Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Series-Parallel Photovoltaic Configuration to Enhance Efficiency under Partial Shading

Partial shading is a phenomenon where photovoltaics (PV) array experiences irregular level of irradiances. Such mismatch can cause a significant reduction in power yield. To mitigate the effect of partial shading, PV modules in an array are connected in various configurations namely Series-Parallel (SP), Total-Cross-Tied (TCT), Bridge-Linked (BL) and Honey-Comb (HC) etc. However, all these techniques introduce redundancy and complexity while improving the performance by very little. In this paper, a new PV configuration is proposed to solve the limitations of the existing PV configurations. The proposed configuration is a modified version of SP, hence referred as MSP configuration. To justify the performance of the proposed scheme, several experiments have been carried in MATLAB Simulink. Total 14 partial shading cases are simulated to compare the output performance between generic SP and proposed MSP configuration. The proposed MSP configuration is validated to be superior compared to normal SP configuration under majority of the cases. Depending on shading pattern, the efficiency of the PV array can be enhanced up to 37%, if MSP configuration is implemented.
Models and Algorithms for Episodic Time-Series

This thesis is built on the idea of modeling episodes of multiple time series which can be briefly defined as multivariate time series whose individual dimensions vary in time and nature. This kind of data arises naturally when we observe repeatedly scenarios where collections of individual elements that may or may not take part in the collective observed behaviour. We illustrate the ideas constructed around this kind of data making use of datasets related to crowdfunding and video-on-demand. These datasets are prolonged periods of observation of these scenarios and provide natural examples to the ideas we develop. How to relate seemingly disconnected individual episodes and how to incorporate information from them into the general view of the multiple episodes is the main goal of this thesis. We focus on constructing this two-way flux so that even more complex models than the ones present in this work can be constructed using the proposed features. We describe models and algorithms that mix supervised and unsupervised tasks. Specifically, we construct models that connect Topic Models, unsupervised learning models that aim to summarize big corpora of texts with regression models on time series. We also discuss how summaries of past episodes may be helpfull in predicting future series of observations of same category
Work Engagement & Effective Factors Among Nurses In General Teaching Hospital Of Shiraz University Of Medical Sciences

Background and Aim: Work engagement is defined as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption. Since the quality of care delivery to patients depends on nurse’s practices, the objective of this study was to determine work engagement & its effective factors among nurses of general hospitals of Shiraz University of Medical Sciences. Materials and Methods: This cross sectional study was performed among 273 in-patient nurses of Namazi and Faghihi hospitals using 2 stages of sampling. Our questionnaire was validated by experts opinions and Cronbach's alpha index (86%). Descriptive statistics and correlation tests were used for the analysis of data by SPSS. Results: According to the results obtained, mean work engagement was 58.95 ± 10.51 and there was a significant correlation between work engagement and organizational – supervisor support, organizational justice, job characteristics, reward & recognition, organizational citizenship behavior, organizational commitment, job satisfaction, turnover( p<0.05). The relationship between work engagement and reward was the strongest(r=0.512). Conclusion: The results imply the importance of work engagement and its related factors. Nurses are one of the biggest health system groups, and their burnout disadvantages hospitals and community. Thus, work engagement should be noticed in all policy-making related to nurses.
Documenting clinical interventions in community pharmacy: PROMISe III

Intervention documentation software was developed and installed in 185 community pharmacies across three States of Australia. The intervention behaviour in 38 of these pharmacies, and 24 additional pharmacies without the software, was observed for one week. Surveys of participating and non-participating pharmacists, pharmacy owners, and consumers provided information on barriers and facilitators for the intervention process, allowing the research team to determine the factors that affected intervention frequency and the nature of the interventions. Pharmacy workload and pharmacist software training were strongly related to intervention frequency. An electronic prompt for a specific intervention significantly increased prompted interventions, as well as intervention frequency overall.
Introduction of OSCE for clinical assessment in obstetrics and gynaecology in Hue, Vietnam

This paper presents a case study of the new introduction of a recognized assessment methodology, the Objective Structured Clinical Exam (OSCE), into the Hue College of Medicine and Pharmacy (HCMP) in Hue, Vietnam. Following introduction of the OSCE, students pass rates successively improved with each new group of students and new application of the assessment. Discussions with both students and faculty indicate that the new assessment methodology, combined with new learning resources for students, has positively impacted students’ learning, especially of clinical skills.
Mode of Impact of Genetic Determinants of Hypertension in People of African Descent

Blood pressure (BP) is a heritable trait. However, the loci res ponsible and the mechanisms by which these genes determine BP are uncertain. B ased on widely published data regarding frequent phenotypic characteristics that e xemplify essential hypertension (EHT) in persons of African ancestry, in the prese nt th sis I explored the role of gene candidates most likely to contribute to BP in this group. In this regard a high frequency of persons of African descent experience increases in BP i response to an enhanced salt intake (salt-sensitive hypertension). In addition, man y patients of African origin with EHT fail to respond to inhibition of angiotensin-converting e zyme (ACE) with an appropriate decrease in BP, a factor that cannot be explaine d entirely on the basis of reduced plasma renin levels in this group. Thus, I evaluated the r ole of several gene variants that could influence either renal salt handling or the act ivity and effects of the renin-angiotensin system on BP in subjects of African ancestry. Although the angiotensinogen (AGT) gene has at least 3 variants in the promoter region that influence angiotensinogen expression and which occur with a remarkably high frequency in populations of African ancestry, their role in this group is still controversial. To-date, interactions between these variants have not been considered. Using a casecontrol study design in a sample of 1325 subjects, as well as ass ociation analysis with 24 hour ambulatory BP (ABP) values in 626 hypertensives, I confirmed tha t an independent effect of functional AGT gene variants on the risk for EHT or 24 hour ABP was weak at best. Importantly, however, interactions between the -20A →C and -217G→A variants were noted to strongly impact on the risk for EHT as well as ABP. Furthermore, interactions between the -20A →C and -217G→A variants played a major role in
Comparison of perspective of children with major thalassemia and their parents about their quality of life in Shahrekord

چکیده: زمینه و هدف: آنچه که در درمان یک بیماری مزمن چون تالاسمی دارای اهمیت است علاوه بر کنترل علایم بیماری، بهبود کیفیت زندگی بیمار است. با توجه به اینکه تأثیر تالاسمی و عوارض ناشی از آن بر روی کیفیت زندگی از دیدگاه کودکان بیمار و افراد درگیر با آن هنوز ناشناخته باقی مانده است. لذا این مطالعه با هدف بررسی مقایسه ای کیفیت زندگی کودکان و نوجوانان مبتلا به تالاسمی از دو دیدگاه خود و والدینشان انجام شد. روش بررسی: در یک مطالعه توصیفی– تحلیلی کیفیت زندگی 39 کودک و نوجوان مبتلا به تالاسمی ماژور مراجعه کننده به بخش تالاسمی مر کز آموزشی، درمانی هاجر شهرکرد مورد بررسی قرار گرفت. ابزار گردآوری داده ها پرسشنامه های کیفیت زندگی (PedSQL) در دو دیدگاه کودکان و والدین در چهار بعد جسمی (8 سؤال)، عاطفی (5 سؤال)، اجتماعی (5 سؤال) و عملکرد مدرسه (5 سؤال) بود. اطلاعات با استفاده از روشهای آمار استنباطی (آزمون t زوج، من ویتنی وهبستگی اسپیرمن) مورد تجزیه و تحلیل قرار گرفت. یافته ها: میانگین نمره کمی و کیفیت زندگی این کودکان از دیدگاه خودشان 6/0±06/1 و از دیدگاه والدینشان 88/±43/1 بود (001/0p
Rapid jamming detection approach based on fuzzy in WSN

It is necessary for securing wireless sensor networks from jamming, since these networks are easily attacked by jammers. Jamming may be made by either external jammer node or internal node, which may become as an adversary in future. In this paper, two approaches are proposed in the cluster‐based sensor network to detect the maliciousness level of nodes to secure sensor networks from jamming attacks. First approach detects maliciousness level of nodes using two modules, namely, certification module and monitoring module. Certification module defends the network from the jammers. Monitoring module discovers the sensor nodes that are jammed by a jammer. Second approach uses fuzzy logic for optimizing the jamming metrics to determine the occurrence of jamming accurately. The proposed system achieves 99.58% detection ratio to determine node's maliciousness level.
Using human judgments to examine the validity of automated grammar, syntax, and mechanical errors in writing

This study introduces GAMET, which was developed to help writing researchers examine the types and percentages of structural and mechanical errors in texts. GAMET is a desktop application that expands LanguageTool v3.2 through a user-friendly, graphic user interface that affords the automatic assessment of writing samples for structural and mechanical errors. GAMET is freely available, works on a variety of operating systems, affords document batch processing, and groups errors into a number of structural and mechanical error categories. This study also tests LanguageTool’s validity using hand-coded assessment for accuracy and meaningfulness on first language (L1) and second language (L2) writing corpora. The study also examines how well LanguageTool replicates human coding of structural and mechanical errors in an L1 corpus as well as assesses associations between GAMET and human ratings of essay quality. Results indicate that LanguageTool can be used to successful locate errors within text. However, while the accuracy of LanguageTool is high, the recall of errors is low, especially in terms of punctuation errors. Nevertheless, the errors coded by LanguageTool show significant correlations with human ratings of writing and grammar and mechanics errors. Overall, the results indicate that while LanguageTool fails to flag a number of errors, the errors flagged provide an accurate profile of the structural and mechanical errors made by writers.
High Reliability, Low Latency and Cost Effective Network Planning for Industrial Wireless Mesh Networks

In this paper, we study high reliability, low latency and cost effective network planning for industrial wireless mesh networks. Based on the requirements of routing reliability, minimum end-to-end delay and reduced deployment cost in wireless mesh networks, such as WirelessHART network, we propose three network planning approaches following the principles of the shortest hops, the least routers and balance of the shortest hops and the least routers, respectively. We then implement the proposed algorithms respectively to generate the network deployment for a given factory layout. Simulation results show that there exists a performance trade-off between these three algorithms. The proposed algorithms have also been implemented and validated in an NS-2 WirelessHART network simulator.
A Modification to Improve the Realism of Networks Generated with the LFR Model

Community detection consists in searching cohesive subgroups of nodes in complex networks. It has recently become one of the domain pivotal questions for scientists in many different fields where networks are used as modeling tools. Algorithms performing community detection are usually tested on real, but also on artificial networks, the former being costly and difficult to obtain. In this context, being able to generate networks with realistic properties is crucial for the reliability of algorithms testing. Recently, Lancichinetti et al. [1] designed a method called LFR, able to produce realistic networks with a community structure, and power law distributed degrees and community sizes. However, other realistic properties such as degree correlation and transitivity are missing. In this work, we propose a modification of the LFR model, based on the preferential attachment model, in order to remedy this limitation. We analyze the properties of the generated networks and compare them to the original approach. We then apply different community detection algorithms and observe significant changes in their performances when compared to results on networks generated with the original approach.
Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.
Numerical simulation of seasonal mesoscale atmospheric flow‐field variables using ARW over the Singapore region: impact of land use land cover

In the present study, the effect of land use land cover information on the simulation of seasonal planetary boundary layer (PBL) flow‐field variables over the Singapore region is analysed using the Advanced Research Weather Research and Forecasting (ARW) mesoscale model. The results achieved are compared using the standard ARW Moderate Resolution Imaging Spectroradiometer (ARW‐MODIS) global land cover data and a regional land cover map produced at the Centre for Remote Imaging, Sensing and Processing (ARW‐CRISP), Singapore. The high‐resolution simulations are conducted with triple‐nested domains (horizontal grid spacing 27, 9 and 3 km) and 51 unequally spaced vertical sigma levels. The evolution of PBL variables and rainfall are modelled for four seasons (northeast monsoon season, first inter‐monsoon period, southwest monsoon season and second inter‐monsoon period). For the purpose of numerical simulations, 30 days in each season were considered and the model‐simulated variables were validated with in situ available meteorological observations and rainfall at four stations over Singapore. The simulation results highlight the broad variation of the low‐level flow‐field in different seasons. The ARW mesoscale model simulated diurnal trends in surface meteorological variables and rainfall are in better agreement with observations by using the ARW‐CRISP regional land cover map compared to ARW‐MODIS global land cover data. Stronger winds are found during monsoon seasons compared with inter‐monsoon periods. Overall, the results of the present study indicate that regional level air quality monitoring and prediction capabilities around Singapore can be improved by using up‐to‐date land cover products designed to take into account regional land cover characteristics.
A New Asymmetric Multilevel Inverter With Reduced Number of Components

This article presents a new asymmetric voltage-source multilevel inverter with a reduced number of components. It is composed of several basic units, where each one can be designed to generate multilevel voltage with positive, zero, and negative levels. The basic units are connected in a cascaded fashion to provide the advantages of higher output voltage quality. To specify the amplitude of dc voltage sources of the proposed inverter, two methods are presented. Optimal topologies of the proposed inverter based on different criterions are also presented, which are important in the design process. The superiority of the proposed inverter is verified by comparison with some recent multilevel inverters in terms of IGBTs number, number of driver circuits, the total standing voltage on switches, and the number of output voltage levels. Losses analysis is also presented to study the effect of the modulation index and output power factor on the inverter efficiency. The ability of the proposed inverter to generate high-quality multilevel output voltage is verified through simulations and experimental results.
Shapeshift 3D Repair TM – A fully Automated and Unsupervised Cloud API for the Reliable Reconstruction of Raw 3D Surface Scans Data

This paper presents a study to quantify the reliability of the automatic reconstruction tool Shapeshift 3D Repair TM to create watertight, genus 0, precise and accurate 3D surface scan of the human body. Our methodology uses a precise baseline 3D scan acquired from a full body 3D scanner as an input of a scanning process simulator that emulates the properties of a common 3D scanner, the Structure TM by Occipital TM , and the behavior of a typical untrained handheld 3D scanner operator. The output of the simulator is a raw scan (noisy and incomplete). Afterward, the raw scan is fed to Shapeshift 3D Repair TM which outputs a reconstructed scan. We express the reliability of the process in terms of Standard Error of Measurement (SEM). Using the girth difference between the baseline scan and the reconstructed scan, we express the compatibility in terms of Signed Mean Difference (Bias) and Mean Absolute Error (MAE). We compare our results with common reconstruction methods found in the literature and with other studies about the reliability of 3D Scanning, Plaster Casting and Traditional Anthropometry. Context: To create custom medical devices and wearables, the patient’s 3D geometry can be acquired using a 3D scanner. The raw 3D scans require post-processing as they are often noisy and incomplete. While organizations using 3D scanners put in place training programs, scans are often of poor quality. To this day, this issue is a hindrance to business models centered on 3D scanning such as the novel 3Dscan-to-3Dprint business model; inadequate scans must be manually corrected by the operators, which is a time-consuming offline process. The study is focused on the simulation of the scanning process and the scan reconstruction of the knee. Results: A fully automated and unsupervised cloud processing service for the reconstruction of the knee has been implemented and is ready to be tested by users and vendors of 3D scanners. Reconstructed scans exhibit leg, knee and max thigh girth error under 0.1 cm, 0.3 cm and 0.4 cm respectively with 95% confidence level while producing properly defined surface that are manifold, genus 0, have good triangle aspect ratio, and have a single surface. With the recent boom of devices featuring an embedded 3D scanner, we believe that in due time, our technology can be accessible to millions of users without the needs of industry-specific hardware or skills.
Quantum Algorithmic Techniques for Fault-Tolerant Quantum Computers

Quantum computers have the potential to push the limits of computation in areas such as quantum chemistry, cryptography, optimization, and machine learning. Even though many quantum algorithms show asymptotic improvement compared to classical ones, the overhead of running quantum computers limits when quantum computing becomes useful. Thus, by optimizing components of quantum algorithms, we can bring the regime of quantum advantage closer. My work focuses on developing efficient subroutines for quantum computation. I focus specifically on algorithms for scalable, fault-tolerant quantum computers. While it is possible that even noisy quantum computers can outperform classical ones for specific tasks, high-depth and therefore fault-tolerance is likely required for most applications. In this thesis, I introduce three sets of techniques that can be used by themselves or as subroutines in other algorithms. The first components are coherent versions of classical sort and shuffle. We require that a quantum shuffle prepares a uniform superposition over all permutations of a sequence. The quantum sort is used within the shuffle and as well as in the next algorithm in this thesis. The quantum shuffle is an essential part of state preparation for quantum chemistry computation in first quantization. Second, I review the progress of Hamiltonian simulations and give a new algorithm for simulating time-dependent Hamiltonians. This algorithm scales polylogarithmic in the inverse error, and the query complexity does not depend on the derivatives of the Hamiltonian. A time-dependent Hamiltonian simulation was recently used for interaction picture simulation with applications to quantum chemistry. Next, I present a fully quantum Boltzmann machine. I show that our algorithm can train on quantum data and learn a classical description of
Machine Learning in Network Security Using KNIME Analytics

Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Detection of Spoofing Medium Contours for Face Anti-Spoofing

Face anti-spoofing is an important step for secure face recognition. In this paper, we target on building a general classifier to detect the face images with spoofing medium contours (termed as SMCs for simplicity). To this end, we consider the task of face anti-spoofing as the detection of SMCs from the image. We propose and train a Contour Enhanced Mask R-CNN (CEM-RCNN) model for the detection. This model detects the existence of the SMCs by incorporating the contour objectness which measures how likely an object contains the SMCs. The experimental results demonstrate the generality of the CEM-RCNN for identifying the face images with SMCs, which performs significantly better than the state-of-the-art on the cross-database scenario.