Search results for: cost prediction

Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Busra Mutlu Ipek, Merve Mutlu, Ahmet Mutlu

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Novel coronavirus COVID-19, which has recently influenced the world, poses a great threat to humanity. In order to overcome this challenging situation, scientists are working on developing effective vaccine against coronavirus. Many experts and researchers have also produced articles and done studies on this highly important subject. In this direction, this special topic was chosen for article to make a contribution to this area. The purpose of this article is to perform RNA sequence analysis of selected virus forms in the Coronaviridae family and predict/classify SARS-CoV-2 (COVID-19) from other selected complete genomes in coronaviridae family using proposed Artificial Neural Network(ANN) algorithm.

Keywords: Coronaviridae family, COVID-19, RNA sequencing, ANN, neural network

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Authors: Lívia B. Meirelles, Erika C. A. N. Chrisman, Flávia B. de Andrade, Lilian C. M. de Oliveira

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True Boiling Point distillation (TBP) is one of the most common experimental techniques for the determination of petroleum properties. This curve provides information about the performance of petroleum in terms of its cuts. The experiment is performed in a few days. Techniques are used to determine the properties faster with a software that calculates the distillation curve when a little information about crude oil is known. In order to evaluate the accuracy of distillation curve prediction, eight points of the TBP curve and specific gravity curve (348 K and 523 K) were inserted into the HYSYS Oil Manager, and the extended curve was evaluated up to 748 K. The methods were able to predict the curve with the accuracy of 0.6%-9.2% error (Software X ASTM), 0.2%-5.1% error (Software X Spaltrohr).

Keywords: distillation curve, petroleum distillation, simulation, true boiling point curve

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Authors: Leonor Varandas, Pedro D. Gaspar, Martim L. Aguiar

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One of the biggest concerns in the field of agriculture is around the energy efficiency of robots that will perform agriculture’s activity and their charging methods. In this paper, two different charging methods for agricultural standalone docking stations are shown that will take into account various variants as field size and its irregularities, work’s nature to which the robot will perform, deadlines that have to be respected, among others. Its features also are dependent on the orchard, season, battery type and its technical specifications and cost. First charging base method focuses on wireless charging, presenting more benefits for small field. The second charging base method relies on battery replacement being more suitable for large fields, thus avoiding the robot stop for recharge. Existing many methods to charge a battery, the CC CV was considered the most appropriate for either simplicity or effectiveness. The choice of the battery for agricultural purposes is if most importance. While the most common battery used is Li-ion battery, this study also discusses the use of graphene-based new type of batteries with 45% over capacity to the Li-ion one. A Battery Management Systems (BMS) is applied for battery balancing. All these approaches combined showed to be a promising method to improve a lot of technical agricultural work, not just in terms of plantation and harvesting but also about every technique to prevent harmful events like plagues and weeds or even to reduce crop time and cost.

Keywords: agricultural mobile robot, charging methods, battery replacement method, wireless charging method

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Authors: Nikolai Brosch, Alwine Mohnen

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Academics and business leaders increasingly call for a (re)definition of a corporate purpose beyond profit-maximization to contribute to the welfare of society. This study investigates the effect of communicating such a pro-social corporate purpose on three employee-level outcomes that constitute major cost components for most organizations: workers reservation wage, work quality, and work misbehavior. To provide causal evidence, two natural field experiments were conducted with almost 2,000 workers recruited from different online labor marketplaces. Workers were randomly assigned to treatments manipulating whether or not they received information about the employer’s corporate purpose and subsequently performed a short, real-effort task for payment. The main findings in both experiments show that receiving information about an employer’s pro-social corporate purpose causes workers to accept lower wages (9% lower in the first experiment and 28% lower in the second experiment) for the same job. Workers that personally assess high importance to organizations having a pro-social purpose are most responsive. At the same time, sacrificing wage for a corporate purpose comes at no cost of quality and even decreases the likelihood of engaging in work misbehavior. In a broader context, the results provide some evidence that the (re)definition of corporate purpose in commercial organizations is not ultimately at odds with creating profits.

Keywords: corporate purpose, natural field experiment, reservation wage, work misbehavior, work quality

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Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

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We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

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Authors: Parmjit S. Panesar, Rupinder Kaur, Ram S. Singh

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Enzymes are the biocatalysts which catalyze the biochemical processes and thus have a wide variety of applications in the industrial sector. β-Galactosidase (E.C. 3.2.1.23) also known as lactase, is one of the prime enzymes, which has significant potential in the dairy and food processing industries. It has the capability to catalyze both the hydrolytic reaction for the production of lactose hydrolyzed milk and transgalactosylation reaction for the synthesis of prebiotics such as lactulose and galactooligosaccharides. These prebiotics have various nutritional and technological benefits. Although, the enzyme is naturally present in almonds, peaches, apricots and other variety of fruits and animals, the extraction of enzyme from these sources increases the cost of enzyme. Therefore, focus has been shifted towards the production of low cost enzyme from the microorganisms such as bacteria, yeast and fungi. As compared to yeast and bacteria, fungal β-galactosidase is generally preferred as being extracellular and thermostable in nature. Keeping the above in view, the present study was carried out for the isolation of the β-galactosidase producing fungal strain from the food as well as the agricultural wastes. A total of more than 100 fungal cultures were examined for their potential in enzyme production. All the fungal strains were screened using X-gal and IPTG as inducers in the modified Czapek Dox Agar medium. Among the various isolated fungal strains, the strain exhibiting the highest enzyme activity was chosen for further phenotypic and genotypic characterization. The strain was identified as Rhizomucor pusillus on the basis of 5.8s RNA gene sequencing data.

Keywords: beta-galactosidase, enzyme, fungal, isolation

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Authors: Motwkel M. Alhaj, Bashir M. Elhassan

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The East Nile area is located in Khartoum state. The main source of drinking water in the East Nile Area (Sudan) is groundwater. However, fluoride concentration in the water is more than the maximum allowable dose, which is 1.5 mg/l. This study aims to demonstrate and innovative, affordable, and efficient filter to remove fluoride from drinking water. Many researchers have found that aluminum oxide-coated adsorbent is the most affordable technology for fluoride removal. However, adsorption is pH-dependent, and the water pH in the East Nile area is relatively high (around 8), which is hindering the adsorption process. Locally available charcoal was crushed, sieved, and coated with aluminum oxide. Then, different coating configurations were tested in order to produce an adsorbent with a high pH point of zero charge pH PZC in order to overcome the effect of high pH of water. Moreover, different methods were used to characterize the adsorbent, including: Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDX), Brunauer - Emmett - Teller (BET) method, and pH point of zero charge pH PZC. The produced adsorbent has pH PZC of 8.5, which is essential in enhancing the fluoride adsorption process. A pilot household fluoride filter was also designed and installed in a house that has water with 4.34 mg/l F- and pH of 8.4. The filter was operated at a flow rate 250 cm³/min. The total cost of treating one cubic meter was about 0.63$, while the cost for the same water before adsorbent coating modification was 2.33$⁄cm³.

Keywords: water treatment, fluoride, adsorption, charcoal, Sudan

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

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One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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Authors: Mokrane Selma

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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Chang He, Hiroshi Tamura, Hiroshi Katsuchi, Jiaqi Wang

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In this study, the aim is to infer the slip limit (static friction limit) of contact interfaces in bolt friction joints by analyzing other bolt friction joints with the same contact surface but in a different shape. By using the Weibull distribution to deal with microelements on the contact surface statistically, the slip limit of a certain type of bolt joint was predicted from other types of bolt joint with the same contact surface. As a result, this research succeeded in predicting the slip limit of bolt joins with different numbers of contact surfaces and with different numbers of bolt rows.

Keywords: bolt joints, slip coefficient, finite element method, Weibull distribution

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Authors: Min Wei

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The traditional prediction methods of the forecast for tourism market are paid more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. With the application of Linear Regression Model, this paper attempts to construct a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation. The findings show is that a scientific evaluation system can implement the scientific concept of development, the harmonious development of man and nature co-ordinate.

Keywords: linear regression model, tourism market, forecast, tourism economics

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Authors: Stephanie Aguirre Moreno, Jesús Everardo Olguín Tiznado, Claudia Camargo Wilson, Juan Andrés López Barreras

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This research work presents a proposal to evaluate the environmental performance of a Higher Education Institution (HEI) in Mexico in order to minimize their environmental impact. Given that public education has limited financial resources, it is necessary to conduct studies that support priorities in decision-making situations and thus obtain the best cost-benefit ratio of continuous improvement programs as part of the environmental management system implemented. The methodology employed, adapted from the Environmental Effect Analysis (EEA), weighs the environmental aspects identified in the environmental diagnosis by two characteristics. Number one, environmental priority through the perception of the stakeholders, compliance of legal requirements, and environmental impact of operations. Number two, the possibility of improvement, which depends of factors such as the exchange rate that will be made, the level of investment and the return time of it. The highest environmental priorities, or hot spots, identified in this evaluation were: electricity consumption, water consumption and recycling, and disposal of municipal solid waste. However, the possibility of improvement for the disposal of municipal solid waste is higher, followed by water consumption and recycling, in spite of having an equal possibility of improvement to the energy consumption, time of return and cost-benefit is much greater.

Keywords: environmental performance, environmental priority, possibility of improvement, continuous improvement programs

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Authors: Sina Moradi, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Patrick Hayde, Rose Amal

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Chloramine is commonly used as a disinfectant in drinking water distribution systems (DWDSs), particularly in Australia and the USA. Maintaining a chloramine residual throughout the DWDS is important in ensuring microbiologically safe water is supplied at the customer’s tap. In order to simulate how chloramine behaves when it moves through the distribution system, a water quality network model (WQNM) can be applied. In this work, the WQNM was based on mono-chloramine decomposition reactions, which enabled prediction of mono-chloramine residual at different locations through a DWDS in Australia, using the Bentley commercial hydraulic package (Water GEMS). The accuracy of WQNM predictions is influenced by a number of water quality parameters. Optimization of these parameters in order to obtain the closest results in comparison with actual measured data in a real DWDS would result in both cost reduction as well as reduction in consumption of valuable resources such as energy and materials. In this work, the optimum operating conditions of water quality parameters (i.e. temperature, pH, and initial mono-chloramine concentration) to maximize the accuracy of mono-chloramine residual predictions for two water supply scenarios in an entire network were determined using response surface methodology (RSM). To obtain feasible and economical water quality parameters for highest model predictability, Design Expert 8.0 software (Stat-Ease, Inc.) was applied to conduct the optimization of three independent water quality parameters. High and low levels of the water quality parameters were considered, inevitably, as explicit constraints, in order to avoid extrapolation. The independent variables were pH, temperature and initial mono-chloramine concentration. The lower and upper limits of each variable for two water supply scenarios were defined and the experimental levels for each variable were selected based on the actual conditions in studied DWDS. It was found that at pH of 7.75, temperature of 34.16 ºC, and initial mono-chloramine concentration of 3.89 (mg/L) during peak water supply patterns, root mean square error (RMSE) of WQNM for the whole network would be minimized to 0.189, and the optimum conditions for averaged water supply occurred at pH of 7.71, temperature of 18.12 ºC, and initial mono-chloramine concentration of 4.60 (mg/L). The proposed methodology to predict mono-chloramine residual can have a great potential for water treatment plant operators in accurately estimating the mono-chloramine residual through a water distribution network. Additional studies from other water distribution systems are warranted to confirm the applicability of the proposed methodology for other water samples.

Keywords: chloramine decay, modelling, response surface methodology, water quality parameters

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Authors: David W. Hill, Linda W. Glass, Jakob L. Vingren

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Maximal accumulated oxygen deficit (MAOD), the gold standard measure of anaerobic capacity, is the difference between the oxygen cost of exhaustive severe intensity exercise and the accumulated oxygen consumption (O2; mL·kg–1). In theory, MAOD can be estimated as the sum of independent estimates of the phosphocreatine and glycolysis contributions, which we refer to as PCr+glycolysis. Purpose: The purpose was to test the hypothesis that PCr+glycolysis provides a valid measure of anaerobic capacity in cycling and running. Methods: The participants were 27 women (mean ± SD, age 22 ±1 y, height 165 ± 7 cm, weight 63.4 ± 9.7 kg) and 25 men (age 22 ± 1 y, height 179 ± 6 cm, weight 80.8 ± 14.8 kg). They performed two exhaustive cycling and running tests, at speeds and work rates that were tolerable for ~5 min. The rate of oxygen consumption (VO2; mL·kg–1·min–1) was measured in warmups, in the tests, and during 7 min of recovery. Fingerprick blood samples obtained after exercise were analysed to determine peak blood lactate concentration (PeakLac). The VO2 response in exercise was fitted to a model, with a fast ‘primary’ phase followed by a delayed ‘slow’ component, from which was calculated the accumulated O2 and the excess O2 attributable to the slow component. The VO2 response in recovery was fitted to a model with a fast phase and slow component, sharing a common time delay. Oxygen demand (in mL·kg–1·min–1) was determined by extrapolation from steady-state VO2 in warmups; the total oxygen cost (in mL·kg–1) was determined by multiplying this demand by time to exhaustion and adding the excess O2; then, MAOD was calculated as total oxygen cost minus accumulated O2. The phosphocreatine contribution (area under the fast phase of the post-exercise VO2) and the glycolytic contribution (converted from PeakLac) were summed to give PCr+glycolysis. There was not an interaction effect involving sex, so values for anaerobic capacity were examined using a two-way ANOVA, with repeated measures across method (PCr+glycolysis vs MAOD) and mode (cycling vs running). Results: There was a significant effect only for exercise mode. There was no difference between MAOD and PCr+glycolysis: values were 59 ± 6 mL·kg–1 and 61 ± 8 mL·kg–1 in cycling and 78 ± 7 mL·kg–1 and 75 ± 8 mL·kg–1 in running. Discussion: PCr+glycolysis is a valid measure of anaerobic capacity in cycling and running, and it is as valid for women as for men.

Keywords: alactic, anaerobic, cycling, ergometer, glycolysis, lactic, lactate, oxygen deficit, phosphocreatine, running, treadmill

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Authors: Jian Li

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Carotenoids area group of metabolites in mostly photosynthetic organisms such as plants and microalgae and have wide applications in cosmetics, food, feed, and health industries. Although phototrophic cultivation of microalgae has been developed to produce some carotenoids for decades, most carotenoids are currently synthesized chemically at industrial scales because of affordable production costs. Chemical carotenoids are regarded not as safe for human beings as natural carotenoids and are restricted only for animal feed markets, and the industries call for inexpensive sources of natural products. Microalgae grow much quicker in mixotrophy than in phototrophy, and thus mixotrophic cultivation processes have great potential to reduce the production cost of carotenoids from microalgae. However, much more expensive photobioreactor systems and more strictly controlled sterile processes are needed to avoid contamination by heterotrophic organisms during mixotrophic cultivation processes, which makes mixotrophy, in fact, much more expensive than phototrophic cultivation. Recently technical breakthroughs have been reported to overcome contamination problems in photobioreactor systems traditionally used for phototrophic cultivation, and a much lower process cost of mixotrophic cultivation than that of phototrophic cultivation might be achieved for carotenoid production. These reviews intend to summarize recent technical advancements in mixotrophic cultivation of microalgae, to evaluate the economic viability of carotenoid production from mixotrophically cultivated microalgae, and to prospect mixotrophy as a strategy to produce a variety of carotenoids for industrial applications.

Keywords: microalgae, carotenoid, mixotrophy, biotechnology

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Authors: Nima Mahmoudi, Hamzeh Khazaei

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Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

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Authors: Taehyun Kim

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As a result of rapid urbanization, energy demands for lighting, heating and cooling of households have been concentrated in metropolitan areas. The energy resources for housing in urban areas are dominantly fossil fuel whose uses contribute to increase cost of living and carbon dioxide (CO2) emission. To achieve environmentally and economically sustainable residential development, it is important to know how energy use and cost of living can be reduced by planning and design. The purpose of this study is to examine which type of building requires less energy for housing. To do so, carbon footprint (CF) quiz survey was employed which estimates the amount of carbon dioxide required to support households’ consumption of energy uses for housing. The housing carbon footprints (HCF) of 500 households of Seoul, Korea in summer and winter were estimated and compared in three major types of housing: single-family (detached), row-house and apartment. In addition, its differences of HCF were estimated between tower and flat type of apartment. The results of T-test and analysis of variance (ANOVA) provide statistical evidence that housing type is related to housing energy use. Average HCF of detached house was higher than other housing types. Between two types of apartment, tower type shows higher HCF than flat type in winter. These findings may provide new perspectives on CF application in sustainable architecture and urban design.

Keywords: analysis of variance, carbon footprint, energy use, housing type

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Authors: Jing Nan

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The air breakdown voltage in compact circuit breakers is a critical factor in the design and reliability of electrical distribution systems. This voltage determines the threshold at which the air insulation between conductors will fail or 'break down,' leading to an arc. This phenomenon is highly sensitive to the conditions within the breaker, such as the temperature and the distance between electrodes. Typically, air breakdown voltage models have been reliable for predicting failure under standard operational temperatures. However, in conditions post-arcing, where temperatures can soar above 2000K, these models face challenges due to the complex physics of ionization and electron behaviour at such high-energy states. Building upon the foundational understanding that the breakdown mechanism is initiated by free electrons and propelled by electric fields, which lead to ionization and, potentially, to avalanche or streamer formation, we acknowledge the complexity introduced by high-temperature environments. Recognizing the limitations of existing experimental data, a notable research gap exists in the accurate prediction of breakdown voltage at elevated temperatures, typically observed post-arcing, where temperatures exceed 2000K.To bridge this knowledge gap, we present a method that integrates gap distance and high-temperature effects into air breakdown voltage assessment. The proposed model is grounded in the physics of ionization, accounting for the dynamic behaviour of free electrons which, under intense electric fields at elevated temperatures, lead to thermal ionization and potentially reach the threshold for streamer formation as Meek's criterion. Employing the Saha equation, our model calculates equilibrium electron densities, adapting to the atmospheric pressure and the hot temperature regions indicative of post-arc temperature conditions. Our model is rigorously validated against established experimental data, demonstrating substantial improvements in predicting air breakdown voltage in the high-temperature regime. This work significantly improves the predictive power for air breakdown voltage under conditions that closely mimic operational stressors in compact circuit breakers. Looking ahead, the proposed methods are poised for further exploration in alternative insulating media, like SF6, enhancing the model's utility for a broader range of insulation technologies and contributing to the future of high-temperature electrical insulation research.

Keywords: air breakdown voltage, high-temperature insulation, compact circuit breakers, electrical discharge, saha equation

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Authors: M. Lebrini, L. Salhi, C. Deyrat, C. Roos, O. Nait-Rabah

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The paper presents a large experimental study on the corrosion of zinc in tropical soil and in the ground water at the various depths. Through this study, the corrosion rate prediction was done on the basis of two methods the electrochemical method and the gravimetric. The electrochemical results showed that the corrosion rate is more important at the depth levels 0 m to 0.5 m and 0.5 m to 1 m and beyond these depth levels, the corrosion rate is less important. The electrochemical results indicated also that a passive layer is formed on the zinc surface. The found SEM and EDX micrographs displayed that the surface is extremely attacked and confirmed that a zinc oxide layer is present on the surface whose thickness and relief increase as the contact with soil increases.

Keywords: soil corrosion, galvanized steel, electrochemical technique, SEM and EDX

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Authors: William J. Crowther, Conor Marsh

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Object-based flow simulation allows fast computation of arbitrarily complex aerodynamic models made up of simple objects with limited flow interactions. The proposed approach is universally applicable to objects made from arbitrarily scaled ellipsoid primitives at arbitrary aerodynamic attitude and angular rate. The use of a component-based aerodynamic modelling approach increases efficiency by allowing selective inclusion of different physics models at run-time and allows extensibility through the development of new models. Insight into the numerical stability of the model under first order fixed-time step integration schemes is provided by stability analysis of the drag component. The compute cost of model components and functions is evaluated and compared against numerical benchmarks. Model static outputs are verified against theoretical expectations and dynamic behaviour using falling plate data from the literature. The model is applied to a range of case studies to demonstrate the efficacy of its application in extensibility, ease of use, and low computational cost. Dynamically complex multi-body systems can be implemented in a transparent and efficient manner, and we successfully demonstrate large scenes with hundreds of objects interacting with diverse flow fields.

Keywords: aerodynamics, real-time simulation, low-order model, flight dynamics

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Authors: Mohamed H. Sorour, Hayam F. Shaalan, Heba A. Hani, Eman S. Sayed

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Ion exchange adsorption has a long standing history of success for seawater softening and selective ion removal from saline sources. Strong, weak and mixed types ion exchange systems could be designed and optimized for target separation. In this paper, different types of adsorbents comprising zeolite 13X and kaolin, in addition to, poly acrylate/zeolite (AZ), poly acrylate/kaolin (AK) and stand-alone poly acrylate (A) hydrogel types were prepared via microwave (M) and ultrasonic (U) irradiation techniques. They were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The developed adsorbents were evaluated on bench scale level and based on assessment results, a composite bed has been formulated for performance evaluation in pilot scale column investigations. Owing to the hydrogel nature of the partially crosslinked poly acrylate, the developed adsorbents manifested a swelling capacity of about 50 g/g. The pilot trials have been carried out using magnesium enriched Red Seawater to simulate Red Seawater desalination brine. Batch studies indicated varying uptake efficiencies, where Mg adsorption decreases according to the following prepared hydrogel types AU>AM>AKM>AKU>AZM>AZU, being 108, 107, 78, 69, 66 and 63 mg/g, respectively. Composite bed adsorbent tested in the up-flow mode column studies indicated good performance for Mg uptake. For an operating cycle of 12 h, the maximum uptake during the loading cycle approached 92.5-100 mg/g, which is comparable to the performance of some commercial resins. Different regenerants have been explored to maximize regeneration and minimize the quantity of regenerants including 15% NaCl, 0.1 M HCl and sodium carbonate. Best results were obtained by acidified sodium chloride solution. In conclusion, developed cation exchange adsorbents comprising clay or zeolite support indicated adequate performance for Mg recovery under saline environment. Column design operated at the up-flow mode (approaching expanded bed) is appropriate for such type of separation. Preliminary cost indicators for Mg recovery via ion exchange have been developed and analyzed.

Keywords: batch and dynamic magnesium separation, seawater, polyacrylate hydrogel, cost evaluation

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Authors: Mahua Banerjee, Debtanu Maiti

Abstract:

Zero-tillage cultivation is a farming practice that reduces costs while maintaining harvests and protecting the environment. Innovative partnerships among researchers, farmers, and other actors in the agricultural value chain have enabled the adoption of zero-tillage to sow rice in the Indo-Gangetic Plains, increasing farmers' incomes, fostering more sustainable use of soil and water, and providing a platform for cropping diversification and the introduction of other resource-conserving practices. A field experiment was conducted in the farmer’s field of Ausgram I Block, Burdwan, West Bengal, India under sandy loam soil with soil pH of 5.2, which is low in Nitrogen, medium in Phosphorus and Potassium. There were three techniques of tillage-T1: Zero tillage in Rice, T2: conventional tillage in Rice, T3: Rice grown with Drum seeder and three varieties namely V1: MTU 7029 V2-MTU 1010, V3: Pratikha thus making nine treatment combinations which were replicated thrice and the experiment was laid out in Factorial Randomised Block Design. Among the three varieties, rice variety MTU 7029 gave higher yield in all the tillage techniques. The highest yield was obtained under Zero tillage followed by conventional tillage. From economic analysis it was revealed that the benefit:cost ratio was higher in Zero tillage and rice cultivation by drum seeder. Zero-till is increasingly being adopted because it gives more yield at less cost, saves labour and farmer time. Farmers will be interested in this technology once they overcome their tillage biases.

Keywords: economics, Indo-Gangetic plain, rice, zero tillage, yield

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Authors: Rouhallah Bagheri, Morteza Mahmoudi, Hadi Moheb-Alizadeh

Abstract:

This paper aims at developing a multi-objective model for supplier selection and order allocation problem in stochastic environment, where purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability distribution. In this regard, dependent chance programming is used which maximizes probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. The abovementioned stochastic multi-objective programming problem is then transformed into a stochastic single objective programming problem using minimum deviation method. In the next step, the further problem is solved applying a genetic algorithm, which performs a simulation process in order to calculate the stochastic objective function as its fitness function. Finally, the impact of stochastic parameters on the given solution is examined via a sensitivity analysis exploiting coefficient of variation. The results show that whatever stochastic parameters have greater coefficients of variation, the value of the objective function in the stochastic single objective programming problem is deteriorated.

Keywords: supplier selection, order allocation, dependent chance programming, genetic algorithm

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Authors: Shubham Damke, Md Imtiaz, Sanchita Dei

Abstract:

To stimulate the well and to enhance the production from the shaly formations, fracturing is essential. Presently the chiefly employed technology is Hydraulic Fracturing. However Hydraulic Fracturing accompanies itself with problems like disposing large volumes of fracturing wastewater, removal of water from the pores, formation damage due to injection of large amount of chemicals into underground formations and many more. Therefore embarking on the path of innovation new techniques have been developed which uses different gases such as Nitrogen, Carbon dioxide, Frac Oil, LPG, etc. are used as a base fluid for fracturing formation. However LPG proves to be the most favorable of them which eliminates the use of water and chemicals. When using it as a fracturing fluid, within the surface equipment, it is stored, gelled, and proppant blended at a constant pressure. It is then pressurized with high pressure pumps to the required surface injection pressure With lowering the total cost and increasing the productivity, LPG is also very noteworthy for fracturing shale, where if the hydraulic fracturing is done the water ‘swells’ the formation and creates surface tension, both of which inhibit the flow of oil and gas. Also fracturing with LPG increases the effective fracture length and since propane, butane and pentane is used which are already present in the natural gas therefore there is no problem of back flow because these gases get mixed with the natural gas. LPG Fracturing technology can be a promising substitute of the Hydraulic Fracturing, which could substantially reduce the capital cost of fracturing shale and will also restrict the problems with the disposal of water and on the same hand increasing the fracture length and the productivity from the shale.

Keywords: Fracking, Shale, Surface Tension, Viscosity

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Authors: Natrah Mohd Saad, Muhammad Irfan Abdul Jalal, Ahmad Termidzi Mohd Azhar, Mohd Arman Kamaruddin, Mohd Raziff Alias, Nazihah Abd Jalal, Norliza Ismail, Chin Siok-Fong, Goh Ying-Xian, Noraidatulakma Abdullah, Ismail Sagap, Zairul Azwan Mohd Azman, Azmawati Mohammed Nawi, Nor Halizam Ismail, Rahman Jamal, Azimatun Noor Aizuddin, Nor Azian Abdul Murad

Abstract:

Colorectal cancer (CRC) ranks among the leading causes of incidence and mortality globally, including in Malaysia. Despite the availability of multiple screening modalities that have proven effective in saving lives and improving survival rates, most patients present at a late stage. The World Health Organization (WHO) recommends large-scale population screening due to its effectiveness and cost-efficiency. This study aims to evaluate the budget impact of implementing cohort and community-based approaches for CRC screening. A budget impact analysis was conducted following the ISPOR Task Force guidelines, projecting a 5-year expenditure for each screening strategy. All relevant costs for the intervention approaches are from the provider’s perspective. Calculation of costs incurred for risk stratification of the eligible population, awareness and health education, CRC screening using iFOBT, diagnostic colonoscopy, patient navigation, consultation for positive iFOBT test participants, and referral for further management if CRC was diagnosed was done using a cost calculator programmed in Microsoft Excel, following the costing template produced by the National Institute for Health and Care Excellence in the UK. The current CRC screening strategy in Malaysia costs USD11,489,983.73 to detect 2,478 CRC cases in the first year, with a 176% increase in costs over 5 years. The Malaysian Cohort (TMC) CRC Screening Programme is projected to cost USD34,476,768.86 for detecting 13,557 cases in the first year, with a 34% increase over 5 years. The community intervention approach is estimated to cost USD54,890,417.02 for detecting 6,735 CRC cases in the first year, with a 78% increase over 5 years. The budget impact is highly sensitive to CRC screening uptake. The analysis reveals that implementing the cohort and community intervention approaches would incur costs 3 times and 4.7 times higher, respectively, compared to the current strategy. Assuming consistent costs for screening, iFOBT positive rates, and detection rates, the cohort approach requires USD2,542.76 to detect one CRC case, while the current approach costs USD4,636.55 per case. This suggests that the cohort approach is more affordable. A larger-scale screening programme would increase the number of early-stage CRC detection, ultimately reducing the need for treatment.

Keywords: BIA, cancer screening, CRC, economic evaluation, iFOBT

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Authors: Johanna Perdomo, Riki Lamont, Edmund Pickering, Naomi C. Paxton, Maria A. Woodruff

Abstract:

Background: Tissue Engineering (TE) has combined advanced materials, such as biomaterials, to create affordable scaffolds and dynamic systems to generate stimulation of seeded cells on these scaffolds, improving and maintaining the cellular growth process in a cell culture. However, Few TE skin products have been clinically translated, and more research is required to produce highly biomimetic skin substitutes that mimic the native elasticity of skin in a controlled manner. Therefore, this work will be focused on the fabrication of a novel mechanical system to enhance the TE treatment approaches for the reparation of damaged tissue skin. Aims: To archive this, a soft robotic device will be created to emulate different deformation of skin stress. The design of this soft robot will allow the attachment of scaffolds, which will then be mechanically actuated. This will provide a novel and highly adaptable platform for dynamic cell culture. Methods: Novel, low-cost soft robot is fabricated via 3D printed moulds and silicone. A low cost, electro-mechanical device was constructed to actuate the soft robot through the controlled combination of positive and negative air pressure to control the different state of movements. Mechanical tests were conducted to assess the performance and calibration of each electronic component. Similarly, pressure-displacement test was performed on scaffolds, which were attached to the soft robot, applying various mechanical loading regimes. Lastly, digital image correlation test was performed to obtain strain distributions over the soft robot’s surface. Results: The control system can control and stabilise positive pressure changes for long hours. Similarly, pressure-displacement test demonstrated that scaffolds with 5µm of diameter and wavy geometry can displace at 100%, applying a maximum pressure of 1.5 PSI. Lastly, during the inflation state, the displacement of silicone was measured using DIC method, and this showed a parameter of 4.78 mm and strain of 0.0652. Discussion And Conclusion: The developed soft robot system provides a novel and low-cost platform for the dynamic actuation of tissue scaffolds with a target towards dynamic cell culture.

Keywords: soft robot, tissue engineering, mechanical stimulation, dynamic cell culture, bioreactor

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