Search results for: cost of living

Authors: O. Ojeda Mena, Y. Cressault, P. Teulet, J. P. Gonnet, D. F. N. Santos, MD. Cunha, M. S. Benilov

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The assumption of plasma in local thermal equilibrium (LTE) is commonly used to perform electric arc simulations for industrial applications. This assumption allows to model the arc using a set of magneto-hydromagnetic equations that can be solved with a computational fluid dynamic code. However, the LTE description is only valid in the arc column, whereas in the regions close to the electrodes the plasma deviates from the LTE state. The importance of these near-electrode regions is non-trivial since they define the energy and current transfer between the arc and the electrodes. Therefore, any accurate modelling of the arc must include a good description of the arc-electrode phenomena. Due to the modelling complexity and computational cost of solving the near-electrode layers, a simplified description of the arc-electrode interaction was developed in a previous work to study a steady high-pressure arc discharge, where the near-electrode regions are introduced at the interface between arc and electrode as boundary conditions. The present work proposes a similar approach to simulate the arc ignition in a free-burning arc configuration following an LTE description of the plasma. To obtain the transient evolution of the arc characteristics, appropriate boundary conditions for both the near-cathode and the near-anode regions are used based on recent publications. The arc-cathode interaction is modeled using a non-linear surface heating approach considering the secondary electron emission. On the other hand, the interaction between the arc and the anode is taken into account by means of the heating voltage approach. From the numerical modelling, three main stages can be identified during the arc ignition. Initially, a glow discharge is observed, where the cold non-thermionic cathode is uniformly heated at its surface and the near-cathode voltage drop is in the order of a few hundred volts. Next, a spot with high temperature is formed at the cathode tip followed by a sudden decrease of the near-cathode voltage drop, marking the glow-to-arc discharge transition. During this stage, the LTE plasma also presents an important increase of the temperature in the region adjacent to the hot spot. Finally, the near-cathode voltage drop stabilizes at a few volts and both the electrode and plasma temperatures reach the steady solution. The results after some seconds are similar to those presented for thermionic cathodes.

Keywords: arc-electrode interaction, thermal plasmas, electric arc simulation, cold electrodes

Procedia PDF Downloads 115

Authors: Tennah Safia, Azzag Naouelle, Derdour Salima, Hafsi Fella, Laouadi Mourad, Laamari Abdalouahab, Ghalmi Farida, Kafidi Nacerredine

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Currently, goat farming is widely practiced among the rural population of Algeria. Although milk yield of goats is low (110 liters per goat and per year on average), this milk partly ensures the feeding of small children and provides raw milk, curd, and fermented milk to the whole family. In addition, given its investment cost, which is ten times lower than that of a cow, this level of production is still of interest. This interest is reinforced by the qualities of goat's milk, highly sought after for its nutritional value superior to that of cow's milk. In the same way, its aptitude for the transformation, in particular in quality cheeses, is very sought after. The objective of this study is to give the situation of goat milk production in rural areas of Algeria and to establish a classification of goat breeds according to their production potential. For this, a survey was carried out with goat farmers in Algerian steppe. Three indigenous breeds were encountered in this study: the breed Arabia, Mozabite, and Mekatia; Arabia being the most dominant. The Mekatia breed and the Mozabite breed appear to have higher production and milking abilities than other local breeds. They are therefore indicated to play the role of local dairy breeds par excellence. The other breed that could be improved milk performance is the Arabia breed. There, however, the milk performance of this breed is low. However, in order to increase milk production, uncontrolled crosses with imported breeds (mainly Saanen and Alpine) were carried out. The third population that can be included in the category for dairy production is the dairy breed group of imported origin. There are farms in Algeria composed of Alpine and Saanen breeds born locally. Improved milk performance of local goats, Crusader population, and dairy breeds of imported origin could be done by selection. For this, it is necessary to set up a milk control to detect the best animals. This control could be carried out among interested farmers in each large goat breeding area. In conclusion, sustained efforts must be made to enable the sustainable development of the goat sector in Algeria. It will, therefore, be necessary to deepen the reflection on a national strategy to valorize goat's milk, taking into account the specificities of the environment, the genetic biodiversity, and the eating habits of the Algerian consumer.

Keywords: goat, milk, Algeria, biodiversity

Procedia PDF Downloads 175

Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

Procedia PDF Downloads 82

Authors: Yu-Ju Hsieh, Lin-Hung Lin, Wen-Hui Chang

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This study was designed as an interventional research and intended to validate whether the introduction of drinking vinegar every day can reduce and even prevent urinary tract infection in Taiwan home stayed disabilities who using indwelling catheter. The data was collected from the subjects who have received home care case at northern Taiwan, according to the questionnaire and a medical records retroactive methodology, the subjects were informed and consent to drink 15ml of table vinegar in a daily diet, and through routine urine testing and culture study. Home care nurses would assist collecting urine at the point of before and after a meal from total 35 studied subjects per month, and total collected 4 times for testing. The results showed that when the average age of study subjects was 65.46 years and catheter indwelling time was 15 years, drinking table vinegar could inhibit the activity of E. coli O157: H7 and reduce its breeding. Before drinking table vinegar daily, the subjects’ urine pH value was 7.0-8.0, and the average was 7.5, and the urine PH value dropped to 6.5 after drinking table vinegar for a month. There were two purple urine cases whose urine were changed from purple to normal color after two weeks of drinking, and the protein and bacteria values of urine gradually improved. Urine smell unpleasant before attending to this study, and the symptom improved significantly only after 1 week, and the urine smell returned to normal ammonia and became clean after 1 month later. None of these subjects received treatment in a hospital due to urinary tract infection, and there were no signs of bleeding in all cases during this study. The subjects of this study are chronic patients with a long-term bedridden catheterization; drinking cranberry juice is an economic burden for them, and also highly prohibited for diabetes patients. By adapting to use cheaper table vinegar to acidified urine and improve its smell and ease Purple Urine Syndrome, to furthermore, proven urinary tract infection, it can also to reduce the financial burden on families, the cost of social resources and the rate of re-admission.

Keywords: table vinegar, urinary tract infection, disability patients, long-term indwelling catheter

Procedia PDF Downloads 256

Authors: Yuanxiaoyue Yang

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Emissions trading, or ‘cap-and-trade', has been long promoted by economists as a more cost-effective pollution control approach than traditional performance standard approaches. While there is a large body of empirical evidence for the overall effectiveness of emissions trading, relatively little attention has been paid to other unintended consequences brought by emissions trading. One important consequence is that cap-and-trade could introduce the risk of creating high-level emission concentrations in areas where emitting facilities purchase a large number of emission allowances, which may cause an unequal distribution of environmental benefits. This study will contribute to the current environmental policy literature by linking trading activity with environmental injustice concerns and empirically analyzing the causal relationship between trading activity and emissions reduction under a cap-and-trade program for the first time. To investigate the potential environmental injustice concern in cap-and-trade, this paper uses a differences-in-differences (DID) with instrumental variable method to identify the causal effect of allowance trading behaviors on emission reduction levels under the clean air interstate rule (CAIR), a cap-and-trade program targeting on the power sector in the eastern US. The major data source is the facility-year level emissions and allowance transaction data collected from US EPA air market databases. While polluting facilities from CAIR are the treatment group under our DID identification, we use non-CAIR facilities from the Acid Rain Program - another NOx control program without a trading scheme – as the control group. To isolate the causal effects of trading behaviors on emissions reduction, we also use eligibility for CAIR participation as the instrumental variable. The DID results indicate that the CAIR program was able to reduce NOx emissions from affected facilities by about 10% more than facilities who did not participate in the CAIR program. Therefore, CAIR achieves excellent overall performance in emissions reduction. The IV regression results also indicate that compared with non-CAIR facilities, purchasing emission permits still decreases a CAIR participating facility’s emissions level significantly. This result implies that even buyers under the cap-and-trade program have achieved a great amount of emissions reduction. Therefore, we conclude little evidence of environmental injustice from the CAIR program.

Keywords: air pollution, cap-and-trade, emissions trading, environmental justice

Procedia PDF Downloads 139

Authors: Mikiale Gebreslase Gebremariam, Chai Huaqi, Tesfay Gebretsdkan Gebremichael, Dawit Nega Bekele

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In recent years, brownfield redevelopment projects (BRPs) have contributed to the overarching paradigm of the United Nations 2030 agendas. In the present circumstance, most developed nations adopted BRPs, an efficacious urban policy tool. However, in developing and some advanced countries, BRPs are lacking due to limitations of awareness, policy tools, and financial capability for cleaning up brownfield sites. For example, the growth and development of Ethiopian cities were achieved at the cost of poor urban planning, including no community consultations and excessive urbanization for future growth. The demand for land resources is more and more urgent as the result of an intermigration to major cities and towns for socio-economic reasons and population growth. In the past, the development mode of spreading major cities has made horizontal urbanizations stretching outwards. Expansion in search of more land resources, while the outer cities are growing, the inner cities are polluted by environmental pollution. It is noteworthy that the rapid development of cities has not brought about an increase in people's happiness index. Thus, the proposed management framework for managing brownfields in Ethiopia as a lesson to the developing nation facing similar challenges and growth will add immense value in solving the problems and give insights into brownfield land utilization. Under the umbrella of the grey incidence decision-making model and with the consideration of multiple stakeholders and tight environmental and economic constraints, the proposed management framework integrates different criteria from economic, social, environmental, technical, and risk aspects into the grey incidence decision-making model and gives useful guidance to manage brownfields in Ethiopia. Furthermore, it will contribute to the future development of the social economy and the missions of the 2030 UN sustainable development goals.

Keywords: Brownfields, environmental sustainability, Ethiopia, grey-incidence decision-making, sustainable urban development

Procedia PDF Downloads 82

Authors: Yuvraj S. Malghe, Atul B. Lavand

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In recent years, semiconductor photocatalytic degradation processes have attracted a lot of attention and are used widely for the destruction of organic pollutants present in waste water. Among various semiconductors, titanium dioxide (TiO2) is the most popular photocatalyst due to its excellent chemical stability, non-toxicity, relatively low cost and high photo-oxidation power. It has been known that zinc oxide (ZnO) with band gap energy 3.2 eV is a suitable alternative to TiO2 due to its high quantum efficiency, however it corrodes in acidic medium. Unfortunately TiO2 and ZnO both are active only in UV light due to their wide band gaps. Sunlight consist about 5-7% UV light, 46% visible light and 47% infrared radiation. In order to utilize major portion of sunlight (visible spectrum), it is necessary to modify the band gap of TiO2 as well as ZnO. This can be done by several ways such as semiconductor coupling, doping the material with metals/non metals. Doping of TiO2 using transition metals like Fe, Co and non-metals such as N, C or S extends its absorption wavelengths from UV to visible region. In the present work, we have synthesized ZnO-TiO2 nanocomposite using reverse microemulsion method. Visible light photocatalytic activity of synthesized nanocomposite was investigated for degradation of aqueous solution of malachite green (MG). To increase the photocatalytic activity of ZnO-TiO2 nanocomposite, it is decorated with C and Fe. Pure, carbon (C) doped and carbon, iron(C, Fe) co-doped nanosized ZnO-TiO2 nanocomposites were synthesized using reverse microemulsion method. These composites were characterized using, X-ray diffraction (XRD), Energy dispersive X-ray spectroscopy (EDX), Scanning electron microscopy (SEM), UV visible spectrophotometery and X-ray photoelectron spectroscopy (XPS). Visible light photocatalytic activities of synthesized nanocomposites were investigated for degradation of aqueous malachite green (MG) solution. C, Fe co-doped ZnO-TiO2 nanocomposite exhibit better photocatalytic activity and showed threefold increase in photocatalytic activity. Effect of amount of catalyst, pH and concentration of MG solution on the photodegradation rate is studied. Stability and reusability of photocatalyst is also studied. C, Fe decorated ZnO-TiO2 nanocomposite shows threefold increase in photocatalytic activity.

Keywords: malachite green, nanocomposite, photocatalysis, titanium dioxide, zinc oxide

Procedia PDF Downloads 281

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

Procedia PDF Downloads 157

Authors: Rosine Angelique Uwacu

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Poor household sanitation is rife in Uganda, especially in Kampala. This study was carried out with he goal of establishing the main causes and effects of poor household sanitation in Kansanga parish. The study objectively sought to: To identify various ways through which wastes are generated and disposed of in Kansanga parish, identify different hygiene procedures/behaviors of waste handling in Kansanga parish and assess health effects of poor household sanitation and suggest the recommended appropriate measures of addressing cases of lack of hygiene in Kansanga parish. The study used a survey method where cluster sampling was employed. This is because there is no register of population or sufficient information, or geographic distribution of individuals is widely scattered. Data was collected through the use of interviews accompanied by observation and questionnaires. The study involved a sample of 100 households. The study revealed that; some households use wheeled bin collection, skip hire and roll on/off contained others take their wastes to refuse collection vehicles. Surprisingly, majority of the households submitted that they use polythene bags 'Kavera' and at times plastic sacs to dispose of their wastes which are dumped in drainage patterns or dustbins and other illegal dumping site. The study showed that washing hands with small jerrycans after using the toilet was being adopted by most households as there were no or few other alternatives. The study revealed that the common health effects that come as a result of poor household sanitation in Kansanga Parish are diseases outbreaks such as malaria, typhoid and diarrhea. Finally, the study gave a number of recommendations or suggestions on maintaining and achieving an adequate household sanitation in Kansanga Parish such as sensitization of community members by their leaders like Local Counselors could help to improve the situation, establishment of community sanitation days for people to collectively and voluntarily carry out good sanitation practices like digging trenches, burning garbage and proper waste management and disposal. Authorities like Kampala Capital City Authority should distribute dumping containers or allocate dumping sites where people can dispose of their wastes preferably at a minimum cost for proper management.

Keywords: household sanitation, kansanga parish, Uganda, waste

Procedia PDF Downloads 185

Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

Procedia PDF Downloads 101

Authors: Mayowa Saheed Sanusi A, Musiliu Olushola Sunmonua, Abdulquadri Alakab Owolabi Raheema, Adeyemi Ikimot Adejokea

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The development of healthy cookies for health-conscious consumers cannot be overemphasized in the present global health crisis. This study was aimed to evaluate and model the influence of ripeness levels of papaya puree (unripe, ripe and overripe), oven temperature (130°C, 150°C and 170°C) and oven rack speed (stationary, 10 and 20 rpm) on physicochemical properties of papaya-based cookies using Response Surface Methodology (RSM). The physicochemical properties (baking time, cookies mass, cookies thickness, spread ratio, proximate composition, Calcium, Vitamin C and Total Phenolic Content) were determined using standard procedures. The data obtained were statistically analysed at p≤0.05 using ANOVA. The polynomial regression model of response surface methodology was used to model the physicochemical properties. The adequacy of the models was determined using the coefficient of determination (R²) and the response optimizer of RSM was used to determine the optimum physicochemical properties for the papaya-based cookies. Cookies produced from overripe papaya puree were observed to have the shortest baking time; ripe papaya puree favors cookies spread ratio, while the unripe papaya puree gives cookies with the highest mass and thickness. The highest crude protein content, fiber content, calcium content, Vitamin C and Total Phenolic Content (TPC) were observed in papaya based-cookies produced from overripe puree. The models for baking time, cookies mass, cookies thickness, spread ratio, moisture content, crude protein and TPC were significant, with R2 ranging from 0.73 – 0.95. The optimum condition for producing papaya based-cookies with desirable physicochemical properties was obtained at 149°C oven temperature, 17 rpm oven rack speed and with the use of overripe papaya puree. The Information on the use of puree from unripe, ripe and overripe papaya can help to increase the use of underutilized unripe or overripe papaya and also serve as a strategic means of obtaining a fat substitute to produce new products with lower production cost and health benefit.

Keywords: papaya based-cookies, modeling, response surface methodology, physicochemical properties

Procedia PDF Downloads 158

Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Nimet Yildirim Tirgil, Ahmed Busnaina, April Z. Gu

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Environmental waters are monitored worldwide to protect people from infectious diseases primarily caused by enteric pathogens. All long, Escherichia coli (E. coli) is a good indicator for potential enteric pathogens in waters. Thus, a rapid and simple detection method for E. coli is very important to predict the pathogen contamination. In this study, to the best of our knowledge, as the first time we developed a rapid, direct and reusable SWCNTs (single walled carbon nanotubes) based biosensor system for sensitive and selective E. coli detection in water samples. We use a novel and newly developed flexible biosensor device which was fabricated by high-rate nanoscale offset printing process using directed assembly and transfer of SWCNTs. By simple directed assembly and non-covalent functionalization, aptamer (biorecognition element that specifically distinguish the E. coli O157:H7 strain from other pathogens) based SWCNTs biosensor system was designed and was further evaluated for environmental applications with simple and cost-effective steps. The two gold electrode terminals and SWCNTs-bridge between them allow continuous resistance response monitoring for the E. coli detection. The detection procedure is based on competitive mode detection. A known concentration of aptamer and E. coli cells were mixed and after a certain time filtered. The rest of free aptamers injected to the system. With hybridization of the free aptamers and their SWCNTs surface immobilized probe DNA (complementary-DNA for E. coli aptamer), we can monitor the resistance difference which is proportional to the amount of the E. coli. Thus, we can detect the E. coli without injecting it directly onto the sensing surface, and we could protect the electrode surface from the aggregation of target bacteria or other pollutants that may come from real wastewater samples. After optimization experiments, the linear detection range was determined from 2 cfu/ml to 10⁵ cfu/ml with higher than 0.98 R² value. The system was regenerated successfully with 5 % SDS solution over 100 times without any significant deterioration of the sensor performance. The developed system had high specificity towards E. coli (less than 20 % signal with other pathogens), and it could be applied to real water samples with 86 to 101 % recovery and 3 to 18 % cv values (n=3).

Keywords: aptamer, E. coli, environmental detection, nanobiosensor, SWCTs

Procedia PDF Downloads 189

Authors: Sairi Satari

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Introduction: Six-sigma is a metric that quantifies the performance of processes as a rate of Defects-Per-Million Opportunities. Sigma methodology can be applied in chemical pathology laboratory for evaluating process performance with evidence for process improvement in quality assurance program. In the laboratory, these methods have been used to improve the timeliness of troubleshooting, reduce the cost and frequency of quality control and minimize pre and post-analytical errors. Aim: The aim of this study is to evaluate the sigma values of the Cobas 8000 analyzer based on the minimum requirement of the specification. Methodology: Twenty-one analytes were chosen in this study. The analytes were alanine aminotransferase (ALT), albumin, alkaline phosphatase (ALP), Amylase, aspartate transaminase (AST), total bilirubin, calcium, chloride, cholesterol, HDL-cholesterol, creatinine, creatinine kinase, glucose, lactate dehydrogenase (LDH), magnesium, potassium, protein, sodium, triglyceride, uric acid and urea. Total error was obtained from Clinical Laboratory Improvement Amendments (CLIA). The Bias was calculated from end cycle report of Royal College of Pathologists of Australasia (RCPA) cycle from July to December 2016 and coefficient variation (CV) from six-month internal quality control (IQC). The sigma was calculated based on the formula :Sigma = (Total Error - Bias) / CV. The analytical performance was evaluated based on the sigma, sigma > 6 is world class, sigma > 5 is excellent, sigma > 4 is good and sigma < 4 is satisfactory and sigma < 3 is poor performance. Results: Based on the calculation, we found that, 96% are world class (ALT, albumin, ALP, amylase, AST, total bilirubin, cholesterol, HDL-cholesterol, creatinine, creatinine kinase, glucose, LDH, magnesium, potassium, triglyceride and uric acid. 14% are excellent (calcium, protein and urea), and 10% ( chloride and sodium) require more frequent IQC performed per day. Conclusion: Based on this study, we found that IQC should be performed frequently for only Chloride and Sodium to ensure accurate and reliable analysis for patient management.

Keywords: sigma matrics, analytical performance, total error, bias

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Authors: M. Shahab Uddin, Pennung Warnitchai

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Currently more than half of the world’s populations are living in cities, and the number and sizes of cities are growing faster than ever. Cities rely on the effective functioning of complex and interdependent critical infrastructures networks to provide public services, enhance the quality of life, and save the community from hazards and disasters. In contrast, complex connectivity and interdependency among the urban critical infrastructures bring management challenges and make the urban system prone to the domino effect. Unplanned rapid growth, increased connectivity, and interdependency among the infrastructures, resource scarcity, and many other socio-political factors are affecting the typical state of an urban system and making it susceptible to numerous sorts of diversion. In addition to internal vulnerabilities, urban systems are consistently facing external threats from natural and manmade hazards. Cities are not just complex, interdependent system, but also makeup hubs of the economy, politics, culture, education, etc. For survival and sustainability, complex urban systems in the current world need to manage their vulnerabilities and hazardous incidents more wisely and more interactively. Coordinated management in such systems makes for huge potential when it comes to absorbing negative effects in case some of its components were to function improperly. On the other hand, ineffective management during a similar situation of overall disorder from hazards devastation may make the system more fragile and push the system to an ultimate collapse. Following the quantum, the current research hypothesizes that a hazardous event starts its journey as an emergency, and the system’s internal vulnerability and response capacity determine its destination. Connectivity and interdependency among the urban critical infrastructures during this stage may transform its vulnerabilities into dynamic damaging force. An emergency may turn into a disaster in the absence of effective management; similarly, mismanagement or lack of management may lead the situation towards a catastrophe. Situation awareness and factual decision-making is the key to win a battle. The current research proposed a contextual decision support system for an urban critical infrastructure system while integrating three different models: 1) Damage cascade model which demonstrates damage propagation among the infrastructures through their connectivity and interdependency, 2) Restoration model, a dynamic restoration process of individual infrastructure, which is based on facility damage state and overall disruptions in surrounding support environment, and 3) Optimization model that ensures optimized utilization and distribution of available resources in and among the facilities. All three models are tightly connected, mutually interdependent, and together can assess the situation and forecast the dynamic outputs of every input. Moreover, this integrated model will hold disaster managers and decision makers responsible when it comes to checking all the alternative decision before any implementation, and support to produce maximum possible outputs from the available limited inputs. This proposed model will not only support to reduce the extent of damage cascade but will ensure priority restoration and optimize resource utilization through adaptive and collaborative management. Complex systems predictably fail but in unpredictable ways. System understanding, situation awareness, and factual decisions may significantly help urban system to survive and sustain.

Keywords: disaster prevention, decision support system, emergency response, urban critical infrastructure system

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Authors: Sahar Abbaszadeh, Sharifah Rafidah Wan Alwi, Colin Webb, Nahid Ghasemi, Ida Idayu Muhamad

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Toxic heavy metal discharges into environment due to rapid industrialization is a serious pollution problem that has drawn global attention towards their adverse impacts on both the structure of ecological systems as well as human health. Lead as toxic and bio-accumulating elements through the food chain, is regularly entering to water bodies from discharges of industries such as plating, mining activities, battery manufacture, paint manufacture, etc. The application of conventional methods to degrease and remove Pb(II) ion from wastewater is often restricted due to technical and economic constrains. Therefore, the use of various agro-wastes as low-cost bioadsorbent is found to be attractive since they are abundantly available and cheap. In this study, activated carbon of papaya peel (AC-PP) (as locally available agricultural waste) was employed to evaluate its Pb(II) uptake capacity from single-solute solutions in sets of batch mode experiments. To assess the surface characteristics of the adsorbents, the scanning electron microscope (SEM) coupled with energy disperse X-ray (EDX), and Fourier transform infrared spectroscopy (FT-IR) analysis were utilized. The removal amount of Pb(II) was determined by atomic adsorption spectrometry (AAS). The effects of pH, contact time, the initial concentration of Pb(II) and adsorbent dosage were investigated. The pH value = 5 was observed as optimum solution pH. The optimum initial concentration of Pb(II) in the solution for AC-PP was found to be 200 mg/l where the amount of Pb(II) removed was 36.42 mg/g. At the agitating time of 2 h, the adsorption processes using 100 mg dosage of AC-PP reached equilibrium. The experimental results exhibit high capability and metal affinity of modified papaya peel waste with removal efficiency of 93.22 %. The evaluation results show that the equilibrium adsorption of Pb(II) was best expressed by Freundlich isotherm model (R2 > 0.93). The experimental results confirmed that AC-PP potentially can be employed as an alternative adsorbent for Pb(II) uptake from industrial wastewater for the design of an environmentally friendly yet economical wastewater treatment process.

Keywords: activated carbon, bioadsorption, lead removal, papaya peel, wastewater treatment

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Authors: Kartik Gupta, Virendra Kumar, Sanjeev Sinha, N. Jagannathan

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Introduction: A significant number of patients having human immunodeficiency virus (HIV) infection show a neurocognitive decline (NCD) ranging from minor cognitive impairment to severe dementia. The possible causes of NCD in HIV-infected patients include brain injury by HIV before cART, neurotoxic viral proteins and metabolic abnormalities. In the present study, we compared the level of NCD in asymptomatic HIV-infected patients with changes in brain metabolites measured by using magnetic resonance spectroscopy (MRS). Methods: 43 HIV-positive patients (30 males and 13 females) coming to ART center of the hospital and HIV-seronegative healthy subjects were recruited for the study. All the participants completed MRI and MRS examination, detailed clinical assessments and a battery of neuropsychological tests. All the MR investigations were carried out at 3.0T MRI scanner (Ingenia/Achieva, Philips, Netherlands). MRI examination protocol included the acquisition of T2-weighted imaging in axial, coronal and sagittal planes, T1-weighted, FLAIR, and DWI images in the axial plane. Patients who showed any apparent lesion on MRI were excluded from the study. T2-weighted images in three orthogonal planes were used to localize the voxel in left frontal lobe white matter (FWM) and left basal ganglia (BG) for single voxel MRS. Single voxel MRS spectra were acquired with a point resolved spectroscopy (PRESS) localization pulse sequence at an echo time (TE) of 35 ms and a repetition time (TR) of 2000 ms with 64 or 128 scans. Automated preprocessing and determination of absolute concentrations of metabolites were estimated using LCModel by water scaling method and the Cramer-Rao lower bounds for all metabolites analyzed in the study were below 15\%. Levels of total N-acetyl aspartate (tNAA), total choline (tCho), glutamate + glutamine (Glx), total creatine (tCr), were measured. Cognition was tested using a battery of tests validated for Indian population. The cognitive domains tested were the memory, attention-information processing, abstraction-executive, simple and complex perceptual motor skills. Z-scores normalized according to age, sex and education standard were used to calculate dysfunction in these individual domains. The NCD was defined as dysfunction with Z-score ≤ 2 in at least two domains. One-way ANOVA was used to compare the difference in brain metabolites between the patients and healthy subjects. Results: NCD was found in 23 (53%) patients. There was no significant difference in age, CD4 count and viral load between the two groups. Maximum impairment was found in the domains of memory and simple motor skills i.e., 19/43 (44%). The prevalence of deficit in attention-information processing, complex perceptual motor skills and abstraction-executive function was 37%, 35%, 33% respectively. Subjects with NCD had a higher level of Glutamate in the Frontal region (8.03 ± 2.30 v/s. 10.26 ± 5.24, p-value 0.001). Conclusion: Among newly diagnosed, ART-naïve retroviral disease patients from India, cognitive decline was found in 53\% patients using tests validated for this population. Those with neurocognitive decline had a significantly higher level of Glutamate in the left frontal region. There was no significant difference in age, CD4 count and viral load at initiation of ART between the two groups.

Keywords: HIV, neurocognitive decline, neurometabolites, magnetic resonance spectroscopy

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Authors: Md. Shoffikul Islam, Hongqing Hu

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Reducing trace elements' mobility and bioavailability through amendment addition, especially biochar (BC), is a cost-effective and efficient method to address their toxicity in the soil environment. However, the low molecular weight organic acids (LMWOAs) in the rhizosphere could affect BC's efficiency to stabilize trace metals as the LMWOAs could either mobilize or fix metals in the soils. Therefore, understanding the BC's and LMWOAs' interaction mechanisms on metals stabilization in the rhizosphere is crucial. The present study explored the impact of BC derived from rice husk and citric acid (CA) and the combination of BC and CA on the redistribution of cadmium (Cd), lead (Pb), and zinc (Zn) among their geochemical forms through incubation experiment. The changes of zeta potential and X-ray diffraction (XRD) pattern of BC and BC-amended soils to investigate the probable mechanisms of trace elements' immobilization by BC under the CA attack were also examined. The rice husk BC at 5% (w/w) was mixed with the air-dry soil (an Anthrosols) contaminated with Cd, Pb, and Zn in the plastic pot. The 2, 5, 10, and 20 mM kg-1 (w/v) of CA were added separately into the pot. All the ingredients were mixed thoroughly with the soil. A control (CK) treatment was also prepared without BC and CA addition. After 7, 15, and 60 days of incubation with 60% (w/v) moisture level at 25 °C, the incubated soils were determined for pH and EC and were sequentially extracted to assess the metals' transformation in soil. The electronegative charges and XRD peaks of BC and BC-amended soils were also measured. Compared to CK, the application of BC, low level of CA (2 mM kg-1 soil) (CA2), and BC plus the low concentration of CA (BC-CA2) considerably declined the acid-soluble Cd, Pb, and Zn in which BC-CA2 was found to be the most effective treatment. The reversed trends were observed concerning the high levels of CA (>5-20 mM kg-1 soil) and the BC plus high concentrations of CA treatments. BC-CA2 changed the highest amounts of acid-soluble and reducible metals to the oxidizable and residual forms with time. The most increased electronegative charges of BC-CA2 indicate its (BC-CA2) highest Cd, Pb, and Zn immobilizing efficiency, probably through metals adsorption and fixation with the negative charge sites. The XRD study revealed the presence of P, O, CO32-, and Cl1- in BC, which might be responsible for the precipitation of CdCO3, pyromorphite, and hopeite in the case of Cd, Pb, and Zn immobilization, respectively. The findings depicted that the low concentration of CA increased metals' stabilization, whereas the high levels of CA enhanced their mobilization. The BC-CA2 emerged as the best amendment among treatments for metals stabilization in contaminated soils.

Keywords: Biochar, citric acid, immobilization, trace elements contaminated soil

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Authors: Daniel Muwooya

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This paper analyses economic factors that have affected the competitiveness of petroleum refinery and petrochemical projects in sub-Saharan Africa in the past and continue to plague greenfield projects today. Traditional factors like plant sizing and complexity, low-capacity utilization, changing regulatory environment, and tighter product specifications have been important in the past. Additional factors include the development of excess refinery capacity in Asia and the growth of renewable sources of energy – especially for transportation. These factors create both challenges and opportunities for the development of greenfield refineries and petrochemical projects in areas of increased demand growth and new low-cost crude oil production – like sub-Saharan Africa. This paper evaluates the strategies available to project developers and host countries to address contemporary issues of energy transition and the apparent reduction of funds available for greenfield oil and gas projects. The paper also evaluates the structuring of greenfield refinery and petrochemical projects for limited recourse project finance bankability. The methodology of this paper includes analysis of current industry data, conference proceedings, academic papers, and academic books on the subjects of petroleum refinery economics, refinery financing, refinery operations, and project finance generally and specifically in the oil and gas industry; evaluation of expert opinions from journal articles; working papers from international bodies like the World Bank and the International Energy Agency; and experience from playing an active role in the development and financing of US$ 10 Billion greenfield oil development project in Uganda. The paper also applies the discounted cash flow modelling to illustrate the circumstances of an inland greenfield refinery project in Uganda. Greenfield refinery and petrochemical projects are still necessary in sub-Saharan Africa to, among other aspirations, support the transition from traditional sources of energy like biomass to such modern forms as liquefied petroleum gas. Project developers and host governments will be required to structure projects that support global climate change goals without occasioning undue delays to project execution.

Keywords: financing, refinery and petrochemical economics, Africa, project finance

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Authors: Usman Dahiru, Faisal Saleem, Kui Zhang, Adam Harvey

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Volatile organic compounds (VOCs) are atmospheric contaminants predominantly derived from petroleum spills, solvent usage, agricultural processes, automobile, and chemical processing industries, which can be detrimental to the environment and human health. Environmental problems such as the formation of photochemical smog, organic aerosols, and global warming are associated with VOC emissions. Research showed a clear relationship between VOC emissions and cancer. In recent years, stricter emission regulations, especially in industrialized countries, have been put in place around the world to restrict VOC emissions. Non-thermal plasmas (NTPs) are a promising technology for reducing VOC emissions by converting them into less toxic/environmentally friendly species. The dielectric barrier discharge (DBD) plasma is of interest due to its flexibility, moderate capital cost, and ease of operation under ambient conditions. In this study, a dielectric barrier discharge (DBD) reactor has been developed for the decomposition of cyclohexane (as a VOC model compound) using nitrogen, dry, and humidified air carrier gases. The effect of specific input energy (1.2-3.0 kJ/L), residence time (1.2-2.3 s) and concentration (220-520 ppm) were investigated. It was demonstrated that the removal efficiency of cyclohexane increased with increasing plasma power and residence time. The removal of cyclohexane decreased with increasing cyclohexane inlet concentration at fixed plasma power and residence time. The decomposition products included H₂, CO₂, H₂O, lower hydrocarbons (C₁-C₅) and solid residue. The highest removal efficiency (98.2%) was observed at specific input energy of 3.0 kJ/L and a residence time of 2.3 s in humidified air plasma. The effect of humidity was investigated to determine whether it could reduce the formation of solid residue in the DBD reactor. It was observed that the solid residue completely disappeared in humidified air plasma. Furthermore, the presence of OH radicals due to humidification not only increased the removal efficiency of cyclohexane but also improves product selectivity. This work demonstrates that cyclohexane can be converted to smaller molecules by a dielectric barrier discharge (DBD) non-thermal plasma reactor by varying plasma power (SIE), residence time, reactor configuration, and carrier gas.

Keywords: cyclohexane, dielectric barrier discharge reactor, non-thermal plasma, removal efficiency

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Authors: K. V. Venugopalan

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Micro, Small and Medium Enterprises (MSMEs) have been accepted as the engine of economic growth and for promoting equitable development. The major advantage of the sector is its employment potential at low capital cost. The labour intensity of the MSME sector is much higher than that of the large enterprises. The MSMEs constitute over 90% of total enterprises in most of the economies and are credited with generating the highest rates of employment growth and account for a major share of industrial production and exports. Kerala is a small state in India with the limited land area with high potential in educated human resources need micro, small and medium enterprises for development. Kerala has the highest Physical Quality of Life Index (PQLI) in India and the highest Human Development Index (HDI) at par with the developed countries SME play an important role in alleviating poverty and contribute significantly towards the growth of developing economies. Financial institutions can play a vital role for the promotion of micro, small and medium enterprises in Kerala. The study entitled “Financial Institutions and its role in the promotion of Small and Medium Enterprises in Kerala “examine the progress of MSME in Kerala and India and also the role of financial institutions and the problems faced by entrepreneurs for getting advances with reference to ‘Kerala Financial Corporation’-an agency set up by the government for promoting small and medium enterprises in the state. This study is based on both secondary and primary data. Primary data for the study was collected from those entrepreneurs who availed advances from financial institutions. The secondary data include the investment made, goods and services provided, the employment generated and the number of units registered in MSME sector for the last 10 years in Kerala. The study concluded that financial institutions providing finance with simple procedures and charging smaller interest rates will increase the number of MSME's and also contribute gross state domestic product and reduce the unemployment problem and poverty in the economy.

Keywords: gross state domestic product, human development index, micro, small and medium enterprises

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Authors: Scott R. Butler

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Ontario, Canada’s largest province, is in the midst of an interesting experiment in mandated asset management planning for local governments. At the beginning of 2021, Ontario’s 444 municipalities were responsible for the management of 302,864 lane kilometers of roads that have a replacement cost of $97.545 billion CDN. Roadways are by far the most complex, expensive, and extensive assets that a municipality is responsible for overseeing. Since adopting Ontario Regulation 588/47: Asset Management Planning for Municipal Infrastructure in 2017, the provincial government has established prescriptions for local road authorities regarding asset category and levels of service being provided. This provincial regulation further stipulates that asset data such as extent, condition, and life cycle costing are to be captured in manner compliant with qualitative descriptions and technical metrics. The Ontario Good Roads Association undertook an exercise to aggregate the road-related data contained within the 444 asset management plans that municipalities have filed with the provincial government. This analysis concluded that collectively Ontario municipal roadways have a $34.7 billion CDN in deferred maintenance. The ill-state of repair of Ontario municipal roads has lasting implications for province’s economic competitiveness and has garnered considerable political attention. Municipal efforts to address the maintenance backlog are stymied by the extremely limited fiscal parameters municipalities must operate within in Ontario. Further exacerbating the program are provincially designed programs that are ineffective, administratively burdensome, and not necessarily aligned with local priorities or strategies. This paper addresses how municipal asset management plans – and more specifically, the data contained in these plans – can be used to design innovative policy frameworks, flexible funding programs, and new levels of service that respond to these funding challenges, as well as emerging issues such as local economic development and climate change. To fully unlock the potential that Ontario Regulation 588/17 has imposed will require a resolute commitment to data standardization and horizontal collaboration between municipalities within regions.

Keywords: transportation, municipal asset management, subnational policy design, subnational funding program design

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Authors: Beryl Rene R. Lopez, Lesley Anne M. Lipat, Rhogene Barbette C. Lirio, Laurice Joy H. Llanes, Karl Philippe M. Llapitan, Einstein James R. Lopez, Socorro S. GuanHing

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Background: Measles remain as one of the most common childhood diseases despite the availability of the vaccine that is safe and cost-effective. Because of morbidity and mortality associated with the recent measles outbreak in the Philippines, there is an increasing concern from the health care professionals. Objective: The purpose of this study is to determine the relationship between the compliance of Filipino mothers to measles vaccination and their health beliefs when grouped according to the given socio-demographic factors using a researcher-made questionnaire. Research Methodology: This research utilized the descriptive-correlational research design. With the use of purposive sampling technique, the study involved 200 Filipino mothers aged 18 years old and above excluding those who are healthcare professionals with children aged 2-3 years old with either urban or rural as their settlements. Pre-testing was done prior to the actual data gathering. A questionnaire composed of 26 items involving socio-demographic, compliance, and health beliefs was distributed to the sample population. Statistical analysis was done with the use of Exploratory Factor Analysis (EFA) for the first research question and Structural Equation Model (SEM) for the second research question. Results: Four dimensions were generated with the use of EFA namely: Vulnerability-Oriented Beliefs (VOB), Knowledge-Oriented Beliefs (KOB), Accessibility-Oriented Beliefs (AOB), and Outcomes-Oriented Beliefs (OOB). These were then correlated with the mothers’ socio-demographic factors (age, educational attainment, the area of residence, the number of children, and family income) and their compliance to the measles vaccination schedule. Results showed significant and direct relationships between area of residence and compliance, family income and compliance, KOB and compliance, education and KOB, KOB and VOB, KOB and OOB, AOB and KOB, AOB and OOB, AOB and VOB, and lastly, OOB and VOB. Conclusion: The Knowledge – Oriented Belief dimension greatly influence compliance to measles vaccination. Other determinants of compliance like the area of residence, educational attainment, and family income significantly increase the Filipino mothers’ likelihood of compliance to measles vaccination, which have implications to health education.

Keywords: socio-demographic, health beliefs, compliance, measles vaccination

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Authors: Chi-Chang Lin, Jian-Rong Wu, Jiun-Yan Chiu

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Myopia, ubiquitous symptom that is necessary to correct the eyesight by optical lens struggles many people for their daily life. Recent years, younger people raise interesting on using contact lens because of its convenience and aesthetics. In clinical, the risk of eye infections increases owing to the behavior of incorrectly using contact lens unsupervised cleaning which raising the infection risk of cornea, named ocular keratitis. In order to overcome the identification needs, new detection or analysis method with rapid and more accurate identification for clinical microorganism is importantly needed. In our study, we take advantage of Raman spectroscopy having unique fingerprint for different functional groups as the distinct and fast examination tool on microorganism. As we know, Raman scatting signals are normally too weak for the detection, especially in biological field. Here, we applied special SERS enhancement substrates to generate higher Raman signals. SERS filter we designed in this article that prepared by deposition of silver nanoparticles directly onto cellulose filter surface and suspension nanoparticles - gold nanostars (AuNSs) also be introduced together to achieve better enhancement for lower concentration analyte (i.e., various bacteria). Research targets also focusing on studying the shape effect of synthetic AuNSs, needle-like surface morphology may possible creates more hot-spot for getting higher SERS enhance ability. We utilized new designed SERS technology to distinguish the bacteria from ocular keratitis under strain level, and specific Raman and SERS fingerprint were grouped under pattern recognition process. We reported a new method combined different SERS substrates can be applied for clinical microorganism detection under strain level with simple, rapid preparation and low cost. Our presenting SERS technology not only shows the great potential for clinical bacteria detection but also can be used for environmental pollution and food safety analysis.

Keywords: bacteria, gold nanostars, Raman spectroscopy surface-enhanced Raman scattering filter

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Authors: Ádám Vass, István Bakos, Irina Borbáth, Zoltán Pászti, István Sajó, András Tompos

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Important requirements for the anode side electrocatalysts of polymer electrolyte membrane (PEM) fuel cells are CO-tolerance, stability and corrosion resistance. Carbon is still the most common material for electrocatalyst supports due to its low cost, high electrical conductivity and high surface area, which can ensure good dispersion of the Pt. However, carbon becomes degraded at higher potentials and it causes problem during application. Therefore it is important to explore alternative materials with improved stability. Molybdenum-oxide can improve the CO-tolerance of the Pt/C catalysts, but it is prone to leach in acidic electrolyte. The Mo was stabilized by isovalent substitution of molybdenum into the rutile phase titanium-dioxide lattice, achieved by a modified multistep sol-gel synthesis method optimized for preparation of Ti0.7Mo.3O2-C composite. High degree of Mo incorporation into the rutile lattice was developed. The conductivity and corrosion resistance across the anticipated potential/pH window was ensured by mixed oxide – activated carbon composite. Platinum loading was carried out using NaBH4 and ethylene glycol; platinum content was 40 wt%. The electrocatalyst was characterized by both material investigating methods (i.e. XRD, TEM, EDS, XPS techniques) and electrochemical methods (cyclic-voltammetry, COads stripping voltammetry, hydrogen oxidation reaction on rotating disc electrode). The electrochemical activity of the sample was compared to commercial 40 wt% Pt/C (Quintech) and PtRu/C (Quintech, Pt= 20 wt%, Ru= 10 wt%) references. Enhanced CO tolerance of the electrocatalyst prepared using the Ti0.7Mo.3O2-C composite material was evidenced by the appearance of a CO-oxidation related 'pre-peak' and by the pronounced shift of the maximum of the main CO oxidation peak towards less positive potential compared to Pt/C. Fuel cell polarization measurements were also carried out using Bio-Logic and Paxitech FCT-150S test device. All details on the design, preparation, characterization and testing by both electrochemical measurements and fuel cell test device of electrocatalyst supported on Ti0.7Mo.3O2-C composite material will be presented and discussed.

Keywords: anode electrocatalyst, composite material, CO-tolerance, TiMoOx

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Authors: Asim Abbasi, Muhammad Sufyan, Iqra, Hafiza Javaria Ashraf

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The share of vector-borne diseases (VBDs) in the global burden of infectious diseases is almost 17%. The advent of new drugs and latest research in medical science helped mankind to compete with these lethal diseases but still diseases transmitted by different mosquito species, including filariasis, malaria, viral encephalitis and dengue are serious threats for people living in disease endemic areas. Injudicious and repeated use of pesticides posed selection pressure on mosquitoes leading to development of resistance. Hence biological control agents are under serious consideration of scientific community to be used in vector control programmes. Fish have a history of predating immature stages of different aquatic insects including mosquitoes. The noteworthy examples in Africa and Asia includes, Aphanius discolour and a fish in the Panchax group. Moreover, common mosquito fish, Gambusia affinis predates mostly on temporary water mosquitoes like anopheline as compared to permanent water breeders like culicines. Mosquitoes belonging to genus Toxorhynchites have a worldwide distribution and are mostly associated with the predation of other mosquito larvae habituating with them in natural and artificial water containers. These species are harmless to humans as their adults do not suck human blood but feeds on floral nectar. However, their activity is mostly temperature dependent as Toxorhynchites brevipalpis consume 359 Aedes aegypti larvae at 30-32 ºC in contrast to 154 larvae at 20-26 ºC. Although many bacterial species were isolated from mosquito cadavers but those belonging to genus Bacillus are found highly pathogenic against them. The successful species of this genus include Bacillus thuringiensis and Bacillus sphaericus. The prime targets of B. thuringiensis are mostly the immatures of genus Aedes, Culex, Anopheles and Psorophora while B. sphaericus is specifically toxic against species of Culex, Psorophora and Culiseta. The entomopathogenic nematodes belonging to family, mermithidae are also pathogenic to different mosquito species. Eighty different species of mosquitoes including Anopheles, Aedes and Culex proved to be highly vulnerable to the attack of two mermithid species, Romanomermis culicivorax and R. iyengari. Cytoplasmic polyhedrosis virus was the first described pathogenic virus, isolated from the cadavers of mosquito specie, Culex tarsalis. Other viruses which are pathogenic to culicine includes, iridoviruses, cytopolyhedrosis viruses, entomopoxviruses and parvoviruses. Protozoa species belonging to division microsporidia are the common pathogenic protozoans in mosquito populations which kill their host by the chronic effects of parasitism. Moreover, due to their wide prevalence in anopheline mosquitoes and transversal and horizontal transmission from infected to healthy host, microsporidia of the genera Nosema and Amblyospora have received much attention in various mosquito control programmes. Fungal based mycopesticides are used in biological control of insect pests with 47 species reported virulent against different stages of mosquitoes. These include both aquatic fungi i.e. species of Coelomomyces, Lagenidium giganteum and Culicinomyces clavosporus, and the terrestrial fungi Metarhizium anisopliae and Beauveria bassiana. Hence, it was concluded that the integrated use of all these biological control agents can be a healthy contribution in mosquito control programmes and become a dire need of the time to avoid repeated use of pesticides.

Keywords: entomopathogenic nematodes, protozoa, Toxorhynchites, vector-borne

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Authors: Amin Karimi, Lele Zhang, Mark Fackrell

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Rural-to-urban population migrations are a global phenomenon, with projections indicating that by 2050, 68% of the world's population will inhabit densely populated urban centers. Concurrently, the popularity of e-commerce shopping has surged, evidenced by a 51% increase in total e-commerce sales from 2017 to 2021. Consequently, distribution and logistics systems, integral to effective supply chain management, confront escalating hurdles in efficiently delivering and distributing products within bustling urban environments. Additionally, events like environmental challenges and the COVID-19 pandemic have indicated that decision-makers are facing numerous sources of uncertainty. Therefore, to design an efficient and reliable logistics system, uncertainty must be considered. In this study, it examine fleet sizing and routing while considering uncertainty in demand rate. Fleet sizing is typically a strategic-level decision, while routing is an operational-level one. In this study, a carrier must make two types of decisions: strategic-level decisions regarding the number and types of drones to be purchased, and operational-level decisions regarding planning routes based on available fleet and realized demand. If the available fleets are insufficient to serve some customers, the carrier must outsource that delivery at a relatively high cost, calculated per order. With this hierarchy of decisions, it can model the problem using two-stage stochastic programming. The first-stage decisions involve planning the number and type of drones to be purchased, while the second-stage decisions involve planning routes. To solve this model, it employ logic-based benders decomposition, which decomposes the problem into a master problem and a set of sub-problems. The master problem becomes a mixed integer programming model to find the best fleet sizing decisions, and the sub-problems become capacitated vehicle routing problems considering battery status. Additionally, it assume a heterogeneous fleet based on load and battery capacity, and it consider that battery health deteriorates over time as it plan for multiple periods.

Keywords: drone-delivery, stochastic demand, VRP, fleet sizing

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Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

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Authors: Martina Maria Keitsch, Sangeeta Singh

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There has been a growing recognition that sustainable development needs to consider economic, social and environmental aspects including gender. In Nepal, the majority of the population lives in rural areas, and many households do not have access to electricity. In rural areas, the difficulty of accessing energy is becoming one of the greatest constraints for improving living conditions. This is particularly true for women and children, who spent much time for collecting firewood and cooking and thus are often deprived of time for education, political- and business activities. The poster introduces an education and research project financed by the Norwegian Government. The project runs from 2015-2020 and is a collaboration between the Norwegian University of Science (NTNU) and Technology Institute of Engineering (IOE), Tribhuvan University. It has the title Master program and Research in Energy for Sustainable Social Development Energy for Sustainable Social Development (MSESSD). The project addresses engineering and architecture students and comprises several integral activities towards gender mainstreaming. The following activities are conducted; 1. Creating academic opportunities, 2. Updating administrative personnel on strategies to effectively include gender issues, 3. Integrating female and male stakeholders in the design process, 4. Sensitizing female and male students for gender issues in energy systems. The project aims to enable students to design end-user-friendly solutions which can, for example, save time that can be used to generate and enhance income. Relating to gender mainstreaming, design concepts focus on smaller-scale technologies, which female stakeholders can take control of and manage themselves. Creating academic opportunities, we have a 30% female students’ rate in each master student batch in the program with the goal to educate qualified female personnel for academia and policy-making/government. This is a very ambitious target in a Nepalese context. The rate of female students, who completed the MSc program at IOE between 1998 and January 2015 is 10% out of 180 students in total. For recruiting, female students were contacted personally and encouraged to apply for the program. Further, we have established a Master course in gender mainstreaming and energy. On an administrative level, NTNU has hosted a training program for IOE on gender-mainstreaming information and -strategies for academic education. Integrating female and male stakeholders, local women groups such as, e.g., mothers group are actively included in research and education for example in planning, decision-making, and management to establish clean energy solutions. The project meets women’s needs not just practically by providing better technology, but also strategically by providing solutions that enhance their social and economic decision-making authority. Sensitizing the students for gender issues in energy systems, the project makes it mandatory to discuss gender mainstreaming based on the case studies in the Master thesis. All activities will be discussed in detail comprising an overview of MSESSD, the gender mainstreaming master course contents’, and case studies where energy solutions were co-designed with men and women as lead-users and/or entrepreneurs. The goal is to motivate educators to develop similar forms of transnational gender collaboration.

Keywords: knowledge generation on gender mainstreaming, sensitizing students, stakeholder inclusion, education strategies for design and architecture in gender mainstreaming, facilitation for cooperation

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