Search results for: network capacity

Authors: Yoon Suh Song

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Though music education can broaden one’s capacity for mathematical performance, many countries lag behind in music education. Little empirical evidence is found to identify the connection between math and music. Therefore, this research was set out to explore what music-related variables are associated with mathematical performance. The result of our analysis is as follows: A Pearson's Correlation analysis revealed that PISA math score is strongly correlated with students' Intelligence Quotient (IQ). This lays the foundation for further research as to what factors in students’ IQ lead to a better performance in math.

Keywords: music education, mathematical performance, education, IQ

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Authors: Antoine Karam, Lotfi Khiari, Bruno Breton, Alfred Jaouich

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Anthropogenic sources of zinc (Zn), including industrial emissions and effluents, Zn–rich fertilizer materials and pesticides containing Zn, can contribute to increasing the concentration of soluble Zn at levels toxic to plants in acid sandy soils. The application of municipal sewage sludge or biosolids (MBS) which contain metal immobilizing agents on coarse-textured soils could improve the metal sorption capacity of the low-CEC soils. The purpose of this experiment was to evaluate the sorption of Zn in surface samples (0-15 cm) of six Quebec (Canada) soils amended with MBS (pH 6.9) from Val d’Or (Quebec, Canada). Soil samples amended with increasing amounts (0 to 20%) of MBS were equilibrated with various amounts of Zn as ZnCl2 in 0.01 M CaCl2 for 48 hours at room temperature. Sorbed Zn was calculated from the difference between the initial and final Zn concentration in solution. Zn sorption data conformed to the linear form of Freundlich equation. The amount of sorbed Zn increased considerably with increasing MBS rate. Analysis of variance revealed a highly significant effect (p ≤ 0.001) of soil texture and MBS rate on the amount of sorbed Zn. The average values of the Zn-sorption capacity of MBS-amended coarse-textured soils were lower than those of MBS-amended fine textured soils. The two sandy soils (86-99% sand) amended with MBS retained 2- to 5-fold Zn than those without MBS (control). Significant Pearson correlation coefficients between the Zn sorption isotherm parameter, i.e. the Freundlich sorption isotherm (KF), and commonly measured physical and chemical entities were obtained. Among all the soil properties measured, soil pH gave the best significant correlation coefficients (p ≤ 0.001) for soils receiving 0, 5 and 10% MBS. Furthermore, KF values were positively correlated with soil clay content, exchangeable basic cations (Ca, Mg or K), CEC and clay content to CEC ratio. From these results, it can be concluded that (i) municipal biosolids provide sorption sites that have a strong affinity for Zn, (ii) both soil texture, especially clay content, and soil pH are the main factors controlling anthropogenic Zn sorption in the municipal biosolids-amended soils, and (iii) the effect of municipal biosolids on Zn sorption will be more pronounced for a sandy soil than for a clay soil.

Keywords: metal, recycling, sewage sludge, trace element

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Authors: A. K. M. Kafi, S. N. Nina, Mashitah M. Yusoff

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In this work, we have described a new 3-dimensional (3D) network of crosslinked Horseradish Peroxidase/Carbon Nanotube (HRP/CNT) on a thiol-modified Au surface in order to build up the effective electrical wiring of the enzyme units with the electrode. This was achieved by the electropolymerization of aniline-functionalized carbon nanotubes (CNTs) and 4-aminothiophenol -modified-HRP on a 4-aminothiophenol monolayer-modified Au electrode. The synthesized 3D HRP/CNT networks were characterized with cyclic voltammetry and amperometry, resulting the establishment direct electron transfer between the redox active unit of HRP and the Au surface. Electrochemical measurements reveal that the immobilized HRP exhibits high biological activity and stability and a quasi-reversible redox peak of the redox center of HRP was observed at about −0.355 and −0.275 V vs. Ag/AgCl. The electron transfer rate constant, KS and electron transfer co-efficient were found to be 0.57 s-1 and 0.42, respectively. Based on the electrocatalytic process by direct electrochemistry of HRP, a biosensor for detecting H2O2 was developed. The developed biosensor exhibits excellent electrocatalytic activity for the reduction of H2O2. The proposed biosensor modified with HRP/CNT 3D network displays a broader linear range and a lower detection limit for H2O2 determination. The linear range is from 1.0×10−7 to 1.2×10−4M with a detection limit of 2.2.0×10−8M at 3σ. Moreover, this biosensor exhibits very high sensitivity, good reproducibility and long-time stability. In summary, ease of fabrication, a low cost, fast response and high sensitivity are the main advantages of the new biosensor proposed in this study. These obvious advantages would really help for the real analytical applicability of the proposed biosensor.

Keywords: biosensor, nanomaterials, redox enzyme, thiol-modified Au surface

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Authors: Zelal Polat, Şebnem Harsa, Semra Ülkü

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Lactic acid exists in nature optically in two forms, L(+), D(-)-lactic acid, and has been used in food, leather, textile, pharmaceutical and cosmetic industries. Moreover, L(+)-lactic acid constitutes the raw material for the production of poly-L-lactic acid which is used in biomedical applications. Microbially produced lactic acid was aimed to be recovered from the fermentation media efficiently and economically. Among the various downstream operations, ion exchange chromatography is highly selective and yields a low cost product recovery within a short period of time. In this project, Lactobacillus casei NRRL B-441 was used for the production of L(+)-lactic acid from whey by fermentation at pH 5.5 and 37°C that took 12 hours. The product concentration was 50 g/l with 100% L(+)-lactic acid content. Next, the suitable resin was selected due to its high sorption capacity with rapid equilibrium behavior. Dowex marathon WBA, weakly basic anion exchanger in OH form reached the equilibrium in 15 minutes. The batch adsorption experiments were done approximately at pH 7.0 and 30°C and sampling was continued for 20 hours. Furthermore, the effect of temperature and pH was investigated and their influence was found to be unimportant. All the adsorption/desorption experiments were applied to both model lactic acid and biomass free fermentation broth. The ion exchange equilibria of lactic acid and L(+)-lactic acid in fermentation broth on Dowex marathon WBA was explained by Langmuir isotherm. The maximum exchange capacity (qm) for model lactic acid was 0.25 g La/g wet resin and for fermentation broth 0.04 g La/g wet resin. The equilibrium loading and exchange efficiency of L(+)-lactic acid in fermentation broth were reduced as a result of competition by other ionic species. The competing ions inhibit the binding of L(+)-lactic acid to the free sites of ion exchanger. Moreover, column operations were applied to recover adsorbed lactic acid from the ion exchanger. 2.0 M HCl was the suitable eluting agent to recover the bound L(+)-lactic acid with a flowrate of 1 ml/min at ambient temperature. About 95% of bound L(+)-lactic acid was recovered from Dowex marathon WBA. The equilibrium was reached within 15 minutes. The aim of this project was to investigate the purification of L(+)-lactic acid with ion exchange method from fermentation broth. The additional goals were to investigate the end product purity, to obtain new data on the adsorption/desorption behaviours of lactic acid and applicability of the system in industrial usage.

Keywords: fermentation, ion exchange, lactic acid, purification, whey

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Authors: Nasibeh Mohammadpour, Ali Rajabzadeh, Adel Azar, Hamid Zargham Borujeni,

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Generally, various industries development depends on their stakeholders and beneficiaries supports. One of the most important stakeholders in tourism industry ( which has become one of the most important lucrative and employment-generating activities at the international level these days) are host communities in tourist destination which are affected and effect on this industry development. Recognizing host community and its segmentations can be important to get their support for future decisions and policy making. In order to identify these segments, in this study, clustering of the residents has been done by using some tools that are designed to encounter human complexities and have ability to model and generalize complex systems without any needs for the initial clusters’ seeds like classic methods. Neural networks can help to meet these expectations. The research have been planned to design neural networks-based mathematical model for clustering the host community effectively according to multi criteria, and identifies differences among segments. In order to achieve this goal, the residents’ segmentation has been done by demographic characteristics, their attitude towards the tourism development, the level of satisfaction and the type of their support in this field. The applied method is self-organized neural networks and the results have compared with K-means. As the results show, the use of Self- Organized Map (SOM) method provides much better results by considering the Cophenetic correlation and between clusters variance coefficients. Based on these criteria, the host community is divided into five sections with unique and distinctive features, which are in the best condition (in comparison other modes) according to Cophenetic correlation coefficient of 0.8769 and between clusters variance of 0.1412.

Keywords: Artificial Nural Network, Clustering , Resident, SOM, Tourism

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Authors: Afnan Al-Romi, Iman Al-Momani

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The urge of applying the Software Engineering (SE) processes is both of vital importance and a key feature in critical, complex large-scale systems, for example, safety systems, security service systems, and network systems. Inevitably, associated with this are risks, such as system vulnerabilities and security threats. The probability of those risks increases in unsecured environments, such as wireless networks in general and in Wireless Sensor Networks (WSNs) in particular. WSN is a self-organizing network of sensor nodes connected by wireless links. WSNs consist of hundreds to thousands of low-power, low-cost, multi-function sensor nodes that are small in size and communicate over short-ranges. The distribution of sensor nodes in an open environment that could be unattended in addition to the resource constraints in terms of processing, storage and power, make such networks in stringent limitations such as lifetime (i.e. period of operation) and security. The importance of WSN applications that could be found in many militaries and civilian aspects has drawn the attention of many researchers to consider its security. To address this important issue and overcome one of the main challenges of WSNs, security solution systems have been developed by researchers. Those solutions are software-based network Intrusion Detection Systems (IDSs). However, it has been witnessed, that those developed IDSs are neither secure enough nor accurate to detect all malicious behaviours of attacks. Thus, the problem is the lack of coverage of all malicious behaviours in proposed IDSs, leading to unpleasant results, such as delays in the detection process, low detection accuracy, or even worse, leading to detection failure, as illustrated in the previous studies. Also, another problem is energy consumption in WSNs caused by IDS. So, in other words, not all requirements are implemented then traced. Moreover, neither all requirements are identified nor satisfied, as for some requirements have been compromised. The drawbacks in the current IDS are due to not following structured software development processes by researches and developers when developing IDS. Consequently, they resulted in inadequate requirement management, process, validation, and verification of requirements quality. Unfortunately, WSN and SE research communities have been mostly impermeable to each other. Integrating SE and WSNs is a real subject that will be expanded as technology evolves and spreads in industrial applications. Therefore, this paper will study the importance of Requirement Engineering when developing IDSs. Also, it will study a set of existed IDSs and illustrate the absence of Requirement Engineering and its effect. Then conclusions are drawn in regard of applying requirement engineering to systems to deliver the required functionalities, with respect to operational constraints, within an acceptable level of performance, accuracy and reliability.

Keywords: software engineering, requirement engineering, Intrusion Detection System, IDS, Wireless Sensor Networks, WSN

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Authors: Robel Habtemariam

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Globally, wind energy and photovoltaics (PV) solar energy are among the leading renewable energy sources (RES) in terms of installed capacity. In order to increase the contribution of RES to the power supply system, large scale energy integration is required, mainly due to wind energy and PV. In this paper, an investigation has been made on the electrical power supply systems of Taiwan and Greece in order to integrate high level of wind and photovoltaic (PV) to increase the penetration of renewable energy resources. Currently, both countries heavily depend on fossil fuels to meet the demand and to generate adequate electricity. Therefore, this study is carried out to look into the two cases power supply system by developing a methodology that includes major power units. To address the analysis, an approach for simulation of power systems is formulated and applied. The simulation is based on the non-dynamic analysis of the electrical system. This simulation results in calculating the energy contribution of different types of power units; namely the wind, PV, non-flexible and flexible power units. The calculation is done for three different scenarios (2020, 2030, & 2050), where the first two scenarios are based on national targets and scenario 2050 is a reflection of ambitious global targets. By 2030 in Taiwan, the input of the power units is evaluated as 4.3% (wind), 3.7% (PV), 65.2 (non-flexible), 25.3% (flexible), and 1.5% belongs to hydropower plants. In Greece, much higher renewable energy contribution is observed for the same scenario with 21.7% (wind), 14.3% (PV), 38.7% (non-flexible), 14.9% (flexible), and 10.3% (hydro). Moreover, it examines the ability of the power systems to deal with the variable nature of the wind and PV generation. For this reason, an investigation has also been done on the use of the combined wind power with pumped storage systems (WPS) to enable the system to exploit the curtailed wind energy & surplus PV and thus increase the wind and PV installed capacity and replace the peak supply by conventional power units. Results show that the feasibility of pumped storage can be justified in the high scenario (that is the scenario of 2050) of RES integration especially in the case of Greece.

Keywords: large scale energy integration, photovoltaics solar energy, pumped storage systems, renewable energy sources

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Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: Arvind Kumar

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The city of Kanpur is located in the terrestrial plain area on the bank of the river Ganges and is the second largest city in the state of Uttar Pradesh. The city generates approximately 1400-1600 tons per day of MSW. Kanpur has been known as a major point and non-points-based pollution hotspot for the river Ganges. The city has a major industrial hub, probably the largest in the state, catering to the manufacturing and recycling of plastic and other dry waste streams. There are 4 to 5 major drains flowing across the city, which receive a significant quantity of waste leakage, which subsequently adds to the Ganges flow and is carried to the Bay of Bengal. A river-to-sea flow approach has been established to account for leaked waste into urban drains, leading to the build-up of marine litter. Throughout its journey, the river accumulates plastic – macro, meso, and micro, from various sources and transports it towards the sea. The Ganges network forms the second-largest plastic-polluting catchment in the world, with over 0.12 million tonnes of plastic discharged into marine ecosystems per year and is among 14 continental rivers into which over a quarter of global waste is discarded 3.150 Kilo tons of plastic waste is generated in Kanpur, out of which 10%-13% of plastic is leaked into the local drains and water flow systems. With the Support of Kanpur Municipal Corporation, 1TPD capacity MRF for drain waste management was established at Krishna Nagar, Kanpur & A German startup- Plastic Fisher, was identified for providing a solution to capture the drain waste and achieve its recycling in a sustainable manner with a circular economy approach. The team at Plastic Fisher conducted joint surveys and identified locations on 3 drains at Kanpur using GIS maps developed during the survey. It suggested putting floating 'Boom Barriers' across the drains with a low-cost material, which reduced their cost to only 2000 INR per barrier. The project was built upon the self-sustaining financial model. The project includes activities where a cost-efficient model is developed and adopted for a socially self-inclusive model. The project has recommended the use of low-cost floating boom barriers for capturing waste from drains. This involves a one-time time cost and has no operational cost. Manpower is engaged in fishing and capturing immobilized waste, whose salaries are paid by the Plastic Fisher. The captured material is sun-dried and transported to the designated place, where the shed and power connection, which act as MRF, are provided by the city Municipal corporation. Material aggregation, baling, and transportation costs to end-users are borne by Plastic Fisher as well.

Keywords: Kanpur, marine environment, drain waste management, plastic fisher

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Luke Sniewski, Pante Farvid, Phil Carter, Rita Csako

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Background and Aims: Self-Perceived Problematic Porn Use (SPPPU) refers to individuals who identify as or perceive themselves to be addicted to porn. These individuals feel they are unable to regulate their porn consumption and experience adverse consequences as a result of their use in everyday life. To the author’s best knowledge, this research represents the first study to intervene with pornography use with mindfulness meditation, and aims to investigate the experiences and challenges of men with SPPPU as they engage in a mindfulness meditation intervention. As meditation is commonly characterized by sitting and observing one’s internal experience with non-reaction and acceptance, the study’s principal hypothesis was that consistent practice of meditation would develop the participant’s capacity to respond to cravings, urges, and unwanted thoughts in less reactive, more productive ways. Method: This 12-mixed method research utilised Single Case Experimental Design (SCED) methodology, with a standard AB design. Each participant was randomly assigned to an initial baseline time period between 2 to 5 weeks before learning the meditation technique and practicing it for the remainder of the 12-week study. The pilot study included 3 participants, while the intervention study included 12. The meditation technique used for the study involved a 15-minute guided breathing exercise in the morning, along with a 15-minute guided concentration meditation in the evening. Results: At the time of submission, only pilot study results were available. Results from the pilot study indicate an improved capacity for self-awareness of the uncomfortable mental and emotional states that drove their participants’ pornography use. Statistically significant reductions were also observed in daily porn use, total weekly time spent viewing porn, as well as lowered Pornography Craving Questionnaire (PCQ) and Problematic Pornography Use Scale (PPUS) scores. Conclusion: Pilot study results suggest that meditation could serve as a complementary tool for health professionals to provide clients in conjunction with therapeutic interventions. Study limitations, directions for future research, and clinical implications to be discussed as well.

Keywords: meditation, behavioural change, pornography, mindfulness

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

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

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

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Authors: Puran Prasad Adhikari

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Considering the fact that when we are able to communicate science and economics effectively to broader nonprofessional audiences, it promotes a great understanding of its wider relevance to society and encourages more informed and confident decision-making at all levels, from the government to communities to individuals. The study has been conducted. This study is aimed to examine the understanding of the general public of economics and the basic sciences functioning in our surroundings in our day-to-day life. Data was gathered through historical documents related to science communication and through interviews with the public. The statistical result shows that there is a great lack of knowledge in the general public about the basic sciences and how economics impacts their life daily. The difficulties faced by the public include the view that these things can only be understood by professionals and it is beyond their capacity to grasp these concepts, the use of technical words and jargon by the professionals, and the lack of the medium to understand even if they want to learn it. The result further indicates that the lack of this basic knowledge also leads to bad decision-making, which causes frustration and anxiety. The result shows the great correlation between the confidence level of a person and the knowledge of basic science and economics. The factor behind this was the right decision-making capacity of the individual, which boosts the happy hormones of the individual. So indirectly, we found the correlation between mental health and the understanding of science and economics. The public wants to have a basic understanding and concepts of these topics, but they complain that there is no effective medium through which they can gain the understanding; the medium which is available is full of jargon and technical terms directed to professional and highly educated which they consider is beyond their reach. So, communicating the basic concepts to the general public is of great importance in the 21st century for the overall progress of society. The professional one can make this possible by considering the level of public understanding and making the communication and the programs comprehensible to the layman. Various means can be used to make this successful and effective, e.g., cartoon guide books, Q&A with the layman, animations use, and daily life examples. This study’s implication will help educators of high-level institutions and policymakers improve general public [layman] access to comprehensible knowledge.

Keywords: layman, comprehensible, decision making, frustration, confidence

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Authors: Kam Wen Hang, Tan Kee Teng, Huang Poh Ching, Chia Kai Xiang, H. V. Annegowda, H. S. Naveen Kumar

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Gynura procumbens (G. procumbens) leaves, commonly known as ‘sambung nyawa’ in Malaysia is a well-known medicinal plant commonly used as folk medicines in controlling blood glucose, cholesterol level as well as treating cancer. These medicinal properties were believed to be related to the polyphenolic content present in G. procumbens extract, therefore optimization of its extraction process is vital to obtain highest possible antioxidant activities. The current study was conducted to investigate the effect of different concentrations of extracting solvent (ethanol) on the amount of polyphenolic content and antioxidant activities of G. procumbens leaf extract. The concentrations of ethanol used were 30-70%, with the temperature and time kept constant at 50°C and 30 minutes, respectively using ultrasound-assisted extraction. The polyphenolic content of these extracts were quantified by Folin-Ciocalteu colorimetric method and results were expressed as milligram gallic acid equivalent (mg GAE)/g. Phosphomolybdenum method and 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assays were used to investigate the antioxidant properties of the extract and the results were expressed as milligram ascorbic acid equivalent (mg AAE)/g and effective concentration (EC50) respectively. Among the three different (30%, 50% and 70%) concentrations of ethanol studied, the 50% ethanolic extract showed total phenolic content of 31.565 ± 0.344 mg GAE/g and total antioxidant activity of 78.839 ± 0.199 mg AAE/g while 30% ethanolic extract showed 29.214 ± 0.645 mg GAE/g and 70.701 ± 1.394 mg AAE/g, respectively. With respect to DPPH radical scavenging assay, 50% ethanolic extract had exhibited slightly lower EC50 (314.3 ± 4.0 μg/ml) values compared to 30% ethanol extract (340.4 ± 5.3 μg/ml). Out of all the tested extracts, 70% ethanolic extract exhibited significantly (p< 0.05) highest total phenolic content (38.000 ± 1.009 mg GAE/g), total antioxidant capacity (95.874 ± 2.422 mg AAE/g) and demonstrated the lowest EC50 in DPPH assay (244.2 ± 5.9 μg/ml). An excellent correlations were drawn between total phenolic content, total antioxidant capacity and DPPH radical scavenging activity (R2 = 0.949 and R2 = 0.978, respectively). It was concluded from this study that, 70% ethanol should be used as the optimal polarity solvent to obtain G. procumbens leaf extract with maximum polyphenolic content with antioxidant properties.

Keywords: antioxidant activity, DPPH assay, Gynura procumbens, phenolic compounds

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Authors: Lucy Horrobin, Rebecca Fores

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Background: Muay Thai is an increasingly popular combat sport worldwide. Further academic research in the sport will contribute to its professional development. This research sought to produce normative data in relation to the physical and physiological characteristics of professional Muay Thai boxers, as, currently no such data exists. The ultimate aim being to inform appropriate training programs and to facilitate coaching. Methods: N = 9 professional, adult, male Muay Thai boxers were assessed for the following anthropometric, physical and physiological characteristics, using validated methods of assessment: body fat, hamstring flexibility, maximal dynamic upper body strength, lower limb peak power, upper body muscular endurance and aerobic capacity. Raw data scores were analysed for mean, range and SD and where applicable were expressed relative to body mass (BM). Results: Results showed similar characteristics to those found in other combat sports. Low percentages of body fat (mean±SD) 8.54 ± 1.16 allow for optimal power to weight ratios. Highly developed aerobic capacity (mean ±SD) 61.56 ± 5.13 ml.min.kg facilitate recovery and power maintenance throughout bouts. Lower limb peak power output values of (mean ± SD) 12.60 ± 2.09 W/kg indicate that Muay Thai boxers are amongst the most powerful of combat sport athletes. However, maximal dynamic upper body strength scores of (mean±SD) 1.14 kg/kg ± 0.18 were in only the 60th percentile of normative data for the general population and muscular endurance scores (mean±SD) 31.55 ± 11.95 and flexibility scores (mean±SD) 19.55 ± 11.89 cm expressed wide standard deviation. These results might suggest that these characteristics are insignificant in Muay Thai or under-developed, perhaps due to deficient training programs. Implications: This research provides the first normative data of physical and physiological characteristics of Muay Thai boxers. The findings of this study would aid trainers and coaches when designing effective evidence-based training programs. Furthermore, it provides a foundation for further research relating to physiology in Muay Thai. Areas of further study could be determining the physiological demands of a full rules bout and the effects of evidence-based training programs on performance.

Keywords: fitness testing, Muay Thai, physiology, strength and conditioning

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Authors: Zulkifli Zainal Abidin, Ahmad Shahril Mohd Ghani

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Agent-based systems technology has been addressed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated systems that act autonomously across open and distributed environments in solving problems. Nevertheless, it is impractical to rely on a single agent to do all computing processes in solving complex problems. An increasing number of applications lately require multiple agents to work together. A multi-agent system (MAS) is a loosely coupled network of agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. However, the network of MAS still requires a main system to govern or oversees the operation of the agents in order to achieve a unified goal. We had developed a fleet management system (FMS) in order to manage the fleet of agents, plan route for the agents, perform real-time data processing and analysis, and issue sets of general and specific instructions to the agents. This FMS should be able to perform real-time data processing, communicate with the autonomous surface vehicle (ASV) agents and generate bathymetric map according to the data received from each ASV unit. The first algorithm is developed to communicate with the ASV via radio communication using standard National Marine Electronics Association (NMEA) protocol sentences. Next, the second algorithm will take care of the path planning, formation and pattern generation is tested using various sample data. Lastly, the bathymetry map generation algorithm will make use of data collected by the agents to create bathymetry map in real-time. The outcome of this research is expected can be applied on various other multi-agent systems.

Keywords: autonomous surface vehicle, fleet management system, multi agent system, bathymetry

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Authors: Juraj Londák, Radoslav Vargic, Pavol Podhradský

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In this contribution, we focus on the technical and economic aspects of using hybrid storage in microgrids for peak shaving. We perform a feasibility analysis of hybrid storage consisting of conventional supercapacitors and chemical batteries. We use multiple real-life consumption profiles from various industry-oriented microgrids. The primary purpose is to construct a digital twin model for reserved capacity simulation and prediction. The main objective is to find the equilibrium between technical innovations, acquisition costs and energy cost savings

Keywords: microgrid, peak shaving, energy storage, digital twin

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Authors: Hyunsun Lee, Yi Zhu

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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: Mardin Abdalqadir, Paul Adzakro, Tannaz Pak, Sina Rezaei Gomari

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Climate change and global warming recently became significant concerns due to the massive emissions of greenhouse gases into the atmosphere, predominantly CO2 gases. Therefore, it is necessary to find sustainable and inexpensive methods to capture the greenhouse gasses and protect the environment for live species. The application of naturally available and cheap adsorbents of carbon such as clay minerals became a great interest. However, the minerals prone to low storage capacity despite their high affinity to adsorb carbon. This paper aims to explore ways to improve the pore volume and surface area of two selected clay minerals, ‘montmorillonite and kaolinite’ by acid treatment to overcome their low storage capacity. Montmorillonite and kaolinite samples were treated with different sulfuric acid concentrations (0.5, 1.2 and 2.5 M) at 40 °C for 8 hours to achieve the above aim. The grain size distribution and morphology of clay minerals before and after acid treatment were explored with Scanning Electron Microscope to evaluate surface area improvement. The ImageJ software was used to find the porosity and pore volume of treated and untreated clay samples. The structure of the clay minerals was also analyzed using an X-ray Diffraction machine. The results showed that the pore volume and surface area were increased substantially through acid treatment, which speeded up the rate of carbon dioxide adsorption. XRD pattern of kaolinite did not change after sulfuric acid treatment, which indicates that acid treatment would not affect the structure of kaolinite. It was also discovered that kaolinite had a higher pore volume and porosity than montmorillonite before and after acid treatment. For example, the pore volume of untreated kaolinite was equal to 30.498 um3 with a porosity of 23.49%. Raising the concentration of acid from 0.5 M to 2.5 M in 8 hours’ time reaction led to increased pore volume from 30.498 um3 to 34.73 um3. The pore volume of raw montmorillonite was equal to 15.610 um3 with a porosity of 12.7%. When the acid concentration was raised from 0.5 M to 2.5 M for the same reaction time, pore volume also increased from 15.610 um3 to 20.538 um3. However, montmorillonite had a higher specific surface area than kaolinite. This study concludes that clay minerals are inexpensive and available material sources to model the realistic conditions and apply the results of carbon capture to prevent global warming, which is one of the most critical and urgent problems in the world.

Keywords: acid treatment, kaolinite, montmorillonite, pore volume, porosity, surface area

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Authors: Jaechun No

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This paper focuses on I/O optimizations of N-hybrid (New-Form of hybrid), which provides a hybrid file system space constructed on SSD and HDD. Although the promising potentials of SSD, such as the absence of mechanical moving overhead and high random I/O throughput, have drawn a lot of attentions from IT enterprises, its high ratio of cost/capacity makes it less desirable to build a large-scale data storage subsystem composed of only SSDs. In this paper, we present N-hybrid that attempts to integrate the strengths of SSD and HDD, to offer a single, large hybrid file system space. Several experiments were conducted to verify the performance of N-hybrid.

Keywords: SSD, data section, I/O optimizations, hybrid system

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Authors: Daniel Silander

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This article explores the EU Initiated European Neighborhood Policy (ENP) towards Ukraine. It also explores Russian geopolitics in the region. We argue that Ukraine is sandwiched between two regional powers in the EU and Russia. By analyzing EU democracy promotion towards Ukraine and neighbors, we assess a weak EU normative capacity. Instead of building a “ring of friends”, as argued by the EU Commission, in an enlarged democratic community, the EU has achieved poor democratic records in Ukraine which opened for a revival of Russia in the region and causes the international crisis over Crime of 2014.

Keywords: regional neighborhood policy, European Union, Russia, Ukraine, domestic elites

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Authors: Alexandra Sweny

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This paper contends with the intersection between the act of witnessing and the act of reading in order to consider the relevance of literary testimony and fiction as tools for postcolonial readings of history. J. M. Coetzee's Waiting for the Barbarians elucidates what Primo Levi deems the 'paradoxical' task of testimony: that suffering can only be fully narrated by the sufferer themselves, whose voice and narrative capacity is often foreclosed by the very extent of their trauma. By examining the fictional Magistrate's position as both a reader and translator of history, this paper posits Waiting for the Barbarians as an ethical command against the appropriation of trauma.

Keywords: ethical criticism, limit-experience, postcolonialism, psychic trauma in literature, testimony

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Daniel Muwooya

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

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

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Authors: Fethi Bouksila, Nessrine Zemni, Fairouz Slama, Magnus Persson, Ronny Berndasson, Akissa Bahri

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Electromagnetic sensor EM 38 was used to predict and map soil salinity (ECe) in arid oasis. Despite the high spatial variation of soil moisture and shallow watertable, significant ECe-EM relationships were developed. The low drainage network efficiency is the main factor of soil salinization

Keywords: soil salinity map, electromagnetic induction, EM38, oasis, shallow watertable

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Petre Voicu, Mircea Oaida, Radu Vasiu, Catalin Gheorghiu, Aurel Dumitru

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The present work deals with the way in which food and tobacco waste can be used in agriculture. As a result of the lack of efficient technologies for their recycling, we are currently faced with the appearance of appreciable quantities of residual organic residues that find their use only very rarely and only after long storage in landfills. The main disadvantages of long storage of organic waste are the unpleasant smell, the high content of pathogenic agents, and the high content in the water. The release of these enormous amounts imperatively demands the finding of solutions to ensure the avoidance of environmental pollution. The measure practiced by us consists of the processing of this waste in special installations, testing in pilot experimental perimeters, and later administration on agricultural lands without harming the quality of the soil, agricultural crops, and the environment. The current crisis of raw materials and energy also raises special problems in the field of organic waste valorization, an activity that takes place with low energy consumption. At the same time, their composition recommends them as useful secondary sources in agriculture. The transformation of food scraps and other residues concentrated organics thus acquires a new orientation, in which these materials are seen as important secondary resources. The utilization of food and tobacco waste in agriculture is also stimulated by the increasing lack of chemical fertilizers and the continuous increase in their price, under the conditions that the soil requires increased amounts of fertilizers in order to obtain high, stable, and profitable production. The need to maintain and increase the humus content of the soil is also taken into account, as an essential factor of its fertility, as a source and reserve of nutrients and microelements, as an important factor in increasing the buffering capacity of the soil, and the more reserved use of chemical fertilizers, improving the structure and permeability for water with positive effects on the quality of agricultural works and preventing the excess and/or deficit of moisture in the soil.

Keywords: ecology, soil, organic waste, fertility

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Authors: Richard Grant, Landolf Rhode-Barbarigos, Shouraseni Sen Roy, Lucas Brittan, Change Li, Aiden Rowe

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Ports are critical for the US economy, connecting farmers, manufacturers, retailers, consumers and an array of transport and storage operators. Port facilities vary widely in terms of their productivity, footprint, specializations, and governance. In this context, Port Miami is considered as one of the busiest ports providing both cargo and cruise services in connecting the wider region of the Caribbean and Mesoamerica to the global networks. It is considered as the “Cruise Capital of the World and Global Gateway of the Americas” and “leading container port in Florida.” Furthermore, it has also been ranked as one of the top container ports in the world and the second most efficient port in North America. In this regard, Port Miami has made significant investments in the strategic and capital infrastructure of about US$1 billion, including increasing the channel depth and other onshore infrastructural enhancements. Therefore, this study involves a detailed analysis of Port Miami’s network, using publicly available multiple years of data about marine vessel traffic, cargo, and connectivity and performance indices from 2015-2021. Through the analysis of cargo and cruise vessels to and from Port Miami and its relative performance at the global scale from 2015 to 2021, this study examines the port’s long-term resilience and future growth potential. The main results of the analyses indicate that the top category for both inbound and outbound cargo is manufactured products and textiles. In addition, there are a lot of fresh fruits, vegetables, and produce for inbound and processed food for outbound cargo. Furthermore, the top ten port connections for Port Miami are all located in the Caribbean region, the Gulf of Mexico, and the Southeast USA. About half of the inbound cargo comes from Savannah, Saint Thomas, and Puerto Plata, while outbound cargo is from Puerto Corte, Freeport, and Kingston. Additionally, for cruise vessels, a significantly large number of vessels originate from Nassau, followed by Freeport. The number of passenger's vessels pre-COVID was almost 1,000 per year, which dropped substantially in 2020 and 2021 to around 300 vessels. Finally, the resilience and competitiveness of Port Miami were also assessed in terms of its network connectivity by examining the inbound and outbound maritime vessel traffic. It is noteworthy that the most frequent port connections for Port Miami were Freeport and Savannah, followed by Kingston, Nassau, and New Orleans. However, several of these ports, Puerto Corte, Veracruz, Puerto Plata, and Santo Thomas, have low resilience and are highly vulnerable, which needs to be taken into consideration for the long-term resilience of Port Miami in the future.

Keywords: port, Miami, network, cargo, cruise

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Authors: A. Ghribi, M. Bagane

Abstract:

The discharge of dye in industrial effluents is of great concern because their presence and accumulation have a toxic or carcinogenic effect on living species. The removals of such compounds at such low levels are a difficult problem. Physicochemical technique such as coagulation, flocculation, ozonation, reverse osmosis and adsorption on activated carbon, manganese oxide, silica gel and clay are among the methods employed. The adsorption process is an effective and attractive proposition for the treatment of dye contaminated wastewater. Activated carbon adsorption in fixed beds is a very common technology in the treatment of water and especially in processes of decolouration. However, it is expensive and the powdered one is difficult to be separated from aquatic system when it becomes exhausted or the effluent reaches the maximum allowable discharge level. The regeneration of exhausted activated carbon by chemical and thermal procedure is also expensive and results in loss of the sorbent. Dye molecules also have very high affinity for clay surfaces and are readily adsorbed when added to clay suspension. The elimination of the organic dye by clay was studied by serval researchers. The focus of this research was to evaluate the adsorption potential of the raw clay in removing congo red from aqueous solutions using a laboratory fixed-bed column. The continuous sorption process was conducted in this study in order to simulate industrial conditions. The effect of process parameters, such as inlet flow rate, adsorbent bed height and initial adsorbate concentration on the shape of breakthrough curves was investigated. A glass column with an internal diameter of 1.5 cm and height of 30 cm was used as a fixed-bed column. The pH of feed solution was set at 7.Experiments were carried out at different bed heights (5-20 cm), influent flow rates (1.6- 8 mL/min) and influent congo red concentrations (10-50 mg/L). The obtained results showed that the adsorption capacity increases with the bed depth and the initial concentration and it decreases at higher flow rate. The column regeneration was possible for four adsorption–desorption cycles. The clay column study states the value of the excellent adsorption capacity for the removal of congo red from aqueous solution. Uptake of congo red through a fixed-bed column was dependent on the bed depth, influent congo red concentration and flow rate.

Keywords: adsorption, breakthrough curve, clay, congo red, fixed bed column, regeneration

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