Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Genome Analysis of Malaria Parasite Species

With over 200 million infections and up to one million deaths every year, malaria remains one of the most devastating infectious diseases affecting humans. Over the last few years, complete genome sequences of both human and non-human malaria parasite species have become available, adding comparative genomics to the toolbox of molecular biologists to study the genetic basis of human virulence. In this thesis, I computationally compared the published genomes of seven malaria parasite species with the aim to gain new insights into genes underlying human virulence. This comparison was performed using two complementary approaches. In the first approach, I used whole-genome synteny analysis to find genes present in human but not non-human malaria parasites. In the second approach, I first clustered virulence-associated genes into gene families and then examined these gene families for species-specific differences. Both comparisons resulted in interesting gene lists. Synteny analysis identified three key enzymes of the thiamine (vitamin B1) biosynthesis pathway to be present in human but not rodent malaria parasites, indicating that these two groups of parasites differ in their ability to synthesize vitamin B1 de novo. My gene family classification exposed within the largest and highly divergent surface antigen gene family pir a group of unusually well conserved orthologs, which should be considered as high-priority targets for experimental characterization and vaccine development. In conclusion, this thesis highlights genes and pathways that are different between human and non-human malaria parasites and therefore could play important roles in human virulence. Experimental studies can now be initiated to confirm virulence-associated functions and to explore their potential value for drug and vaccine development.
Software Support for Metrology - Good Practice Guide No.17. Distributed computing for metrology applications.

This guide aims to facilitate the effective use of distributed computing methods and techniques by other National Measurement System (NMS) programmes and by metrologists in general. It focuses on the needs of those developing applications for distributed computing systems, and on PC desktop grids in particular, and seeks to ensure that application developers are given enough knowledge of system issues to be able to appreciate what is needed for the optimum performance of their own applications. Within metrology, the use of more comprehensive and realistic mathematical models that require extensive computing resources for their solution is increasing. The motivation for the guide is that in several areas of metrology the computational requirements of such models are so demanding that there is a strong requirement for distributed processing using parallel computing on PC networks. Those who need to use such technology can benefit from guidance on how best to structure the models and software to allow the effective use of distributed computing. This work aims to ensure that metrologists can, when appropriate, take maximum advantage of the significant improvements in computational speed that are offered by cost-effective distributed computing. In addition, it contains case studies and examples that demonstrate what can be achieved with distributed computing technologies.
TENDER EVALUATION: MCDM TECHNIQUES

The decision of tender evaluation and allotment is one of the important step of any construction project. There has been a trend away in tender evaluation process from considering quantitative parameters ("lowest price win", "maximum benefit win") to other qualitative parameters (past performance, time of completion). This paper reviewed past and recent literature in order to identify the current tendering process and the various techniques that have been applied previously. The study utilized different MCDM techniques for evaluation of tendering process. Multi-criteria decision making research has become one of the main areas of research for dealing complex and critical decision problems. This paper outlines the use of various systematic decision making tool for the tender selection. Qualitative and quantitative criteria are compared, and the uncertainty associated with the selection of right contractor is highly reduced. By using a case study the tender evaluation is fully investigated applying various MCDM techniques. Results from the different methods are compared and fuzzy extend analysis, fuzzy TOPSIS showed slight difference in results from the other methods.
Cloud Removal from Satellite Images using Spatiotemporal Generator Networks

Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground information extraction more difficult. Existing pipelines typically perform cloud removal with simple temporal composites and hand-crafted filters. In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds. We train our model on a new large-scale spatiotemporal dataset that we construct, containing 97640 image pairs covering all continents. We demonstrate experimentally that the proposed STGAN model outperforms standard models and can generate realistic cloud-free images with high PSNR and SSIM values across a variety of atmospheric conditions, leading to improved performance in downstream tasks such as land cover classification.
Choice of Renal Function Estimator Influences Adverse Outcomes with Dabigatran Etexilate in Patients with Atrial Fibrillation

Abstract Background Clinical significance of dosing dabigatran with different estimates of renal function for treatment of atrial fibrillation (AF) is unknown. Renal function is routinely estimated by the chronic kidney disease epidemiology initiative equation (CKD-EPI) and used to guide dosing. The aim of this study was to investigate the risk of adverse outcomes for patients with AF when different estimators of renal function are used. Material and Methods AF patient data were extracted from national administrative databases. Renal function was estimated using Cockcroft–Gault, CKD-EPI, and CKD-EPI adjusted for body surface area (CKD-EPI-BSA). Outcomes of cerebrovascular accident (CVA), systemic embolism (SE), and hemorrhage were extracted. Results In total, 2,425 patients were identified, of which there were hospitalizations for 138 (5.7%) hemorrhagic events, 45 (1.9%) CVA/SE, and 33 (1.4%) unspecified CVA. The level of agreement between Cockcroft–Gault with CKD-EPI and CKD-EPI-BSA yielded a weighted kappa statistic of 0.47 and 0.71, respectively. CKD-EPI and CKD-EPI-BSA significantly overestimated renal function in elderly patients resulting in higher recommended doses compared with Cockcroft–Gault. The hazard ratio for a hemorrhagic event was 2.32 (95% confidence interval, 1.22–4.42; p = 0.01) when a high dose was given compared with normal dose, based on Cockcroft–Gault. Conclusion Both CKD-EPI and CKD-EPI-BSA equations significantly overestimated renal function in the elderly population compared with the Cockcroft–Gault equation. This may lead to dose selection errors for dabigatran, particularly for those with severe impairment, increasing the risk of adverse outcome. Hence, CKD-EPI and CKD-EPI-BSA equations should not be substituted for the Cockcroft–Gault equation in the elderly for the purpose of renal dosage adjustments.
review on automated irrigation system by using wireless sensor network .

Automated irrigation system is used to optimize water use for agricultural crops. The aim of farmer is to produce “more crops per drop”, hence there are irrigation techniques which consumes less fresh water. These techniques are helpful in the regions where there is a scarcity of fresh water. Irrigation is the artificial of water to the soil usually for supporting in rising crops. To implement the proposed system uses ARM968E-S processor running at frequencies of up to 125 MHz maximum. Our embedded project is to design and develop a low cost feature which is based on embedded platform foe water irrigation system. This project uses temperature and soil moisture sensor to detect the water quality present in agriculture. The project uses ARM9 LPC2929 combined with 125 MHz ARM968ES CPU core, which is 32-bit controller to process the information. The aim of our embedded project is to monitor status of the sensor which can be monitored on web page through microcontroller. The web-server is connected to internet. This page contains all the information about the status of the sensor or else the owner can also monitor result through mobile. GSM modem is controlled with the help of standard set of AT (Attention) commands. These commands are used to control majority of the functions of GSM modem. Application of this system is use to monitor and control different parameters in agriculture and industrial field and also hazardous area such as chemical or nuclear plant.
Detection of spam review on mobile app stores, evaluation of helpfulness of user reviews and extraction of quality aspects using machine learning techniques

As mobile devices have overtaken fixed Internet access, mobile applications and distribution platforms have gained in importance. App stores enable users to search and purchase mobile applications and then to give feedback in the form of reviews and ratings. A review might contain critical information about user experience, feature requests and bug reports. User reviews are valuable not only to developers and software organizations interested in learning the opinion of their customers but also to prospective users who would like to find out what others think about an app. Even though some surveys have inventoried techniques and methods in opinion mining and sentiment analysis, no systematic literature review (SLR) study had yet reported on mobile app store opinion mining and spam review detection problems. Mining opinions from app store reviews requires pre-processing at the text and content levels, including filtering-out nonopinionated content and evaluating trustworthiness and genuineness of the reviews. In addition, the relevance of the extracted features are not cross-validated with main software engineering concepts. This research project first conducted a systematic literature review (SLR) on the evaluation of mobile app store opinion mining studies. Next, to fill the identified gaps in the literature, we used a novel convolutional neural network to learn document representation for deceptive spam review detection by characterizing an app store review dataset which includes truthful and spam reviews for the first time in the literature. Our experiments reported that our neural network based method achieved 82.5% accuracy, while a baseline Support Vector Machine (SVM) classification model reached only 70% accuracy despite leveraging various feature combinations. We next compared four classification models to assess app store user review helpfulness and proposed a predictive model which makes use of review meta-data along with structural and lexical features for helpfulness prediction. In the last part of this research study, we constructed an annotated app store review dataset for the aspect extraction task, based on ISO 25010 - Systems and software Product Quality Requirements and Evaluation standard and two deep neural network models: Bi-directional Long-Short Term Memory and Conditional Random Field (Bi-LSTM+CRF) and Deep Convolutional Neural Networks and Conditional Random Field (CNN+CRF) for aspect extraction from app store user reviews. Both models achieved nearly 80% F1 score (the weighted average of precision and recall which takes both false positives and false negatives into account) in exact aspect matching and 86% F1 score in partial aspect matching.
Opening the Black Box: How to Design Intelligent Decision Support Systems for Crowdsourcing

In crowdsourcing, reviewing and evaluating textual data is a latent challenge. While text mining and machine learning represent promising technologies to solve this problem, it is still unclear how information systems based on these technologies (i.e., intelligent decision support systems) should be designed. In this study, we address this gap and develop overarching design requirements, design principles, and design features for intelligent decision support systems in crowdsourcing. The study follows a design science research approach with a cross-industry research consortium comprising 8 organizations. Our results are based on 41 semi-structured interviews, 13 expert workshops with 53 participants, statistical analyses with data from 676 crowdsourcing projects, and 2 field tests. For research, we introduce transparency and control as two additional meta-requirements for intelligent decision support systems and capture seven guiding principles for designing such systems. For practitioners, we describe specific design features that show how to instantiate these principles. design feasibility and effectiveness of such artifacts different . Decision-makers that used our DSS were able to reduce the required time to process the contributions by 50%. Based on our findings, we recommend managers of crowdsourcing initiatives to make use of such systems and implement text mining and machine learning algorithms on crowdsourcing platforms. We found that these technologies are both technically feasible and economically viable to support decision-makers in evaluating large amounts of crowdsourced contributions.
Making Sense of Clinical Examination of the Adult Patient: A Hands on Guide

The ability to record an accurate and complete patient history, and to examine the patient appropriately in response to the history described, are fundamental skills that all student doctors need to acquire at an early stage in training. Written from a very practical standpoint, Dr Douglas Model shares his extensive experience of teaching this subject. Real life scenarios are interspersed throughout the text.
Perancangan dan Implementasi Sistem Penjadwalan Servis Berkala Kendaraan Bermotor Berbasis Website Menggunakan Notifikasi SMS Gateway

Cemara cycle is one of Official Yamaha motor dealers that services Yamaha products, such as motorcycle, and sell a wide range of genuine Yamaha spare parts. However, in term of service, the dealer often gets complained from costumers because it takes a longer time to service a motor and often late in the services. Then, the data of services cannot also be found even the costumers have done the services. These matters cause the process of business cannot run maximally. This study conducted by using a design and an implementation of a website that serves as scheduling and as an information media using CodeIgniter framework and Bootstrap framework in order to facilitate a more structured program developed and features Responsive Web. The system is designed by using a prototype method to receive changes in the framework of development so that in the end can produce a system that can provide convenience to the users. The test results showed that the development website in scheduling service helps the owner of the dealer maximizing the services. In addition, the costumers will get notification from SMS gateway as a reminder if the due date of service has exceeded and it makes the costumers easier to obtain information about the service that has been undertaken before.
Automated Driving: Development of a Drowsiness Management Concept and Evaluation of Related Key Elements

This thesis considers driver drowsiness as another system limit of a Level 3 (L3) Automated Driving System (ADS). To prevent potential safety impairments caused by driver drowsiness, a Drowsiness Management Concept (DMC) is developed that takes the perspectives of users and manufacturers into account. By using a Wizard-of-Oz approach, this thesis investigates key elements of the DMC. Overall, three studies with a total of 126 participants were conducted in real traffic.
Developing interactive applets with GeoGebra: processes, technologies

With continuing technological improvements, it is increasingly easy to produce high-quality interactive online resources to support learning of mathematics & statistics concepts. Software like GeoGebra (www.geogebra.org) allows users to produce dynamic, interactive constructions, with relatively low technical demands and moderate time and resource requirements. In this paper, we describe a project at the School of Mathematics & Statistics, The University of Melbourne, to produce a collection of interactive applets to enhance teaching in a range of undergraduate subjects offered by the school. The applets target specific learning & teaching needs of our school and are tailored precisely to our local learning & teaching context. We will give an overview of the project, and describe the approach taken by the project to identify teaching needs that could be serviced by new applets, and the design and feedback processes used to produce the applets. We give a preliminary survey of the outcomes of the project and discuss its impact on teaching & learning, both directly on the student experience as well as on academic staff through professional development. We offer a discussion of the technical merits and drawbacks of GeoGebra as a technological platform for the development of interactive applets for undergraduate mathematics and statistics, including data on the development time required to produce applets using GeoGebra.
Implementation of Intelligent Control Strategy for Boost Converter-Inverter for Hybrid Vehicle

This paper introduces a new control strategy “PWAM” method for HEV/EV motor drive system. This modulation method is quite different from other PWM methods that have been well researched or commonly used for the inverter in HEV/EV system. By using this method, only one phase leg of the inverter is doing switching action for every PWM-carrier period. The system configuration including green power generator, energy storage element, dc appliance and equipment, and energy management system with fuzzy logic will be introduced. The proposed integrated circuit allows the machine to operate in motor mode or acts as boost inductors of the boost converter, and thereby boosting the output torque coupled to the same transmission system or dc-link voltage of the inverter connected to the output of the integrated circuit. In motor mode, the proposed integrated circuit acts as an inverter and it becomes a boost-type boost converter, while using the motor windings as the boost inductors to boost the converter output voltage. Enhancement of a renewable power management system with intelligence control techniques (Fuzzy) for a micro grid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/ Simulink.
High Density Highperformance Computing Systems Cooling

: The paper deals with the cooling approaches and aims at using solutions for the energy efficient and economically viable high density computing system construction and usage. Power consumption of the standalone high density computing systems and supercomputing clusters with different types of cooling has been considered and their characteristics have been estimated. The influence on the overall budget of the system construction and exploitation for 5 years of life time has been analyzed
Reproduction, gonad histology, and spawning cycles of North Atlantic billfishes (Istiophoridae)

The gonads of 498 istiophorid fishes, mostly from the western North Atlantic Ocean, were examined histologically. Female sailfish (Istiophorus platypterus), white marlin (Tetrapturus albidus), and blue marlin (Makaira nigricans) may spawn up to four times during their sexually active reproductive seasons. Males of all of these species can spawn throughout the year. Histologically, the only difference among the above species and other istiophorids examined-black marlin (M. indica), striped marlin (T. audax), and longbill spearfish (T. pfluegeri)-was in the size of the ova, which is smallest in the sailfish and largest in the blue marlin. The number of previous mature generations of ova can be ascertained by the different stages of involution of atretic ovarian follicles. Indications of previous spawnings are the presence of ruptured ovarian follicles and resorbing ovulated ova. Based upon our histological studies, and on macroscopic field analyses and occurrence of larval stages reported in the literature, the following spawning seasons and weights at first maturity of the female are as follows: sailfish (April through October; 13-18 kg), white marlin (March through June; 20 kg), blue marlin (July through October; 120 kg), and longbill spearfish (November to May; 17-19 kg).
FMN: Two-stage web image fine-grained text detection method based on full convolutional network

In the Internet, there are large-scale web images with different image sizes and different resolutions. In order to accurately and efficiently identify fine-grained texts in web images, this paper uses FCN to support the ability to semantically segment images, and treats text and background as different detection targets. A two-stage web image fine-grained text detection method FMN is proposed. For the problem that the output result of the whole convolutional network is not fine enough, the MSER+NMS algorithm is used to extract the fine-grained features of the FCN output, and the ellipse fitting method is introduced to detect the text. Features such as character tilt and morphological differences ultimately result in text detection of the image. Compared with the existing methods, the FMN algorithm proposed in this paper has achieved certain improvement in detail resolution and precision.
A Mechanism for Scalable Redundancy in Parallel File Systems

As parallel file systems span larger and larger numbers of nodes in order to provide the performance and scalability necessary for modern cluster applications, the need for fault-tolerance and high data availability file systems has arisen. Modern parallel file systems spanning tens, hundreds, or even thousands of servers will require fault tolerance to avoid job failure and catastrophic data loss due to a single disk failure or server loss. Effective fault tolerance in parallel file systems must provide a high degree of data resiliency, consistency, and scalable performance. In this thesis we provide an in depth description of the resiliency and consistency requirements of parallel file systems. We then describe a data replication mechanism that meets the resiliency and consistency requirements of parallel file systems and provides scalable performance. We also provide an in depth description of how the file system responds during a system fault and how the system may be recovered to its original, fully redundant state after a failure. Finally, we measure the performance of our proposed mechanism by implementing it in a popular parallel file system, PVFS2. We primarily focus on measuring the performance costs and scalability impacts associated with consistency and resiliency.
Zero-skipping in CapsNet. Is it worth it?

Capsule networks (CapsNet) are the next generation of neural networks. CapsNet can be used for classification of data of different types. Today’s General Purpose Graphical Processing Units (GPGPUs) are more capable than before and let us train these complex networks. However, time and energy consumption remains a challenge. In this work, we investigate if skipping trivial operations i