Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum Cell Operation Control for Power Loss Reduction in MMC-Based STATCOM

This article proposes a minimum cell operation control to reduce power losses in static synchronous compensator (STATCOM) based on the modular multilevel converter (MMC). The main principle is to bypass cells from the MMC, according to the reactive power reference. For such, analytical expressions for the minimum dc-link voltage to keep the converter operation in the modulator linear region are derived. These expressions are used to compute the number of cells, which can be bypassed for given operation conditions. Moreover, a simplified analytical modeling of conduction losses in MMC is presented. The limits of the proposed strategy are also investigated. Simulation studies are performed during cell bypass conditions to analyze its impact on the converter dynamics. In addition, the output current distortion and power losses under different operating conditions are evaluated. Finally, a one-year energy-saving study was conducted for a real reactive power profile, which presented a 7.37% reduction in the yearly energy losses. The results indicated that this methodology is a breakthrough solution to reduce power losses and operational costs. The proposed technique can be applied in MMC-based STATCOMs with more than ten cells per arm and does not require additional hardware.
Enhancing mental health practice in Sierra Leone: A social intervention development study

Inadequacy in mental health care in low and middle-income countries has been an important contributor to the rising global burden of disease. The evidence base is building for innovative solutions to reduce the mental health treatment gap. However, what has largely been missing is the development of interventions which incorporate the nature of the social environment that contributes to the risk, cause, and maintenance of mental health conditions. This thesis aimed to explore the potential for social interventions to reduce the mental health treatment gap (greater than 95%) and the burden on resource-poor services in Sierra Leone. This thesis presents a methodological framework for social intervention development in low-resource settings. First, a feasibility and acceptability study examined stakeholder perceptions (n=59) using rapid ethnographic methods. Second, the evidence base and feasibility findings were modelled and validated in focus groups with Sierra Leone stakeholders (n=9) and members of the United Kingdom the Diaspora community (n=5). Third, a pilot study assessed the impact of the adapted intervention on district level mental health nurses’ knowledge, skills, attitudes, and behaviour (n=20) using qualitative data and a training evaluation tool developed co-productively with stakeholders. The intervention model endured several iterations as the context in the country changed due to the Ebola outbreak. The pilot study showed post-training improvement of skills in communicating and building relationships with service users, identifying assets and linking to community resources. Barriers to embed the model into nurse practice were identified such as stigma, reluctance to change from district health managers and policy makers, and significant financial and time constraints. This study suggests that it is important to harness local understanding of mental health conditions, build capacity of the existing workforce and enhance community engagement with services for mental health social intervention strategies to be effective.
Code Reviews With Divergent Review Scores: An Empirical Study of the OpenStack and Qt Communities

Code review is a broadly adopted software quality practice where developers critique each others’ patches. In addition to providing constructive feedback, reviewers may provide a score to indicate whether the patch should be integrated. Since reviewer opinions may differ, patches can receive both positive and negative scores. If reviews with divergent scores are not carefully resolved, they may contribute to a tense reviewing culture and may slow down integration. In this article, we study patches with divergent review scores in the OpenStack and Qt communities. Quantitative analysis indicates that patches with divergent review scores: (1) account for 15–37 percent of patches that receive multiple review scores; (2) are integrated more often than they are abandoned; and (3) receive negative scores after positive ones in 70 percent of cases. Furthermore, a qualitative analysis indicates that patches with strongly divergent scores that: (4) are abandoned more often suffer from external issues (e.g., integration planning, content duplication) than patches with weakly divergent scores and patches without divergent scores; and (5) are integrated often address reviewer concerns indirectly (i.e., without changing patches). Our results suggest that review tooling should integrate with release schedules and detect concurrent development of similar patches to optimize review discussions with divergent scores. Moreover, patch authors should note that even the most divisive patches are often integrated through discussion, integration timing, and careful revision.
Performance Analysis of NICs and Its Interactivity with Internet Services

This paper analyses the problem of slow data or video streaming on the World Wide Web internet through the network interface cards (NICs). The service of the entire network and internet connectivity largely rest on the installed Network Interface Cards (NICs). The performance of the various NICs such as 10Mbps, 100Mbps, 1,000Mbps and 10,000Mbps were examined using different message data sizes on the network. Also, the paper extensively discusses the performance of 10Gbps Ethernet card such as: data transmission time and throughput. The fact remained that the major backbone of the Internet network is the transmission control protocol (TCP) which is designed for reliable sending of data over the internet. The TCP as the protocol of the NIC is used for browsing web and downloading or uploading files. Additionally, the paper also examines the TCP feedback mechanism that is able to detect loss packets and retransmit them. Also, the TCP is able to dynamically adapt its packet sending rate to the network conditions in order to achieve the highest possible throughput. However, this paper proposes the use of the 10Gbps Ethernet card for the Internet Servers and Workstations for effective and fast internet access in all public networks. Keywords: Ethernet Card, Transmission Control Protocol, Data transmission time, throughput.
Treatment of Biofouling in Internal Seawater Systems - Phase 2

Abstract : Biofouling in the internal seawater systems of vessels is considered to pose a high risk for the introduction and/or translocation of marine pests and, as part of Australia's new National System for the Prevention and Management of Marine Pest Incursions, options for treating such biofouling are needed. Mussels are of particular concern. In this study, a range of chemicals, including vinegar, detergents, disinfectants, bleach, descalers, pipework treatments and freshwater, were tested on the southern Australian blue mussel, Mytilus galloprovincialis planulatus. The effectiveness of descalers in digesting mussel shells was assessed, and the toxicity of other treatments determined in 6 and 14 h (hour) exposures. The most effective treatments were two disinfectants, which both contained the active benzalkomum chloride. 14 h treatments with disinfectants of this type were concluded to be the most effective means of killing mussels. However, the toxicity and environmental acceptability of these chemicals warrant investigation in regard to discharge and disposal of treatment solutions.
Analysis of vascular biomarkers on retinal images for early eye disease detection

In this chapter, we introduce the digital image analysis techniques used for retinal image processing pipeline. First of all, we check the quality of the image by a supervised technique classifying the image as good quality or bad quality. If the image has low bad quality, the distortion type, e.g. over/under exposure, eye blinking and lashes presented, is further determined by another supervised classifier. The bad quality images will be rejected from further processing and in real acquisition the photographer must retake a new picture. For the good quality image, we correct the luminosity variation to obtain a illumination-homogeneous image. After that landmarks including the vasculature, the optic disc and the fovea are detected and segmented. The vessel bifurcations and crossovers are detected for the study of biomarkers and the types of the vessels, being artery or vein, are determined. Based on: F. Huang, B. Dashtbozorg, and B. M. ter Haar Romeny. Artery/vein classification using reflection features in retina fundus images. Machine Vision and Applications, 29(1):23–34, 2018. B. Dashtbozorg, J. Zhang, F. Huang, and B. M. ter Haar Romeny. Automatic optic disc and fovea detection in retinal images using super-elliptical convergence index filters. In Image Analysis and Recognition, Lecture Notes in Computer Science, 9730:697–706, 2016. F. Huang, B. Dashtbozorg, T. Tan, and B. M. ter Haar Romeny. Retinal artery/vein classification using genetic-search feature selection. Computer Methods and Programs in Biomedicine, 161:197–207, 2018. F. Huang, B. Dashtbozorg, T. Tan, and B. M. ter Haar Romeny. Retinal artery/vein classification using a graph convolutional neural network, 2018. In preparation. 2
Association Between the Ratio of Omega-6/Omega-3 Fatty Acids Intake to Plasma Malondialdehyde Level in Patients with Knee Osteoarthritis

One of several factors in the pathogenesis of osteoarthritis (OA) is the generation of oxidative stress, inducing lipid peroxidation, and producing malondialdehyde (MDA). Omega-3 fatty acids have role in inhibiting the oxidative stress, but their levels are determined by the omega-6/omega-3 ratio. This study aims to investigate the association between the ratio of omega-6/omega-3 intake and plasma MDA level in knee OA patients. This study was conducted at orthopedic clinic at Bhayangkara Hospital and Cipto Mangunkusumo Hospital, in grade 2-4 of knee OA patients, aged between 40-60 years (n=57). The 1-month-history of omega-3 and omega-6 intake was obtained by using semi-quantitative food frequency questionnaire. The omega-6/omega-3 ratio was calculated by dividing the average daily intake of total omega-6 by the average daily intake of total omega-3. The plasma MDA level was measured by Wills spectrophotometry. The median for omega-3 and omega-6 intake were 0,864 and 6,830 g/day. Thus the ratio of omega-6/omega-3 intake was 8,8:1, and the mean plasma MDA level was 0,773 nmol/mL. Through multiple linear regression test, the results found were the increase of 1 unit of omega-6/omega-3 intake ratio would increase MDA level of 0,023 nmol/mL (β = 0,023, 95% CI = 0,004 − 0,042, p = 0,017).
Face Recognition in Social Networks

: Popularity of face recognition in image galleries and social networks is growing. But none of these real world applications let the user adjust parameters. The goal of this thesis is to develop a library for face detection and recognition which can be easily used in some other program and also a web application recognizing faces, with environment similar to the one of social networks, which uses the library. Of course we do not try to compete with big social networks in the (cid:28)eld of face recognition, because it will be almost impossible due to their enormous databases. In the experimental part of the work we try to (cid:28)nd most suitable parameters such that face detection and face recognition would be as accurate as possible.
The Role of Smart Personal Assistant for improving personal Healthcare

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. The focus of this paper is to improve personal healthcare using smart personal assistant which make use of the combination of machine learning and cloud. Making a doctor appointment through phone call is a tedious process and it may take more time. People who did not made the prior appointment have to wait on the queue which sometimes leads to dissatisfactions to the patients. To overcome this gap, smart personal assistant application is proposed using which users can get appointment of various doctors according to the current availability and at anytime and anywhere. This will improve time saving from patients' side as well as they will be satisfied by timely service.
Scaling DBSCAN towards exascale computing for clustering of big data sets

Progress in sensor technology allows us to collect environmental data in more detail and with better resolution than ever before. One example are 3D laser scanners that generate 3D point-cloud datasets for land survey. Clustering can then be performed on these datasets to identify objects such as buildings, trees, or rocks in the unstructured point-clouds. Segmenting huge point-clouds (of whole cities or even whole countries) into objects is a computationally expensive operation and therefore requires parallel processing. Density-based spatial clustering of applications with noise (DBSCAN) is a popular clustering algorithm and HPDBSCAN is an efficient parallel implementation of it running on supercomputing clusters. Tomorrow’s supercomputers will be able to provide exascale computing performance by exploiting specialised hardware accelerators, however, existing software needs to be adapted to make use of the best fitting accelerators. To address this problem, we present a mapping of HPDBSCAN to a pre-exascale platform currently being developed by the European DEEP-EST project. It is based on the Modular Supercomputer Architecture (MSA) that provides a set of accelerator modules which we exploit in novel ways to enhance HPDBSCAN to reach exascale performance. These MSA modules include: a Cluster Module (CM) with powerful multicore CPUs; the Extreme Scale Booster (ESB) module with manycore CPUs; the Network Attached Memory (NAM) module which stores datasets and provides extremely fast access to them; a fast interconnect fabric speeds up inter-process message passing together with the Global Collective Engine (GCE), which includes a multi-purpose Field Programmable Gate Array (FPGA) for, e.g., summing up values transmitted in messages collected. HPDBSCAN exploits the above accelerator modules as follows: the data that is to be clustered can be stored in the NAM, it gets subsequently distributed and load balanced, which is accelerated by the GCE, to the CPU nodes of the CM; the parallel clustering itself is performed by the powerful CPUs of the CM which also merges the obtained cluster IDs; the merged cluster IDs are stored in the NAM for further level of detail (LoD) studies, i.e. zooming in and out based on continuous, instead of fixed, levels of importance for each point, which can be regarded as an added dimension. The ESB module (supported by GCE) is most suitable to calculate these continuous level of importance (cLoI) values and add them to the dataset in the NAM. Based on the added cLoI data, the LoD studies can then be performed by re-clustering as described previously, i.e. distribution and load balancing of the cLoI value-enriched dataset followed by parallel clustering. The described approach will allow to scale HPDBSCAN-based clusterings on tomorrow’s hardware towards exascale performance.
Survey of Current Network Intrusion Detection Techniques

The significance of network security has grown enormously and a number of devices have been introduced to perk up the security of a network. NIDS is a retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current open mode. The goal of a network intrusion detection system is to identify, preferably in real time, unauthorized use, misuse and abuse of computer systems by insiders as well as from outside perpetrators. This paper presents a nomenclature of intrusion detection systems that is used to do a survey and identify a number of research prototypes. Keywords: Security, Intrusion Detection, Misuse and Anomaly Detection, Pattern Matching.
Sleep quality in relation to sleep hygiene knowledge and practice, chronotype and lifestyle behaviour among healthcare students

SummaryIntroduction. The purpose of this study was to evaluate sleep qual ity among healthcare science students and to assess its association with sleep hygiene knowledge and practices, circadian typology and lifestyle factors. Material and Methods . The research was conducted as a cross-sectional questionnaire-based internet study on a sample of 268 students. The Pittsburgh Sleep Quality Index and The Self-Morningness-Eveningness Questionnaire were applied as re search instruments to assess sleep quality and chronotypes respec -tively, while evaluation of the other variables was conducted using he Sleep Hygiene Knowledge Questionnaire, socio-demographic questionnaire and the questionnaire of lifestyle factors. Results. The average of the Pittsburgh Sleep Quality Index global score for all students was 6.9 ± 3.2. The poor sleep quality (The Pittsburgh Sleep Quality Index > 5) was reported in 62.7% of students. Sleep effi ciency less than 85% was found in 43.0% of students, and 78% of students reported daytime dysfunctionality. Sleep quality was sig nificantly worse among female students; coffee, alcohol and energy drink consumers and long-term cell phone users. Only 11.9% of students were classified as the morning chronotype and they had the best quality of sleep and the best sleep hygiene knowledge and prac tices, whereas the evening chronotype had the worst quality of sleep. A significant negative correlation was identified between sleep hy giene knowledge (r = - 0.133) and practice (r = 0.501) and sleep qual ity whereby the lower t he Sleep Hygiene Knowledge Questionnaire and Sleep Hygiene Practice Scale scores follow a higher the Pittsburgh Sleep Quality Index score. Conclusion. Majority of students had a suboptimal level of overall sleep quality, satisfactory knowledge of sleep hygiene, but they did not have the sleep hygiene practices which suggests that knowledge is not a factor of deterring from un healthy behaviors.
An Approach towards Effective File Retrieval and its Indexing through Content based Pattern Matching

The systems constantly assume the user is conscious regarding the file they are wanting. But average users rarely even apprehend that file exists. File sharing systems that do ponder the user experience and allow users to travel searching for files by their name, usually gift centralized management which they show several severe vulnerabilities that make the system unreliable and insecure. The aim of this methodology is to vogue a further complete distributed file sharing system that is not entirely trustable, climbable and secure, but in addition leverages the user's psychological feature employment.
Comparing Prediction Accuracy for Supervised Techniques in Gene Expression Data

Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data classification etc. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. One challenging area in the studies of gene expression data is the classification of different types of tumors into correct classes. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. The methods are applied to datasets from four recently published cancer gene expression studies. Four publicly available microarray data sets are Leukemia, Lymphoma, SRBCT & Prostate. The performance of the classification technique has been evaluated according to the percentage of misclassification through hold-out cross validation.
Hybrid energy storage system optimal sizing for urban electrical bus regarding battery thermal behavior

This paper proposes an algorithm for sizing the hybrid energy storage system of an urban electrical bus regarding battery thermal behavior. The aim of this study is to get the supercapacitors optimal contribution part in the hybrid energy storage system to keep the battery temperature within its allowable limit. A semi-active parallel topology that uses supercapacitors as a main source of energy is considered. According to the bus mechanical parameters and the ARTEMIS driving cycle, the power and energy demand are calculated. Using mathematical models for the battery, supercapacitors and DC-DC converter, several simulations are performed for different hybridization percentages. While observing the evolution of battery temperature, the most favorable hybridization percentage is defined.
Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.
Methods of Identifying the Optimal Positioning of Different Products within a Warehouse with Regard to Minimising the Costs Associated with Order Picking

Abstract The most important factors conditioning the competitiveness of a warehouse are time and money. A randomly or improperly chosen storage process influences three types of costs: the movement of goods, waiting time, transportation costs. To increase the effectiveness of goods completion, i.e. to decrease processing time and cost, appropriately selected methods of product classification are used. In the article, the authors have done simulations of product classification for an ABC analysis, an ABC analysis together with a COI index, an ABC analysis together with an XYZ analysis, a COI index only, and the method of free product storage places. Simulations were made for two variants taking into account the need and there is no need to shifting products on the completion trolley during the whole completion process.