Search results for: forecast combination

Authors: Niya Chen, Jennifer Chan

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In the financial market, risk management helps to minimize potential loss and maximize profit. There are two ways to assess risks; the first way is to calculate the risk directly based on the volatility. The most common risk measurements are Value at Risk (VaR), sharp ratio, and beta. Alternatively, we could look at the quantile of the return to assess the risk. Popular return models such as GARCH and stochastic volatility (SV) focus on modeling the mean of the return distribution via capturing the volatility dynamics; however, the quantile/expectile method will give us an idea of the distribution with the extreme return value. It will allow us to forecast VaR using return which is direct information. The advantage of using these non-parametric methods is that it is not bounded by the distribution assumptions from the parametric method. But the difference between them is that expectile uses a second-order loss function while quantile regression uses a first-order loss function. We consider several quantile functions, different volatility measures, and estimates from some volatility models. To estimate the expectile of the model, we use Realized Conditional Autoregressive Expectile (CARE) model with the bayesian method to achieve this. We would like to see if our proposed models outperform existing models in cryptocurrency, and we will test it by using Bitcoin mainly as well as Ethereum.

Keywords: expectile, CARE Model, CARR Model, quantile, cryptocurrency, Value at Risk

Procedia PDF Downloads 109

Authors: Nishu Atrey, Priyanka Singh, G. P. Talwar, Jagdish Gupta, Alka Kriplani, Rohini Sehgal, Indrani Ganguli, Soni Sinha

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Background: Vaginosis is a widely prevalent syndrome in India and elsewhere. Recurrence is frequent in women treated with antibiotics, whose vagina pH remains above 5.0 indicative of the loss of resident lactobacilli. The objective of the present trial was to determine whether a Polyherbal microbicide BASANT can regress Vaginosis. Another objective was to determine whether the three selected strains of Probiotics endowed with making high amounts of lactic acid can colonise and restore the pH of the vagina to the acidic healthy range. Materials and Procedure: BASANT, was employed in powder form in veg (cellulose) capsules. TRF#36 strain of Lactobacillus fermentum, TRF#8 strain of L.gasseri, and TRF#30 strain of L.salivarius (combination termed as Pro-vag-Health) were employed at 3x109 bacilli lyophilized, packaged in capsules. The trials were conducted in women suffering from vaginosis with vaginal pH above 5.0. Women were given intravaginally either BASANT, Pro-vag-Health or a combination of the two intravaginally for seven days and thereafter once weekly as a maintenance dose. Results: BASANT cleared vaginosis in 14/20 women and Pro-vag-Health in 13/20 women. Interestingly, the combination of BASANT plus Pro-vag-Health was effective in 19/20 women, in contrast to Placebo capsules effective only in 1/20 women. Interpretation and Conclusion: The combination of BASANT and Pro-veg-Health Probiotics taken together intravaginally for seven days relieves 19 out of 20 women from vaginosis to restore acidic pH and healthy vagina. Extension of trial with this combination in larger number is indicated.

Keywords: microbicide, probiotics, vaginal pH, vaginosis

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Authors: S. A. Nirmal, J. M. Ingale, G. S. Asane, S. C. Pal, Subhash C. Mandal

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The present work was undertaken to determine the effects of Amaranthus roxburghianus Nevski. (Amaranthaceae) root alone and in combination with piperine in treating ulcerative colitis (UC) in mice. Swiss albino mice were divided into seven groups (n = 6). Standard group received prednisolone (5 mg/kg, i.p.). Treatment groups received hydroalcoholic extract of roots of A. roxburghianus (50 and 100 mg/kg, p.o.) and a combination of hydroalcoholic extract of roots of A. roxburghianus (50 and 100 mg/kg, p.o.) and piperine (5 mg/kg, p.o.). Ulcer index, colitis severity, myeloperoxidase (MPO), malondialdehyde and glutathione were estimated from blood and tissue. Column chromatography of the extract was done and purified fractions were analyzed by gas chromatography-mass spectroscopy (GC-MS). Treatment with the combination of hydroalcoholic extract of A. roxburghianus and piperine showed minimal ulceration, hemorrhage, necrosis and leucocyte infiltration by histopathological observation. Acetic acid increased MPO levels in blood and colon tissue to 355 U/mL and 385 U/mg, respectively. The combination of hydroalcoholic extract (100 mg/kg) and piperine (5 mg/kg) significantly decreased MPO in blood and tissue to 182 U/mL and 193 U/mg, respectively. Similarly, this combination significantly reduced MPO and increased glutathione levels in blood and tissue. Various phytoconstituents were detected by GC-MS. The combination of hydroalcoholic extract and piperine is effective in the treatment of UC and the effects are comparable with the standard drug prednisolone. 4H-pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl, eugenol and benzene, and 1-(1,5-dimethyl-4-hexenyl)-4-methyl are reported having analgesic, anti-inflammatory, and antioxidant properties; they may play a role in the biological activity of A. roxburghianus root.

Keywords: Amaranthus roxburghianus, ulcerative colitis, anti-inflammatory, ulcerative colitis

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi

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This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series.

Keywords: stochastic models, ARIMA, extreme streamflow, Karkheh river

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Authors: Maitreyee Datta

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In Anātmalaksana Sutra, Buddha talks about the importance of anattā (no-self). This notion of no-self is a critical response towards the Brahmanical tradition of classical India in which self has been taken to be eternal. Though self is taken to be eternal, ‘I’ refer to Person who is the self as determined by non-eternal body. Buddha raises questions regarding the possibility of the association between eternal self and non-eternal body. According to him, such an association is not possible. Thus, instead of an eternal self and its association with the non-eternal body, he speaks about association among five different non-eternal parts (skandhas). He holds that ‘I’ refers to Person, but this Person is not eternal self as determined by the non-eternal body. It is the combination of five different skandhas each of which is non-eternal. So according to Buddha, there is no eternal self which in association with non-eternal body is referred to as ‘I,’ but ‘I’ is a convenient designator which designates the combination of five non-eternal skandhas. If ‘I’ is taken to refer the combination of five non-eternal skandhas, then the problematic of the association between eternal self (attā) and non-eternal body will not be there. The realization that ‘I’ does not refer to any eternal self as determined by non-eternal body, but instead refer to the combination of five non-eternal skandhas leads to the cessation of suffering (duhkkha). The root of suffering lies in craving for something or the other. Thus, as soon as one realizes that the person is not constituted of any eternal self but is constituted of non-eternal skandhas, his desire to acquire and possess will be stopped. Thus, in the whole conceptual framework of Buddhist philosophy, anattā occupies a pivotal role the realization of which is admitted to be the cause of the cessation of suffering. In the present paper, an effort will be made to analyse this notion of anattā to show how the realization of the truth that person is a combination of five skandhas each of which is non-eternal helps an individual to get rid of the bondage. If eternal self is to be admitted, then there always remains the problem of connecting the eternal self with the non-eternal body, because this connection only gives rise to the notion of person in such framework.

Keywords: anatta, atta, duhkkha, skandha

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Authors: Dina Fatmawati, Sofia Mubarika, Mae Sri Wahyuningsih

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Chemotherapy for breast cancer is largely ineffective, but innovative combinations of chemotherapeutic agents and natural compounds represent a promising strategy. In our previous study, the combination of Doxorubicin (Dox) and ethanolic extract of Dioscorea esculenta tuber ((EED) was found to have a synergistic effect on T47D human breast cancer cell line. In this study, we investigated the apoptotic effect of the combination on T47D human breast cancer cells and normal fibroblasts cell line and its effects on the expression of Caspase-3 and cleaved poly (ADP-Ribose) Polymerase-1 (cPARP-1) protein. T47D cell lines and fibroblasts cells were treated with the combination of Dox and EED. Apoptotic effect of the combination was determined using flow cytrometry assay. Protein expressions were determined by immunocytochemistry staining. The percentage of apoptotic cells were significantly higher in T47D cell lines (75%) than that of in fibroblast cells (23%). The expression of Caspase 3 (84.53%) and cPARP-1 (83.36%) were significantly higher in the cancer cell lines than those of normal cells. These results indicate that the combination of doxorubicin and Dioscorea esculenta is a promising candidate for the treatment of breast cancer cells.

Keywords: Dioscorea esculenta, Doxorubicin, apoptosis, immunocytochemistry, cancer cells

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Authors: Jeonghwan Jeon, Mintak Han, Youngjun Kim

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Due to the rapid development of information and communication technology (ICT), a substantial transformation is currently happening in the society. As the range of intelligent technologies and services is continuously expanding, ‘things’ are becoming capable of communicating one another and even with people. However, such “Internet of Things” has the technical weakness so that a great amount of such information transferred in real-time may be widely exposed to the threat of security. User’s personal data are a typical example which is faced with a serious security threat. The threats of security will be diversified and arose more frequently because next generation of unfamiliar technology develops. Moreover, as the society is becoming increasingly complex, security vulnerability will be increased as well. In the existing literature, a considerable number of private and public reports that forecast future society have been published as a precedent step of the selection of future technology and the establishment of strategies for competitiveness. Although there are previous studies that forecast security technology, they have focused only on technical issues and overlooked the interrelationships between security technology and social factors are. Therefore, investigations of security threats in the future and security technology that is able to protect people from various threats are required. In response, this study aims to derive potential security threats associated with the development of technology and to explore the security technology that can protect against them. To do this, first of all, private and public reports that forecast future and online documents from technology-related communities are collected. By analyzing the data, future issues are extracted and categorized in terms of STEEP (Society, Technology, Economy, Environment, and Politics), as well as security. Second, the components of potential security threats are developed based on classified future issues. Then, points that the security threats may occur –for example, mobile payment system based on a finger scan technology– are identified. Lastly, alternatives that prevent potential security threats are proposed by matching security threats with points and investigating related security technologies from patent data. Proposed approach can identify the ICT-related latent security menaces and provide the guidelines in the ‘problem – alternative’ form by linking the threat point with security technologies.

Keywords: future society, information and communication technology, security technology, technology forecasting

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Authors: Pushpendra S. Bharti

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Process yield of electric discharge machining (EDM) is directly related to optimal combination(s) of process parameters. Optimization of process parameters of EDM is a multi-objective optimization problem owing to the contradictory behavior of performance measures. This paper employs Grey Relation Analysis (GRA) method as a multi-objective optimization technique for the optimal selection of process parameters combination. In GRA, multi-response optimization is converted into optimization of a single response grey relation grade which ultimately gives the optimal combination of process parameters. Experiments were carried out on die-sinking EDM by taking D2 steel as work piece and copper as electrode material. Taguchi's orthogonal array L36 was used for the design of experiments. On the experimental values, GRA was employed for the parametric optimization. A significant improvement has been observed and reported in the process yield by taking the parametric combination(s) obtained through GRA.

Keywords: electric discharge machining, grey relation analysis, material removal rate, optimization

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Authors: Yuan Huang, Xiangping Chen, Xiaonan Luo

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Current software are continuously updating. The change between two versions usually involves multiple program entities (e.g., packages, classes, methods, attributes) with multiple purposes (e.g., changed requirements, bug fixing). It is hard for developers to understand which changes are made for the same purpose. Whether two changes are related is not decided by the relationship between this two entities in the program. In this paper, we summarized 4 coupling rules(16 instances) and 4 state-combination types at the class, method and attribute levels for software change. Related Change Vector (RCV) are defined based on coupling rules and state-combination types, and applied to classify related software changes by using Probabilistic Neural Network during a software updating.

Keywords: PNN, related change, state-combination, logical coupling, software entity

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Authors: Amir M. Shikhli, Refaat H. Abdel-Razek, Salaheddine Bendak

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Innovation is considered to be one of the key factors that influence long-term success of any company. The problem of many organizations in developing countries is trying to implement innovation without a strong basis within the organizational culture to support it. The objective of this study is to assess the effects of organizational culture on innovation in one of the biggest information technology organizations in UAE, Injazat Data System. First, an Organizational Culture Assessment Instrument (OCAI) was used as a survey and Competing Value Framework as a model to analyze the existing culture within the organization and determine its characteristics. Following that, a modified version of the Community Innovation Survey (CIS) was used to determine innovation types introduced by the organization. Then multiple linear regression analysis was used to find out the effects of existing organizational culture on innovation. Results show that existing organizational culture is composed of a combination of Hierarchy (29.4%), Clan (25.8%), Market (24.9%) and Adhocracy (19.9%). Results of the second survey show that the organization focuses on organizational innovation (26.8%) followed by market and product innovations (25.6%) and finally process innovation (22.0%). Regression analysis results reveal that for each innovation type there is a recommended combination of the four culture types. For product innovation, the combination is 47.4% Clan, 17.9% Adhocracy, 1.0% Market and 33.3% Hierarchy; for process innovation it is 19.7% Clan, 45.2% Adhocracy, 32.0% Market and 3.1% Hierarchy; for organizational innovation the combination is 5.4% Clan, 32.7% Adhocracy, 6.0% Market and 55.9% Hierarchy; and for market innovation it is 25.5% Clan, 42.6% Adhocracy, 32.6% Market and 8.4% Hierarchy. Based on these recommended combinations, this study suggests two ways to enhance the innovation culture in the organization. First, if the management decides on the innovation type to be enhanced, a comparison between the existing culture and the recommended combination of selected innovation types will lead to difference in percentages of each culture type. Then further analysis should show how to modify the existing culture to match the recommended combination. Second, if the innovation type is not selected, but the management wants to enhance innovation culture in the organization, the difference in percentages of each culture type will lead to finding out the recommended combination of culture types that gives the narrowest gap between existing culture and recommended combination.

Keywords: developing countries, organizational culture, innovation types, product innovation, process innovation, organizational innovation, marketing innovation

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Authors: Emdad K. Z. Balanji, M. Neaz Sheikh, Muhammad N. S. Hadi

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This paper presents results of an experimental investigation on the behaviour of Hybrid Steel Fibre Reinforced High Strength Concrete (HSFR-HSC) cylinder specimens (150 mm x 300 mm) under uniaxial compression. Three different combinations of HSFR-HSC specimens and reference specimens without steel fibres were prepared. The first combination of HSFR-HSC included 1.5% Micro Steel (MS) fibre and 1% Deformed Steel (DS) fibre. The second combination included 1.5% MS fibre and 1.5% Hooked-end Steel (HS) fibre. The third combination included 1% DS fibre and 1.5% HS fibre. The experimental results showed that the addition of hybrid steel fibres improved the ductility of high strength concrete. The combination of MS fibre and HS fibre in high strength concrete mixes showed best stress-strain behaviour compared to the other combinations and the reference specimens.

Keywords: high strength concrete, micro steel fibre (MS), deformed steel fibre (DS), hooked-end steel fibre (HS), hybrid steel fibre

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Authors: Jun Wang, Ge Zhang

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Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

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Authors: Eri Sulyanti, Trimurti Habazar, Eti Farda Husen, Abdi Dharma, Nasril Nasir

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In this study, combination of some biocontrol agents with different mechanisms was an alternative to improve the effectiveness of the biological control agents. Single and combined applications of indigenous Pseudomonas fluorescens and Arbuscular Mychorrhizae Fungi (AM Fungi) isolates were tested to induce the resistance on susceptible Cavendish banana against F.oxysporum f. sp. cubense race 4 under greenhouse conditions. These isolates originally isolated from healthy banana rhizosphere at endemic Fusarium wilt areas in the centre of production banana in West Sumatra. These researches were conducted with Randomized Block Design with 16 treatments and 10 replications. The treatments were three indigenous isolates of Pseudomonas fluorescens (Par1-Cv, Par4-Rj1, Par2-Jt1) and 3 isolates of AM Fungi (Gl1BuA4, Gl2BuA6, and Gl1KeP3. The biocontrol agents were applied as single agents and combination two of them. This study demonstrated that the application of combination biocontrol organisms Pseudomonas fluorescens and AM Fungi provided were more effective than single application. The combination of Par1-Cv and Gl1BuA4 isolates was the most effective to control Fusarium wilt and followed by the combination of Par1-Cv and Gl2BuA6 and Par2-Jt1 and Gl1P3.

Keywords: pseudomonad fluorescens (Pf), arbuscular mychorrhizae fungi (AM Fungi) indigenous isolates, fusarium oxysporum f. sp. cubense, soil rhizosphere

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Authors: Eyad Almaita

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In this paper, a novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm is used to forecast the hour by hour electrical load demand in Jordan. A small and effective RBFNN model is used to forecast the hourly total load demand based on a small number of features. These features are; the load in the previous day, the load in the same day in the previous week, the temperature in the same hour, the hour number, the day number, and the day type. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminates the need to retrain the RBFNN model again. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data for the period Jan./2012-April/2013 is used train the RBFNN models and the data for the period May/2013- Sep. /2013 is used to validate the models effectiveness.

Keywords: load forecasting, adaptive neural network, radial basis function, short-term, electricity consumption

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Authors: Mona Khabiri, Fahimeh Farahani

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The present study was an attempt to investigate the comparative effect of teaching NLP, critical thinking, and a combination of NLP and critical thinking on EFL learners' reading comprehension. To fulfill the purpose of this study, a group of 82 female and male intermediate EFL learners at a Language School in Iran took a piloted sample PET as a proficiency test and 63 of them were selected as homogenous learners and were randomly assigned to three experimental groups. Within a treatment process of 10 sessions the teacher/researcher provided the participants of each group with handouts, explanations, practices, homework, and questionnaires on techniques of NLP, critical thinking, and a combination of both. During these 10 sessions, 10 same reading comprehension texts extracted from the multi-skill course book suggested by the language school where thought to the participants of each experimental group using skills and strategies of NLP, critical thinking, and a combination of both. On the eleventh session, the participants sat for a reading posttest. The results of one-way ANOVA showed no significant difference among the three groups in terms of reading comprehension. Justifications and implications for the findings of the study and suggestions for further research are presented.

Keywords: neuro-linguistic programming (NLP), critical thinking, reading comprehension

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Authors: Amir AlAmeeri, Mohamed Ibrahim

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Various data points are constantly being measured in the production system, and due to the nature of the wells, these data points, such as pressure, temperature, water cut, etc.., fluctuations are constant, which requires high frequency monitoring and collection. It is a very difficult task to analyze these parameters manually using spreadsheets and email. An automated system greatly enhances efficiency, reduce errors, the need for constant emails which take up disk space, and frees up time for the operator to perform other critical tasks. Various production data is being recorded in an oil field, and this huge volume of data can be seen as irrelevant to some, especially when viewed on its own with no context. In order to fully utilize all this information, it needs to be properly collected, verified and stored in one common place and analyzed for surveillance and monitoring purposes. This paper describes how data is recorded by different parties and departments in the field, and verified numerous times as it is being loaded into a repository. Once it is loaded, a final check is done before being entered into a production monitoring system. Once all this is collected, various calculations are performed to report allocated production. Calculated production data is used to report field production automatically. It is also used to monitor well and surface facility performance. Engineers can use this for their studies and analyses to ensure field is performing as it should be, predict and forecast production, and monitor any changes in wells that could affect field performance.

Keywords: automation, oil production, Cheleken, exploration and production (E&P), Caspian Sea, allocation, forecast

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Authors: Firas Rashad Al-Samarai

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Introduction: Benign prostatic hyperplasia (BPH) is a common condition as men get older. An enlarged prostate gland can cause uncomfortable urinary symptoms, such as blocking the flow of urine out of the bladder. It can also cause bladder, urinary tract, or kidney problems. Objective: to evaluate the efficacy and interaction of tamsulosin with finasteride treatment on induced benign prostate hyperplasia (BPH) in mice. Methods: BPH was induced by subcutaneous injection of testosterone propionate (20 mg/kg) for 30 days. Eighty-five mice were divided into five groups. The first group (G1): twenty-five mice induced BPH treated with tamsulosin orally and divided into five equal subgroups with doses (0.017, 0.052, 0.087, 0. 123, and 0.158) mg/kg, the second group (G2): twenty-five mice induced BPH treated with finasteride orally and divided into five equal subgroups with doses (0.175, 0.527, 0.878, 1.23, and 1.580) mg/kg. the third group (G3): twenty-five mice induced BPH treated with a combination of tamsulosin with finasteride orally, and divided into five equal subgroups with doses (0.0085, 0.0875), (0.026, 0.2635), (0.0435, 0.439) , (0.0615, 0.615) and ( 0.079 , 0.790 ) mg/kg respectively. Fourth group (G4): five mice induced BPH and treated distilled water. Fifth group (G5): five mice were not inducing BPH and without any treatment. Results: The results showed a gradual significant increase in prostate weight % and prostate index % Inhibitions until reached saturation in the last two doses of tamsulosin, finasteride, and combination groups, the maximum effective dose of tamsulosin and finasteride were (0.156) and (1.495) mg/kg respectively. Moreover, the effective dose of the combination (tamsulosin and finasteride) was estimated (0.06876, 0.6876) mg/kg, respectively, as well as the type of interaction was synergism and the value of the combination index was 0.046. Conclusions: We concluded that the combination of tamsulosin with finasteride showed a synergistic effect in BPH treatment by minimizing the side effect of each drug as s result of decreasing the dose of each one.

Keywords: Tamsulosin, Finasteride, combination, BPH

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Authors: Alexandra Papagianni, George Filis, Panagiotis Papadopoulos

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The liberalization of the energy market and the increasing penetration of fluctuating renewables (e.g., wind and solar power) have heightened the importance of the spot market for ensuring efficient electricity supply. This is further emphasized by the EU’s goal of achieving net-zero emissions by 2050. The day-ahead market (DAM) plays a key role in European energy trading, accounting for 80-90% of spot transactions and providing critical insights for next-day pricing. Therefore, short-term electricity price forecasting (EPF) within the DAM is crucial for market participants to make informed decisions and improve their market positioning. Existing literature highlights out-of-sample performance as a key factor in assessing EPF accuracy, with influencing factors such as predictors, forecast horizon, model selection, and strategy. Several studies indicate that electricity demand is a primary price determinant, while renewable energy sources (RES) like wind and solar significantly impact price dynamics, often lowering prices. Additionally, incorporating data from neighboring countries, due to market coupling, further improves forecast accuracy. Most studies predict up to 24 steps ahead using hourly data, while some extend forecasts using higher-frequency data (e.g., half-hourly or quarter-hourly). Short-term EPF methods fall into two main categories: statistical and computational intelligence (CI) methods, with hybrid models combining both. While many studies use advanced statistical methods, particularly through different versions of traditional AR-type models, others apply computational techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). Recent research combines multiple methods to enhance forecasting performance. Despite extensive research on EPF accuracy, a gap remains in understanding how forecasting strategy affects prediction outcomes. While iterated strategies are commonly used, they are often chosen without justification. This paper contributes by examining whether the choice of forecasting strategy impacts the quality of day-ahead price predictions, especially for multi-step forecasts. We evaluate both iterated and direct methods, exploring alternative ways of conducting iterated forecasts on benchmark and state-of-the-art forecasting frameworks. The goal is to assess whether these factors should be considered by end-users to improve forecast quality. We focus on the Greek DAM using data from July 1, 2021, to March 31, 2022. This period is chosen due to significant price volatility in Greece, driven by its dependence on natural gas and limited interconnection capacity with larger European grids. The analysis covers two phases: pre-conflict (January 1, 2022, to February 23, 2022) and post-conflict (February 24, 2022, to March 31, 2022), following the Russian-Ukraine conflict that initiated an energy crisis. We use the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) for evaluation, as well as the Direction of Change (DoC) measure to assess the accuracy of price movement predictions. Our findings suggest that forecasters need to apply all strategies across different horizons and models. Different strategies may be required for different horizons to optimize both accuracy and directional predictions, ensuring more reliable forecasts.

Keywords: short-term electricity price forecast, forecast strategies, forecast horizons, recursive strategy, direct strategy

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Authors: T. W. Kekana, J. J. Baloyi, M. C. Muya, F. V. Nherera

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Secondary metabolites (thiosulphinates) from Allium sativum are able to stimulate the production of volatile fatty acids. This study was carried out to investigate the effects of feeding Garlic powder or probiotics or a combination of both on feed intake and growth performance of Holstein calves. Neonatal calves were randomly allocated, according to birth weight, to four dietary treatments, each with 8 calves. The treatments were: C control, no additive (C), G: supplemented with either 5g/d garlic powder (G) or 4 g/d probiotics (P) or GP 5g/d garlic powder and 4 g/d probiotics compound (GP) with the total viable count of 1.3 x 107 cfu/g. Garlic and probiotics were diluted in the daily milk allocation from day 4. Commercial (17.5% CP) starter feed and fresh water were available ad libitum from day 4 until day 42 of age. Calves fed GP (0.27 kg day-1) tended (P=0.055) to have higher DMI than C (0.22 kg day-1). Milk, water, CP, fat intake and FCR were not affected (P>0.05) by the treatments. Metibolisable energy (ME) intake for GP group tended (P=0.058) to be higher than C calves. Combination of G and P (60.3 kg) tended (P = 0.056) to be higher than C (56.0 kg) calves on final BW. Garlic, probiotics or their combination did not affect calve’s HG, ADG and BL (P>0.05). The results of the current study indicated that combination of garlic and probiotics may improve nutrients intake and body weight when fed to calves during the first 42 days of life.

Keywords: garlic powder, probiotics, intake, growth, Holstein calves

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Blessing A. Aderibigbe

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Combination therapy is a treatment approach that is used to prevent the emergence of drug resistance. This approach is used for the treatment of many chronic and infectious diseases. Potentiating agents are currently explored in combination therapy, resulting in excellent therapeutic outcomes. Breast cancer and malaria are two chronic conditions responsible globally for high death rates. In this research, a class of polymer-drug conjugates containing potentiating agents with either antimalarial or anticancer drugs were prepared by Michael Addition Polymerization reaction and ring-opening polymerization reaction. Conjugation of potentiating agents with bioactive compounds into the polymers resulted in conjugates with good water solubility, highly selective and non-toxic. In vitro cytotoxicity and in vitro antiplasmodial evaluation on the conjugates revealed that the conjugates were more effective when compared to the free drugs. The drug release studies further showed that the release profile of the drugs from the conjugates was sustained. The findings revealed the potential of polymer-drug conjugates to overcome drug toxicity and drug resistance, which is common with the currently used antimalarial and anticancer drugs.

Keywords: anticancer, antimalarials, combination therapy, polymer-drug conjugates

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Authors: Picha Sriprachan

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The increase in electricity prices affects the cost of high-rise building construction. The objectives of this research are to study the electrical cost, trend of electrical cost and to forecast electrical cost of high-rise building construction. The methods of this research are: 1) to study electrical payment formats, cost data collection methods, and the factors affecting electrical cost of high-rise building construction, 2) to study the quantity and trend of cumulative percentage of the electrical cost, and 3) to forecast the electrical cost for different types of high-rise buildings. The results of this research show that the average proportion between electrical cost and the value of the construction project is 0.87 percent. The proportion of electrical cost for residential, office and commercial, and hotel buildings are closely proportional. If construction project value increases, the proportion of electrical cost and the value of the construction project will decrease. However, there is a relationship between the amount of electrical cost and the value of the construction project. During the structural construction phase, the amount of electrical cost will increase and during structural and architectural construction phase, electrical cost will be maximum. The cumulative percentage of the electrical cost is related to the cumulative percentage of the high-rise building construction cost in the same direction. The amount of service space of the building, number of floors and the duration of the construction affect the electrical cost of construction. The electrical cost of construction forecasted by using linear regression equation is close to the electrical cost forecasted by using the proportion of electrical cost and value of the project.

Keywords: high-rise building construction, electrical cost, construction phase, architectural phase

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: Omaimah Algohary

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Kaolin is one of the principle intestinal adsorbents, has traditionally been used internally in the treatment of various enteric disorders, colitis, enteritis, dysentery, and diarrhea associated with food and alkaloidal poisoning and in traveler’s diarrhea. It binds to and traps bacteria and its toxins and gases in the gut. It also binds to water in the gut, which helps to make the stools firmer, hence giving symptomatic relief. Metronidazole is a synthetic antibacterial agent that is used primarily in the treatment of various anaerobic infections such as intra-abdominal infections, antiprotozoal, and as amebicidal. The need for safe, therapeutically effective antidiarrheal combination continuously lead to effective treatment. Metronidazol used for treatment of anaerobic bacteria and kaolin , when administered simultaneously, Metronidazole–Kaolin interactions have been reported by FDA but not studied. This project is the first to study the effect of Metronidazole–Kaolin interactions on the antimicrobial activity of metronidazole. Agar diffusion method performed to test the antimicrobial activity of metronidazole–kaolin antidiarrheal combination from aqueous solutions at an in-vivo simulated pHs conditions that obtained at 37+0.5 °C on Helicobacter pylori as anaerobic bacteria and E.coli as aerobic bacteria and used as a control for the technique. The antimicrobial activity of metronidazole combination as 1:1 and 1:2 with kaolin was abolished in acidic media as no zones of inhibition shown compared to only metronidazole that used as a control. In alkaline media metronidazole combination as 1:1 and 1:2 with kaolin showed diminutive activity compared to the control. These results proved that the kaolin adsorb metronidazole and abolish its antimicrobial activity and such combination should be avoided.

Keywords: kaolin, metronidazole, interaction, Helicobacter pylori. E. coli, antimicrobial activity

Procedia PDF Downloads 389

Authors: Zhipeng Yan, Janice Wing-Tung Kwong, Ching-Lung Lai

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Background: Advanced melanoma accounts for the majority of skin cancer death due to its poor prognosis. Nivolumab and ipilimumab are monoclonal antibodies targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocytes antigen 4 (CTLA-4). Nivolumab and ipilimumab combination therapy has been proven to be effective for advanced melanoma. This systematic review and meta-analysis are to evaluate its clinical efficacy and adverse events. Method: A systematic search was done on databases (Pubmed, Embase, Medline, Cochrane) on 21 June 2020. Search keywords were nivolumab, ipilimumab, melanoma, and randomised controlled trials. Clinical trials fulfilling the inclusion criteria were selected to evaluate the efficacy of combination therapy in terms of prolongation of progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). The odd ratios and distributions of grade 3 or above adverse events were documented. Subgroup analysis was performed based on PD-L1 expression-status and BRAF-mutation status. Results: Compared with nivolumab monotherapy, the hazard ratios of PFS, OS and odd ratio of ORR in combination therapy were 0.64 (95% CI, 0.48-0.85; p=0.002), 0.84 (95% CI, 0.74-0.95; p=0.007) and 1.76 (95% CI, 1.51-2.06; p < 0.001), respectively. Compared with ipilimumab monotherapy, the hazard ratios of PFS, OS and odd ratio of ORR were 0.46 (95% CI, 0.37-0.57; p < 0.001), 0.54 (95% CI, 0.48-0.61; p < 0.001) and 6.18 (95% CI, 5.19-7.36; p < 0.001), respectively. In combination therapy, the odds ratios of grade 3 or above adverse events were 4.71 (95% CI, 3.57-6.22; p < 0.001) compared with nivolumab monotherapy, and 3.44 (95% CI, 2.49-4.74; p < 0.001) compared with ipilimumab monotherapy, respectively. High PD-L1 expression level and BRAF mutation were associated with better clinical outcomes in patients receiving combination therapy. Conclusion: Combination therapy is effective for the treatment of advanced melanoma. Adverse events were common but manageable. Better clinical outcomes were observed in patients with high PD-L1 expression levels and positive BRAF-mutation.

Keywords: nivolumab, ipilimumab, advanced melanoma, systematic review, meta-analysis

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Authors: Ikram Mohamed Eltayeb, Ustina Saeed Barsoumbolice

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Cinnamomum zeylanicum belong to the family Lauraceae and Artemisia absinthium belong to the family Asteraceae. Both were traditionally used as antiemetic, anti-inflammatory and antidiabetic. In Sudan, the mixtures of the two plants were traditionally used for the treatment of diabetes. Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia. It is mainly classified into two major groups, type-1 and type-2. Type-2 is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. The treatment of type-2 diabetes mellitus (DM) with synthetic drugs have many side effects so many researches were conducted to overcome or reduce this side effects by using alternative medicine. The objective of this study is to investigate and compare the anti-diabetic activity of C. zeylanicum and A. absinthium and their combination with difference ratio. C. zeylanicum and A. absinthium were extracted by 96% ethanol using Soxhlet apparatus. Thirty-two rats were divided into eight groups; each group contains four rats. 1st group was administered with distilled water at dose of 10ml/kg, 2nd group had received glucose only at dose of 2g/kg intraperitoneal, the standard group (3rd group) had received Glibenclamide orally at dose of 0.45mg/kg, 4th group received 100 mg C. zeylanicum + 300 mg A. absinthium with a ratio of (25:75), 5th group received 300 mg C. zeylanicum + 100 mg A. absinthium with a ratio of (75:25), 6th group received 200 mg C. zeylanicum + 200 mg A. absinthiumwith a ratio of (50:50), 7th group received 400 mg of A. absinthium, 8th group received 400 mg of C. zeylanicum. Then the blood samples were taken Retro-orbitally at 0, 1, 2 and 4 hours and the glucose level was measured. Each plant alone and their combination with different ratios shows antidiabetic effect. The significant activity was shown by A. absinthium extract (400 mg/kg), combination of ratio of (75:25) A. absinthium: C. zeylanicum(400mg/kg) and then C. zeylanicum(400mg/kg) with p-value 0.001, 0.022, 0.030 respectively, the activity was found to be increased with time. The other combinations showed less activity with p-value > 0.05. The result concludes that the good antidiabetic activity was performed by A. absinthium alone and its activity decreased by increase combination ratio with C. zeylanicum. Which maybe explains by the antagonistic effect between the compounds of C. zeylanicum and A. absinthium.

Keywords: antidiabetic, Artemisia absinthium , cinnamomum zeylanicum, combination

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

Abstract:

Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

Procedia PDF Downloads 154

Authors: Surachai Ampawasuvan, Supornchai Utainarumol

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Most of fuel station’ customers, who drive on the major arterial road wants to use the stations to fill fuel to their vehicle during their journey to destinations. According to the survey of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, or questionnaires, it was found that most users prefer to use fuel stations on holiday rather than on working day. They also prefer to use fuel stations in the morning rather than in the evening. When comparing the ratio of the distribution pattern of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, there is no significant difference. However, when comparing the ratio of peak hour (peak hour rate) of the results from questionnaires at 13 to 14 percent with the results obtained by using the methods of the Institute of Transportation Engineering (ITE), it is found that the value is similar. However, it is different from a survey by video camera and automatic traffic counting at 6 to 7 percent of about half. So, this study suggests that in order to forecast trip generation of vehicle using fuel stations on major arterial road which is mostly characterized by Though Traffic, it is recommended to use the value of half of peak hour rate, which would make the forecast for trips generation to be more precise and accurate and compatible to surrounding environment.

Keywords: peak rate, trips generation, fuel station, arterial road

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