Search results for: presence metrics

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Heba M. Mohamed

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Owing to scientific and public concern about health and environment and seeking for a better quality of life; “Green”, “Environmentally” and “Eco” friendly practices have been presented and implemented in different research areas. Subsequently, researchers’ attention is drawn in the direction of greening the analytical methodologies and taking the Green Analytical Chemistry principles (GAC) into consideration. It is of high importance to appraise the environmental impact of each of the implemented green approaches. Compared to the other traditional green metrics (E-factor, Atom economy and the process profile), the eco scale is the optimum choice to assess the environmental impact of the analytical procedures used for pharmaceuticals analysis. For analytical methodologies, Eco-Scale is calculated by allotting penalty points to any factor of the used analytical procedure which disagree and not match with the model green analysis, where the perfect green analysis has its Eco-Scale value of 100. In this work, calculation and comparison of the Eco-Scale for some of the reported green analytical methods was done, to accentuate their greening potentials. Where the different scores can reveal how green the method is, compared to the ideal value. The study emphasizes that greenness measurement is not only about the waste quantity determination but also dictates a holistic scheme, considering all factors.

Keywords: eco scale, green analysis, environmentally friendly, pharmaceuticals analysis

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Vladimir Mantsevich, Natalya Maslova, Petr Arseyev

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We investigated the tunneling current peculiarities in the system of two coupled by means of the external field quantum dots (QDs) weakly connected to the electrodes in the presence of Coulomb correlations between localized electrons by means of Heisenberg equations for pseudo operators with constraint. Special role of multi-electronic states was demonstrated. Various single-electron levels location relative to the sample Fermi level and to the applied bias value in symmetric tunneling contact were investigated. Rabi frequency tuning results in the single-electron energy levels spacing. We revealed the appearance of negative tunneling conductivity and demonstrated multiple switching "on" and "off" of the tunneling current depending on the Coulomb correlations value, Rabi frequency amplitude and energy levels spacing. We proved that Coulomb correlations strongly influence the system behavior. We demonstrated the presence of multi-stability in the coupled QDs with Coulomb correlations when single value of the tunneling current amplitude corresponds to the two values of Rabi frequency in the case when both single-electron energy levels are located slightly above eV and are close to each other. This effect disappears when the single-electron energy levels spacing increases.

Keywords: Coulomb correlations, negative tunneling conductivity, quantum dots, rabi frequency

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Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

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Authors: Abhijith Asok

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This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Jesvin Puay-Hwa Yeo, Laavanya Kathiravelu, Sa’Eda Binte Buang

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In the early days, myths and taboos were used by our ancestors to give explanations to the existence of nature and man, as well as to propitiate fortunes and to avoid unluckiness and harm. Myths and taboos are deeply rooted in our cultures and environment, and they form certain characteristics of any society, even in modern societies. With decades of the three main ethnic communities in Singapore – Malay, Indian and Chinese – living together, there has been intermingling and intermixing of traditions and practices. This may mean that what we think is a ‘Malay’ practice is actually one that is a hybrid of the Chinese and Malay. A good example would be the practice of covering all mirrors in a house of mourning. Therefore, the proposed seeks to explore and understand the underlying social influences of Singapore’s oral tradition. As part of a bigger cultural research project: Designing Cultures, the proposed paper focused on using secondary data to contribute to the overall cultural understanding of the integral connections between oral traditions, people and landscapes. The proposed paper will discuss in details the initials findings of the research project, including the two manners that contributed to the intermixing of myths and taboos. The first is the presence of social institutions such as religions, and the second is the presence of cross-cultural minorities such as the Straits Chinese. As well as other observations included the use and influence of Chinese oral traditions such as folklore among the early Chinese immigrants through social institutions.

Keywords: cultural belief, multiracial society, myths, oral tradition

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Authors: Gabriel Roveda-Hoyos, Margarita Ramirez-Gomez, Adrian Perez, Diana Paola Serralde

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The objective of this study was to determine the variability of arbuscular mycorrhizal fungi (HFMA) communities associated with wild agraz plants (Vaccinium meridionale Swartz) in the Colombian Andes. This species is one of the most promising fruits within the genus Vaccinium because of the high content of anthocyanins and antioxidants in its fruits, and like other species of the Ericaceae family, it depends on the association with HFM for its development in the natural environment. In this study, the presence of mycorrhizae in wild communities of V. meridionale was evaluated, and their relationship with the edaphic and climatic conditions of the study area was analyzed. Sampling was conducted in the rural area of the municipalities of Raquira, and Chiquinquira, Chia, and Tabio in the departments of Cundinamarca and Boyaca, Colombia. Seven sites were selected, and in each site, 5 plants were randomly selected, root and soil samples were taken from each plant in the rhizosphere zone for the quantification of colonization and the presence of spores. The samples were collected on different soils, taxonomic orders Entisols, Inceptisols, and Alfisols, located at altitudes between 2,600 and 3,000 above sea level in the Eastern Cordillera of Colombia. The physicochemical characteristics of the soil were compared with the density of spores and the percentage of presence of mycorrhizae in the roots and variables with the morphometric and physiological characteristics of the plants. Four types of mutual associations were found: arbuscular mycorrhizae, ectendomycorrhiza, ericoid mycorrhizae, and endophytic septate fungi. The main results obtained show a predominance of spores of the genera Glomus and Acaulsopora, in most of the soils analyzed. The spore density of Glomeromycete fungi in the soil varied considerably between the different sites; it was higher ( > 50 spores/g of dry soil) in soil samples with lower bulk density and higher content of organic matter; in these soils a higher cation exchange capacity was found, as well as of nitrogen, calcium, magnesium, manganese and zinc concentration. It can be concluded that Vaccinium meridionale is able to establish in a natural way, association with HFMA.

Keywords: Ericaceae, Arbuscular mycorrhizae, Andes, soils, Glomus sp.

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Authors: Zahra Asgar Pour

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Two types of Ti-Al-containing zeolitic beads with an average diameter of 450 to 750 µm and hierarchical porosity were synthesized using a hard template method and tested as heterogeneous catalysts in the epoxidation of cycloalkenes (i.e. cyclohexene and cis-cyclooctene) with aqueous hydrogen peroxide (H₂O₂) or tert-butyl hydroperoxide(TBHP) as the oxidant agent. The first type of zeolitic beads was prepared by hydrothermal treatment of a primarygel (containing the Si, Ti, and Al precursors) in the presence of porous anion-exchange resin beads as the hard shaping template. After calcination, these beads (Ti-Al-Beta-HDT-B) consisted of both crystalline zeolite Beta and an amorphous silicate phase. The second type of zeolitic beads (Ti-Beta-PS-deAl-14.4-B) was obtained by post-synthesis dealumination of Al-containing zeolite Beta beads using 14.4 M HNO₃, followed by Ti grafting (3 wt% per gram of zeolite). The prepared materials were characterised by means of XRD, N2-physisorption, UV-vis, XRF, SEM, and TEM and tested as heterogeneous epoxidation catalysts. This post-synthetically prepared catalyst demonstrated higher activity (cyclohexene conversion of 22.7 % and epoxide selectivity of 33.5 %) after 5 h at60 °C, which emanates from the crystalline structure and higher degrees of hydrophobicity. In addition, the post-synthetically prepared beads were prone to partial Ti leaching in the presence of H₂O₂, whereas they showed to be resistant against Ti leaching using tert-butyl hydroperoxide as the oxidant agent.

Keywords: epoxidation, structured catalysts, hierarchical porosity, bead-shape catalysts

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Authors: Amar Partap Singh Pharwaha, Balkrishan Jindal

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In this paper, a new method is developed for hiding image in a digital image with multilayer security. In the proposed method, the secret image is encrypted in the first instance using a flexible matrix based symmetric key to add first layer of security. Then another layer of security is added to the secret data by encrypting the ciphered data using Pythagorean Theorem method. The ciphered data bits (4 bits) produced after double encryption are then embedded within digital image in the spatial domain using Least Significant Bits (LSBs) substitution. To improve the image quality of the stego-image, an improved form of pixel adjustment process is proposed. To evaluate the effectiveness of the proposed method, image quality metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), entropy, correlation, mean value and Universal Image Quality Index (UIQI) are measured. It has been found experimentally that the proposed method provides higher security as well as robustness. In fact, the results of this study are quite promising.

Keywords: Pythagorean theorem, pixel adjustment, ciphered data, image hiding, least significant bit, flexible matrix

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Authors: Mallikarjuna Rao Yamarthy, Subramanyam Makam Venkata, Satya Prasad Kodati

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The main objective of the proposed work is to reduce the overall network traffic incurred by mobility management, packet delivery cost and to increase the resource utilization. The proposed algorithm, An Efficient Resource Management Algorithm (ERMA) for mobility management in wireless mesh networks, relies on pointer based mobility management scheme. Whenever a mesh client moves from one mesh router to another, the pointer is set up dynamically between the previous mesh router and current mesh router based on the distance constraints. The algorithm evaluated for signaling cost, data delivery cost and total communication cost performance metrics. The proposed algorithm is demonstrated for both internet sessions and intranet sessions. The proposed algorithm yields significantly better performance in terms of signaling cost, data delivery cost, and total communication cost.

Keywords: data delivery cost, mobility management, pointer forwarding, resource management, wireless mesh networks

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Sana Gul, Sheeba Murad, Aneela Javed

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Introduction: Human Papilloma Virus (HPV) is small DNA virus mostly infecting mucosa and cutaneous keratinocytes. So far, more than 200 Human papillomaviruses are known. HPV have been divided into high- and low-risk on the basis of their oncogenic potential. High risk HPV is considered to be the main etiological cause for cervical cancer. Objective: Current study was designed to screen the local cervical cancer patients from the twin cities of Pakistan for the occurance of high risk HPV. Methodology: A total of 67 formalin fixed paraffin-embedded samples of cervical cancer biopsies were obtained from the government hospitals in Islamabad and Rawalpindi. Cervical cancer biopsies were examined for the presence of HPV DNA. Polymerase chain reaction (PCR) was used for the amplification of a region in the HPV-L1 gene for the general detection of the Papilloma virus and for the genotype specific detection of high risk HPV 16 and 18 using the GP5/GP6 primers and genotype specific primers respectively. Results: HPV DNA was detected in 59 out of 67 samples analyzed. 30 samples showed the presence of HPV16 while 22 samples were positive for HPV 18 . HPV subtype could not be determined in 7 samples. Conclusion: Our results show a strong association between HPV infection and cervical cancer among women in twin cities of Pakistan. One way to minimize the disease burden in relation to HPV infection in Pakistani population is the use of prophylactic vaccines and routine screening. An early diagnosis of HPV infection will allow better health management to reduce the risk of developing cervical cancer.

Keywords: cervical cancer, Pakistan, human papillomavirus, HPV 16

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Authors: L. Beltran, S. Solano, T. Lacruz, M. Blanco, M. Rojo, M. Graell, A. R. Sepulveda

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Introduction and Objective: Given the difficulties of assessing Binge Eating Disorder during childhood, episodes of Loss of Control (LOC) eating can be a key symptom. The objective is to know the prevalence of food psychopathology depending on the type of evaluation and find out which psychological characteristics differentiate overweight or obese children who present LOC from those who do not. Material and Methods: 170 children from 8 to 12 years of age with overweight or obesity (P > 85) were evaluated through the Primary Care Centers of Madrid. Sociodemographic data and psychological measures were collected through the Kiddie-Schedule for Affective Disorders & Schizophrenia, Present & Lifetime Version (K-SADS-PL) diagnostic interview and self-applied questionnaires: Children's eating attitudes (ChEAT), depressive symptomatology (CDI), anxiety (STAIC), general self-esteem (LAWSEQ), body self-esteem (BES), perceived teasing (POTS) and perfectionism (CAPS). Results: 15.2% of the sample exceeded the ChEAT cut-off point, presenting a risk of pathological eating; 5.88% presented an Eating Disorder through the diagnostic interview (2.35% Binge Eating disorder), and 33.53% had LOC episodes. No relationship was found between the presence of LOC and clinical diagnosis of eating disorders according to DSM-V; however, the group with LOC presented a higher risk of eating psychopathology using the ChEAT (p < .02). Significant differences were found in the group with LOC (p < .02): higher z-BMI, lower body self-esteem, greater anxious symptomatology, greater frequency of teasing towards weight, and greater effect of teasing both towards weight and competitions; compared to their peers without LOC. Conclusion: According to previous studies in samples with overweight children, in this Spanish sample of children with obesity, we found a prevalence of moderate eating disorder and a high presence of LOC episodes, which is related to both eating and general psychopathology. These findings confirm that the exclusion of LOC episodes as a diagnostic criterion can underestimate the presence of eating psychopathology during this developmental stage. According to these results, it is highly recommended to promote school context programs that approach LOC episodes in order to reduce associated symptoms. This study is included in a Project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, eating psychopathology, loss-of-control eating, psychological symptomatology

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Authors: Hafiza Khaddija Saman, Muhammad Sufyan

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The increase in availability and popularity of mobile wireless devices has led researchers to develop a wide variety of Mobile Ad-hoc Networking (MANET) protocols to exploit the unique communication opportunities presented by these devices. Devices are able to communicate directly using the wireless spectrum in a peer-to-peer fashion, and route messages through intermediate nodes, however, the nature of wireless shared communication and mobile devices result in many routing and security challenges which must be addressed before deploying a MANET. In this paper, we investigate the range of MANET routing protocols available and discuss the functionalities of several ranging from early protocols such as DSDV to more advanced such as MAODV, our protocol study focuses upon works by Perkins in developing and improving MANET routing. A range of literature relating to the field of MANET routing was identified and reviewed, we also reviewed literature on the topic of securing AODV based MANETs as this may be the most popular MANET protocol. The literature review identified a number of trends within research papers such as exclusive use of the random waypoint mobility model, excluding key metrics from simulation results and not comparing protocol performance against available alternatives.

Keywords: protocol, MANET, ad-Hoc, communication

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Zuriati Ismail, Naomie Salim

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Seeking and sharing knowledge on online forums have made them popular in recent years. Although online forums are valuable sources of information, due to variety of sources of messages, retrieving reliable threads with high quality content is an issue. Majority of the existing information retrieval systems ignore the quality of retrieved documents, particularly, in the field of thread retrieval. In this research, we present an approach that employs various quality features in order to investigate the quality of retrieved threads. Different aspects of content quality, including completeness, comprehensiveness, and politeness, are assessed using these features, which lead to finding not only textual, but also conceptual relevant threads for a user query within a forum. To analyse the influence of the features, we used an adopted version of voting model thread search as a retrieval system. We equipped it with each feature solely and also various combinations of features in turn during multiple runs. The results show that incorporating the quality features enhances the effectiveness of the utilised retrieval system significantly.

Keywords: content quality, forum search, thread retrieval, voting techniques

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Authors: Khalid S. Hashim, Andy Shaw, Rafid Alkhaddar

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Dissolved oxygen concentration (DO) plays a key role in the electrocoagulation process (EC) as it oxidizes the heavy metals, ammonia, and cyanide into other forms that can be removed easily from water. For instance, the DO oxidises Fe (II) to Fe (III), As (III) to As (V), and cyanide to cyanate and then to ammonia. As well as, removal of nitrogenous compounds accomplishes by the presence of DO. Hence, many of the previous investigations used external aerators to provide the required DO inside EC reactors especially when the water being treated has low DO (such as leachate and highly polluted waters with organic matter); or when the DO depleted during the EC treatment. Although the external aeration process effectively enhances the DO concentration, it has a significant impact on energy consumption. Where, the presence of air bubbles increases the electrical resistance of the EC cell that increase the energy consumption in consequence. Thus, the present project aims to fill this gap by an innovative use of perforated flow columns in the designing of a new EC reactor (ECR1). The new reactor (ECR1) consisted of a Perspex made cylinder container having a controllable working volume of 0.5 to 1 L. It supplied with a flow column that consisted of perorated discoid electrodes that made from aluminium. In order to investigate the performance of ECR1; water samples with a controlled DO concentration were pumped at different flow rates (110, 220, and 440 ml/min) to the ECR1 for 10 min. The obtained results demonstrated that the ECR1 increased the DO concentration from 5.0 to 9.54, 10.53, and 11.0 mg/L which equivalent to 90.8%, 110.6%, and 120% at flow rates of 110, 220, and 440 mL/min respectively.

Keywords: dissolved oxygen, flow column, electrocoagulation, aluminium electrodes

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Authors: J. B. Minari, O. B. Oloyede

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Polymorphonuclear neutrophils (PMNs) play a crucial role in a variety of infections caused by bacteria, fungi, and parasites. Indeed, the involvement of PMNs in host defence against Plasmodium falciparum is well documented both in vitro and in vivo. Many of the antimalarial drugs such as chloroquine used in the treatment of human malaria significantly reduce the immune response of the host in vitro and in vivo. Myeloperoxidase is the most abundant enzyme found in the polymorphonuclear neutrophil which plays a crucial role in its function. This study was carried out to investigate the effect of chloroquine on the enzyme. In investigating the effects of the drug on myeloperoxidase, the influence of concentration, pH, partition ratio estimation and kinetics of inhibition were studied. This study showed that chloroquine is concentration-dependent inhibitor of myeloperoxidase with an IC50 of 0.03 mM. Partition ratio estimation showed that 40 enzymatic turnover cycles are required for complete inhibition of myeloperoxidase in the presence of chloroquine. The influence of pH on the effect of chloroquine on the enzyme showed significant inhibition of myeloperoxidase at physiological pH. The kinetic inhibition studies showed that chloroquine caused a non-competitive inhibition with an inhibition constant Ki of 0.27mM. The results obtained from this study shows that chloroquine is a potent inhibitor of myeloperoxidase and it is capable of inactivating the enzyme. It is therefore considered that the inhibition of myeloperoxidase in the presence of chloroquine as revealed in this study may partly explain the impairment of polymorphonuclear neutrophil and consequent immunosuppression of the host defence system against secondary infections.

Keywords: myeloperoxidase, chloroquine, inhibition, neutrophil, immune

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

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Authors: Mohanad Alhabo, Naveed Nawaz, Mahmoud Al-Faris

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The dense deployment of small cells is a promising solution to enhance the coverage and capacity of the heterogeneous networks (HetNets). However, the unplanned deployment could bring new challenges to the network ranging from interference, unnecessary handovers and handover failures. This will cause a degradation in the quality of service (QoS) delivered to the end user. In this paper, we propose an integrated Grey Rational Analysis Standard Deviation based handover method (GRA-SD) for HetNet. The proposed method integrates the Standard Deviation (SD) technique to acquire the weight of the handover metrics and the GRA method to select the best handover base station. The performance of the GRA-SD method is evaluated and compared with the traditional Multiple Attribute Decision Making (MADM) methods including Simple Additive Weighting (SAW) and VIKOR methods. Results reveal that the proposed method has outperformed the other methods in terms of minimizing the number of frequent unnecessary handovers and handover failures, in addition to improving the energy efficiency.

Keywords: energy efficiency, handover, HetNets, MADM, small cells

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Authors: Abrahão S. Fontes, Carlos A. V. Cardoso, Levi P. B. Oliveira

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The induction motors are used worldwide in various industries. Several maintenance techniques are applied to increase the operating time and the lifespan of these motors. Among these, the predictive maintenance techniques such as Motor Current Signature Analysis (MCSA), Motor Square Current Signature Analysis (MSCSA), Park's Vector Approach (PVA) and Park's Vector Square Modulus (PVSM) are used to detect and diagnose faults in electric motors, characterized by patterns in the stator current frequency spectrum. In this article, these techniques are applied and compared on a real motor, which has the fault of eccentricity in the air-gap. It was used as a theoretical model of an electric induction motor without fault in order to assist comparison between the stator current frequency spectrum patterns with and without faults. Metrics were purposed and applied to evaluate the sensitivity of each technique fault detection. The results presented here show that the above techniques are suitable for the fault of eccentricity in the air gap, whose comparison between these showed the suitability of each one.

Keywords: eccentricity in the air-gap, fault diagnosis, induction motors, predictive maintenance

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Authors: Saugata Bose, Ritambhra Korpal

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The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.

Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram

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Authors: Fouzia Perveen, Rumana Qureshi

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The presently studied twelve anticancer drugs are the cytotoxic agents which inhibit the replication of DNA and activity of enzymes involved in DNA replication namely topoisomerase-II, polymerase and helicase and have shown remarkable anticancer activity in clinical trials. In this study, we performed molecular docking studies of twelve antitumor drugs against DNA and DNA enzymes in the presence and absence of ascorbic acid (AA) and developed the quantitative structure-activity relationship (QSAR) model for anticancer activity screening. A number of electronic and steric descriptors were calculated using MOE software package. QSAR was established showing a correlation of binding strength with various physicochemical descriptors. Out of these twelve, eight cytotoxic drugs were tested on Non-Small Cell Lung Cancer cell lines (H-157 and H-1299) in the absence and presence of ascorbic acid and experimental IC50 values were calculated. From the docking studies, binding constants were calculated indicating the strength of drug-DNA and drug-enzyme complex formation and it was correlated to the IC50 values (both experimental and theoretical). These results can offer useful references for directing the molecular design of DNA enzyme inhibitor with improved anticancer activity.

Keywords: ascorbic acid, binding constant, cytotoxic agents, cell culture, DNA, DNA enzymes, molecular docking

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Authors: Alexander Hempfing

Abstract:

The desire to understand business cycle fluctuations, trade interdependencies and co-movement has a long tradition in economic thinking. From input-output economics to business cycle theory, researchers aimed to find appropriate answers from an empirical as well as a theoretical perspective. This paper empirically analyses how the production structure of the global economy and several states developed over time, what their distributional properties are and if there are network specific metrics that allow identifying structurally important nodes, on a global, national and sectoral scale. For this, the World Input-Output Database was used, and different statistical methods were applied. Empirical evidence is provided that the importance of the Eastern hemisphere in the global production network has increased significantly between 2000 and 2014. Moreover, it was possible to show that the sectoral eigenvector centrality indices on a global level are power-law distributed, providing evidence that specific national sectors exist which are more critical to the world economy than others while serving as a hub within the global production network. However, further findings suggest, that global production cannot be characterized as a scale-free network.

Keywords: economic integration, industrial organization, input-output economics, network economics, production networks

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Authors: Scott Ashby, Emily Granger, Mark Connellan

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Background: In-hospital management of Out-of-Hospital Cardiac Arrest (OHCA) is complex as the aetiologies are varied. Acute coronary angiography has been shown to improve outcomes for patients with coronary occlusion as the cause; however, these patients are difficult to identify. ECG results may help identify these patients, but the accuracy of this diagnostic test is under debate, and requires further investigation. Methods: Arrest and hospital management information was collated retrospectively for OHCA patients who presented to a single clinical site between 2009 and 2013. Angiography results were then collected and checked for significance with survival to discharge. The presence of a severe lesion (>70%) was then compared to categorised ECG findings, and the accuracy of the test was calculated. Results: 104 patients were included in this study, 44 survived to discharge, 52 died and 8 were transferred to other clinical sites. Angiography appears to significantly correlate with survival to discharge. ECG showed 54.8% sensitivity for detecting the presence of a severe lesion within the group that received angiography. A combined criterion including any ECG pathology showed 100% sensitivity and negative predictive value, however, a low specificity and positive predictive value. Conclusion: In the cohort investigated, ST elevation on ECG is not a sensitive enough screening test to be used to determine whether OHCA patients have coronary stenosis as the likely cause of their arrest, and more investigation into whether screening with a combined ECG criterion, or whether all patients should receive angiography routinely following OHCA is needed.

Keywords: out of hospital cardiac arrest, coronary angiography, resuscitation, emergency medicine

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Authors: Guilherme Reis Ranieri, Guilherme Leite Gaudereto, Michele Toledo, Luis Fernando Amato-Lourenco, Thais Mauad

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Given the importance to urban sustainability and the growing relevance of the term ‘community garden’, this paper aims to conduct a bibliometric analysis of the term. Using SCOPUS as database, we analyzed 105 articles that contained the keywords ‘community garden’, and conducted a cluster analysis with the associated keywords. As results, we found 205 articles and 404 different keywords. Among the keywords, 334 are not repeated anytime, 44 are repeated 2 times and 9 appear 3 times. The most frequent keywords are: community food systems (74), urban activism (14), Communities of practice (6), food production (6) and public rethoric (5). Within the areas, which contains more articles are: social sciences (74), environmental science (29) and agricultural and biological sciences (24).The three main countries that concentrated the papers are United States (54), Canada (15) and Australia (12). The main journal with these keywords is Local Environment (10). The first publication was in 1999, and by 2010 concentrated 30,5% of the publications. The other 69,5% occurred 2010 to 2015, indicating an increase in frequency. We can conclude that the papers, based on the distribution of the keywords, are still scattered in various research topics and presents high variability between subjects.

Keywords: bibliometrics, community garden, metrics, urban agriculture

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Authors: Mohammed Mashrei

Abstract:

Ferrocement is a type of thin reinforced concrete made of cement-sand matrix with closely spaced relatively small diameter wire meshes, with or without steel bars of small diameter called skeletal steel. This work concerns on the behavior of square ferrocement slabs of dimensions (500) mm x (500) mm and 30 mm subjected to a central load. This study includes testing thirteen ferrocement slabs. The main variables considered in the experimental work are the number of wire mesh layers, percentage of silica fume and the presence of steel fiber. The effects of these variables on the behavior and load carrying capacity of tested slabs under central load were investigated. From the experimental results, it is found that by increasing the percentage of silica fume from (0 to 1.5, 3, 4.5 and 6) of weight of cement the ultimate loads are affected. Also From this study, it is observed that the load carrying capacity increases with the presence of steel fiber reinforcement, the ductility is high in the case of steel fibers. The increasing wire mesh layer from six to ten layers increased the load capacity by 76%. Also, a reduction in width of crack with increasing in number of cracks in the samples that content on steel fibers comparing with samples without steel fibers was observed from the results.

Keywords: ferrocement, fibre, silica fume, slab, strength

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