Search results for: curve feature

Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Pappu Kumar, Ajai Singh, Anshuman Singh

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There are different water user sectors such as rural, urban, mining, subsistence and commercial irrigated agriculture, commercial forestry, industry, power generation which are present in the catchment in Subarnarekha River Basin and Ranchi city. There is an inequity issue in the access to water. The development of the rural area, construction of new power generation plants, along with the population growth, the requirement of unmet water demand and the consideration of environmental flows, the revitalization of small-scale irrigation schemes is going to increase the water demands in almost all the water-stressed catchment. The WEAP Model was developed by the Stockholm Environment Institute (SEI) to enable evaluation of planning and management issues associated with water resources development. The WEAP model can be used for both urban and rural areas and can address a wide range of issues including sectoral demand analyses, water conservation, water rights and allocation priorities, river flow simulation, reservoir operation, ecosystem requirements and project cost-benefit analyses. This model is a tool for integrated water resource management and planning like, forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas and Hydropower generation, In the present study, efforts have been made to access the utility of the WEAP model for water supply and demand analysis for Ranchi city. A detailed works have been carried out and it was tried to ascertain that the WEAP model used for generating different scenario of water requirement, which could help for the future planning of water. The water supplied to Ranchi city was mostly contributed by our study river, Hatiya reservoir and ground water. Data was collected from various agencies like PHE Ranchi, census data of 2011, Doranda reservoir and meteorology department etc. This collected and generated data was given as input to the WEAP model. The model generated the trends for discharge of our study river up to next 2050 and same time also generated scenarios calculating our demand and supplies for feature. The results generated from the model outputs predicting the water require 12 million litter. The results will help in drafting policies for future regarding water supplies and demands under changing climatic scenarios.

Keywords: WEAP model, water demand analysis, Ranchi, scenarios

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Ignacio W. Loor

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In the context of informal settlements in Quito, this paper provides evidence that slopes and deep ravines typical of Andean cities, around which marginalized urban communities sit, constitute a platform for green infrastructure that supports mobility for pedestrians in an incremental fashion. This is informally shaped green infrastructure that provides connectivity to other mobility infrastructures such as roads and public transport, which permits relegated dwellers reach their daily destinations and reclaim their rights to the city. This is relevant in that walking has been increasingly neglected as a viable mean of transport in Latin American cities, in favor of rather motorized means, for which the mobility benefits of green infrastructure have remained invisible to policymakers, contributing to the progressive isolation of informal settlements. This research leverages greatly on an ecological rejuvenation programme led by the municipality of Quito and the Andean Corporation for Development (CAN) intended for rehabilitating the ecological functionalities of ravines. Accordingly, four ravines in different stages of rejuvenation were chosen, in order to through ethnographic methods, capture the practices they support to dwellers of informal settlements across different stages, particularly in terms of issues of mobility. Then, by presenting fragments of interviews, description of observed phenomena, photographs and narratives published in institutional reports and media, the production process of mobility infrastructure over unoccupied slopes and ravines, and the roles that this infrastructure plays in the mobility of dwellers and their quotidian practices are explained. For informal settlements, which normally feature scant urban infrastructure, mobility embodies an unfavourable driver for the possibilities of dwellers to actively participate in the social, economic and political dimensions of the city, for which their rights to the city are widely neglected. Nevertheless, informal green infrastructure for mobility provides some alleviation. This infrastructure is incremental, since its features and usability gradually evolves as users put into it knowledge, labour, devices, and connectivity to other infrastructures in different dimensions which increment its dependability. This is evidenced in the diffusion of knowledge of trails and routes of footpaths among users, the implementation of linking stairs and bridges, the improved access by producing public spaces adjacent to the ravines, the illuminating of surrounding roads, and ultimately, the restoring of ecological functions of ravines. However, the perpetuity of this type of infrastructure is also fragile and vulnerable to the course of urbanisation, densification, and expansion of gated privatised spaces.

Keywords: green infrastructure, informal settlements, urban mobility, walkability

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Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Huinan Zhang, Wenjie Jiang

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Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Masoomeh Estaji, Ahdie Tahamtani

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Considering the necessity of conducting research and publishing academic papers during Master’s and Ph.D. programs, graduate students are in dire need of improving their writing skills through either writing courses or self-study planning. One key feature that could aid academic papers to look more sophisticated is the application of grammatical metaphors (GMs). These types of metaphors represent the ‘non-congruent’ and ‘implicit’ ways of decoding meaning through which one grammatical category is replaced by another, more implied counterpart, which can alter the readers’ understanding of the text as well. Although a number of studies have been conducted on the application of GMs across various disciplines, almost none has been devoted to the field of environmental management, and the scope of the previous studies has been relatively limited compared to the present work. In the current study, attempts were made to analyze different types of GMs used in academic papers published in top-tiered journals in the field of environmental management, and make a list of the most frequently used GMs based on their functions in this particular discipline to make the teaching of academic writing courses more explicit and the composition of academic texts more well-structured. To fulfill these purposes, a corpus-based analysis based on the two theoretical models of Martin et al. (1997) and Liardet (2014) was run. Through two stages of manual analysis and concordancers, ten recent academic articles entailing 132490 words published in two prestigious journals were precisely scrutinized. The results yielded that through the whole IMRaD sections of the articles, among all types of ideational GMs, material processes were the most frequent types. The second and the third ranks would apply to the relational and mental categories, respectively. Regarding the use of interpersonal GMs, objective expanding metaphors were the highest in number. In contrast, subjective interpersonal metaphors, either expanding or contracting, were the least significant. This would suggest that scholars in the field of Environmental Management tended to shift the focus on the main procedures and explain technical phenomenon in detail, rather than to compare and contrast other statements and subjective beliefs. Moreover, since no instances of verbal ideational metaphors were detected, it could be deduced that the act of ‘saying or articulating’ something might be against the standards of the academic genre. One other assumption would be that the application of ideational GMs is context-embedded and that the more technical they are, the least frequent they become. For further studies, it is suggested that the employment of GMs to be studied in a wider scope and other disciplines, and the third type of GMs known as ‘textual’ metaphors to be included as well.

Keywords: English for specific purposes, grammatical metaphor, academic texts, corpus-based analysis

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Authors: Nusrat Zabeen Islam

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Partnership between local Non-Government organizations (NGO) and International development organizations has become an important feature in the development sector of Bangladesh. It is an important challenge for International development organizations to work with local NGOs with proper HR practice. Local NGOs have a lack of quality working environment and this affects the employee’s work experiences and overall performance at individual, partnership with International development organizations and organizational level. Many local development organizations due to the size of the organization and scope do not have a human resource (HR) unit. Inadequate Human Resource Policies, skills, leadership and lack of effective strategy is now a common scenario in Non-Government organization sector of Bangladesh. So corruption, nepotism, and fraud, risk of Political Contribution in office /work space, Sexual/ gender based abuse, insecurity take place in work place of development sector. The Complaint Management Mechanism (CMM) in human resource management could be one way to improve human resource competence in these organizations. The responsibility of Complaint Management Unit (CMU) of an International development organization is to make workplace maltreating, discriminating communities free. The information of impact of CMM was collected through case study of an International organization and some of its partner national organizations in Bangladesh who are engaged in different projects/programs. In this mechanism International development organizations collect complaints from beneficiaries/ staffs by complaint management unit and investigate by segregating the type and mood of the complaint and find out solution to improve the situation within a very short period. A complaint management committee is formed jointly with HR and management personnel. Concerned focal point collect complaints and share with CM unit. By conducting investigation, review of findings, reply back to CM unit and implementation of resolution through this mechanism, a successful bridge of communication and feedback can be established within beneficiaries, staffs and upper management. The overall result of Complaint management mechanism application indicates that by applying CMM accountability and transparency of workplace and workforce in development organization can be increased significantly. Evaluations based on outcomes, and measuring indicators such as productivity, satisfaction, retention, gender equity, proper judgment will guide organizations in building a healthy workforce, and will also clearly articulate the return on investment and justify any need for further funding.

Keywords: human resource management in NGOs, challenges in human resource, workplace environment, complaint management mechanism

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Authors: Nawras Kurzom, Avi Mendelsohn

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The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: V. A. Dubovsky, V. V. Savchenko, A. A. Baryskevich

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The current trend in the automotive industry towards automatic vehicles is creating new challenges related to human factors. This occurs due to the fact that the driver is increasingly relieved of the need to be constantly involved in driving the vehicle, which can negatively impact his/her situation awareness when manual control is required, and decrease driving skills and abilities. These new problems need to be studied in order to provide road safety during the transition towards self-driving vehicles. For this purpose, it is important to develop an appropriate conceptual model of the interaction between the driver and the automated vehicle, which could serve as a theoretical basis for the development of mathematical and simulation models to explore different aspects of driver behaviour in different road situations. Well-known driver behaviour models describe the impact of different stages of the driver's cognitive process on driving performance but do not describe how the driver controls and adjusts his actions. A more complete description of the driver's cognitive process, including the evaluation of the results of his/her actions, will make it possible to more accurately model various aspects of the human factor in different road situations. This paper presents a conceptual model of the 'driver – highly automated vehicle' system based on the P.K. Anokhin's theory of functional systems, which is a theoretical framework for describing internal processes in purposeful living systems based on such notions as goal, desired and actual results of the purposeful activity. A central feature of the proposed model is a dynamic coupling mechanism between the decision-making of a driver to perform a particular action and changes of road conditions due to driver’s actions. This mechanism is based on the stage by stage evaluation of the deviations of the actual values of the driver’s action results parameters from the expected values. The overall functional structure of the highly automated vehicle in the proposed model includes a driver/vehicle/environment state analyzer to coordinate the interaction between driver and vehicle. The proposed conceptual model can be used as a framework to investigate different aspects of human factors in transitions between automated and manual driving for future improvements in driving safety, and for understanding how driver-vehicle interface must be designed for comfort and safety. A major finding of this study is the demonstration that the theory of functional systems is promising and has the potential to describe the interaction of the driver with the vehicle and the environment.

Keywords: automated vehicle, driver behavior, human factors, human-machine system

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Authors: Radul Shishkov, Orlin Davchev

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The digital era has revolutionized architectural practices, with building information modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research introduces a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data -a collection of data points in space, typically produced by 3D scanners- into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. Our methodology has been tested on several real-world case studies, demonstrating its capability to handle diverse architectural styles and complexities. The results showcase a substantial reduction in time and resources required for BIM model generation while maintaining high levels of accuracy and detail. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historic preservation.

Keywords: BIM, 3D point cloud, algorithmic modeling, computational design, architectural reconstruction

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Sumeet Rai, Anuj Tyagi, B. T. Naveen Kumar, Shubhkaramjeet Kaur, Niraj K. Singh

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Since their discovery, indiscriminate use of antibiotics in human, veterinary and aquaculture systems has resulted in global emergence/spread of multidrug-resistant bacterial pathogens. Thus, the need for alternative approaches to control bacterial infections has become utmost important. High selectivity/specificity of bacteriophages (phages) permits the targeting of specific bacteria without affecting the desirable flora. In this study, a lytic phage (Ahp1) specific to Aeromonas hydrophila subsp. hydrophila was isolated from finfish aquaculture pond. The host range of Ahp1 range was tested against 10 isolates of A. hydrophila, 7 isolates of A. veronii, 25 Vibrio cholerae isolates, 4 V. parahaemolyticus isolates and one isolate each of V. harveyi and Salmonella enterica collected previously. Except the host A. hydrophila subsp. hydrophila strain, no lytic activity against any other bacterial was detected. During the adsorption rate and one-step growth curve analysis, 69.7% of phage particles were able to get adsorbed on host cell followed by the release of 93 ± 6 phage progenies per host cell after a latent period of ~30 min. Phage nucleic acid was extracted by column purification methods. After determining the nature of phage nucleic acid as dsDNA, phage genome was subjected to next-generation sequencing by generating paired-end (PE, 2 x 300bp) reads on Illumina MiSeq system. De novo assembly of sequencing reads generated circular phage genome of 42,439 bp with G+C content of 58.95%. During open read frame (ORF) prediction and annotation, 22 ORFs (out of 49 total predicted ORFs) were functionally annotated and rest encoded for hypothetical proteins. Proteins involved in major functions such as phage structure formation and packaging, DNA replication and repair, DNA transcription and host cell lysis were encoded by the phage genome. The complete genome sequence of Ahp1 along with gene annotation was submitted to NCBI GenBank (accession number MF683623). Stability of Ahp1 preparations at storage temperatures of 4 °C, 30 °C, and 40 °C was studied over a period of 9 months. At 40 °C storage, phage counts declined by 4 log units within one month; with a total loss of viability after 2 months. At 30 °C temperature, phage preparation was stable for < 5 months. On the other hand, phage counts decreased by only 2 log units over a period of 9 during storage at 4 °C. As some of the phages have also been reported as glycerol sensitive, the stability of Ahp1 preparations in (0%, 15%, 30% and 45%) glycerol stocks were also studied during storage at -80 °C over a period of 9 months. The phage counts decreased only by 2 log units during storage, and no significant difference in phage counts was observed at different concentrations of glycerol. The Ahp1 phage discovered in our study had a very narrow host range and it may be useful for phage typing applications. Moreover, the endolysin and holin genes in Ahp1 genome could be ideal candidates for recombinant cloning and expression of antimicrobial proteins.

Keywords: Aeromonas hydrophila, endolysin, phage, narrow host range

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Authors: M. Sarraf, J. E. Moros, M. C. Sánchez

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Oleogels are self-standing systems that are able to trap edible liquid oil into a tridimensional network and also help to use less fat by forming crystallization oleogelators. There are different ways to generate oleogelation and oil structuring, including direct dispersion, structured biphasic systems, oil sorption, and indirect method (emulsion-template). The selection of processing conditions as well as the composition of the oleogels is essential to obtain a stable oleogel with characteristics suitable for its purpose. In this sense, one of the ingredients widely used in food products to produce oleogels and emulsions is polysaccharides. Basil seed gum (BSG), with the scientific name Ocimum basilicum, is a new native polysaccharide with high viscosity and pseudoplastic behavior because of its high molecular weight in the food industry. Also, proteins can stabilize oil in water due to the presence of amino and carboxyl moieties that result in surface activity. Whey proteins are widely used in the food industry due to available, cheap ingredients, nutritional and functional characteristics such as emulsifier and a gelling agent, thickening, and water-binding capacity. In general, the interaction of protein and polysaccharides has a significant effect on the food structures and their stability, like the texture of dairy products, by controlling the interactions in macromolecular systems. Using edible oleogels as oil structuring helps for targeted delivery of a component trapped in a structural network. Therefore, the development of efficient oleogel is essential in the food industry. A complete understanding of the important points, such as the ratio oil phase, processing conditions, and concentrations of biopolymers that affect the formation and stability of the emulsion, can result in crucial information in the production of a suitable oleogel. In this research, the effects of oil concentration and pressure used in the manufacture of the emulsion prior to obtaining the oleogel have been evaluated through the analysis of droplet size and rheological properties of obtained emulsions and oleogels. The results show that the emulsion prepared in the high-pressure homogenizer (HPH) at higher pressure values has smaller droplet sizes and a higher uniformity in the size distribution curve. On the other hand, in relation to the rheological characteristics of the emulsions and oleogels obtained, the predominantly elastic character of the systems must be noted, as they present values of the storage modulus higher than those of losses, also showing an important plateau zone, typical of structured systems. In the same way, if steady-state viscous flow tests have been analyzed on both emulsions and oleogels, the result is that, once again, the pressure used in the homogenizer is an important factor for obtaining emulsions with adequate droplet size and the subsequent oleogel. Thus, various routes for trapping oil inside a biopolymer matrix with adjustable mechanical properties could be applied for the creation of the three-dimensional network in order to the oil absorption and creating oleogel.

Keywords: basil seed gum, particle size, viscoelastic properties, whey protein

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Authors: Samuel O. Oduyela

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Over the last ten years, many studies have examined the media coverage of terrorism across the world. Nevertheless, most of these studies have been inclined to the western narrative, more so in relation to the international media. This study departs from that partiality to explore the Nigerian press and its coverage of the Boko Haram. The study intends to illustrate how the Nigerian press has reported its homegrown terrorism within its borders. On 14 April 2014, the Shekau-led Boko Haram kidnapped over 200 female students from Chibok in the Borno State. This study analyses a structured sample of news stories, feature articles, editorial comments, and opinions from the Nation newspaper. The study examined the representation of the Chibok girls kidnaps by concentrating on four main viewpoints. The news framing of the Chibok girls’ kidnap under Presidents Goodluck Jonathan (2014) and Mohammadu Buhari (2016-2018), the sourcing model present in the news reporting of the kidnap and the challenges Nation reporters face in reporting Boko Haram. The study adopted the use of qualitative news framing analysis to provide further insights into significant developments established from the examination of news contents. The study found that the news reportage mainly focused on the government response to Chibok girls kidnap, international press and Boko Haram. Boko Haram was also framed, as a political conspiracy, as prevailing, and as instilling fear. Political, and economic influence appeared to be a significant determinant of the reportage. The study found that the Nation newspaper's portrayal of the crisis under President Jonathan differed significantly from under President Buhari. While the newspaper framed the action of President Jonathan as lacklustre, dismissive, and confusing, it was less critical of President Buhari's government's handling of the crisis. The Nation newspaper failed to promote or explore non-violent approaches. News reports of the kidnap, thus, were presented mainly from a political and ethnoreligious perspective. The study also raised questions of what roles should journalists play in covering conflicts? Should they merely report comments on and interpret it, or should they be actors in the resolution or, more importantly, the prevention of conflicts? The study underlined the need for the independence of the media, more training for journalists to advance a more nuanced and conflict-sensitive news coverage in the Nigerian context.

Keywords: boko haram, chibok girls kidnap, conflict in nigeria, media framing

Procedia PDF Downloads 121

Authors: Jialin Ma, Jia Meng

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Genomic features, the genome-based coordinates, are commonly used for the representation of biological features such as genes, RNA transcripts and transcription factor binding sites. For the analysis of RNA-related genomic features, such as RNA modification sites, a common task is to correlate these features with transcript components (5'UTR, CDS, 3'UTR) to explore their distribution characteristics in terms of transcriptomic coordinates, e.g., to examine whether a specific type of biological feature is enriched near transcription start sites. Existing approaches for performing these tasks involve the manipulation of a gene database, conversion from genome-based coordinate to transcript-based coordinate, and visualization methods that are capable of showing RNA transcript components and distribution of the features. These steps are complicated and time consuming, and this is especially true for researchers who are not familiar with relevant tools. To overcome this obstacle, we develop a dedicated web app TARF, which represents web toolkit for annotating RNA-related genomic features. TARF web tool intends to provide a web-based way to easily annotate and visualize RNA-related genomic features. Once a user has uploaded the features with BED format and specified a built-in transcript database or uploaded a customized gene database with GTF format, the tool could fulfill its three main functions. First, it adds annotation on gene and RNA transcript components. For every features provided by the user, the overlapping with RNA transcript components are identified, and the information is combined in one table which is available for copy and download. Summary statistics about ambiguous belongings are also carried out. Second, the tool provides a convenient visualization method of the features on single gene/transcript level. For the selected gene, the tool shows the features with gene model on genome-based view, and also maps the features to transcript-based coordinate and show the distribution against one single spliced RNA transcript. Third, a global transcriptomic view of the genomic features is generated utilizing the Guitar R/Bioconductor package. The distribution of features on RNA transcripts are normalized with respect to RNA transcript landmarks and the enrichment of the features on different RNA transcript components is demonstrated. We tested the newly developed TARF toolkit with 3 different types of genomics features related to chromatin H3K4me3, RNA N6-methyladenosine (m6A) and RNA 5-methylcytosine (m5C), which are obtained from ChIP-Seq, MeRIP-Seq and RNA BS-Seq data, respectively. TARF successfully revealed their respective distribution characteristics, i.e. H3K4me3, m6A and m5C are enriched near transcription starting sites, stop codons and 5’UTRs, respectively. Overall, TARF is a useful web toolkit for annotation and visualization of RNA-related genomic features, and should help simplify the analysis of various RNA-related genomic features, especially those related RNA modifications.

Keywords: RNA-related genomic features, annotation, visualization, web server

Procedia PDF Downloads 187

Authors: Andrew N. Saylor, James R. Peters

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Scoliosis is a complex 3D deformity of the thoracic and lumbar spines, clinically diagnosed by measurement of a Cobb angle of 10 degrees or more on a coronal X-ray. The Cobb angle is the angle made by the lines drawn along the proximal and distal endplates of the respective proximal and distal vertebrae comprising the curve. Traditionally, Cobb angles are measured manually using either a marker, straight edge, and protractor or image measurement software. The task of measuring the Cobb angle can also be represented by a function taking the spine geometry rendered using X-ray imaging as input and returning the approximate angle. Although the form of such a function may be unknown, it can be approximated using artificial neural networks (ANNs). The performance of ANNs is affected by many factors, including the choice of activation function and network architecture; however, the effects of these parameters on the accuracy of scoliotic deformity measurements are poorly understood. Therefore, the objective of this study was to systematically investigate the effect of ANN architecture and activation function on Cobb angle measurement from the coronal X-rays of scoliotic subjects. The data set for this study consisted of 609 coronal chest X-rays of scoliotic subjects divided into 481 training images and 128 test images. These data, which included labeled Cobb angle measurements, were obtained from the SpineWeb online database. In order to normalize the input data, each image was resized using bi-linear interpolation to a size of 500 × 187 pixels, and the pixel intensities were scaled to be between 0 and 1. A fully connected (dense) ANN with a fixed cost function (mean squared error), batch size (10), and learning rate (0.01) was developed using Python Version 3.7.3 and TensorFlow 1.13.1. The activation functions (sigmoid, hyperbolic tangent [tanh], or rectified linear units [ReLU]), number of hidden layers (1, 3, 5, or 10), and number of neurons per layer (10, 100, or 1000) were varied systematically to generate a total of 36 network conditions. Stochastic gradient descent with early stopping was used to train each network. Three trials were run per condition, and the final mean squared errors and mean absolute errors were averaged to quantify the network response for each condition. The network that performed the best used ReLU neurons had three hidden layers, and 100 neurons per layer. The average mean squared error of this network was 222.28 ± 30 degrees2, and the average mean absolute error was 11.96 ± 0.64 degrees. It is also notable that while most of the networks performed similarly, the networks using ReLU neurons, 10 hidden layers, and 1000 neurons per layer, and those using Tanh neurons, one hidden layer, and 10 neurons per layer performed markedly worse with average mean squared errors greater than 400 degrees2 and average mean absolute errors greater than 16 degrees. From the results of this study, it can be seen that the choice of ANN architecture and activation function has a clear impact on Cobb angle inference from coronal X-rays of scoliotic subjects.

Keywords: scoliosis, artificial neural networks, cobb angle, medical imaging

Procedia PDF Downloads 107

Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Devi Eka Septiyani Arifin, Jrjeng Ruan

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As an approach to manipulate the performance of polymer thin film, epitaxy crystallization within polymer blends of poly(vinylidene fluoride) (PVDF) and its copolymer poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) was studied in this research, which involves the competition between phase separation and crystal growth of constitutive semicrystalline polymers. The unique piezoelectric feature of poly(vinylidene fluoride) crystalline phase is derived from the packing of molecular chains in all-trans conformation, which spatially arranges all the substituted fluorene atoms on one side of the molecular chain and hydrogen atoms on the other side. Therefore, the net dipole moment is induced across the lateral packing of molecular chains. Nevertheless, due to the mutual repulsion among fluorene atoms, this all-trans molecular conformation is not stable, and ready to change above curie temperature, where thermal energy is sufficient to cause segmental rotation. This research attempts to explore whether the epitaxial interactions between piezoelectric crystals and crystal lattice of hexamethylbenzene (HMB) crystalline platelet is able to stabilize this metastable all-trans molecular conformation or not. As an aromatic crystalline compound, the melt of HMB was surprisingly found able to dissolve the poly(vinylidene fluoride), resulting in homogeneous eutectic solution. Thus, after quenching this binary eutectic mixture to room temperature, subsequent heating or annealing processes were designed to explore the involve phase separation and crystallization behavior. The phase transition behaviors were observed in-situ by X-ray diffraction and differential scanning calorimetry (DSC). The molecular packing was observed via transmission electron microscope (TEM) and the principles of electron diffraction were brought to study the internal crystal structure epitaxially developed within thin films. Obtained results clearly indicated the occurrence of heteroepitaxy of PVDF/PVDF-TrFE on HMB crystalline platelet. Both the concentration of poly(vinylidene fluoride) and the mixing ratios of these two constitutive polymers have been adopted as the influential factors for studying the competition between the epitaxial crystallization of PVDF and P(VDF-TrFE) on HMB crystalline. Furthermore, the involved epitaxial relationship is to be deciphered and studied as a potential factor capable of guiding the wide spread of piezoelectric crystalline form.

Keywords: epitaxy, crystallization, crystalline platelet, thin film and mixing ratio

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Authors: H. Agbaria, A. Salman, M. Huleihel, G. Beck, D. H. Rich, S. Mordechai, J. Kapelushnik

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Viral and bacterial infections are responsible for variety of diseases. These infections have similar symptoms like fever, sneezing, inflammation, vomiting, diarrhea and fatigue. Thus, physicians may encounter difficulties in distinguishing between viral and bacterial infections based on these symptoms. Bacterial infections differ from viral infections in many other important respects regarding the response to various medications and the structure of the organisms. In many cases, it is difficult to know the origin of the infection. The physician orders a blood, urine test, or 'culture test' of tissue to diagnose the infection type when it is necessary. Using these methods, the time that elapses between the receipt of patient material and the presentation of the test results to the clinician is typically too long ( > 24 hours). This time is crucial in many cases for saving the life of the patient and for planning the right medical treatment. Thus, rapid identification of bacterial and viral infections in the lab is of great importance for effective treatment especially in cases of emergency. Blood was collected from 50 patients with confirmed viral infection and 50 with confirmed bacterial infection. White blood cells (WBCs) and plasma were isolated and deposited on a zinc selenide slide, dried and measured under a Fourier transform infrared (FTIR) microscope to obtain their infrared absorption spectra. The acquired spectra of WBCs and plasma were analyzed in order to differentiate between the two types of infections. In this study, the potential of FTIR microscopy in tandem with multivariate analysis was evaluated for the identification of the agent that causes the human infection. The method was used to identify the infectious agent type as either bacterial or viral, based on an analysis of the blood components [i.e., white blood cells (WBC) and plasma] using their infrared vibrational spectra. The time required for the analysis and evaluation after obtaining the blood sample was less than one hour. In the analysis, minute spectral differences in several bands of the FTIR spectra of WBCs were observed between groups of samples with viral and bacterial infections. By employing the techniques of feature extraction with linear discriminant analysis (LDA), a sensitivity of ~92 % and a specificity of ~86 % for an infection type diagnosis was achieved. The present preliminary study suggests that FTIR spectroscopy of WBCs is a potentially feasible and efficient tool for the diagnosis of the infection type.

Keywords: viral infection, bacterial infection, linear discriminant analysis, plasma, white blood cells, infrared spectroscopy

Procedia PDF Downloads 202

Authors: Priya Kulkarni, Soumya Koppikar, Narendrakumar Wagh, Dhanshri Ingle, Onkar Lande, Abhay Harsulkar

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Cartilage is an extracellular matrix composed of aggrecan, which imparts it with a great tensile strength, stiffness and resilience. Disruption in cartilage metabolism leading to progressive degeneration is a characteristic feature of Osteoarthritis (OA). The process involves enzymatic depolymerisation of cartilage specific proteoglycan, releasing free glycosaminoglycan (GAG). This released GAG in synovial fluid (SF) of knee joint serves as a direct measure of cartilage loss, however, limited due to its invasive nature. Western Ontario and McMaster Universities Arthritis Index (WOMAC) is widely used for assessing pain, stiffness and physical-functions in OA patients. The scale is comprised of three subscales namely, pain, stiffness and physical-function, intends to measure patient’s perspective of disease severity as well as efficacy of prescribed treatment. Twenty SF samples obtained from OA patients were analysed for their GAG values in SF using DMMB based assay. LK 1.0 vernacular version was used to attain WOMAC scale. The results were evaluated using SAS University software (Edition 1.0) for statistical significance. All OA patients revealed higher GAG values compared to the control value of 78.4±30.1µg/ml (obtained from our non-OA patients). Average WOMAC calculated was 51.3 while pain, stiffness and function estimated were 9.7, 3.9 and 37.7, respectively. Interestingly, a strong statistical correlation was established between WOMAC function subscale and GAG (p = 0.0102). This subscale is based on day-to-day activities like stair-use, bending, walking, getting in/out of car, rising from bed. However, pain and stiffness subscale did not show correlation with any of the studied markers and endorsed the atypical inflammation in OA pathology. On one side, where knee pain showed poor correlation with GAG, it is often noted that radiography is insensitive to cartilage degenerative changes; thus OA remains undiagnosed for long. Moreover, active cartilage degradation phase remains elusive to both, patient and clinician. Through analysis of large number of OA patients we have established a close association of Kellgren-Lawrence grades and increased cartilage loss. A direct attempt to correlate WOMAC and radiographic progression of OA with various biomarkers has not been attempted so far. We found a good correlation in GAG levels in SF and the function subscale.

Keywords: cartilage, Glycosaminoglycan, synovial fluid, western ontario and McMaster Universities Arthritis Index

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Authors: Nalluri Lakshmi Ramu, C. Venkat Nihit, Narayana Kumar, Dillep

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Masonry bridges are the iconic heritage transportation infrastructure throughout the world. Continuous increase in traffic loads and speed have kept engineers in dilemma about their structural performance and capacity. Henceforth, research community has an urgent need to propose an effective methodology and validate on real-time bridges. The presented research aims to assess the structural health of an Eighty-year-old masonry railway bridge in India using wireless accelerometer sensors. The bridge consists of 44 spans with length of 24.2 m each and individual pier is 13 m tall laid on well foundation. To calculate the dynamic characteristic properties of the bridge, ambient vibrations were recorded from the moving traffic at various speeds and the same are compared with the developed three-dimensional numerical model using finite element-based software. The conclusions about the weaker or deteriorated piers are drawn from the comparison of frequencies obtained from the experimental tests conducted on alternative spans. Masonry is a heterogeneous anisotropic material made up of incoherent materials (such as bricks, stones, and blocks). It is most likely the earliest largely used construction material. Masonry bridges, which were typically constructed of brick and stone, are still a key feature of the world's highway and railway networks. There are 1,47,523 railway bridges across India and about 15% of these bridges are built by masonry, which are around 80 to 100 year old. The cultural significance of masonry bridges cannot be overstated. These bridges are considered to be complicated due to the presence of arches, spandrel walls, piers, foundations, and soils. Due to traffic loads and vibrations, wind, rain, frost attack, high/low temperature cycles, moisture, earthquakes, river overflows, floods, scour, and soil under their foundations may cause material deterioration, opening of joints and ring separation in arch barrels, cracks in piers, loss of brick-stones and mortar joints, distortion of the arch profile. Few NDT tests like Flat jack Tests are being employed to access the homogeneity, durability of masonry structure, however there are many drawbacks because of the test. A modern approach of structural health assessment of masonry structures by vibration analysis, frequencies and stiffness properties is being explored in this paper.

Keywords: masonry bridges, condition assessment, wireless sensors, numerical analysis modal frequencies

Procedia PDF Downloads 150

Authors: Yung-Chih Kuo, I-Hsin Wang

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Catanionic solid lipid nanoparticles (CASLNs) with surface wheat germ agglutinin (WGA) and lactoferrin (Lf) were formulated for entrapping and releasing etoposide (ETP), crossing the blood–brain barrier (BBB), and inhibiting the growth of glioblastoma multiforme (GBM). Microemulsified ETP-CASLNs were modified with WGA and Lf for permeating a cultured monolayer of human brain-microvascular endothelial cells (HBMECs) regulated by human astrocytes and for treating malignant U87MG cells. Experimental evidence revealed that an increase in the concentration of catanionic surfactant from 5 μM to 7.5 μM reduced the particle size. When the concentration of catanionic surfactant increased from 7.5 μM to 12.5 μM, the particle size increased, yielding a minimal diameter of WGA-Lf-ETP-CASLNs at 7.5 μM of catanionic surfactant. An increase in the weight percentage of BW from 25% to 75% enlarged WGA-Lf-ETP-CASLNs. In addition, an increase in the concentration of catanionic surfactant from 5 to 15 μM increased the absolute value of zeta potential of WGA-Lf-ETP-CASLNs. It was intriguing that the increment of the charge as a function of the concentration of catanionic surfactant was approximately linear. WGA-Lf-ETP-CASLNs revealed an integral structure with smooth particle contour, displayed a lighter exterior layer of catanionic surfactant, WGA, and Lf and showed a rigid interior region of solid lipids. A variation in the concentration of catanionic surfactant between 5 μM and 15 μM yielded a maximal encapsulation efficiency of ETP ata 7.5 μM of catanionic surfactant. An increase in the concentration of Lf/WGA decreased the grafting efficiency of Lf/WGA. Also, an increase in the weight percentage of ETP decreased its encapsulation efficiency. Moreover, the release rate of ETP from WGA-Lf-ETP-CASLNs reduced with increasing concentration of catanionic surfactant, and WGA-Lf-ETP-CASLNs at 12.5 μM of catanionic surfactant exhibited a feature of sustained release. The order in the viability of HBMECs was ETP-CASLNs ≅ Lf-ETP-CASLNs ≅ WGA-Lf-ETP-CASLNs > ETP. The variation in the transendothelial electrical resistance (TEER) and permeability of propidium iodide (PI) was negligible when the concentration of Lf increased. Furthermore, an increase in the concentration of WGA from 0.2 to 0.6 mg/mL insignificantly altered the TEER and permeability of PI. When the concentration of Lf increased from 2.5 to 7.5 μg/mL and the concentration of WGA increased from 2.5 to 5 μg/mL, the enhancement in the permeability of ETP was minor. However, 10 μg/mL of Lf promoted the permeability of ETP using Lf-ETP-CASLNs, and 5 and 10 μg/mL of WGA could considerably improve the permeability of ETP using WGA-Lf-ETP-CASLNs. The order in the efficacy of inhibiting U87MG cells was WGA-Lf-ETP-CASLNs > Lf-ETP-CASLNs > ETP-CASLNs > ETP. As a result, WGA-Lf-ETP-CASLNs reduced the TEER, enhanced the permeability of PI, induced a minor cytotoxicity to HBMECs, increased the permeability of ETP across the BBB, and improved the antiproliferative efficacy of U87MG cells. The grafting of WGA and Lf is crucial to control the medicinal property of ETP-CASLNs and WGA-Lf-ETP-CASLNs can be promising colloidal carriers in GBM management.

Keywords: catanionic solid lipid nanoparticle, etoposide, glioblastoma multiforme, lactoferrin, wheat germ agglutinin

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Authors: Jia-Chao Wang

Abstract:

A model of the universe without invoking space expansion is proposed to explain the observed redshift-distance relation and the cosmic microwave background radiation (CMB). The main hypothesized feature of the model is that photons traveling in space interact with the CMB photon gas. This interaction causes the photons to gradually lose energy through dissipation and, therefore, experience redshift. The interaction also causes some of the photons to be scattered off their track toward an observer and, therefore, results in beam intensity attenuation. As observed, the CMB exists everywhere in space and its photon density is relatively high (about 410 per cm³). The small average energy of the CMB photons (about 6.3×10⁻⁴ eV) can reduce the energies of traveling photons gradually and will not alter their momenta drastically as in, for example, Compton scattering, to totally blur the images of distant objects. An object moving through a thermalized photon gas, such as the CMB, experiences a drag. The cause is that the object sees a blue shifted photon gas along the direction of motion and a redshifted one in the opposite direction. An example of this effect can be the observed CMB dipole: The earth travels at about 368 km/s (600 km/s) relative to the CMB. In the all-sky map from the COBE satellite, radiation in the Earth's direction of motion appears 0.35 mK hotter than the average temperature, 2.725 K, while radiation on the opposite side of the sky is 0.35 mK colder. The pressure of a thermalized photon gas is given by Pγ = Eγ/3 = αT⁴/3, where Eγ is the energy density of the photon gas and α is the Stefan-Boltzmann constant. The observed CMB dipole, therefore, implies a pressure difference between the two sides of the earth and results in a CMB drag on the earth. By plugging in suitable estimates of quantities involved, such as the cross section of the earth and the temperatures on the two sides, this drag can be estimated to be tiny. But for a photon traveling at the speed of light, 300,000 km/s, the drag can be significant. In the present model, for the dissipation part, it is assumed that a photon traveling from a distant object toward an observer has an effective interaction cross section pushing against the pressure of the CMB photon gas. For the attenuation part, the coefficient of the typical attenuation equation is used as a parameter. The values of these two parameters are determined by fitting the 748 µ vs. z data points compiled from 643 supernova and 105 γ-ray burst observations with z values up to 8.1. The fit is as good as that obtained from the lambda cold dark matter (ΛCDM) model using online cosmological calculators and Planck 2015 results. The model can be used to interpret Hubble's constant, Olbers' paradox, the origin and blackbody nature of the CMB radiation, the broadening of supernova light curves, and the size of the observable universe.

Keywords: CMB as the lowest energy state, model of the universe, origin of CMB in a static universe, photon-CMB photon gas interaction

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Authors: Eleonora Di Salvo, Antonio Panebianco, Graziella Ziino

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Background: Vibrio parahaemolyticus is a human pathogen that is widely distributed in marine environments. It is frequently isolated from raw seafood, particularly shellfish. Consumption of raw or undercooked seafood contaminated with V. parahaemolyticus may lead to acute gastroenteritis. Vibrio spp. has excellent resistance to low temperatures so it can be found in frozen products for a long time. Recently, the viable but non-culturable state (VBNC) of bacteria has attracted great attention, and more than 85 species of bacteria have been demonstrated to be capable of entering this state. VBNC cells cannot grow in conventional culture medium but are viable and maintain metabolic activity, which may constitute an unrecognized source of food contamination and infection. Also V. parahaemolyticus could exist in VBNC state under nutrient starvation or low-temperature conditions. Aim: The aim of the present study was to optimize methods and investigate V. parahaemolyticus VBNC cells and their presence in frozen bivalve molluscs, regularly marketed. Materials and Methods: propidium monoazide (PMA) was integrated with real-time polymerase chain reaction (qPCR) targeting the tl gene to detect and quantify V. parahaemolyticus in the VBNC state. PMA-qPCR resulted highly specific to V. parahaemolyticus with a limit of detection (LOD) of 10-1 log CFU/mL in pure bacterial culture. A standard curve for V. parahaemolyticus cell concentrations was established with the correlation coefficient of 0.9999 at the linear range of 1.0 to 8.0 log CFU/mL. A total of 77 samples of frozen bivalve molluscs (35 mussels; 42 clams) were subsequently subjected to the qualitative (on alkaline phosphate buffer solution) and quantitative research of V. parahaemolyticus on thiosulfate-citrate-bile salts-sucrose (TCBS) agar (DIFCO) NaCl 2.5%, and incubation at 30°C for 24-48 hours. Real-time PCR was conducted on homogenate samples, in duplicate, with and without propidium monoazide (PMA) dye, and exposed for 45 min under halogen lights (650 W). Total DNA was extracted from cell suspension in homogenate samples according to bolliture protocol. The Real-time PCR was conducted with species-specific primers for V. parahaemolitycus. The RT-PCR was performed in a final volume of 20 µL, containing 10 µL of SYBR Green Mixture (Applied Biosystems), 2 µL of template DNA, 2 µL of each primer (final concentration 0.6 mM), and H2O 4 µL. The qPCR was carried out on CFX96 TouchTM (Bio-Rad, USA). Results: All samples were negative both to the quantitative and qualitative detection of V. parahaemolyticus by the classical culturing technique. The PMA-qPCR let us individuating VBNC V. parahaemolyticus in the 20,78% of the samples evaluated with a value between the Log 10-1 and Log 10-3 CFU/g. Only clams samples were positive for PMA-qPCR detection. Conclusion: The present research is the first evaluating PMA-qPCR assay for detection of VBNC V. parahaemolyticus in bivalve molluscs samples, and the used method was applicable to the rapid control of marketed bivalve molluscs. We strongly recommend to use of PMA-qPCR in order to identify VBNC forms, undetectable by the classic microbiological methods. A precise knowledge of the V.parahaemolyticus in a VBNC form is fundamental for the correct risk assessment not only in bivalve molluscs but also in other seafood.

Keywords: food safety, frozen bivalve molluscs, PMA dye, Real-time PCR, VBNC state, Vibrio parahaemolyticus

Procedia PDF Downloads 114

Authors: Suliman Al-Fayoumi, Sherri Amberg, Huafeng Zhou, Jack W. Singer, James P. Dean

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Pacritinib is an oral kinase inhibitor with specificity for JAK2, FLT3, IRAK1, and CSF1R. In clinical studies, pacritinib was well tolerated with clinical activity in patients with myelofibrosis. The most frequent adverse events (AEs) observed with pacritinib are gastrointestinal (diarrhea, nausea, and vomiting; mostly grade 1-2 in severity) and typically resolve within 2 weeks. A human ADME mass balance study demonstrated that pacritinib is predominantly cleared via hepatic metabolism and biliary excretion (>85% of administered dose). The major hepatic metabolite identified, M1, is not thought to materially contribute to the pharmacological activity of pacritinib. Hepatic diseases are known to impair hepatic blood flow, drug-metabolizing enzymes, and biliary transport systems and may affect drug absorption, disposition, efficacy, and toxicity. This phase 1 study evaluated the pharmacokinetics (PK) and safety of pacritinib and the M1 metabolite in study subjects with mild, moderate, or severe hepatic impairment (HI) and matched healthy subjects with normal liver function to determine if pacritinib dosage adjustments are necessary for patients with varying degrees of hepatic insufficiency. Study participants (aged 18-85 y) were enrolled into 4 groups based on their degree of HI as defined by Child-Pugh Clinical Assessment Score: mild (n=8), moderate (n=8), severe (n=4), and healthy volunteers (n=8) matched for age, BMI, and sex. Individuals with concomitant renal dysfunction or progressive liver disease were excluded. A single 400 mg dose of pacritinib was administered to all participants. Blood samples were obtained for PK evaluation predose and at multiple time points postdose through 168 h. Key PK parameters evaluated included maximum plasma concentration (Cmax), time to Cmax (Tmax), area under the plasma concentration time curve (AUC) from hour zero to last measurable concentration (AUC0-t), AUC extrapolated to infinity (AUC0-∞), and apparent terminal elimination half-life (t1/2). Following treatment, pacritinib was quantifiable for all study participants at 1 h through 168 h postdose. Systemic pacritinib exposure was similar between healthy volunteers and individuals with mild HI. However, there was a significant difference between those with moderate and severe HI and healthy volunteers with respect to peak concentration (Cmax) and plasma exposure (AUC0-t, AUC0-∞). Mean Cmax decreased by 47% and 57% respectively in participants with moderate and severe HI vs matched healthy volunteers. Similarly, mean AUC0-t decreased by 36% and 45% and mean AUC0-∞ decreased by 46% and 48%, respectively in individuals with moderate and severe HI vs healthy volunteers. Mean t1/2 ranged from 51.5 to 74.9 h across all groups. The variability on exposure ranged from 17.8% to 51.8% across all groups. Systemic exposure of M1 was also significantly decreased in study participants with moderate or severe HI vs. healthy participants and individuals with mild HI. These changes were not significantly dissimilar from the inter-patient variability in these parameters observed in healthy volunteers. All AEs were grade 1-2 in severity. Diarrhea and headache were the only AEs reported in >1 participant (n=4 each). Based on these observations, it is unlikely that dosage adjustments would be warranted in patients with mild, moderate, or severe HI treated with pacritinib.

Keywords: pacritinib, myelofibrosis, hepatic impairment, pharmacokinetics

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Authors: Lynnette Lyzwinski, Liam Caffery, Matthew Bambling, Sisira Edirippulige

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Introduction: A novel method of delivering mindfulness interventions for populations at risk of weight gain and stress-related eating, in particular, college students, is through mHealth. While there have been qualitative studies on mHealth for weight loss, there has not been a study on mHealth for weight loss using mindfulness that has explored student perspectives on a student centred mindfulness app and mindfulness-based text messages for eating and stress. Student perspective data will provide valuable information for creating a specific purpose weight management app and mindfulness-based text messages (for the Mindfulness App study). Methods: A qualitative focus group study was undertaken at St Lucia campus at the University of Queensland in March 2017. Students over the age of 18 were eligible to participate. Interviews were audiotaped and transcribed. One week following the focus group, students were sent sample mindfulness-based text messages based on their responses. Students provided written feedback via email. Data were analysed using N Vivo software. Results: The key themes in a future mindfulness-based app are a simple design interface, a focus on education/practical tips, and real-life practical exercises. Social media should be avoided. Key themes surrounding barriers include the perceived difficulty of mindfulness and a lack of proper guidance or knowledge. The mindfulness-based text messages were received positively. Key themes were creating messages with practical tips about how to be mindful and how to integrate mindful reflection of both one’s body and environment while on campus. Other themes including creating positive, inspirational messages. There was lack of agreement on the ideal timing for messages. Discussion: This is the first study that explored student perspectives on a mindfulness-app and mindfulness-based text messages for stress and weight management as a pre-trial study for the Mindfulness App trial for stress, lifestyle, and weight in students. It is important to consider maximizing the potential facilitators of use and minimize potential identified barriers when developing and designing a future mHealth mindfulness-based intervention tailored to the student consumer. Conclusion: Future mHealth studies may consider integrating mindfulness-based text messages in their interventions for weight and stress as this is a novel feature that appears to be acceptable for participants. The results of this focus group provide the basis to develop content for a specific purpose student app for weight management.

Keywords: mindfulness, college students, mHealth, weight loss

Procedia PDF Downloads 166

Authors: P. Crosby, Shiva Krishna Pavuluri, S. Rajkumar

Abstract:

Purpose: Occupied built-up space can be broadly classified as air-conditioned and naturally ventilated. Regardless of the building type, the objective of all occupied built-up space is to provide a thermally acceptable environment for human occupancy. Considering this aspect, air-conditioned spaces allow a greater degree of flexibility to control and modulate the comfort parameters during the operation phase. However, in the case of naturally ventilated space, a number of design features favoring indoor thermal comfort should be mandatorily conceptualized starting from the design phase. One such primary design feature that requires to be prioritized is, selection of building envelope material, as it decides the flow of energy from outside environment to occupied spaces. Research Methodology: In India and many countries across globe, the standardized material used for building envelope is re-enforced concrete (i.e. M-25 grade concrete). The comfort inside the RC built environment for warm & humid climate (i.e. mid-day temp of 30-35˚C, diurnal variation of 5-8˚C & RH of 70-90%) is unsatisfying to say the least. This study is mainly focused on reviewing the impact of mix design of conventional M25 grade concrete on inside thermal comfort. In this mix design, air entrainment in the range of 2000 to 2100 kg/m3 is introduced to reduce the density of M-25 grade concrete. Thermal performance parameters & indoor comfort indices are analyzed for the proposed mix and compared in relation to the conventional M-25 grade. There are diverse methodologies which govern indoor comfort calculation. In this study, three varied approaches specifically a) Indian Adaptive Thermal comfort model, b) Tropical Summer Index (TSI) c) Air temperature less than 33˚C & RH less than 70% to calculate comfort is adopted. The data required for the thermal comfort study is acquired by field measurement approach (i.e. for the new mix design) and simulation approach by using design builder (i.e. for the conventional concrete grade). Findings: The analysis points that the Tropical Summer Index has a higher degree of stringency in determining the occupant comfort band whereas also providing a leverage in thermally tolerable band over & above other methodologies in the context of the study. Another important finding is the new mix design ensures a 10% reduction in indoor air temperature (IAT) over the outdoor dry bulb temperature (ODBT) during the day. This translates to a significant temperature difference of 6 ˚C IAT and ODBT.

Keywords: Indian adaptive thermal comfort, indoor air temperature, thermal comfort, tropical summer index

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Authors: Pedro Llanos, Diego García

Abstract:

This paper is addressed to expanding our understanding of the effects of hypoxia training on our bodies to better model its dynamics and leverage some of its implications and effects on human health. Hypoxia training is a recommended practice for military and civilian pilots that allow them to recognize their early hypoxia signs and symptoms, and Scientist Astronaut Candidates (SACs) who underwent hypobaric hypoxia (HH) exposure as part of a training activity for prospective suborbital flight applications. This observational-analytical study describes physiologic responses and symptoms experienced by a SAC group before, during and after HH exposure and proposes a model for assessing predicted versus observed physiological responses. A group of individuals with diverse Science Technology Engineering Mathematics (STEM) backgrounds conducted a hypobaric training session to an altitude up to 22,000 ft (FL220) or 6,705 meters, where heart rate (HR), breathing rate (BR) and core temperature (Tc) were monitored with the use of a chest strap sensor pre and post HH exposure. A pulse oximeter registered levels of saturation of oxygen (SpO₂), number and duration of desaturations during the HH chamber flight. Hypoxia symptoms as described by the SACs during the HH training session were also registered. This data allowed to generate a preliminary predictive model of the oxygen desaturation and O₂ pressure curve for each subject, which consists of a sixth-order polynomial fit during exposure, and a fifth or fourth-order polynomial fit during recovery. Data analysis showed that HR and BR showed no significant differences between pre and post HH exposure in most of the SACs, while Tc measures showed slight but consistent decrement changes. All subjects registered SpO₂ greater than 94% for the majority of their individual HH exposures, but all of them presented at least one clinically significant desaturation (SpO₂ < 85% for more than 5 seconds) and half of the individuals showed SpO₂ below 87% for at least 30% of their HH exposure time. Finally, real time collection of HH symptoms presented temperature somatosensory perceptions (SP) for 65% of individuals, and task-focus issues for 52.5% of individuals as the most common HH indications. 95% of the subjects experienced HH onset symptoms below FL180; all participants achieved full recovery of HH symptoms within 1 minute of donning their O₂ mask. The current HH study performed on this group of individuals suggests a rapid and fully reversible physiologic response after HH exposure as expected and obtained in previous studies. Our data showed consistent results between predicted versus observed SpO₂ curves during HH suggesting a mathematical function that may be used to model HH performance deficiencies. During the HH study, real-time HH symptoms were registered providing evidenced SP and task focusing as the earliest and most common indicators. Finally, an assessment of HH signs of symptoms in a group of heterogeneous, non-pilot individuals showed similar results to previous studies in homogeneous populations of pilots.

Keywords: slow onset hypoxia, hypobaric chamber training, altitude sickness, symptoms and altitude, pressure cabin

Procedia PDF Downloads 101