaction on the challenges of organisational productivity, in particular where an organisation experiences a capacity problem during its growth stage during tough economic conditions. Recommendations were made which, if implemented by management, could further enhance the organisation’s sustained competitive operations.

Keywords: high demand for products, high organisational performance, limited production capacity, limited resources

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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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 238

Authors: Alvaro Figueira, Bruno Cabral

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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

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Authors: Muziwandile Luthuli

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Although numerous factors predicting adherence to antiretroviral therapy (ART) among people living with HIV/AIDS (PLWHA) have been broadly studied on both regional and global level, up-to-date adherence of patients to ART remains an overarching, dynamic and multifaceted problem that needs to be investigated over time and across various contexts. There is a rarity of empirical data in the literature on interactive mechanisms by which psychosocial factors influence adherence to ART among PLWHA within the South African context. Therefore, this study was designed to investigate the relationship between depression, HIV stigma, and adherence to ART among adult patients living with HIV at a tertiary hospital in Durban, South Africa, and the mediating roles of self-efficacy and social support. The health locus of control theory and the social support theory were the underlying theoretical frameworks for this study. Using a cross-sectional research design, a total of 201 male and female adult patients aged between 18-75 years receiving ART at a tertiary hospital in Durban, KwaZulu-Natal were sampled, using time location sampling (TLS). A self-administered questionnaire was employed to collect the data in this study. Data were analysed through SPSS version 27. Several statistical analyses were conducted in this study, namely univariate statistical analysis, correlational analysis, Pearson’s chi-square analysis, cross-tabulation analysis, binary logistic regression analysis, and mediational analysis. Univariate analysis indicated that the sample mean age was 39.28 years (SD=12.115), while most participants were females 71.0% (n=142), never married 74.2% (n=147), and most were also secondary school educated 48.3% (n=97), as well as unemployed 65.7% (n=132). The prevalence rate of participants who had high adherence to ART was 53.7% (n=108), and 46.3% (n=93) of participants had low adherence to ART. Chi-square analysis revealed that employment status was the only statistically significant socio-demographic influence of adherence to ART in this study (χ2 (3) = 8.745; p < .033). Chi-square analysis showed that there was a statistically significant difference found between depression and adherence to ART (χ2 (4) = 16.140; p < .003), while between HIV stigma and adherence to ART, no statistically significant difference was found (χ2 (1) = .323; p >.570). Binary logistic regression indicated that depression was statistically associated with adherence to ART (OR= .853; 95% CI, .789–.922, P < 001), while the association between self-efficacy and adherence to ART was statistically significant (OR= 1.04; 95% CI, 1.001– 1.078, P < .045) after controlling for the effect of depression. However, the findings showed that the effect of depression on adherence to ART was not significantly mediated by self-efficacy (Sobel test for indirect effect, Z= 1.01, P > 0.31). Binary logistic regression showed that the effect of HIV stigma on adherence to ART was not statistically significant (OR= .980; 95% CI, .937– 1.025, P > .374), but the effect of social support on adherence to ART was statistically significant, only after the effect of HIV stigma was controlled for (OR= 1.017; 95% CI, 1.000– 1.035, P < .046). This study promotes behavioral and social change effected through evidence-based interventions by emphasizing the need for additional research that investigates the interactive mechanisms by which psychosocial factors influence adherence to ART. Depression is a significant predictor of adherence to ART. Thus, to alleviate the psychosocial impact of depression on adherence to ART, effective interventions must be devised, along with special consideration of self-efficacy and social support. Therefore, this study is helpful in informing and effecting change in health policy and healthcare services through its findings

Keywords: ART adherence, depression, HIV/AIDS, PLWHA

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

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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

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Authors: Haftay Abraha Tadesse

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Background: Staphylococcus is a genus of worldwide distributed bacteria correlated to several infectious of different sites in human and animals. They are among the most important causes of infection that are associated with the consumption of contaminated food. Objective: The objective of this study was to determine the isolates, antimicrobial susceptibility patterns and public health significance for Staphylococcus aureus in raw meat from butchery and abattoir houses of Mekelle, Northern Ethiopia. Methodology: A cross-sectional study was conducted from April to October 2019. Sociodemographic data and public health significance were collected using predesigned questionnaire. The raw meat samples were collected aseptically in the butchery and abattoir houses and transported using ice box to Mekelle University, College of Veterinary Sciences for isolating and identification of Staphylococcus aureus. Antimicrobial susceptibility tests were determined by disc diffusion method. Data obtained were cleaned and entered in to STATA 22.0 and logistic regression model with odds ratio were calculated to assess the association of risk factors with bacterial contamination. P-value < 0.05 was considered as statistically significant. Results: In present study, 88 out of 250 (35.2%) were found to be contamination with Staphylococcus aureus. Among the raw meat specimens to be positivity rate of Staphylococcus aureus were 37.6% (n=47) and (32.8% (n=41), butchery and abattoir houses, respectively. Among the associated risk factories not using gloves reduces risk was found to (AOR=0.222; 95% CI: 0.104-0.473), Strict Separation b/n clean & dirty (AOR= 1.37; 95% CI: 0.66-2.86) and poor habit of hand washing (AOR=1.08; 95%CI: 0.35-3.35) were found to be statistically significant and ha ve associated with Staphylococcus aureus contamination. All isolates thirty sevevn of Staphyloco ccus aureus were checked displayed (100%) sensitive to doxycycline, trimethoprim, gentamicin, sulphamethoxazole, amikacin, CN, Co trimoxazole and nitrofurantoi. whereas the showed resistance of cefotaxime (100%), ampicillin (87.5%), Penicillin (75%), B (75%), and nalidixic acid (50%) from butchery houses. On the other hand, all isolates of Staphylococcus aur eu isolate 100% (n= 10) showed sensitive chloramphenicol, gentamicin and nitrofurantoin whereas the showed 100% resistance of Penicillin, B, AMX, ceftriaxone, ampicillin and cefotaxime from abattoirs houses. The overall multi drug resistance pattern for Staphylococcus aureus were 90% and 100% of butchery and abattoirs houses, respectively. Conclusion: 35.3% Staphylococcus aureus isolated were recovered from the raw meat samples collected from the butchery and abattoirs houses. More has to be done in the developed of hand washing behavior, and availability of safe water in the butchery houses to reduce burden of bacterial contamination. The results of the present finding highlight the need to implement protective measures against the levels of food contamination and alternative drug options. The development of antimicrobial resistance is nearly always as a result of repeated therapeutic and/or indiscriminate use of them. Regular antimicrobial sensitivity testing helps to select effective antibiotics and to reduce the problems of drug resistance development towards commonly used antibiotics. Key words: abattoir houses, antimicrobial resistance, butchery houses, Ethiopia,

Keywords: abattoir houses, antimicrobial resistance, butchery houses, Ethiopia, staphylococcus aureuse, MDR

Procedia PDF Downloads 73

Authors: Soheila Sadeghi

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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

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Authors: A. Moghadasein, M. Ghasemi, S. Fazelifar

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Aim: Chemerin is a novel adipokine that play an important role in regulating lipid metabolism and abiogenesis. Chemerin is dependent on autocrine and paracrine signals for the differentiation and maturation of fat cells; it also regulates glucose uptake in fat cells and stimulates lipolysis. It has been reported that in adipocytes, chemerin enhances the insulin-stimulated glucose and causes the phosphorylation of tyrosine in Insulin receptor substrate. According to the studies, Chemerin may increase insulin sensitivity in adipose tissue and is largely associated with Body mass index, triglycerides, and blood pressure in those with normal glucose tolerance. There is limited information available regarding the effect of exercise training on serum chemerin concentrations. The purpose of this study was to investigate the effect of endurance training on serum chemerin levels and lipids of plasma in overweight women. Methodology: This study was a quasi-experimental research with a pre-post test design. After required examination and verification of high pressure by the physician, 22 obese subjects (age: 35.64±5.55 yr, weight: 75.62±9.30 kg, body mass index: 32.4±1.6 kg/m2) were randomly assigned to aerobic training (n= 12) and control (n= 12) groups. Participants completed a questionnaire indicating the lack of sports history during the past six months, the lack of anti-hypertension drugs use, hormone therapy, cardiovascular problems, and complete stoppage of menstrual cycle. Aerobic training was performed 3 times weekly for 8 weeks. Resting levels of chemerin plasma, metabolic parameters were measured prior to and after the intervention. The control group did not participate in any training program. In this study, ethical considerations included the complete description of the objectives to the study participants, ensuring the confidentiality of their information. Kolmogorov-Smirnov and Levin test were used for determining the normal distribution of data and homogeneity of variances, respectively. Analyze of variance with repeated measure were used to investigate the changes in the intra-group and the differences in inter-group of variables. Statistical operations were performed using SPSS 16 and the significance level of the tests was considered at P < 0.05. Results: After an 8 week aerobic training, levels of chemerin plasma were significantly decreased in aerobic trained group when compared with their control groups (p < 0.05).Concurrently, levels of HDL-c were significantly decreased (p < 0.05) whereas, levels of cholesterol, TG and LDL-c, showed no significant changes (p > 0.05). No significant correlations between chemerin levels and weight loss were observed in subjects with overweight women. Conclusion: The present study demonstrated, 8 weeks aerobic training, reduced serum chemerin concentrations in overweight women. Whereas, aerobic training exercise programmers affected the lipid profile response of obese subjects differently. However further research is warranted in order to unravel the molecular mechanism for the range of responses and the role of serum chemerin.

Keywords: chemerin, aerobic training, lipid profile, obese women

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

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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

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Authors: Adnan Celik

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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

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Authors: Mariamawit T. Belete

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Cereal production and marketing are the means of livelihood for millions of households in Ethiopia. However, cereal production is constrained by technical and socio-economic factors. Among the technical factors, cereal crop diseases are the major contributing factors to the low yield. The aim of this research is to develop an integration of data mining and knowledge based system for sorghum anthracnose disease diagnosis that assists agriculture experts and development agents to make timely decisions. Anthracnose diagnosing systems gather information from Melkassa agricultural research center and attempt to score anthracnose severity scale. Empirical research is designed for data exploration, modeling, and confirmatory procedures for testing hypothesis and prediction to draw a sound conclusion. WEKA (Waikato Environment for Knowledge Analysis) was employed for the modeling. Knowledge based system has come across a variety of approaches based on the knowledge representation method; case-based reasoning (CBR) is one of the popular approaches used in knowledge-based system. CBR is a problem solving strategy that uses previous cases to solve new problems. The system utilizes hidden knowledge extracted by employing clustering algorithms, specifically K-means clustering from sampled anthracnose dataset. Clustered cases with centroid value are mapped to jCOLIBRI, and then the integrator application is created using NetBeans with JDK 8.0.2. The important part of a case based reasoning model includes case retrieval; the similarity measuring stage, reuse; which allows domain expert to transfer retrieval case solution to suit for the current case, revise; to test the solution, and retain to store the confirmed solution to the case base for future use. Evaluation of the system was done for both system performance and user acceptance. For testing the prototype, seven test cases were used. Experimental result shows that the system achieves an average precision and recall values of 70% and 83%, respectively. User acceptance testing also performed by involving five domain experts, and an average of 83% acceptance is achieved. Although the result of this study is promising, however, further study should be done an investigation on hybrid approach such as rule based reasoning, and pictorial retrieval process are recommended.

Keywords: sorghum anthracnose, data mining, case based reasoning, integration

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

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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

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Authors: Dina Magnaye

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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 362

Authors: Flavien Marteau, Pedro Affonso Nóbrega, Pascal Biwole, Nicolas Autrusson, Iona De Bievre, Christian Beauger

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Effective water management is essential for the optimal performance of fuel cells. For this reason, many vehicle systems use a membrane humidifier, a passive device that humidifies the air before the cathode inlet. Although they offer good performance, humidifiers are voluminous, costly, and fragile, hence the desire to find an alternative. Direct water injection could be an option, although this method lacks maturity. It consists of injecting liquid water as a spray in the dry heated air coming out from the compressor. This work focuses on the evaluation of direct water injection and its performance compared to the membrane humidifier selected as a reference. Two architectures were experimentally tested to humidify an industrial 2 kW short stack made up of 20 cells of 150 cm² each. For the reference architecture, the inlet air is humidified with a commercial membrane humidifier. For the direct water injection architecture, a pneumatic nozzle was selected to generate a fine spray in the air flow with a Sauter mean diameter of about 20 μm. Initial performance was compared over the entire range of current based on polarisation curves. Then, the influence of various parameters impacting water management was studied, such as the temperature, the gas stoichiometry, and the water injection flow rate. The experimental results obtained confirm the possibility of humidifying the fuel cell using direct water injection. This study, however shows the limits of this humidification method, the mean cell voltage being significantly lower in some operating conditions with direct water injection than with the membrane humidifier. The voltage drop reaches 30 mV per cell (4 %) at 1 A/cm² (1,8 bara, 80 °C) and increases in more demanding humidification conditions. It is noteworthy that the heat of compression available is not enough to evaporate all the injected liquid water in the case of DWI, resulting in a mix of liquid and vapour water entering the fuel cell, whereas only vapour is present with the humidifier. Variation of the injection flow rate shows that part of the injected water is useless for humidification and seems to cross channels without reaching the membrane. The stack was successfully humidified thanks to direct water injection. Nevertheless, our work shows that its implementation requires substantial adaptations and may reduce the fuel cell stack performance when compared to conventional membrane humidifiers, but opportunities for optimisation have been identified.

Keywords: cathode humidification, direct water injection, membrane humidifier, proton exchange membrane fuel cell

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Authors: Abdallah M. Fayed

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The study area is located in south Darb El Arbaieen, western desert of Egypt. It occupies the area between latitudes 22° 00/ and 22° 30/ North and Longitudes 29° 30/ and 30° 00/ East, from southern border of Egypt to the area north Bir Kuraiym and from the area East of East Owienat to the area west Tushka district, its area about 2750 Km2. The famous features; southern part of Darb El Arbaieen road, G Baraqat El Scab El Qarra, Bir Dibis, Bir El Shab and Bir Kuraiym, Interpretation of soil stratification shows layers that are related to Quaternary and Upper-Lower Cretaceous eras. It is dissected by a series of NE-SW striking faults. The regional groundwater flow direction is in SW-NE direction with a hydraulic gradient is 1m / 2km. Mathematical model program has been applied for evaluation of groundwater potentials in the main Aquifer –Nubian Sandstone- in the area of study and Remote sensing technique is considered powerful, accurate and saving time in this respect. These techniques are widely used for illustrating and analysis different phenomenon such as the new development in the desert (land reclamation), residential development (new communities), urbanization, etc. The major issues concerning water development objective of this work is to determine the new development areas in western desert of Egypt during the period from 2003 to 2015 using remote sensing technique, the impacts of the present and future development have been evaluated by using the two-dimensional numerical groundwater flow Simulation Package (visual modflow 4.2). The package was used to construct and calibrate a numerical model that can be used to simulate the response of the aquifer in the study area under implementing different management alternatives in the form of changes in piezometric levels and salinity. Total period of simulation is 100 years. After steady state calibration, two different scenarios are simulated for groundwater development. 21 production wells are installed at the study area and used in the model, with the total discharge for the two scenarios were 105000 m3/d, 210000 m3/d. The drawdown was 11.8 m and 23.7 m for the two scenarios in the end of 100 year. Contour maps for water heads and drawdown and hydrographs for piezometric head are represented. The drawdown was less than the half of the saturated thickness (the safe yield case).

Keywords: remote sensing, management of aquifer systems, simulation modeling, western desert, South Darb El Arbaieen

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Authors: Tamrat Girma Biru

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Multidrug-resistant tuberculosis (MDR-TB) is defined as resistant to at least Refampicin and Isoniazed: the most two power full TB drugs. It is a leading cause of high rates of morbidity and mortality, and increasing psychosocial challenges to patients, especially when co-infected with Human Immunodeficiency Virus (HIV). Ethiopia faces the highest rates of MDR-TB infection in the world. Objectives: The main objective of this study was to identify the psychosocial challenges of MDR-TB patients, to investigate the extent of the psychosocial challenges on (self-esteem, depression, and stigma) that MDR-TB patients encounter, to examine whether there is a sex difference in experiencing psychosocial challenges and assess the counseling needs of MDR-TB patients. Methodology: A cross-sectional study was conducted at St. Peter TB Specialized Hospital, Addis Ababa on 40 patients (25 males and 15 females) who are hospitalized for treatment. The patients were identified by using purposive sampling and made fill a questionnaire measuring their level of self-esteem, depression and stigma. Besides, data were collected from 16 participants, 28 care providers and 8 guardians, using semi-structured interview. The obtained data were analyzed using SPSS statistical program, descriptive statistics, independent t-test, and qualitative description. Results and Discussion: The results of the study showed that the majority (80%) of the respondents had suffered psychological challenges and social discriminations. Thus, the significance of MDR-TB and its association with HIV/AIDS problems is considered. Besides the psychosocial challenges, various aggravating factors such as length of treatment, drug burden and insecurity in economy together highly challenges the life of patients. In addition, 60% of participants showed low level of self-esteem. The patients also reported that they experienced high self-stigma and stigma by other members of the society. The majority of the participants (75%) showed moderate and severe level of depression. In terms of sex there is no difference between the mean scores of males and females in the level of depression and stigmatization by others and by themselves. But females showed lower level of self-esteem than males. The analysis of the t-test also shows that there were no statistically significant sex difference on the level of depression and stigma. Based on the qualitative data MDR-TB patients face various challenges in their life sphere such as: Psychological (depression, low self value, lowliness, anxiety), social (stigma, isolation from social relations, self-stigmatization,) and medical (drug side effect, drug toxicity, drug burden, treatment length, hospital stays). Recommendations: Based on the findings of this study possible recommendations were forwarded: develop and extend MDR-TB disease awareness creation through by media (printing and electronic), school net TB clubs, and door to door community education. Strengthen psychological wellbeing and social relationship of MDR-TB patients using proper and consistent psychosocial support and counseling. Responsible bodies like Ministry of Health (MOH) and its stakeholders and Non Governmental Organizations (NGOs) need to assess the challenges of patients and take measures on this pressing issue.

Keywords: psychosocial challenges, counseling, multi-drug resistant tuberculosis (MDR-TB), tuberculosis therapy

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Authors: Oladele V. Adeniyi, B. Longo-Mbenza, Daniel T. Goon

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Background: Lipids are often low or in the normal ranges and controversial in the atherogenesis among Black Africans. The effect of the severity of obesity on some traditional and novel cardiovascular disease risk factors is unclear before and after a diet and exercise maintenance programme among obese black South Africans with type 2 diabetes mellitus (T2DM). Therefore, this study aimed to identify the risk factors to discriminate obesity classes among patients with T2DM before and after a diet and exercise programme. Methods: This interventional cohort of Black South Africans with T2DM was followed by a very – low calorie diet and exercise programme in Mthatha, between August and November 2013. Gender, age, and the levels of body mass index (BMI), blood pressure, monthly income, daily frequency of meals, blood random plasma glucose (RPG), serum creatinine, total cholesterol (TC), triglycerides (TG), LDL –C, HDL – C, Non-HDL, ratios of TC/HDL, TG/HDL, and LDL/HDL were recorded. Univariate analysis (ANOVA) and multivariate discriminant analysis were performed to separate obesity classes: normal weight (BMI = 18.5 – 24.9 kg/m2), overweight (BMI = 25 – 29.9 kg/m2), obesity Class 1 (BMI = 30 – 34.9 kg/m2), obesity Class 2 (BMI = 35 – 39.9 kg/m2), and obesity Class 3 (BMI ≥ 40 kg/m2). Results: At the baseline (1st Month September), all 327 patients were overweight/obese: 19.6% overweight, 42.8% obese class 1, 22.3% obese class 2, and 15.3% obese class 3. In discriminant analysis, only systolic blood pressure (SBP with positive association) and LDL/HDL ratio (negative association) significantly separated increasing obesity classes. At the post – evaluation (3rd Month November), out of all 327 patients, 19.9%, 19.3%, 37.6%, 15%, and 8.3% had normal weight, overweight, obesity class 1, obesity class 2, and obesity class 3, respectively. There was a significant negative association between serum creatinine and increase in BMI. In discriminant analysis, only age (positive association), SBP (U – shaped relationship), monthly income (inverted U – shaped association), daily frequency of meals (positive association), and LDL/HDL ratio (positive association) classified significantly increasing obesity classes. Conclusion: There is an epidemic of diabesity (Obesity + T2DM) in this Black South Africans with some weight loss. Further studies are needed to understand positive or negative linear correlations and paradoxical curvilinear correlations between these markers and increase in BMI among black South African T2DM patients.

Keywords: atherogenic dyslipidaemia, dietary interventions, obesity, south africans

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Authors: V. I. Kozyavkin, L. F. Shestopalova, T. B. Voloshyn

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Introduction: Rehabilitation of children with Autism is the issue of the day in psychiatry and neurology. It is attributed to constantly increasing quantity of autistic children - Autistic Spectrum Disorders (ASD) Existing rehabilitation approaches in treatment of children with Autism improve their medico- social and social- psychological adjustment. Experience of treatment for different kinds of Autistic disorders in International Clinic of Rehabilitation (ICR) reveals the necessity of complex intensive approach for healing this malady and wider implementation of a Kozyavkin method for treatment of children with ASD. Methods: 19 children aged from 3 to 14 years were examined. They were diagnosed ‘Autism’ (F84.0) with comorbid neurological pathology (from pyramidal insufficiency to para- and tetraplegia). All patients underwent rehabilitation in ICR during two weeks, where INRS approach was used. INRS included methods like biomechanical correction of the spine, massage, physical therapy, joint mobilization, wax-paraffin applications. They were supplemented by art- therapy, ergotherapy, rhythmical group exercises, computer game therapy, team Olympic games and other methods for improvement of motivation and social integration of the child. Estimation of efficacy was conducted using parent’s questioning and done twice- on the onset of INRS rehabilitation course and two weeks afterward. For efficacy assessment of rehabilitation of autistic children in ICR standardized tool was used, namely Autism Treatment Evaluation Checklist (ATEC). This scale was selected because any rehabilitation approaches for the child with Autism can be assessed using it. Results: Before the onset of INRS treatment mean score according to ATEC scale was 64,75±9,23, it reveals occurrence in examined children severe communication, speech, socialization and behavioral impairments. After the end of the rehabilitation course, the mean score was 56,5±6,7, what indicates positive dynamics in comparison to the onset of rehabilitation. Generally, improvement of psychoemotional state occurred in 90% of cases. Most significant changes occurred in the scope of speech (16,5 before and 14,5 after the treatment), socialization (15.1 before and 12,5 after) and behavior (20,1 before and 17.4 after). Conclusion: As a result of INRS rehabilitation course reduction of autistic symptoms was noted. Particularly improvements in speech were observed (children began to spell out new syllables, words), there was some decrease in signs of destructiveness, quality of contact with the surrounding people improved, new skills of self-service appeared. The prospect of the study is further, according to evidence- based medicine standards, deeper examination of INRS and assessment of its usefulness in treatment for Autism and ASD.

Keywords: intensive neurophysiological rehabilitation system (INRS), international clinic od rehabilitation, ASD, rehabilitation

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Authors: Sushil Kumar Singh, Kumar Rahul, Vivek Tewarson, Sarvesh Kumar, Shobhit Kumar

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Objective: Rheumatic mitral stenosis continues to be a major public health problem in developing countries. When the left atrium (LA) is unable to fill the left ventricle (LV) at normal LA pressures due to impaired relaxation and impaired compliance, diastolic dysfunction occurs. The assessment of left ventricular (LV) diastolic function and filling pressures is of clinical importance to identify underlying cardiac disease, its treatment, and to assess prognosis. 2D echocardiography can detect diastolic dysfunction with excellent sensitivity and minimal risk when compared to the gold standard of invasive pressure-volume measurements. Material and Method: This was a one-year study consisting of twenty-nine patients of isolated rheumatic severe mitral stenosis. Data was analyzed preoperative and post operative (at one month follow-up). Transthoracic 2D echocardiographic parameters of the diastolic function are transmitral flow, pulmonary venous flow, mitral annular tissue doppler, and color M-mode doppler. In our study, mitral valve orifice area, ejection fraction, deceleration time, E/A-wave, E/E’-wave, myocardial performance index of left ventricle (Tei index ), and Mitral inflow propagation velocity were included for echocardiographic evaluation. The statistical analysis was performed on SPSS Version 15.0 statistical analysis software. Result: Twenty-nine patients underwent successful closed mitral commissurotomy for isolated mitral stenosis. The outcome measures were observed pre-operatively and at one-month follow-up. The majority of patients were in NYHA grade III (69.0%) in the preoperative period, which improved to NYHA grade I (48.3%) after closed mitral commissurotomy. Post-surgery mitral valve area increased from 0.77 ± 0.13 to 2.32 ± 0.26 cm, ejection fraction increased from 61.38 ± 4.61 to 64.79 ± 3.22. There was a decrease in deceleration time from 231.55 ± 49.31 to 168.28 ± 14.30 ms, E/A ratio from 1.70 ± 0.54 from 0.89 ± 0.39, E/E’ ratio from 14.59 ± 3.34 to 8.86 ± 3.03. In addition, there was improvement in TIE index from 0.50 ± 0.03 to 0.39 ± 0.06 and mitral inflow propagation velocity from 47.28 ± 3.71 to 57.86 ± 3.19 cm/sec. In peri-operative and follow-up, there was no incidence of severe mitral regurgitation (MR). There was no thromboembolic incident and no mortality.

Keywords: closed mitral valvotomy, mitral stenosis, open mitral commissurotomy, balloon mitral valvotomy

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Authors: Mahboobe Daneshvar

Abstract:

Introduction: Clinical studies on the effect of systemic conditions on periodontal diseases have shown that some systemic deficiencies may provide grounds for the onset of periodontal diseases. One of these systemic problems is osteoporosis, which may be a risk factor for the onset and exacerbation of periodontitis. This study tends to evaluate periodontal indices in osteoporotic menopausal women and compare them with healthy controls. Materials and Methods: In this case-control study, participants included 45-75-year-old menopausal women referred to rheumatology wards of the Khatamolanbia Clinic and Shahid Sadoughi Hospital in Yazd; Their bone density was determined by DEXA-scan and by imaging the femoral-lumbar bone. Thirty patients with osteoporosis and 30 subjects with normal BMD were selected. Then, informed consent was obtained for participation in the study. During the clinical examinations, tooth loss (TL), plaque index (PI), gingival recession, pocket probing depth (PPD), clinical attachment loss (CAL), and tooth mobility (TM) were measured to evaluate the periodontal status. These clinical examinations were performed to determine the periodontal status by catheter, mirror and probe. Results: During the evaluation, there was no significant difference in PPD, PI, TM, gingival recession, and CAL between case and control groups (P-value>0.05); that is, osteoporosis has no effect on the above factors. These periodontal factors are almost the same in both healthy and patient groups. In the case of missing teeth, the following results were obtained: the mean of missing teeth was 22.173% of the total teeth in the case group and 18.583% of the total teeth in the control group. In the study of the missing teeth in the case and control groups, there was a significant relationship between case and control groups (P-value = 0.025). Conclusion: In fact, since periodontal disease is multifactorial and microbial plaque is the main cause, osteoporosis is considered a predisposing factor in exacerbation or persistence of periodontal disease. In patients with osteoporosis, usually pathological fractures, hormonal changes, and aging lead to reduced physical activity and affect oral health, which leads to the manifestation of periodontal disease. But this disease increases tooth loss by changing the shape and structure of bone trabeculae and weakening them. Osteoporosis does not seem to be a deterministic factor in the incidence of periodontal disease, since it affects bone quality rather than bone quantity.

Keywords: plaque index, Osteoporosis, tooth mobility, periodontal packet

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Authors: Sumedha Weerasekara, Morgan Miles, Mark Morrison, Branka Krivokapic-Skoko

Abstract:

This paper is aimed at exploring the interrelationships among entrepreneurial ecosystem factors and entrepreneurial cognition in regional and metropolitan ecosystems. Entrepreneurial ecosystem factors examined include: culture, infrastructure, access to finance, informal networks, support services, access to universities, and the depth and breadth of the talent pool. Using a multivariate approach we explore the impact of these ecosystem factors or elements on entrepreneurial cognition. In doing so, the existing body of knowledge from the literature on entrepreneurial ecosystem and cognition have been blended to explore the relationship between entrepreneurial ecosystem factors and cognition in a way not hitherto investigated. The concept of the entrepreneurial ecosystem has received increased attention as governments, universities and communities have started to recognize the potential of integrated policies, structures, programs and processes that foster entrepreneurship activities by supporting innovation, productivity and employment growth. The notion of entrepreneurial ecosystems has evolved and grown with the advancement of theoretical research and empirical studies. Importance of incorporating external factors like culture, political environment, and the economic environment within a single framework will enhance the capacity of examining the whole systems functionality to better understand the interaction of the entrepreneurial actors and factors within a single framework. The literature on clusters underplays the role of entrepreneurs and entrepreneurial management in creating and co-creating organizations, markets, and supporting ecosystems. Entrepreneurs are only one actor following a limited set of roles and dependent upon many other factors to thrive. As a consequence, entrepreneurs and relevant authorities should be aware of the other actors and factors with which they engage and rely, and make strategic choices to achieve both self and also collective objectives. The study uses stratified random sampling method to collect survey data from 12 different regions in regional and metropolitan regions of NSW, Australia. A questionnaire was administered online among 512 Small and medium enterprise owners operating their business in selected 12 regions in NSW, Australia. Data were analyzed using descriptive analyzing techniques and partial least squares - structural equation modeling. The findings show that even though there is a significant relationship between each and every entrepreneurial ecosystem factors, there is a weak relationship between most entrepreneurial ecosystem factors and entrepreneurial cognition. In the metropolitan context, the availability of finance and informal networks have the largest impact on entrepreneurial cognition while culture, infrastructure, and support services having the smallest impact and the talent pool and universities having a moderate impact on entrepreneurial cognition. Interestingly, in a regional context, culture, availability of finance, and the talent pool have the highest impact on entrepreneurial cognition, while informal networks having the smallest impact and the remaining factors – infrastructure, universities, and support services have a moderate impact on entrepreneurial cognition. These findings suggest the need for a location-specific strategy for supporting the development of entrepreneurial cognition.

Keywords: academic achievement, colour response card, feedback

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