Search results for: diesel working process

Authors: Piyush Swami, Bijaya Ketan Panigrahi, Sneh Anand, Manvir Bhatia, Tapan Gandhi

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Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications.

Keywords: electroencephalogram (EEG), epilepsy, ictal patterns, empirical mode decomposition

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Authors: Mengjie Liu

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This essay aims at responding to the Kant arguments on how to fit the self-caused subject into the deterministic object which follows the natural laws. This essay mainly adopts the approach abstracted from Fichte’s “Wissenshaftslehre” (Doctrine of Science) to picture a possible solution to the conciliation of Kantian dualism. The Fichte approach is based on the unity of the theoretical and practical reason, which can be understood as a philosophical abstraction from ordinary experience combining both subject and object. This essay will discuss the general Kantian dualism problem and Fichte’s unity approach in the first part. Then the essay will elaborate on the achievement of this unity of the subject and object through Fichte’s “the I posits itself” process in the second section. The following third section is related to the ethical unity of subject and object based on the Fichte approach. The essay will also discuss the limitation of Fichte’s approach from two perspectives: (1) the theoretical possibility of the existence of the pure I and (2) Schelling’s statement that the Absolute I is a result rather than the originating act. This essay demonstrates a possible approach to unifying the subject and object supported by Fichte’s “Absolute I” and ethical theories and also points out the limitations of Fichte’s theories.

Keywords: Fichte, identity, Kantian dualism, Wissenshaftslehre

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Authors: Adilah Shariff, Radin Hakim, Nurhayati Abdullah

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Utilisation of biomass feedstock for biochar has received increasing attention because of their potential for carbon sequestration and soil amendment. The aim of this study is to investigate the characteristics of rubber wood as a biomass feedstock for biochar via slow pyrolysis process. This was achieved by using proximate, ultimate, and thermogravimetric analysis (TGA) as well as heating value, pH and lignocellulosic determination. Rubber wood contains 4.13 mf wt.% moisture, 86.30 mf wt.% volatile matter, 0.60 mf wt.% ash content, and 13.10 mf wt.% fixed carbon. The ultimate analysis shows that rubber wood consists of 44.33 mf wt.% carbon, 6.26 mf wt.% hydrogen, 19.31 mf wt.% nitrogen, 0.31 mf wt.% sulphur, and 29.79 mf wt.% oxygen. The higher heating value of rubber wood is 22.5 MJ/kg, and its lower heating value is 21.2 MJ/kg. At 27 °C, the pH value of rubber wood is 6.83 which is acidic. The lignocellulosic analysis revealed that rubber wood composition consists of 2.63 mf wt.% lignin, 20.13 mf wt.% cellulose, and 65.04 mf wt.% hemicellulose. The volatile matter to fixed carbon ratio is 6.58. This led to a biochar yield of 25.14 wt.% at 500 °C. Rubber wood is an environmental friendly feedstock due to its low sulphur content. Rubber wood therefore is a suitable and a potential feedstock for biochar production via slow pyrolysis.

Keywords: biochar, biomass, rubber wood, slow pyrolysis

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Authors: E. A. Tkachenko, E. M. Rogova, A. S. Osipenko

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The problems of technological development for the Russian Federation take on special significance in the context of modernization of the production base. The complexity of the position of the Russian economy is that it cannot be attributed fully to developing ones. Russia is a strong industrial power that has gone through the processes of destructive de-industrialization in the conditions of changing its economic and political structure. The need to find ways for re-industrialization is not a unique task for the economies of industrially developed countries. Under the influence of production outsourcing for 20 years, the industrial potential of leading economies of the world was regressed against the backdrop of the ascent of China, a new industrial giant. Therefore, methods, tools, and techniques utilized for industrial renaissance in EU may be used to achieve a technological leap in the Russian Federation, especially since the temporary gap of 5-7 years makes it possible to analyze best practices and use those technological transfer tools that have shown the greatest efficiency. In this article, methods of technological transfer are analyzed, the role of technological audit is justified, and factors are analyzed that influence the successful process of commercialization of technologies.

Keywords: technological transfer, productivity, technological audit, commercialization of technologies

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Authors: Shuen-Tai Wang, Yu-Ching Lin

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With more and more workflow systems adopting cloud as their execution environment, it presents various challenges that need to be addressed in order to be utilized efficiently. This paper introduces a method for resource provisioning based on our previous research of dynamic allocation and its pipeline processes. We present an abstraction for workload scheduling in which independent tasks get scheduled among various available processors of distributed computing for optimization. We also propose an integrated web-based workflow designer by taking advantage of the HTML5 technology and chaining together multiple tools. In order to make the combination of multiple pipelines executing on the cloud in parallel, we develop a script translator and an execution engine for workflow management in the cloud. All information is known in advance by the workflow engine and tasks are allocated according to the prior knowledge in the repository. This proposed effort has the potential to provide support for process definition, workflow enactment and monitoring of workflow processes. Users would benefit from the web-based system that allows creation and execution of pipelines without scripting knowledge.

Keywords: workflow systems, resources provisioning, workload scheduling, web-based, workflow engine

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Authors: Sinead Impey, Gaye Stephens, Declan O’Sullivan

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Mitigating organisational knowledge loss presents challenges for knowledge managers. Expert knowledge is embodied in people and captured in ‘routines, processes, practices and norms’ as well as in the paper system. These knowledge stores have limitations in so far as they make knowledge diffusion beyond geography or over time difficult. However, technology could present a potential solution by facilitating the capture and management of expert knowledge in a codified and sharable format. Before it can be digitised, however, the knowledge of healthcare experts must be captured. Methods: As a first step in a larger project on this topic, a scoping review was conducted to identify how expert healthcare knowledge is captured digitally. The aim of the review was to identify current healthcare knowledge capture practices, identify gaps in the literature, and justify future research. The review followed a scoping review framework. From an initial 3,430 papers retrieved, 22 were deemed relevant and included in the review. Findings: Two broad approaches –direct and indirect- with themes and subthemes emerged. ‘Direct’ describes a process whereby knowledge is taken directly from subject experts. The themes identified were: ‘Researcher mediated capture’ and ‘Digital mediated capture’. The latter was further distilled into two sub-themes: ‘Captured in specified purpose platforms (SPP)’ and ‘Captured in a virtual community of practice (vCoP)’. ‘Indirect’ processes rely on extracting new knowledge using artificial intelligence techniques from previously captured data. Using this approach, the theme ‘Generated using artificial intelligence methods’ was identified. Although presented as distinct themes, some papers retrieved discuss combining more than one approach to capture knowledge. While no approach emerged as superior, two points arose from the literature. Firstly, human input was evident across themes, even with indirect approaches. Secondly, a range of challenges common among approaches was highlighted. These were (i) ‘Capturing an expert’s knowledge’- Difficulties surrounding capturing an expert’s knowledge related to identifying the ‘expert’ say from the very experienced and how to capture their tacit or difficult to articulate knowledge. (ii) ‘Confirming quality of knowledge’- Once captured, challenges noted surrounded how to validate knowledge captured and, therefore, quality. (iii) ‘Continual knowledge capture’- Once knowledge is captured, validated, and used in a system; however, the process is not complete. Healthcare is a knowledge-rich environment with new evidence emerging frequently. As such, knowledge needs to be reviewed, updated, or removed (redundancy) as appropriate. Although some methods were proposed to address this, such as plausible reasoning or case-based reasoning, conclusions could not be drawn from the papers retrieved. It was, therefore, highlighted as an area for future research. Conclusion: The results described two broad approaches – direct and indirect. Three themes were identified: ‘Researcher mediated capture (Direct)’; ‘Digital mediated capture (Direct)’ and ‘Generated using artificial intelligence methods (Indirect)’. While no single approach was deemed superior, common challenges noted among approaches were: ‘capturing an expert’s knowledge’, ‘confirming quality of knowledge’, and ‘continual knowledge capture’. However, continual knowledge capture was not fully explored in the papers retrieved and was highlighted as an important area for future research. Acknowledgments: This research is partially funded by the ADAPT Centre under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

Keywords: expert knowledge, healthcare, knowledge capture and knowledge management

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: V. Sobotková, B. Šarapatka, M. Dumbrovský, J. Uhrová, M. Bednář

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The paper deals with the influence of implemented conservation measures on the quality of surface water resources. Recently, a new process of complex land consolidation in the Czech Republic has provided a unique opportunity to improve the quality of the environment and sustainability of crop production by means of better soil and water conservation. The most important degradation factor in our study area in the Hubenov drinking water reservoir catchment basin was water erosion together with loss of organic matter. Hubenov Reservoir water resources were monitored for twenty years (1990–2010) to collect water quality data for nitrate nitrogen (N-NO3-), total P, and undissolved substances. Results obtained from measurements taken before and after land consolidation indicated a decrease in the linear trend of N-NO3- and total P concentrations, this was achieved through implementation of conservation measures limiting soil degradation in the Hubenov reservoir catchment area.

Keywords: complex land consolidation, degradation, land use, soil and water conservation, surface water resources

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Authors: Muhammad Usman

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Pattern extraction has been done in past to extract hidden and interesting patterns from large datasets. Recently, advancements are being made in these techniques by providing the ability of multi-level mining, effective dimension reduction, advanced evaluation and visualization support. This paper focuses on reviewing the current techniques in literature on the basis of these parameters. Literature review suggests that most of the techniques which provide multi-level mining and dimension reduction, do not handle mixed-type data during the process. Patterns are not extracted using advanced algorithms for large datasets. Moreover, the evaluation of patterns is not done using advanced measures which are suited for high-dimensional data. Techniques which provide visualization support are unable to handle a large number of rules in a small space. We present a theoretical model to handle these issues. The implementation of the model is beyond the scope of this paper.

Keywords: association rule mining, data mining, data warehouses, visualization of association rules

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Authors: Jessica J. Gillies

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Awareness of the tragedy that is human trafficking has increased exponentially over the past two decades. The four pillars widely recognised as global solutions to the problem are prevention, prosecution, protection, and partnership between government and non-government organisations. While ‘sex-trafficking’ initially received major attention, this focus has shifted to other industries that conceal broader experiences of exploitation. However, within the regions of focus for this study, namely Australia and Thailand, trafficking for the purpose of sexual exploitation remains the commonly uncovered narrative of criminal justice investigations. In these regions anti-trafficking action is characterised by government-led prevention and prosecution efforts; whereas protection and reintegration practices have received criticism. Typically, non-government organisations straddle the critical chasm between policy and practice; therefore, they are perfectly positioned to contribute valuable experiential knowledge toward understanding how both sectors can support survivors in the post-trafficking experience. The aim of this research is to inform improved partnerships throughout government and non-government post-trafficking services by illuminating gaps in protection and reintegration initiatives. This research will explore government and non-government responses to human trafficking in Thailand and Australia, in order to understand how meaning is constructed in this context and how the construction of meaning effects survivors in the post-trafficking experience. A qualitative, three-stage methodology was adopted for this study. The initial stage of enquiry consisted of a discursive analysis, in order to deconstruct the broader discourses surrounding human trafficking. The data included empirical papers, grey literature such as publicly available government and non-government reports, and anti-trafficking policy documents. The second and third stages of enquiry will attempt to further explore the findings of the discourse analysis and will focus more specifically on protection and reintegration in Australia and Thailand. Stages two and three will incorporate process observations in government and non-government survivor support services, and semi-structured interviews with employees and volunteers within these settings. Two key findings emerged from the discursive analysis. The first exposed conflicting feminist arguments embedded throughout anti-trafficking discourse. Informed by conflicting feminist discourses on sex-work, a discursive relationship has been constructed between sex-industry policy and anti-trafficking policy. In response to this finding, data emerging from the process observations and semi-structured interviews will be interpreted using a feminist theoretical framework. The second finding progresses from the construction in the first. The discursive construction of sex-trafficking appears to have had influence over perceptions of the legitimacy of survivors, and therefore the support they receive in the post-trafficking experience. For example; women who willingly migrate for employment in the sex-industry, and on arrival are faced with exploitative conditions, are not perceived to be deserving of the same support as a woman who is not coerced, but rather physically forced, into such circumstances, yet both meet the criteria for a victim of human trafficking. The forthcoming study is intended to contribute toward building knowledge and understanding around the implications of the construction of legitimacy; and contextualise this in reference to government led protection and reintegration support services for survivors in the post-trafficking experience.

Keywords: Australia, government, human trafficking, non-government, reintegration, Thailand

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Authors: F. Esfandyari Darabad, Z. Samadi

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The volume of runoff caused by rainfall in the river basin has enticed the researchers in the fields of the water management resources. In this study, first of the hydrological data such as the rainfall and discharge of the Khiyav river basin of Meshkin city in the northwest of Iran collected and then the process of analyzing and reconstructing has been completed. The soil conservation service (scs) has developed a method for calculating the runoff, in which is based on the curve number specification (CN). This research implemented the following model in the Khiyav river basin of Meshkin city by the GIS techniques and concluded the following fact in which represents the usage of weight model in calculating the curve numbers that provides the possibility for the all efficient factors which is contributing to the runoff creation such as; the geometric characteristics of the basin, the basin soil characteristics, vegetation, geology, climate and human factors to be considered, so an accurate estimation of runoff from precipitation to be achieved as the result. The findings also exposed the accident-prone areas in the output of the Khiyav river basin so it was revealed that the Khiyav river basin embodies a high potential for the flood creation.

Keywords: curve number, khiyav river basin, runoff estimation, SCS

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Authors: Upal Barua

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The risk of severe crashes at high-speed right-turns at intersections is a major safety concern these days. The application of a smart right-turn at an intersection is increasing day by day to address is an issue. The design, ‘Smart Right-turn’ consists of a narrow-angle of channelization at approximately 70°. This design increases the cone of vision of the right-tuning drivers towards the crossing pedestrians as well as traffic on the cross-road. As part of the Safety Improvement Program in Austin Transportation Department, several smart right-turns were constructed at high crash intersections where high-speed right-turns were found to be a contributing factor. This paper features the state of the art techniques applied in planning, engineering, designing and construction of this smart right-turn, key factors driving the success, and lessons learned in the process. This paper also presents the significant crash reductions achieved from the application of this smart right-turn design using Empirical Bayes method. The result showed that smart right-turns can reduce overall right-turn crashes by 43% and severe right-turn crashes by 70%.

Keywords: smart right-turn, intersection, cone of vision, empirical Bayes method

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Authors: Aleksandar Hatzivelkos

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The term ”compromise” is mostly used casually within the social choice theory. It is usually used as a mere result of the social choice function, and this omits its deeper meaning and ramifications. This paper is based on a mathematical model for the description of a compromise as a version of the Sorites paradox. It introduces a formal definition of d-measure of divergence from a compromise and models a notion of compromise that is often used only colloquially. Such a model for vagueness phenomenon, which lies at the core of the notion of compromise enables the introduction of new mathematical structures. In order to maximize compromise, different methods can be used. In this paper, we explore properties of a social welfare function TdM (from Total d-Measure), which is defined as a function which minimizes the total sum of d-measures of divergence over all possible linear orderings. We prove that TdM satisfy strict Pareto principle and behaves well asymptotically. Furthermore, we show that for certain domain restrictions, TdM satisfy positive responsiveness and IIIA (intense independence of irrelevant alternatives) thus being equivalent to Borda count on such domain restriction. This result gives new opportunities in social choice, especially when there is an emphasis on compromise in the decision-making process.

Keywords: borda count, compromise, measure of divergence, minimization

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Authors: Kaggwa Abdul, Chi-Chuan Wang

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This research is based on R-1234ze that is considered to substitute R-134a due to its low global warming potential in a microfin tube with outer diameter 9.52 mm, number of fins 70, and fin height 0.17 mm. In comparison, a smooth tube with similar geometries was used to study pressure drop and heat transfer coefficients related to the two fluids. The microfin tube was brazed inside a stainless steel tube and heated electrically. T-type thermocouples used to measure the temperature distribution during the phase change process. The experimental saturation temperatures and refrigerant mass velocities varied from 10 – 20°C and 50 – 300 kg/m2s respectively. The vapor quality from 0.1 to 0.9, and heat flux ranged from 5 – 11kW/m2. The results showed that heat transfer performance of R-134a in both microfin and smooth tube was better than R-1234ze especially at mass velocities above G = 50 kg/m2s. However, at low mass velocities below G = 100 kg/m2s R-1234ze yield better heat transfer coefficients than R-134a. The pressure gradient of R-1234ze was markedly higher than that of R-134a at all mass flow rates.

Keywords: R-1234ze and R-134a, horizontal flow boiling, pressure drop, heat transfer coefficients, micro-fin and smooth tubes

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Authors: Yang L. Frances

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Women's health and wellness has been becoming an increasingly important issue in mass media in the age of globalization. In this context, the current research focuses on comparing the construction of healthy bodies in women's health magazines of China and U.S. Trends Health in China and Women's Health in U.S are chosen. Textual analysis and in depth interviews are combined to examine how the healthy bodies are constructed in two magazines through discursive strategies. The interviews with the Deputy Editorial Director, Creative Director and Senior Visual Design of two magazines are undertaken to make the further comparisons. In both Trends Health and Women's Health, women's subjectivity is realized in the construction of ideal healthy body; nevertheless in the process of constructing healthy body, the disciplinary practices imposed on women's bodies are different in two magazines. This paper argues that women's health magazines in both China and America provide an alternative discourse to speak their voices on the one hand, but on the other hand, Women's Health and Trends Health construct the healthy body through disparate disciplinary practices because of the different socio-cultural contexts in two societies.

Keywords: healthy body, women's health magazines, Foucault, textual analysis

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Authors: Hafiz Muhammad Umer Aslam, Mohammad Rafiq Khan

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This techno-economic study reports the feasibility of generating thermoelectric power from municipal solid waste (MSW) of Kasur City by incineration process. The data was gathered from different establishments of Kasur, through appropriate permission from their heads, and processed to design different alternative projects for installation of a thermal power plant in the city of Kasur. A technique of discounted cash flow was used to evaluate alternative projects so that their Benefit to Cost Ratio, Net Present Value, Internal Rate of Return and Payback Period can be determined. The study revealed that Kasur City currently consumes 18MWh electricity and generates 179 tons/day MSW. The generated waste has the ability to produce 2.1MWh electricity at the cost of USD 0.0581/unit with an expenditure of USD 3,907,692 as initial fixed investment of forming about 1/7th of consumption of Kasur. The cost from this source, when compared to current rate of electricity in Pakistan (USD 0.1346), is roughly half.

Keywords: Kasur City, resource recovery, thermoelectric power, waste management

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Ali Zaimi

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Citizen participation in local government is an opportunity given to citizens and government to increase communication between them, create public support for local government plans and most important grow public trust in government. Also, the citizens’ involvement in the political process is an important part of democracy. This study aims to define the strategies for increasing citizen participation in local governance and concentrated in two important mechanisms such as participatory budget and public policy councils. Three strategies that promote more effective citizen involvement in local governance are understanding and using formal institutions of power, collaboration of citizens’ groups and governments officials to jointly formulate programs plans, electing and appointing local officials. A unique aspect of citizen participation to operate effectively is the transparency of government and the inclusion of actors into decision-making. The citizen engagement in local governance enhances accountability and problem solving, promote more inclusive and cohesive communities and enlarge the quality and quantity of initiatives made by communities.

Keywords: accountability, citizen participation, democracy, government

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Authors: Kuei-Hao Li, Eden Huang

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Motivation suggests the willingness one person has towards taking action. Learners’ motivation has frequently been regarded as the most crucial factor in successful language acquisition. Without sufficient motivation, learners cannot achieve long-term learning goals despite remarkable abilities. Therefore, the study aims to investigate motivation of interaction tasks designed by the researchers for college EFL learners in Travel English class in virtual reality environment, integrating CUE model, Cognition, Usage and Expansion in the course. Thirty college learners were asked to join the virtual language learning website designed by the researchers. Data was collected via feedback questionnaire, interview, and learner interactions. The findings indicated that the course in the CUE model in language learning website of virtual reality environment was effective at motivating EFL learners and improving their oral communication and social interactions in the learning process. Some pedagogical implications are also provided in helping both language instructors and EFL learners in virtual reality environment.

Keywords: motivation, virtual reality, virtual language learning, second language acquisition

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Authors: Pramod Kumar, A. V. N. Swamy, C. V. Sowjanya, C. V. Ramachandra Murthy

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Biosorption is one of the effective methods for the removal of heavy metal ions from aqueous solutions. Results are presented showing the sorption of Pb(II) from solutions by biomass of commonly available marine algae Chlorella sp. The ability of marine algae Chlorella pyrenoidosa to remove heavy metal ion (Pb(II)) from aqueous solutions has been studied in this work. The biosorption properties of the biosorbent like equilibrium agitation time, optimum pH, temperature and initial solute concentration were investigated on metal uptake by showing respective profiles. The maximum metal uptake was found to be 10.27 mg/g. To achieve this metal uptake, the optimum conditions were found to be 30 min as equilibrium agitation time, 4.6 as optimum pH, 60 ppm of initial solute concentration. Lead concentration is determined by atomic absorption spectrometer. The process was found to be well fitted for pseudo-second order kinetics.

Keywords: biosorption, heavy metal ions, agitation time, metal uptake, aqueous solution

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Authors: Rocio Murad, Marcela Sanchez, Mariana Calderon Jaramillo, Danny Rivera, Angela Cifuentes, Daniela Roldán, Juan Carlos Rivillas

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Women and girls have specific health needs, but during health pandemics such as COVID19 they are less likely to have access to quality essential health information, commodities and services, or insurance coverage for routine and catastrophic health expenses, especially in rural and marginalized communities. This is compounded by multiple or intersecting inequalities, such as ethnicity, socioeconomic status, disability, age, geographic location, and sexual orientation, among others. Despite concerted collective action, there is a lack of information on the situation of women, adolescents and youth, including gender inequalities exacerbated by the pandemic. Much more needs to be done to amplify the lived realities of women and adolescents in global and national advocacy and policy responses. The COVID 19 pandemic reflects the need for systematic advocacy policies based on the lived experiences of women and adolescents, underpinned by human rights. This research is part of the initiative of Profamilia Association (Solidarity Study), and its objective is twofold: i) to analyze the behavioral changes and immediate expectations of Colombians during the stage of relaxation of the confinement measures decreed by the national government; and ii) to identify the needs, experiences and resilient practices of adolescents and young women during the COVID-19 crisis in Colombia. Descriptive analysis of data collected by Profamilia through the Solidaridad study, an exploratory cross-sectional descriptive study that used subnational level data from a nonprobabilistic sample survey conducted to 1735 adults, between September 01 and 11, 2020. Interviews were conducted with key stakeholders about their experiences during COVID19, under three key axes: i) main challenges for adolescents and young women; ii) examples of what has worked well in responding to the challenge; and iii) how/what services are/should be provided during COVID-19 (and beyond) to address the challenge. Interviewees were selected based on prior mapping of social groups of interest. In total, 23 adolescents and young women participated in the interviews. The results show that people adopted behavioral changes such as wearing masks, avoiding people with symptoms, and reducing mobility, but there was also a doubling of concerns for many reasons, from effects on mental health, sexual health, and unattended reproductive health to the burden of care and working at home. The favorable perception that people had at the beginning of the quarantine about the response and actions of the national and local government to control Covid-19 decreased over the course of the quarantine. The challenges and needs of adolescents and young women were highlighted during the most restrictive measures to contain the COVID-19 pandemic, which resulted in disruptions to daily activities, education and work, as well as restrictions to mobility and social interaction. Concerns raised by participants included: impact on mental health and wellbeing due to disruption of daily life; limitations in access to formal and informal education; food insecurity; migration; loss of livelihoods; lack of access to health information and services; limitations to sexual and reproductive health and rights; insecurity problems; and problems in communication and treatment among household members.

Keywords: COVID-19, changes in behavior, adolescents, women

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Authors: E. H. N. Gashti, M. Malaska, K. Kujala

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The thermo-mechanical behaviour of concrete energy pile foundations with different single and double U-tube shapes incorporated was analysed using the Comsol Multi-physics package. For the analysis, a 3D numerical model in real scale of the concrete pile and surrounding soil was simulated regarding actual operation of ground heat exchangers (GHE) and the surrounding ambient temperature. Based on initial ground temperature profile measured in situ, tube inlet temperature was considered to range from 6°C to 0°C (during the contraction process) over a 30-day period. Extra thermal stresses and deformations were calculated during the simulations and differences arising from the use of two different systems (single-tube and double-tube) were analysed. The results revealed no significant difference for extra thermal stresses at the centre of the pile in either system. However, displacements over the pile length were found to be up to 1.5-fold higher in the double-tube system than the single-tube system.

Keywords: concrete energy piles, stresses, displacements, thermo-mechanical behaviour, soil-structure interactions

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Authors: Sesethu G. Njokweni, Marelize Botes, Emile W. H. Van Zyl

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An alternative strategy to the production of bioethanol is by examining the degradability of biomass in a natural system such as the rumen of mammals. This anaerobic microbial community has higher cellulolytic activities than microbial communities from other habitats and degrades cellulose to produce volatile fatty acids (VFA), methane and CO₂. VFAs have the potential to serve as intermediate products for electrochemical conversion to hydrocarbon fuels. In vitro mimicking of this process would be more cost-effective than bioethanol production as it does not require chemical pre-treatment of biomass, a sterile environment or added enzymes. The strategies of the carboxylate platform and the co-cultures of a bovine ruminal microbiota from cannulated cows were combined in order to investigate and optimize the bioconversion of agricultural biomass (apple and grape pomace, citrus pulp, sugarcane bagasse and triticale straw) to high value VFAs as intermediates for biofuel production in a consolidated bioprocess. Optimisation of reactor conditions was investigated using five different ruminal inoculum concentrations; 5,10,15,20 and 25% with fixed pH at 6.8 and temperature at 39 ˚C. The ANKOM 200/220 fiber analyser was used to analyse in vitro neutral detergent fiber (NDF) disappearance of the feedstuffs. Fresh and cryo-frozen (5% DMSO and 50% glycerol for 3 months) rumen cultures were tested for the retainment of fermentation capacity and durability in 72 h fermentations in 125 ml serum vials using a FURO medical solutions 6-valve gas manifold to induce anaerobic conditions. Fermentation of apple pomace, triticale straw, and grape pomace showed no significant difference (P > 0.05) in the effect of 15 and 20 % inoculum concentrations for the total VFA yield. However, high performance liquid chromatographic separation within the two inoculum concentrations showed a significant difference (P < 0.05) in acetic acid yield, with 20% inoculum concentration being the optimum at 4.67 g/l. NDF disappearance of 85% in 96 h and total VFA yield of 11.5 g/l in 72 h (A/P ratio = 2.04) for apple pomace entailed that it was the optimal feedstuff for this process. The NDF disappearance and VFA yield of DMSO (82% NDF disappearance and 10.6 g/l VFA) and glycerol (90% NDF disappearance and 11.6 g/l VFA) stored rumen also showed significantly similar degradability of apple pomace with lack of treatment effect differences compared to a fresh rumen control (P > 0.05). The lack of treatment effects was a positive sign in indicating that there was no difference between the stored samples and the fresh rumen control. Retaining of the fermentation capacity within the preserved cultures suggests that its metabolic characteristics were preserved due to resilience and redundancy of the rumen culture. The amount of degradability and VFA yield within a short span was similar to other carboxylate platforms that have longer run times. This study shows that by virtue of faster rates and high extent of degradability, small scale alternatives to bioethanol such as rumen microbiomes and other natural fermenting microbiomes can be employed to enhance the feasibility of biofuels large-scale implementation.

Keywords: agricultural wastes, carboxylate platform, rumen microbiome, volatile fatty acids

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: Shalaka Sharad Dixit

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Rural Development in India is an important part of the rural economy & empowerment. Almost 70 percent of the Indian population lives in villages, hence rural development is important to become self-sustainable. Hence, the process to aiming the self-reliance of people living in rural areas. Maharashtra is one of the leading states in rural development. Hence, further study of the different villages in the five regions of Maharashtra i.e. Kokan, Pashchim, Marathwada, Khandesh, and Vidharbh. The study shows that major amenities lacking in the village. Annapur village case study has been done. The result has shown that the villagers face major problems like Unemployment, Load shedding, missing education facilities, unavailability of Bank and ATM, etc. They are facing lots of problems because scarce of required amenities. Therefore, the aim is to provide the amenities lacking in the Annapur village through a different government scheme. Government plans are devoted to development that includes the PMGSY, MGNREG, and GRAM UJALA. The study concluded that to provide and fulfill the amenities lacking in the Annapur village with the help of this government initiative.

Keywords: self sustainable rural development, government policies, Annapurna village, amenities, smart village

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Authors: C. Han, H. Kim, S. Park

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The purpose of the present study is to analyze the effect of surface roughness on fatigue life of SCM440 steel. Two groups of specimens were made from SCM440 steel with and without surface polished after forging process and resulted in different values of surface roughness. The difference of the surface roughness between two groups was clearly distinguished even to the naked eye. Surface roughness of both groups of the specimens was quantitatively measured by a roughness measuring device, Talysurf series2 (Taylor-Hobson Co., USA). Average roughness (Ra) and maximum roughness depth (Rmax) values were obtained by scanning 45 mm with a speed of 0.25 mm/s. Fatigue tests were conducted using a three-point bending method with a cyclic sinusoidal profile of 5 Hz, stress ratio of R = 0.1 and reference life for fatigue limit of 1 × 106 cycles. Ra and Rmax without surface polished were 10.497 ± 1.721 μm and 87.936 ± 16.210 μm, respectively while those values with surface polished were much smaller (ongoing measurements). Fatigue lives of the surface-polished specimens achieved approximately 1 × 106 cycles under the maximum stress of 900 MPa, which was 10 times longer than those of the surface-untreated specimens with an average roughness of 10.082 μm. The results showed that an increase in surface roughness values led to a decrease in fatigue lives.

Keywords: surface roughness, fatigue test, fatigue life, SCM440 steel

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Authors: Amsar Maulana, Rafdea Syafitri, Topanal Gustiranda, Natasya Permatasari, Herviyanti

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Coal as an alternative source of humic material that has the potential of 973.92 million tons (sub-bituminous amounted to 673.70 million tons) in West Sumatera. The purpose of this research was to study combination Urea and KCl with powder coal Sub-bituminous to increase nutrient content of Ultisols In Limau Manis Padang West Sumatera. The experiment was designed in Completely Randomized Design with 3 replications, those were T1) 0.5% (50g plot-1) of powder coal Sub-bituminous; T2) T1 and 125% (7.03g plot-1 ) of Urea recommendation; T3) T1 and 125% (5.85g plot-1) of KCl recommendation; T4) 1.0% (100g plot-1) of powder coal Sub-bituminous; T5) T4 and 125% (7.03g plot-1 ) of Urea recommendation; T6) T4 and 125% (5.85g plot-1) of KCl recommendation; T7) 1.5% (150g plot-1) of powder coal Sub-bituminous; T8) T7 and 125% (7.03g plot-1 ) of Urea recommendation; T9) T7 and 125% (5.85g plot-1) of KCl recommendation. The results showed that application 1.5% of powder coal Sub-bituminous and 125% of Urea recommendation could increase nutrient content of Ultisols such as pH by 0.33 unit, Organic – C by 2.03%, total – N by 0.31%, Available P by 14.16 ppm and CEC by 19.38 me 100g-1 after 2 weeks of incubation process.

Keywords: KCl, sub-bituminous, ultisols, urea

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Authors: Nuno Biga

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Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

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Authors: René Davids

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Some Western countries are abandoning coal and finding cleaner alternatives, such as solar, wind, hydroelectric, biomass, and geothermal, for the production of energy. As a consequence, industries have closed, and the toxic contaminated slag heaps formed essentially of discarded rock that did not contain coal are being colonized by spontaneously generated plant communities. In becoming green hiking territory, goat farms, viewing platforms, vineyards, great staging posts for species experiencing, and skiing slopes, many of the formerly abandoned hills of refuse have become delightful amenities to the surrounding communities. Together with the transformation of many industrial facilities into cultural venues, these changes to the slag hills have allowed the old coal districts to develop a new identity, but in the process, they have also literally buried the past. This essay reviews a few case studies to analyze the different ways slag heaps have contributed to the cultural landscape in the ex-coal county while arguing that it is important when deciding on their future, that we find ways to make the environmental damage that the extraction industry caused visibly and honor the lives of the people that worked under often appalling conditions in them.

Keywords: slag-heaps, mines, extraction, remediation, pollution

Procedia PDF Downloads 71