Search results for: computational load reduction

Authors: Swati Tomar, Sunil Kumar Gupta

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Anammox is a promising and cost effective alternative to conventional treatment systems that facilitates direct oxidation of ammonium nitrogen under anaerobic conditions with nitrite as an electron acceptor without addition of any external carbon sources. The present study investigates the process kinetics of laboratory scale anammox hybrid reactor (AHR) which combines the dual advantages of attached and suspended growth. The performance & behaviour of AHR was studied under varying hydraulic retention time (HRTs) and nitrogen loading rate (NLRs). The experimental unit consisted of 4 numbers of 5L capacity anammox hybrid reactor inoculated with mixed seed culture containing anoxic and activated sludge. Pseudo steady state (PSS) ammonium and nitrite removal efficiencies of 90.6% and 95.6%, respectively, were achieved during acclimation phase. After establishment of PSS, the performance of AHR was monitored at seven different HRTs of 3.0, 2.5, 2.0, 1.5, 1.0, 0.5 and 0.25 d with increasing NLR from 0.4 to 4.8 kg N/m3d. The results showed that with increase in NLR and decrease in HRT (3.0 to 0.25 d), AHR registered appreciable decline in nitrogen removal efficiency from 92.9% to 67.4 %, respectively. The HRT of 2.0 d was considered optimal to achieve substantial nitrogen removal of 89%, because on further decrease in HRT below 1.5 days, remarkable decline in the values of nitrogen removal efficiency were observed. Analysis of data indicated that attached growth system contributes an additional 15.4 % ammonium removal and reduced the sludge washout rate (additional 29% reduction). This enhanced performance may be attributed to 25% increase in sludge retention time due to the attached growth media. Three kinetic models, namely, first order, Monod and Modified Stover-Kincannon model were applied to assess the substrate removal kinetics of nitrogen removal in AHR. Validation of the models were carried out by comparing experimental set of data with the predicted values obtained from the respective models. For substrate removal kinetics, model validation revealed that Modified Stover-Kincannon is most precise (R2=0.943) and can be suitably applied to predict the kinetics of nitrogen removal in AHR. Lawrence and McCarty model described the kinetics of bacterial growth. The predicted value of yield coefficient and decay constant were in line with the experimentally observed values.

Keywords: anammox, kinetics, modelling, nitrogen removal, sludge wash out rate, AHR

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Authors: Jean Paul Hategekimana, Bampire Claudine, Niyonsenga Nadia, Irakoze Josiane

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Peas, soybeans and rice are crops which are grown in Rwanda and are available in rural and urban local markets and they give contribution in reduction of health problems especially in fighting malnutrition and food insecurity in Rwanda. Several research activities have been conducted on how cereals flour can be mixed with legumes flour for developing baked products which are rich in protein, fiber, minerals as they are found in legumes. However, such activity was not yet well studied in Rwanda. The aim of the present study was to develop bread and biscuit products from peas, soybeans and rice as functional ingredients combined with wheat flour and then analyze the nutritional content and consumer acceptability of new developed products. The malnutrition problem can be reduced by producing bread and biscuits which are rich in protein and are very accessible for every individual. The processing of bread and biscuit were made by taking peas flour, soybeans flour and rice flour mixed with wheat flour and other ingredients then a dough was made followed by baking. For bread, two kind of products were processed, for each product one control and three experimental samples in different three ratios of peas and rice were prepared. These ratios were 95:5, 90:10 and 80:20 for bread from peas and 85:5:10, 80:10:10 and 70:10:20 for bread from peas and rice. For biscuit, two kind of products were also processed, for each product one control sample and three experimental samples in three different ratios were prepared. These ratios are 90:5:5,80:10:10 and 70:10:20 for biscuit from peas and rice and 90:5:5,80:10:10 and 70:10:20 for biscuit from soybean and rice. All samples including the control sample were analyzed for the consumer acceptability (sensory attributes) and nutritional composition. For sensory analysis, bread from of peas and rice flour with wheat flour at ratio 85:5:10 and bread from peas only as functional ingredient with wheat flour at ratio 95:5 and biscuits made from a of soybeans and rice at a ratio 90:5:5 and biscuit made from peas and rice at ratio 90:5:5 were most acceptable compared to control sample and other samples in different ratio. The moisture, protein, fat, fiber and minerals (Sodium and iron.) content were analyzed where bread from peas in all ratios was found to be rich in protein and fiber compare to control sample and biscuit from soybean and rice in all ratios was found to be rich in protein and fiber compare to control sample.

Keywords: bakery products, peas and rice flour, wheat flour, sensory evaluation, proximate composition

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Authors: Abdulaziz Kikanga, Kellen Muchira, Styvers Kathuni, Paul Saitoti

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Background: Provision of adequate levels of water, sanitation, and hygiene in schools is a strategic objective in achieving universal primary education among children in low and middle-income countries. However, lack of proper sanitation and hygiene practices in schools, especially those in informal settlement has resulted to an increased rate of school absenteeism thereby affecting the education and health outcomes of the children in those setting. Intervention or Response: Catholic Relief Services in Kenya supports five schools in informal settlements of Nairobi by painting of key hygiene messages on school walls to promote proper hygiene and sanitation practices among the school children. The mural exhibitions depict the essence of proper hygiene practices, proper latrine use, and hand washing after visiting the latrine. The artwork is context specific and its aimed at improving the uptake of proper hygiene and sanitation practices among the school children. Review of project related documents was conducted including interviews with the school children. Thematic analysis was used to interpret the qualitative information generated. Results and Lessons Learnt: 12 school children have interviewed on proper hygiene and sanitation practices and the exercise revealed that painted murals were the best communication platforms for creating awareness on proper sanitation on issues relating to water, sanitation, and hygiene in schools. The painting mural provided a strong knowledge base for the formation of healthy habits in both the school and informal settlement. In addition, these sanitation messages on the school walls empower the children to share these practices with their siblings, parents, and other family members thereby acting as agents of change to proper hygiene and sanitation in those informal settlements. The findings revealed that by adopting proper sanitation and hygiene practices, there has been a reduction of school absenteeism due to a decrease in disease related to inadequate sanitation and hygiene in schools. Conclusion: The adoption of proper sanitation in schools entails more than just a painted mural wall. Insights revealed that to have a lasting sanitation and hygiene intervention, there is a need to invest in effective hygiene educational programming that encourages the formation of proper hygiene habits and promotes changes in behavior.

Keywords: education outcomes, informal settlement, mural exhibition, school hygiene and sanitation

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Authors: Ann Enright

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More than ever, animal shelters need to identify ways to reduce the number of animals entering shelter facilities and the incidence of euthanasia. Managing animal overpopulation using euthanasia can have detrimental health and emotional consequences for the shelter staff involved. There are also community expectations with moral and financial implications to consider. To achieve the goals of reducing animal intake and the incidence of euthanasia, shelter best practice involves combining programs, procedures and partnerships to increase live release rates (LRR), reduce the incidence of disease, length of stay (LOS) and shelter intake whilst overall remaining financially viable. Analysing daily processes, tracking outcomes and implementing simple strategies enabled shelter staff to more effectively focus their efforts and achieve amazing results. The objective of this retrospective study was to assess the effect of implementing the capacity for care (C4C) management model. Data focusing on the average daily number of animals on site for a two year period (2016 – 2017) was exported from a shelter management system, Customer Logic (CL) Vet to Excel for manipulation and comparison. Following the implementation of C4C practices the average daily number of animals on site was reduced by >50%, (2016 average 103 compared to 2017 average 49), average LOS reduced by 50% from 8 weeks to 4 weeks and incidence of disease reduced from ≥ 70% to less than 2% of the cats on site at the completion of the study. The total number of stray cats entering the shelter due to council contracts reduced by 50% (486 to 248). Improved cat outcomes were attributed to strategies that increased adoptions and reduced euthanasia of poorly socialized cats, including foster programs. To continue to achieve improvements in LRR and LOS, strategies to decrease intake further would be beneficial, for example, targeted sterilisation programs. In conclusion, the study highlighted the benefits of using C4C as a management tool, delivering a significant reduction in animal intake and euthanasia with positive emotional, financial and community outcomes.

Keywords: animal welfare, capacity for care, cat, euthanasia, length of stay, managed intake, shelter

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Authors: Sikiru O. Aminu, Magda Ismail Abdul Mohsin, Fauziah M. Taib

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Fund collections for Waqf projects in some Muslim countries received some boost because of the resuscitation of the cash waqf concept This study envisages that such development can improve the economic empowerment of the poor in contemporary Muslim communities given appropriate collections and effective management of the Waqf institution. Recent developments in the Financial Technology (FINTECH) space portend valuable relevance in the conduct and delivery of social charitable causes such as Waqf around the world. Particularly, emerging areas in FINTECH such as Islamic Crowdfunding (ICF) and blockchain have brought about greater efficiency and effectiveness through cost reduction, faster transactions, wider access, transparency and prompt disclosure of adequate information to relevant stakeholders. These FINTECH options of ICF and blockchain provide veritable opportunities to resuscitate, re-align, synergize and magnify the Islamic Social Finance (ISF) ecosystem of Waqf, Zakat and Sodaqah to generate positive and sustainable impact to the community, environment and the economy at large, with a view to projecting the Maqasid Shari’ah (Objective of the Law Giver). To document the effect of FINTECH on Waqf, this study examined the activities of six banks in Malaysia that signed a pact to utilize FINTECH for waqf collection towards improving the economy. Semi-structured Face to Face interviews were conducted with officers in charge of Waqf in the six banks, founder of the Islamic Crowdfunding platform and senior officers in Waqaf Selangor. Content analysis was used to analyze their responses, and the emergent themes were reported verbatim. Based on the derived themes, survey questionnaires were also administered to 300 customers with respect to the Waqf’s FINTECH functionalities of the identified banks.to further confirm and strengthen the results of the interviews. Simple descriptive analysis was performed on the result of the survey questionnaire to provide clear information on the questions raised. The findings showed a disproportionate level of readiness among the banks, where few of the banks have put structure in place to increase their Waqf collections, others are at their elementary stage. However, the commitment is high across the six banks to achieve their set goals.

Keywords: blockchain, Fintech, Islamic crowdfunding, waqf

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Authors: Priyanka Verma, Vijaya Dixit, Rishabh Bajpai

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This research paper is aimed at selecting appropriate material handling equipment among the given choices so that the automation level in material handling can be enhanced. This work is a practical case scenario of material handling systems in consumer electronic appliances manufacturing organization. The choices of material handling equipment among which the decision has to be made are Automated Guided Vehicle’s (AGV), Autonomous Mobile Robots (AMR), Overhead Conveyer’s (OC) and Battery Operated Trucks/Vehicle’s (BOT). There is a need of attaining a certain level of automation in order to reduce human interventions in the organization. This requirement of achieving certain degree of automation can be attained by material handling equipment’s mentioned above. The main motive for selecting above equipment’s for study was solely based on corporate financial strategy of investment and return obtained through that investment made in stipulated time framework. Since the low cost automation with respect to material handling devices has to be achieved hence these equipment’s were selected. Investment to be done on each unit of this equipment is less than 20 lakh rupees (INR) and the recovery period is less than that of five years. Fuzzy analytic hierarchic process (FAHP) is applied here for selecting equipment where the four choices are evaluated on basis of four major criteria’s and 13 sub criteria’s, and are prioritized on the basis of weight obtained. The FAHP used here make use of triangular fuzzy numbers (TFN). The inability of the traditional AHP in order to deal with the subjectiveness and impreciseness in the pair-wise comparison process has been improved in the FAHP. The range of values for general rating purposes for all decision making parameters is kept between 0 and 1 on the basis of expert opinions captured on shop floor. These experts were familiar with operating environment and shop floor activity control. Instead of generating exact value the FAHP generates the ranges of values to accommodate the uncertainty in decision-making process. The four major criteria’s selected for the evaluation of choices of material handling equipment’s available are materials, technical capabilities, cost and other features. The thirteen sub criteria’s listed under these following four major criteria’s are weighing capacity, load per hour, material compatibility, capital cost, operating cost and maintenance cost, speed, distance moved, space required, frequency of trips, control required, safety and reliability issues. The key finding shows that among the four major criteria selected, cost is emerged as the most important criteria and is one of the key decision making aspect on the basis of which material equipment selection is based on. While further evaluating the choices of equipment available for each sub criteria it is found that AGV scores the highest weight in most of the sub-criteria’s. On carrying out complete analysis the research shows that AGV is the best material handling equipment suiting all decision criteria’s selected in FAHP and therefore it is beneficial for the organization to carry out automated material handling in the facility using AGV’s.

Keywords: fuzzy analytic hierarchy process (FAHP), material handling equipment, subjectiveness, triangular fuzzy number (TFN)

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Authors: Julia A. Ermakova

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This study aimed to investigate the efficacy of a physical rehabilitation program for postpartum women. The program was developed with the purpose of restoring physical performance in women during the postpartum period. The research employed a variety of methods, including an analysis of scientific literature, pedagogical testing and experimentation, mathematical processing of study results, and physical performance assessment using a range of tests. The program recommends refraining from abdominal exercises during the first 6-8 months following a cesarean section and avoiding exercises with weights. Instead, a feasible training regimen that gradually increases in intensity several times a week is recommended, along with moderate cardio exercises such as walking, bodyweight training, and a separate workout component that targets posture improvement. Stretching after strength training is also encouraged. The necessary equipment includes comfortable sports attire with a chest support top, mat, push-ups, resistance band, timer, and clock. The motivational aspect of the program is paramount, and the mentee's positive experience with the workout regimen includes feelings of lightness in the body, increased energy, and positive emotions. The gradual reduction of body size and weight loss due to an improved metabolism also serves as positive reinforcement. The mentee's progress can be measured through various means, including an external assessment of her form, body measurements, weight, BMI, and the presence or absence of slouching in everyday life. The findings of this study reveal that the program is effective in restoring physical performance in postpartum women. The mentee achieved weight loss and almost regained her pre-pregnancy shape while her self-esteem improved. Her waist, shoulder, and hip measurements decreased, and she displayed less slouching in her daily life. In conclusion, the developed physical rehabilitation program for postpartum women is an effective means of restoring physical performance. It is crucial to follow the recommended training regimen and equipment to avoid limitations and ensure safety during the postpartum period. The motivational component of the program is also fundamental in encouraging positive reinforcement and improving self-esteem.

Keywords: physical rehabilitation, postpartum, methodology, postpartum recovery, rehabilitation

Procedia PDF Downloads 75

Authors: Zeenat Rupawalla, Nicole Robinson, Susanne Schmidt, Sijie Li, Selina Carruthers, Elodie Buisset, John Roles, Ben Hankamer, Juliane Wolf

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Agriculture has radically changed the global biogeochemical cycle of nitrogen (N). Fossil fuel-enabled synthetic N-fertiliser is a foundation of modern agriculture but applied to soil crops only use about half of it. To address N-pollution from cropping and the large carbon and energy footprint of N-fertiliser synthesis, new technologies delivering enhanced energy efficiency, decarbonisation, and a circular nutrient economy are needed. We characterised algae fertiliser (AF) as an alternative to synthetic N-fertiliser (SF) using empirical and modelling approaches. We cultivated microalgae in nutrient solution and modelled up-scaled production in nutrient-rich wastewater. Over four weeks, AF released 63.5% of N as ammonium and nitrate, and 25% of phosphorous (P) as phosphate to the growth substrate, while SF released 100% N and 20% P. To maximise crop N-use and minimise N-leaching, we explored AF and SF dose-response-curves with spinach in glasshouse conditions. AF-grown spinach produced 36% less biomass than SF-grown plants due to AF’s slower and linear N-release, while SF resulted in 5-times higher N-leaching loss than AF. Optimised blends of AF and SF boosted crop yield and minimised N-loss due to greater synchrony of N-release and crop uptake. Additional benefits of AF included greener leaves, lower leaf nitrate concentration, and higher microbial diversity and water holding capacity in the growth substrate. Life-cycle-analysis showed that replacing the most effective SF dosage with AF lowered the carbon footprint of fertiliser production from 2.02 g CO₂ (C-producing) to -4.62 g CO₂ (C-sequestering), with a further 12% reduction when AF is produced on wastewater. Embodied energy was lowest for AF-SF blends and could be reduced by 32% when cultivating algae on wastewater. We conclude that (i) microalgae offer a sustainable alternative to synthetic N-fertiliser in spinach production and potentially other crop systems, and (ii) microalgae biofertilisers support the circular nutrient economy and several sustainable development goals.

Keywords: bioeconomy, decarbonisation, energy footprint, microalgae

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Authors: Naira Sahakyan, Margarit Petrosyan, Armen Trchounian

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One of the tasks in contemporary biotechnology, pharmacology and other fields of human activities is to obtain biologically active substances from plants. They are very essential in the treatment of many diseases due to their actually high therapeutic value without visible side effects. However, sometimes the possibility of obtaining the metabolites is limited due to the reduction of wild-growing plants. That is why the plant cell cultures are of great interest as alternative sources of biologically active substances. Besides, during the monitored cultivation, it is possible to obtain substances that are not synthesized by plants in nature. Isolated culture of Ajuga genevensis with high growth activity and ability of regeneration was obtained using MS nutrient medium. The agar-diffusion method showed that aqueous extracts of callus culture revealed high antimicrobial activity towards various gram-positive (Bacillus subtilis A1WT; B. mesentericus WDCM 1873; Staphylococcus aureus WDCM 5233; Staph. citreus WT) and gram-negative (Escherichia coli WKPM M-17; Salmonella typhimurium TA 100) microorganisms. The broth dilution method revealed that the minimal and half maximal inhibitory concentration values against E. coli corresponded to the 70 μg/mL and 140 μg/mL concentration of the extract respectively. According to the photochemiluminescent analysis, callus tissue extracts of leaf and root origin showed higher antioxidant activity than the same quantity of A. genevensis intact plant extract. A. genevensis intact plant and callus culture extracts showed no cytotoxic effect on K-562 suspension cell line of human chronic myeloid leukemia. The GC-MS analysis showed deep differences between the qualitative and quantitative composition of callus culture and intact plant extracts. Hexacosane (11.17%); n-hexadecanoic acid (9.33%); and 2-methoxy-4-vinylphenol (4.28%) were the main components of intact plant extracts. 10-Methylnonadecane (57.0%); methoxyacetic acid, 2-tetradecyl ester (17.75%) and 1-Bromopentadecane (14.55%) were the main components of A. genevensis callus culture extracts. Obtained data indicate that callus culture of A. genevensis can be used as an alternative source of biologically active substances.

Keywords: Ajuga genevensis, antibacterial activity, antioxidant activity, callus cultures

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Authors: Desiree Di Marco

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The aim of this work is to show through the lens of art first which kind of reality was the one represented through fascist maps, and second to study the impact of the fascist regime’s cartography (FRC) on observers eye’s. In this study, it is assumed that the FRC’s representation of reality was simplified, timeless, and even a-spatial because it underrates the concept of territoriality. Cubism and Picasso’s paintings will be used as counter-examples to mystify fascist cartography’s ideological assumptions. The difference between the gaze of an observer looking at the surface of a fascist map and the gaze of someone observing a Picasso painting is impressive. Because there is always something dark, hidden, behind and inside a map, the world of fascist maps was a world built starting from the observation of a “window” that distorted reality and trapped the eyes of the observers. Moving across the map, they seem as if they were hypnotized. Cartohypnosis is the state in which the observer finds himself enslaved by the attractive force of the map, which uses a sort of “magic” geography, a geography that, by means of symbolic language, never has as its primary objective the attempt to show us reality in its complexity, but that of performing for its audience. Magical geography and hypnotic cartography in fascism blended together, creating an almost mystical, magical relationship that demystified reality to reduce the world to a conquerable space. This reduction offered the observer the possibility of conceiving new dimensions: of the limit, of the boundary, elements with which the subject felt fully involved and in which the aesthetic force of the images demonstrated all its strength. But in the early 20th century, the combination of art and cartography gave rise to new possibilities. Cubism which, more than all the other artistic currents showed us how much the observation of reality from a single point of view falls within dangerous logic, is an example. Cubism was an artistic movement that brought about a profound transformation in pictorial culture. It was not only a revolution of pictorial space, but it was a revolution of our conception of pictorial space. Up until that time, men and women were more inclined to believe in the power of images and their representations. Cubist painters rebelled against this blindness by claiming that art must always offer an alternative. Indeed the contribution of this work is precisely to show how art can be able to provide alternatives to even the most horrible regimes and the most atrocious human misfortunes. It also enriches the field of cartography because it "reassures" it by showing how much good it can be for cartography if also for other disciplines come close. Only in this way researcher can increase the chances for the cartography of a greater diffusion at the academic level.

Keywords: cartography, Picasso, fascism, culture

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Authors: Amela Ajanovic, Reinhard Haas

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Electric vehicles (EVs) are considered as an important means to cope with current environmental problems in transport. However, their high capital costs and limited driving ranges state major barriers to a broader market penetration. The core objective of this paper is to investigate the future market prospects of various types of EVs from an economic and ecological point of view. Our method of approach is based on the calculation of total cost of ownership of EVs in comparison to conventional cars and a life-cycle approach to assess the environmental benignity. The most crucial parameters in this context are km driven per year, depreciation time of the car and interest rate. The analysis of future prospects it is based on technological learning regarding investment costs of batteries. The major results are the major disadvantages of battery electric vehicles (BEVs) are the high capital costs, mainly due to the battery, and a low driving range in comparison to conventional vehicles. These problems could be reduced with plug-in hybrids (PHEV) and range extenders (REXs). However, these technologies have lower CO₂ emissions in the whole energy supply chain than conventional vehicles, but unlike BEV they are not zero-emission vehicles at the point of use. The number of km driven has a higher impact on total mobility costs than the learning rate. Hence, the use of EVs as taxis and in car-sharing leads to the best economic performance. The most popular EVs are currently full hybrid EVs. They have only slightly higher costs and similar operating ranges as conventional vehicles. But since they are dependent on fossil fuels, they can only be seen as energy efficiency measure. However, they can serve as a bridging technology, as long as BEVs and fuel cell vehicle do not gain high popularity, and together with PHEVs and REX contribute to faster technological learning and reduction in battery costs. Regarding the promotion of EVs, the best results could be reached with a combination of monetary and non-monetary incentives, as in Norway for example. The major conclusion is that to harvest the full environmental benefits of EVs a very important aspect is the introduction of CO₂-based fuel taxes. This should ensure that the electricity for EVs is generated from renewable energy sources; otherwise, total CO₂ emissions are likely higher than those of conventional cars.

Keywords: costs, mobility, policy, sustainability,

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Authors: R. Nandhini, Gaurab Mudbhari

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Ranging from the field of health care to self-driving cars, machine learning and deep learning algorithms have revolutionized the field with the proper utilization of images and visual-oriented data. Segmentation, regression, classification, clustering, dimensionality reduction, etc., are some of the Machine Learning tasks that helped Machine Learning and Deep Learning models to become state-of-the-art models for the field where images are key datasets. Among these tasks, classification and clustering are essential but difficult because of the intricate and high-dimensional characteristics of image data. This finding examines and assesses advanced techniques in supervised classification and unsupervised clustering for image datasets, emphasizing the relative efficiency of Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Deep Embedded Clustering (DEC), and self-supervised learning approaches. Due to the distinctive structural attributes present in images, conventional methods often fail to effectively capture spatial patterns, resulting in the development of models that utilize more advanced architectures and attention mechanisms. In image classification, we investigated both CNNs and ViTs. One of the most promising models, which is very much known for its ability to detect spatial hierarchies, is CNN, and it serves as a core model in our study. On the other hand, ViT is another model that also serves as a core model, reflecting a modern classification method that uses a self-attention mechanism which makes them more robust as this self-attention mechanism allows them to lean global dependencies in images without relying on convolutional layers. This paper evaluates the performance of these two architectures based on accuracy, precision, recall, and F1-score across different image datasets, analyzing their appropriateness for various categories of images. In the domain of clustering, we assess DEC, Variational Autoencoders (VAEs), and conventional clustering techniques like k-means, which are used on embeddings derived from CNN models. DEC, a prominent model in the field of clustering, has gained the attention of many ML engineers because of its ability to combine feature learning and clustering into a single framework and its main goal is to improve clustering quality through better feature representation. VAEs, on the other hand, are pretty well known for using latent embeddings for grouping similar images without requiring for prior label by utilizing the probabilistic clustering method.

Keywords: machine learning, deep learning, image classification, image clustering

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Authors: Su Chul Han, Seungwoo Park

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As the increasing incidence of cancer, the technology and modality of radiotherapy have advanced and the importance of preclinical model is increasing in the cancer research. Furthermore, the small animal dosimetry is an essential part of the evaluation of the relationship between the absorbed dose in preclinical small animal and biological effect in preclinical study. In this study, we carried out realistic modeling of the preclinical small animal phantom possible to verify irradiated dose using commercial software. The small animal phantom was modeling from 4D Digital Mouse whole body phantom. To manipulate Moby phantom in commercial software (Mimics, Materialise, Leuven, Belgium), we converted Moby phantom to DICOM image file of CT by Matlab and two- dimensional of CT images were converted to the three-dimensional image and it is possible to segment and crop CT image in Sagittal, Coronal and axial view). The CT images of small animals were modeling following process. Based on the profile line value, the thresholding was carried out to make a mask that was connection of all the regions of the equal threshold range. Using thresholding method, we segmented into three part (bone, body (tissue). lung), to separate neighboring pixels between lung and body (tissue), we used region growing function of Mimics software. We acquired 3D object by 3D calculation in the segmented images. The generated 3D object was smoothing by remeshing operation and smoothing operation factor was 0.4, iteration value was 5. The edge mode was selected to perform triangle reduction. The parameters were that tolerance (0.1mm), edge angle (15 degrees) and the number of iteration (5). The image processing 3D object file was converted to an STL file to output with 3D printer. We modified 3D small animal file using 3- Matic research (Materialise, Leuven, Belgium) to make space for radiation dosimetry chips. We acquired 3D object of realistic small animal phantom. The width of small animal phantom was 2.631 cm, thickness was 2.361 cm, and length was 10.817. Mimics software supported efficiency about 3D object generation and usability of conversion to STL file for user. The development of small preclinical animal phantom would increase reliability of verification of absorbed dose in small animal for preclinical study.

Keywords: mimics, preclinical small animal, segmentation, 3D printer

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Martín Pratto Burgos

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The Engineering School of the University of the Republic in Uruguay offers an Introductory Mathematical Course from the second semester of 2019. This course has been designed to assist students in preparing themselves for math courses that are essential for Engineering Degrees, namely Math1, Math2, and Math3 in this research. The research proposes to build a model that can accurately predict the student's activity and academic progress based on their performance in the three essential Mathematical courses. Additionally, there is a need for a model that can forecast the incidence of the Introductory Mathematical Course in the three essential courses approval during the first academic year. The techniques used are Principal Component Analysis and predictive modelling using the Generalised Linear Model. The dataset includes information from 5135 engineering students and 12 different characteristics based on activity and course performance. Two models are created for a type of data that follows a binomial distribution using the R programming language. Model 1 is based on a variable's p-value being less than 0.05, and Model 2 uses the stepAIC function to remove variables and get the lowest AIC score. After using Principal Component Analysis, the main components represented in the y-axis are the approval of the Introductory Mathematical Course, and the x-axis is the approval of Math1 and Math2 courses as well as student activity three years after taking the Introductory Mathematical Course. Model 2, which considered student’s activity, performed the best with an AUC of 0.81 and an accuracy of 84%. According to Model 2, the student's engagement in school activities will continue for three years after the approval of the Introductory Mathematical Course. This is because they have successfully completed the Math1 and Math2 courses. Passing the Math3 course does not have any effect on the student’s activity. Concerning academic progress, the best fit is Model 1. It has an AUC of 0.56 and an accuracy rate of 91%. The model says that if the student passes the three first-year courses, they will progress according to the timeline set by the curriculum. Both models show that the Introductory Mathematical Course does not directly affect the student’s activity and academic progress. The best model to explain the impact of the Introductory Mathematical Course on the three first-year courses was Model 1. It has an AUC of 0.76 and 98% accuracy. The model shows that if students pass the Introductory Mathematical Course, it will help them to pass Math1 and Math2 courses without affecting their performance on the Math3 course. Matching the three predictive models, if students pass Math1 and Math2 courses, they will stay active for three years after taking the Introductory Mathematical Course, and also, they will continue following the recommended engineering curriculum. Additionally, the Introductory Mathematical Course helps students to pass Math1 and Math2 when they start Engineering School. Models obtained in the research don't consider the time students took to pass the three Math courses, but they can successfully assess courses in the university curriculum.

Keywords: machine-learning, engineering, university, education, computational models

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Authors: L. Alejandro Cárdenas, Fernando Herrera, David Nova, Juan Ballesteros

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This paper presents a methodology to optimize the maintenance of grid-connected photovoltaic systems, considering the cleaning and module replacement periods based on an asset management strategy. The methodology is based on the analysis of the energy production of the PV plant, the energy feed-in tariff, and the cost of cleaning and replacement of the PV modules, with the overall revenue received being the optimization variable. The methodology is evaluated as a case study of a 5.6 kWp solar PV plant located on the Bogotá campus of the Universidad Nacional de Colombia. The asset management strategy implemented consists of assessing the PV modules through visual inspection, energy performance analysis, pollution, and degradation. Within the visual inspection of the plant, the general condition of the modules and the structure is assessed, identifying dust deposition, visible fractures, and water accumulation on the bottom. The energy performance analysis is performed with the energy production reported by the monitoring systems and compared with the values estimated in the simulation. The pollution analysis is performed using the soiling rate due to dust accumulation, which can be modelled by a black box with an exponential function dependent on historical pollution values. The pollution rate is calculated with data collected from the energy generated during two years in a photovoltaic plant on the campus of the National University of Colombia. Additionally, the alternative of assessing the temperature degradation of the PV modules is evaluated by estimating the cell temperature with parameters such as ambient temperature and wind speed. The medium-term energy decrease of the PV modules is assessed with the asset management strategy by calculating the health index to determine the replacement period of the modules due to degradation. This study proposes a tool for decision making related to the maintenance of photovoltaic systems. The above, projecting the increase in the installation of solar photovoltaic systems in power systems associated with the commitments made in the Paris Agreement for the reduction of CO2 emissions. In the Colombian context, it is estimated that by 2030, 12% of the installed power capacity will be solar PV.

Keywords: asset management, PV module, optimization, maintenance

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Authors: Eleni Karantzola, Athanasios Karasimos, Vasiliki Makri, Ioanna Skouvara

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Historical linguistics unveils the historical depth of languages and traces variation and change by analyzing linguistic variables over time. This field of linguistics usually deals with a closed data set that can only be expanded by the (re)discovery of previously unknown manuscripts or editions. In some cases, it is possible to use (almost) the entire closed corpus of a language for research, as is the case with the Thesaurus Linguae Graecae digital library for Ancient Greek, which contains most of the extant ancient Greek literature. However, concerning ‘dynamic’ periods when the production and circulation of texts in printed as well as manuscript form have not been fully mapped, representative samples and corpora of texts are needed. Such material and tools are utterly lacking for Early Modern Greek (16th-18th c.). In this study, the principles of the creation of EMoGReC, a pilot representative corpus of Early Modern Greek (16th-18th c.) are presented. Its design follows the fundamental principles of historical corpora. The selection of texts aims to create a representative and balanced corpus that gives insight into diachronic, diatopic and diaphasic variation. The pilot sample includes data derived from fully machine-readable vernacular texts, which belong to 4-5 different textual genres and come from different geographical areas. We develop a hierarchical linguistic annotation scheme, further customized to fit the characteristics of our text corpus. Regarding variables and their variants, we use as a point of departure the bundle of twenty-four features (or categories of features) for prose demotic texts of the 16th c. Tags are introduced bearing the variants [+old/archaic] or [+novel/vernacular]. On the other hand, further phenomena that are underway (cf. The Cambridge Grammar of Medieval and Early Modern Greek) are selected for tagging. The annotated texts are enriched with metalinguistic and sociolinguistic metadata to provide a testbed for the development of the first comprehensive set of tools for the Greek language of that period. Based on a relational management system with interconnection of data, annotations, and their metadata, the EMoGReC database aspires to join a state-of-the-art technological ecosystem for the research of observed language variation and change using advanced computational approaches.

Keywords: early modern Greek, variation and change, representative corpus, diachronic variables.

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Authors: A. Saghatelyan, Sh. Asmaryan, G. Tepanosyan, V. Muradyan

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One of major concern to Armenia has been soil degradation emerged as a result of unsustainable management and use of grasslands, this in turn largely impacting environment, agriculture and finally human health. Hence, assessment of soil degradation is an essential and urgent objective set out to measure its possible consequences and develop a potential management strategy. Since recently, an essential tool for assessing pasture degradation has been remote sensing (RS) technologies. This research was done with an intention to measure preciseness of Linear spectral unmixing (LSU) and NDVI-SMA methods to estimate soil surface components related to degradation (fractional vegetation cover-FVC, bare soils fractions, surface rock cover) and determine appropriateness of these methods for mapping man-induced soil degradation in high mountain pastures. Taking into consideration a spatially complex and heterogeneous biogeophysical structure of the studied site, we used high resolution multispectral QuickBird imagery of a pasture site in one of Armenia’s rural communities - Nerkin Sasoonashen. The accuracy assessment was done by comparing between the land cover abundance data derived through RS methods and the ground truth land cover abundance data. A significant regression was established between ground truth FVC estimate and both NDVI-LSU and LSU - produced vegetation abundance data (R2=0.636, R2=0.625, respectively). For bare soil fractions linear regression produced a general coefficient of determination R2=0.708. Because of poor spectral resolution of the QuickBird imagery LSU failed with assessment of surface rock abundance (R2=0.015). It has been well documented by this particular research, that reduction in vegetation cover runs in parallel with increase in man-induced soil degradation, whereas in the absence of man-induced soil degradation a bare soil fraction does not exceed a certain level. The outcomes show that the proposed method of man-induced soil degradation assessment through FVC, bare soil fractions and field data adequately reflects the current status of soil degradation throughout the studied pasture site and may be employed as an alternate of more complicated models for soil degradation assessment.

Keywords: Armenia, linear spectral unmixing, remote sensing, soil degradation

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Authors: Muhammad Shafi’U Adamu

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This study examined the effects of two groups of Cognitive Behavioral Therapies (CBT) which, includes Cognitive Restructuring (CB) and Rational Emotive Behavioral Therapy (REBT), on the Psychological Distress of awaiting-trial Inmates in Correctional Centers in North-West Nigeria. The study had four specific objectives, four research questions, and four null hypotheses. The study used a quasi-experimental design that involved pre-test and post-test. The population comprised of all 7,962 awaiting-trial inmates in correctional centers in North-west Nigeria. 131 awaiting trial inmates from three intact Correctional Centers were randomly selected using the census technique. The respondents were sampled and randomly put into 3 groups (CR, REBT and Control). Kessler Psychological Distress Scale (K10) was adapted for data collection in the study. The instrument was validated by experts and subjected to a pilot study using Cronbach's Alpha with a reliability coefficient of 0.772. Each group received treatment for 8 consecutive weeks (60 minutes/week). Data collected from the field were subjected to descriptive statistics of mean, standard deviation and mean difference to answer the research questions. Inferential statistics of ANOVA and independent sample t-test were used to test the null hypotheses at P≤ 0.05 level of significance. Results in the study revealed that there was no significant difference among the pre-treatment mean scores of experimental and control groups. Statistical evidence also showed a significant difference among the mean scores of the three groups, and thus, results of the Post Hoc multiple-comparison test indicated the posttreatment reduction of psychological distress in the awaiting-trial inmates. Documented output also showed a significant difference between the post-treatment psychologically distressed mean scores of male and female awaiting-trial inmates, but there was no difference in those exposed to REBT. The research recommends that a standardized structured CBT counseling technique treatment should be designed for correctional centers across Nigeria, and CBT counseling techniques could be used in the treatment of PD in both correctional and clinical settings.

Keywords: awaiting-trial inmates, cognitive restructuring, correctional centers, rational emotive behavioral therapy

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Authors: Dhritilekha Deka, Deepak Patwa, Ravi K., Archana M. Nair

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Bioaccumulation of heavy metal contaminants due to intense anthropogenic interference degrades the environment and ecosystem functions. Conventional physicochemical methods involve energy-intensive and costly methodologies. Phytoremediation, on the other hand, provides an efficient nature-based strategy for the reclamation of heavy metal-contaminated sites. However, the slow process and adaptation to high-concentration contaminant sequestration often limit the efficiency of the method. This necessitates natural amendments such as biochar to improve phytoextraction and stabilize the green cover. Biochar is a highly porous structure with high carbon sequestration potential and containing negatively charged functional groups that provide binding sites for the positively charged metals. This study aims to develop and determine the synergy between sugarcane bagasse biochar content and phytoremediation. A 60-day pot experiment using perennial tussock vetiver grass (Chrysopogon zizanioides) was conducted for different biochar contents of 1%, 2%, and 4% for the removal of cadmium and zinc. A concentration of 500 ppm is maintained for the amended and unamended control (CK) samples. The survival rates of the plants, biomass production, and leaf area index were measured for the plant growth characteristics. Results indicate a visible change in the plant growth and the heavy metal concentration with the biochar content. The bioconcentration factor (BCF) in the plant improved significantly for the 4% biochar content by 57% in comparison to the control CK treatment in Cd-treated soils. The Zn soils indicated the highest reduction in the metal concentration by 50% in the 2% amended samples and an increase in the BCF in all the amended samples. The translocation from the rhizosphere to the shoots was low but not dependent on the amendment content and varied for each contaminant type. The root-to-shoot ratio indicates higher values compared to the control samples. The enhanced tolerance capacities can be attributed to the nutrients released by the biochar in the soil. The study reveals the high potential of biochar as a phytoremediation amendment, but its effect is dependent on the soil and heavy metal and accumulator species.

Keywords: phytoextraction, biochar, heavy metals, chrysopogon zizanioides, bioaccumulation factor

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Authors: Josef Novák, Alena Kohoutková

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The performance of concrete structures in fire depends on several factors which include, among others, the change in material properties due to the fire. Today, fiber reinforced concrete (FRC) belongs to materials which have been widely used for various structures and elements. While the knowledge and experience with FRC behavior under ambient temperature is well-known, the effect of elevated temperature on its behavior has to be deeply investigated. This paper deals with an experimental investigation and stress‑strain relations for hybrid fiber reinforced concrete (HFRC) which contains siliceous aggregates, polypropylene and steel fibers. The main objective of the experimental investigation is to enhance a database of mechanical properties of concrete composites with addition of fibers subject to elevated temperature as well as to validate existing stress-strain relations for HFRC. Within the investigation, a unique heat transport test, compressive test and splitting tensile test were performed on 150 mm cubes heated up to 200, 400, and 600 °C with the aim to determine a time period for uniform heat distribution in test specimens and the mechanical properties of the investigated concrete composite, respectively. Both findings obtained from the presented experimental test as well as experimental data collected from scientific papers so far served for validating the computational accuracy of investigated stress-strain relations for HFRC which have been developed during last few years. Owing to the presence of steel and polypropylene fibers, HFRC becomes a unique material whose structural performance differs from conventional plain concrete when exposed to elevated temperature. Polypropylene fibers in HFRC lower the risk of concrete spalling as the fibers burn out shortly with increasing temperature due to low ignition point and as a consequence pore pressure decreases. On the contrary, the increase in the concrete porosity might affect the mechanical properties of the material. To validate this thought requires enhancing the existing result database which is very limited and does not contain enough data. As a result of the poor database, only few stress-strain relations have been developed so far to describe the structural performance of HFRC at elevated temperature. Moreover, many of them are inconsistent and need to be refined. Most of them also do not take into account the effect of both a fiber type and fiber content. Such approach might be vague especially when high amount of polypropylene fibers are used. Therefore, the existing relations should be validated in detail based on other experimental results.

Keywords: elevated temperature, fiber reinforced concrete, mechanical properties, stress strain relation

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Authors: Dominik Halstrup, Marlene Schriever

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The industry, as one of the main energy consumer, is of critical importance along the way of transforming the energy system to Renewable Energies. The distributed character of the Energy Transition demands for further flexibility being introduced to the grid. In order to shed further light on the acceptance of Electric Energy Storage (ESS) from an industrial point of view, this study therefore examines the German manufacturing industry. The analysis in this paper uses data composed of a survey amongst 101 manufacturing companies in Germany. Being part of a two-stage research design, both qualitative and quantitative data was collected. Based on a literature review an acceptance concept was developed in the paper and four user-types identified: (Dedicated) User, Impeded User, Forced User and (Dedicated) Non-User and incorporated in the questionnaire. Both descriptive and bivariate analysis is deployed to identify the level of acceptance in the different organizations. After a factor analysis has been conducted, variables were grouped to form independent acceptance factors. Out of the 22 organizations that do show a positive attitude towards ESS, 5 have already implemented ESS and show a positive attitude towards ESS. They can be therefore considered ‘Dedicated Users’. The remaining 17 organizations have a positive attitude but have not implemented ESS yet. The results suggest that profitability plays an important role as well as load-management systems that are already in place. Surprisingly, 2 organizations have implemented ESS even though they have a negative attitude towards it. This is an example for a ‘Forced User’ where reasons of overriding importance or supporters with overriding authority might have forced the company to implement ESS. By far the biggest subset of the sample shows (critical) distance and can therefore be considered ‘(Dedicated) Non-Users’. The results indicate that the majority of the respondents have not thought ESS in their own organization through yet. For the majority of the sample one can therefore not speak of critical distance but rather a distance due to insufficient information and the perceived unprofitability. This paper identifies the relative state of acceptance of ESS in the manufacturing industry as well as current reasons for hindrance and perspectives for future growth of ESS in an industrial setting from a policy level. The interest that is currently generated by the media could be channeled and taken into a more substantial and individual discussion about ESS in an industrial setting. If the current perception of profitability could be addressed and communicated accordingly, ESS and their use in for instance cooperative business models could become a topic for more organizations in Germany and other parts of the world. As price mechanisms tend to favor existing technologies, policy makers need to further access the use of ESS and acknowledge the positive effects when integrated in an energy system. The subfields of generation, transmission and distribution become increasingly intertwined. New technologies and business models, such as ESS or cooperative arrangements entering the market, increase the number of stakeholders. Organizations need to find their place within this array of stakeholders.

Keywords: acceptance, energy storage solutions, German energy transition, manufacturing industry

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Moses Kolade Ogun, Ina Korner

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To improve on the sustainable resource management in the wastepaper recycling industry, studies into the valorization of wastes generated by the industry are necessary. The industry produces different residues, among which is the deinking sludge (DS). The DS is generated from the deinking process and constitutes a major fraction of the residues generated by the European pulp and paper industry. The traditional treatment of DS by incineration is capital intensive due to energy requirement for dewatering and the need for complementary fuel source due to DS low calorific value. This could be replaced by a biotechnological approach. This study, therefore, investigated the biogas potential of different DS streams (different dewatering degrees) and the influence of the high calcium carbonate content of DS on its biogas potential. Dewatered DS (solid fraction) sample from filter press and the filtrate (liquid fraction) were collected from a partner wastepaper recycling company in Germany. The solid fraction and the liquid fraction were mixed in proportion to realize DS with different water content (55–91% fresh mass). Spiked samples of DS using deionized water, cellulose and calcium carbonate were prepared to simulate DS with varying calcium carbonate content (0– 40% dry matter). Seeding sludge was collected from an existing biogas plant treating sewage sludge in Germany. Biogas potential was studied using a 1-liter batch test system under the mesophilic condition and ran for 21 days. Specific biogas potential in the range 133- 230 NL/kg-organic dry matter was observed for DS samples investigated. It was found out that an increase in the liquid fraction leads to an increase in the specific biogas potential and a reduction in the absolute biogas potential (NL-biogas/ fresh mass). By comparing the absolute biogas potential curve and the specific biogas potential curve, an optimal dewatering degree corresponding to a water content of about 70% fresh mass was identified. This degree of dewatering is a compromise when factors such as biogas yield, reactor size, energy required for dewatering and operation cost are considered. No inhibitory influence was observed in the biogas potential of DS due to the reported high calcium carbonate content of DS. This study confirms that DS is a potential bioresource for biogas production. Further optimization such as nitrogen supplementation due to DS high C/N ratio can increase biogas yield.

Keywords: biogas, calcium carbonate, deinking sludge, dewatering, water content

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Authors: Sophie Lemoussu, Jean-Charles Chaudemar, Robertus A. Vingerhoeds

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Many innovative projects exist in the field of aeronautics, each addressing specific areas so to reduce weight, increase autonomy, reduction of CO2, etc. In many cases, such innovative developments are being carried out by very small enterprises (VSE’s) or small and medium sized-enterprises (SME’s). A good example concerns airships that are being studied as a real alternative to passenger and cargo transportation. Today, no international regulations propose a precise and sufficiently detailed framework for the development and certification of airships. The absence of such a regulatory framework requires a very close contact with regulatory instances. However, VSE’s/SME’s do not always have sufficient resources and internal knowledge to handle this complexity and to discuss these issues. This poses an additional challenge for those VSE’s/SME’s, in particular those that have system integration responsibilities and that must provide all the necessary evidence to demonstrate their ability to design, produce, and operate airships with the expected level of safety and reliability. The main objective of this research is to provide a methodological framework enabling VSE’s/SME’s with limited resources to organize the development of airships while taking into account the constraints of safety, cost, time and performance. This paper proposes to provide a contribution to this problematic by proposing a Model-Based Systems Engineering approach. Through a comprehensive process modeling approach applied to the development processes, the regulatory constraints, existing best practices, etc., a good image can be obtained as to the process landscape that may influence the development of airships. To this effect, not only the necessary regulatory information is taken on board, also other international standards and norms on systems engineering and project management are being modeled and taken into account. In a next step, the model can be used for analysis of the specific situation for given developments, derive critical paths for the development, identify eventual conflicting aspects between the norms, standards, and regulatory expectations, or also identify those areas where not enough information is available. Once critical paths are known, optimization approaches can be used and decision support techniques can be applied so to better support VSE’s/SME’s in their innovative developments. This paper reports on the adopted modeling approach, the retained modeling languages, and how they all fit together.

Keywords: aeronautics, certification, process modeling, project management, regulation, SME, systems engineering, VSE

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Authors: Kiran Nawaz, Ahmad Ali Shahid, Sehrish Iftikhar, Waheed Anwar, Muhammad Nasir Subhani

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Chili (Solanum annum L.) attacks by many fungal pathogens, including members of Oomycetes which are responsible for root rot in different chili growing areas of the world. Oomycetes pathogens cause economic losses in different regions of the Pakistan. Most of the plant tissues, including roots, crowns, fruit, and leaves, are vulnerable to Phytophthora capsici. It is very difficult to manage the Phytophthora root rot of chili as many commercial varieties are tremendously vulnerable to P. capsici. The causal agent of the disease was isolated on corn meal agar (CMA) and identified on a morphological basis by using available taxonomic keys. The pathogen was also confirmed on the molecular basis through internal transcribed spacer region and with other molecular markers.The Blastn results showed 100% homology with already reported sequences of P. capsici in NCBI database. Most of the farmers have conventionally relied on foliar fungicide applications to control Phytophthora root rot in spite of their incomplete effectiveness. In this study, in vitro plate assay, seed soaking and foliar applications of 6 fungicides were evaluated against root rot of chili. In vitro assay revealed that significant inhibition of linear growth was obtained with Triflumizole at 7.0%, followed by Thiophanate methyl (8.9%), Etridiazole (6.0%), Propamocarb (5.9%) and 7.5% with Mefenoxam and Iprodione for P. capsici. The promising treatments of in vitro plate bioassay were evaluated in pot experiments under controlled conditions in the greenhouse. All fungicides were applied after at 6-day intervals. Results of pot experiment showed that all treatments considerably inhibited the percentage of P. capsici root rot incidence. In addition, application of seed soaking with all six fungicides combined with the foliar spray of the same components showed the significant reduction in root rot incidence. The combine treatments of all fungicides as in vitro bioassay, seed soaking followed by foliar spray is considered non-harmful control methods which have advantages and limitation. Hence, these applications proved effective and harmless for the management of soil-borne plant pathogens.

Keywords: blastn, bioassay, corn meal agar(CMA), oomycetes

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Authors: A. Shaygani, R. Saifi, M. S. Saidi, M. Sani

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Particle size distribution, the most important characteristics of aerosols, is obtained through electrical characterization techniques. The dynamics of charged nano-particles under the influence of electric field in electrical mobility spectrometer (EMS) reveals the size distribution of these particles. The accuracy of this measurement is influenced by flow conditions, geometry, electric field and particle charging process, therefore by the transfer function (transfer matrix) of the instrument. In this work, a wire-cylinder corona charger was designed and the combined field-diffusion charging process of injected poly-disperse aerosol particles was numerically simulated as a prerequisite for the study of a multi-channel EMS. The result, a cloud of particles with non-uniform charge distribution, was introduced to the EMS. The flow pattern and electric field in the EMS were simulated using computational fluid dynamics (CFD) to obtain particle trajectories in the device and therefore to calculate the reported signal by each electrometer. According to the output signals (resulted from bombardment of particles and transferring their charges as currents), we proposed a modification to the size of detecting rings (which are connected to electrometers) in order to evaluate particle size distributions more accurately. Based on the capability of the system to transfer information contents about size distribution of the injected particles, we proposed a benchmark for the assessment of optimality of the design. This method applies the concept of Von Neumann entropy and borrows the definition of entropy from information theory (Shannon entropy) to measure optimality. Entropy, according to the Shannon entropy, is the ''average amount of information contained in an event, sample or character extracted from a data stream''. Evaluating the responses (signals) which were obtained via various configurations of detecting rings, the best configuration which gave the best predictions about the size distributions of injected particles, was the modified configuration. It was also the one that had the maximum amount of entropy. A reasonable consistency was also observed between the accuracy of the predictions and the entropy content of each configuration. In this method, entropy is extracted from the transfer matrix of the instrument for each configuration. Ultimately, various clouds of particles were introduced to the simulations and predicted size distributions were compared to the exact size distributions.

Keywords: aerosol nano-particle, CFD, electrical mobility spectrometer, von neumann entropy

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Authors: Alia Alghosoun, Nabil El Moçayd, Mohammed Seaid

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Modeling dam-break flows over non-flat beds requires an accurate representation of the topography which is the main source of uncertainty in the model. Therefore, developing robust and accurate techniques for reconstructing topography in this class of problems would reduce the uncertainty in the flow system. In many hydraulic applications, experimental techniques have been widely used to measure the bed topography. In practice, experimental work in hydraulics may be very demanding in both time and cost. Meanwhile, computational hydraulics have served as an alternative for laboratory and field experiments. Unlike the forward problem, the inverse problem is used to identify the bed parameters from the given experimental data. In this case, the shallow water equations used for modeling the hydraulics need to be rearranged in a way that the model parameters can be evaluated from measured data. However, this approach is not always possible and it suffers from stability restrictions. In the present work, we propose an adaptive optimal control technique to numerically identify the underlying bed topography from a given set of free-surface observation data. In this approach, a minimization function is defined to iteratively determine the model parameters. The proposed technique can be interpreted as a fractional-stage scheme. In the first stage, the forward problem is solved to determine the measurable parameters from known data. In the second stage, the adaptive control Ensemble Kalman Filter is implemented to combine the optimality of observation data in order to obtain the accurate estimation of the topography. The main features of this method are on one hand, the ability to solve for different complex geometries with no need for any rearrangements in the original model to rewrite it in an explicit form. On the other hand, its achievement of strong stability for simulations of flows in different regimes containing shocks or discontinuities over any geometry. Numerical results are presented for a dam-break flow problem over non-flat bed using different solvers for the shallow water equations. The robustness of the proposed method is investigated using different numbers of loops, sensitivity parameters, initial samples and location of observations. The obtained results demonstrate high reliability and accuracy of the proposed techniques.

Keywords: erodible beds, finite element method, finite volume method, nonlinear elasticity, shallow water equations, stresses in soil

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Authors: Ane Escobar, Paula Angelomé, Mihaela Delcea, Marek Grzelczak, Sergio Enrique Moya

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The antibacterial capacity of bone-anchoring implants can be improved by the use of antibiotics that can be delivered to the media after the surgery. Mesoporous films have shown great potential in drug delivery for orthopedic applications, since pore size and thickness can be tuned to produce different surface area and free volume inside the material. This work shows the synthesis of mesoporous titania films (MTF) by sol-gel chemistry and evaporation-induced self-assembly (EISA) on top of glass substrates. Pores with a diameter of 12nm were observed by Transmission Electron Microscopy (TEM). A film thickness of 100 nm was measured by Scanning Electron Microscopy (SEM). Gentamicin was used to study the antibiotic delivery from the film by means of High-performance liquid chromatography (HPLC). The Staphilococcus aureus strand was used to evaluate the effectiveness of the penicillin loaded films toward inhibiting bacterial colonization. MC3T3-E1 pre-osteoblast cell proliferation experiments proved that MTFs have a good biocompatibility and are a suitable surface for MC3T3-E1 cell proliferation. Moreover, images taken by Confocal Fluorescence Microscopy using labeled vinculin, showed good adhesion of the MC3T3-E1 cells to the MTFs, as well as complex actin filaments arrangement. In order to improve cell proliferation Bone Morphogenetic Protein-2 (BMP-2) was adsorbed on top of the mesoporous film. The deposition of the protein was proved by measurements in the contact angle, showing an increment in the hydrophobicity while the protein concentration is higher. By measuring the dehydrogenase activity in MC3T3-E1 cells cultured in dually functionalized mesoporous titatina films with gentamicin and BMP-2 is possible to find an improvement in cell proliferation. For this purpose, the absorption of a yellow-color formazan dye, product of a water-soluble salt (WST-8) reduction by the dehydrogenases, is measured. In summary, this study proves that by means of the surface modification of MTFs with proteins and loading of gentamicin is possible to achieve an antibacterial effect and a cell growth improvement.

Keywords: antibacterial, biocompatibility, bone morphogenetic protein-2, cell proliferation, gentamicin, implants, mesoporous titania films, osteoblasts

Procedia PDF Downloads 163