Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter-database data quality management : a relational-model based approach

Poor data quality can have a detrimental impact on the overall effectiveness of an organization. However, there exists no complete tool today to manage the quality of organizational data. This thesis presents an inter-database software tool as part of the concerted research effort of Total Data Quality Management (TDQM) project to ensure data consistency. It focuses primarily on referential integrity and in particular, business rules pertaining to organizational needs. A set of inter-database rules is proposed based on generalizations derived from the referential integrity and normalization literature. The rules are implemented in the form of a database using Microsoft Access and Structured Query Language (SQL), presented with an appropriate graphical user interface. The results of executions are delivered in comprehensible reports tailored for users' specific needs. Without the enforcement of such rules, inconsistent data can corrupt organizational databases and impede subsequent operations. The software tool developed in this thesis enables organizations to execute inter-database business rules. With additions of intra-database tool and other techniques, TDQM will evolve into a complete tool to manage organizational data and subsequently improve overall effectiveness. Thesis Supervisor: Richard Wang Title: Associate Professor of Information Technologies, Sloan School of Management
An Active and Reactive Power Controller for Battery Energy Storage System in Microgrids

Battery energy storage systems (BESS) are widely used for renewable energy applications, especially in stabilizing the power system with ancillary services. The objective of this paper is to propose an active and reactive power controller for a BESS in microgrids. The proposed controller can operate the BESS with active and reactive power conditions and realize power smoothing and voltage regulation. The demanded active power flow generated through a low pass filter (LPF) is used to control the power smoothing for microgrids. LPF design is presented based on the BESS sizing and genetic algorithm (GA). The demanded reactive power flow generated according to the reactive current calculation is used to compensate the microgrid voltage to fulfill the voltage regulation. The analysis of the vector diagram is adopted to validate the proposed controller and to explain the applicability and functionality of the voltage regulation for microgrids. To verify the validity of the proposed controller, a 1-kW grid-connected inverter is built to demonstrate the ability of power smoothing and voltage regulation. Both simulations and hardware experimental results agree well with the theoretical analysis.
ccnWheat: A Database for Comparing Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named ccnWheat. ccnWheat is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathway, gene family, protein-protein interactions, microRNA (miRNA), and several lines of epigenome data are integrated in this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using ccnWheat, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CArG specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. ccnWheat is useful for investigating co-expression networks and conducting other associated analyses, and thus facilitates comparative and functional genomic studies in wheat. ccnWheat is freely available at http://bioinformatics.cau. edu.cn/ccnWheat.
Significant Changes in Bacterial Communities Associated with Pocillopora Corals Ingestion by Crown-of-Thorns Starfish: An Important Factor Affecting the Coral’s Health

Coral ingestion by crown-of-thorns starfish (COTS) is an important cause of coral reef degradation, although the impacts of COTS feeding on coral-associated microbial communities are not well understood. Therefore, in this study, we analyzed the coral tissue-weight, Symbiodiniaceae density (SD), bacterial community composition, and the predicted functions of bacterial genes associated with Pocillopora corals in healthy portions and feeding scars, following COTS feeding. Coral tissue-weight loss rate in the feeding scars was 71.3–94.95%. The SDs were significantly lower in the feeding scars, and the SD-loss rate was 92.05% ± 2.12%. The relative abundances of bacterial communities associated with Pocillopora corals after COTS feeding changed significantly and were almost completely reorganized at the phylum and genus levels. Analysis of the microbial metagenomic-functional capacities showed that numerous physiological functions of the coral-bacterial holobionts in the feeding scars were different, including amino acid metabolism, xenobiotic biodegradation and metabolism, lipid metabolism, membrane transport, signal transduction, and cell motility, and all these capacities could be corroborated based on metagenomic, transcriptomic or proteomic technologies. Overall, our research suggests that coral holobionts may be destroyed by COTS, and our findings imply that bacterial communities in feeding scars could affect the health of Pocillopora corals.
Experimental and Industrial Evaluation of Variability Resilient Schemes

As semiconductor devices shrink further, the effects of process variation become more and more pronounced. They negatively impact the speed and power consumption of Integrated Circuits (ICs). Traditionally, a worst-case based design methodology is used to compensate for their potential impact. For modern technology this method means an increased penalty in area and power consumption. Therefore, Variability Resilient Schemes (VRSs) are introduced. They are used to improve the speed of ICs with slow processing and decrease the power consumption of ICs with fast processing. Thanks to these techniques a Better-Than-Worst-Case (BTWC) design methodology can be used. It improves performance, area, and power consumption. The work in this thesis is performed within the Variability Resilient Architectures (VRA) project at NXP Semiconductors. In this project the potential of body biasing, clock stretching, and error avoidance flip-flops as VRSs is investigated. Circuits with VRSs run closer to their optimal performance. This means more extensive testing is necessary compared to worst-case based designs. Also, the usage of error avoidance flip-flops bring new testability challenges. In order for designs with VRSs to be interesting yield and testability need to be high enough. This thesis evaluates the testability challenges of the VRSs used in the VRA project. A cost model is proposed to compare the cost of worst-case based designs with designs with VRSs. The testability of the error avoidance flip-flop is evaluated and a solution is proposed for stuck-at and path delay testing of the error output. Finally, the potential of body biasing to compensate process variation is investigated, by comparing the performance of a worst-case based design and a BTWC design, which has an area reduction of 25%. The performance of both designs is compared by measuring path delays in both simulations and measurements. It is shown that with around 0.2 V forward body biasing the performance of the BTWC design is comparable to the worst-case design.
Genomic Instability in Somatic Hybridization between Poncirus and Citrus Species Aiming to Create New Rootstocks

Rootstocks are an important component for citrus adaptation to increasing biotic and abiotic stresses resulting from global climate change. There is a strong complementarity between Citrus species, which adapt to abiotic stresses, and Poncirus trifoliata and its intergeneric hybrids, which exhibit resistances or tolerances to major diseases and pests. Thus, symmetrical somatic hybridization between complementary diploid rootstocks of these two genera appears to be an efficient way to develop new tetraploid rootstocks in order to address the new challenges of the citrus industry. New intergeneric somatic hybrids were obtained by electrofusion between protoplasts of Citrus and P. trifoliata hybrids. Extensive characterization of the nuclear and cytoplasmic genomes was performed by genotyping-by-sequencing (GBS) analysis. This revealed diploid cybrids and nuclear somatic hybrids. Mitochondrial genomes were mostly inherited from the callus parent, but homologous recombination events were observed for one parental combination. Chloroplasts exhibited random uniparental inheritance. GBS revealed local chromosomal instabilities for all nuclear somatic hybrids and whole chromosome eliminations for two hybrids. However, at the whole genome level, symmetrical addition of the nuclear genomes of both parents was predominant and all somatic hybrids displayed at least one trifoliate orange haplotype throughout the genome.
Research on Tea Tree Growth Monitoring Model Using Soil Information

Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.
Attitude estimation & control of autonomous aerial vehicles

Nowadays, we see a growing popularity of the use of Unmanned Aerial Vehicles (UAV) ofespecially Vertical Take-Off and Landing (VTOL) type. One of the most known VTOL is thequadrotor or Quadcopter which is probably the most used one as a research platform. Thisthesis deal with attitude control and estimation techniques applied to a rigid body movingin 3D space such as Quadcopter VTOL. The first contribution of this thesis is the design ofa new class of complementary linear-like filters allowing the fusion of inertial vector measurementswith angular velocity measurements and combined with algebraic algorithms asTRIAD, QUEST etc. to give an efficient attitude estimation solution. This class of filtersallows several possibilities of implementation such as the order of the filters which can bechosen high in order to reduce more the measurement noise and the form of the filters thatcan be direct or passive and the ability to take into account the possible gyro bias. Lyapunovanalysis shows the global asymptotic convergence of the estimation errors to zero. The sameprinciple of data fusion is used for the proposed new attitude control law in which the complementaryfilters were included to reduce the effect of measurement noise. The obtainedcontroller ensures almost global stability of the desired equilibrium point; it represents thesecond contribution of this thesis. The third contribution takes into consideration an interestingspecial case, where instantaneous measurements of attitude and angular velocity areunavailable. A first order linear auxiliary system based directly on vector measurements isused in an observer-like system to handle the luck of angular velocity. The proposed controllerensures almost global asymptotic stability of the trajectories to the desired equilibriumpoint. Detailed sets of experiments were done to validate the obtained results
The design of a highly penetrated hybrid renewable energy system for the Ha'apai Island group.

Electrical and Computer Engineering Master of Engineering The Design of a Highly Penetrated Hybrid Renewable Energy System for the Ha’apai Island Group by Xueshu Cao Hybrid renewable energy systems (HRESs) have become increasingly popular, especially for isolated regions. This thesis describes the design of a HRES for the isolated Ha’apai Island group in Tonga following a devastating cyclone which happened in 2014. Several renewable power generation and storage possibilities were investigated; solar, wind and battery were found to be feasible for Ha’apai. The conceptual design of a new energy storage system, the Subterranean Ocean Energy Storage System (SOESS), is also discussed as a possible alternative to batteries and a more viable substitute for an ocean renewable energy storage (ORES) system. For the proposed Ha’apai system, the optimum system configuration (solar 450 kW , wind 550 kW , battery 1,216 kAh/4,864 kW ) with 90% renewable penetration was obtained using the HOMER software. Based on the optimum system configuration, load flow simulations of both the previous system and the proposed HRES were performed in DIgSILENT PowerFactory. The results of the load flow analysis show that all the transformers and transmission lines in both systems operate safely in both peak and nominal load conditions, and that the voltage levels of all LV buses are within the acceptable range of ±5%. The detailed system topology of the proposed HRES is discussed from the system implementation point of view. A unique set point control algorithm for the start-up/shutdown of the diesel generators was developed. The system dynamic performance was simulated according to the control logic during the three main switching events in DIgSILENT PowerFactory. The dynamic simulation results indicate that the proposed system would operate safely with acceptable voltage and frequency oscillations. This thesis could be used as a template for the design of other isolated HRESs with high renewable penetrations.
Improved flow performance of a centrifugal compressor based on pit formation on the notum of the whirligig beetle (Gyrinidae Latreille)

Many studies have shown that bionic forms reduce drag through the control fluid medium. In this study, the evenly aligned pits of the whirligig beetle (Gyrinidae Latreille) notum were selected as the bionic form to be studied. They were used in centrifugal compressor impellers, called bionic centrifugal compressors, and the flow characteristics were simulated and compared to the prototype compressor. The simulation results show that compared to the prototype compressor, the pressure drag of bionic centrifugal compressors was reduced dramatically, while the pressure ratio was significantly improved. The pressure drag decreased by 19.24%, while the compression ratio increased by 9.8%. The bionic form of the blade delayed the boundary layer separation of the impeller suction surface, reducing the turbulent kinetic energy loss and improving internal air flow. The effect of drag reduction was shown to have a close relationship with the dimple size and bionic surface area; the greater the area, the better the drag reduction effect. However, there should exist an optimal bionic area, which could be determined by considering the flow velocity inside the impeller.
Method for Prediction of Acute Hypotensive Episodes

Hypotension is type of secondary insult and it is related to poor outcome. The ability to predict adverse hypotensive events, where a patient’s arterial blood pressure drops to abnormally low levels, would be of major benefit to the fields of primary and secondary health care. The aim of the paper is to present the novel method for predicting of acute hypotensive episodes, based on ECG analysis by the complex system theory approach. 45 patients (in four neurointensive care facilities throughout Europe) data were selected for the analysis. 11 patients had EUSIG-defined hypotensive events. The method includes determining of time varying biomarkers corresponding to plurality of physiological processes in patient’s organism as a non-linear dynamic complex system and generating an acute hypotension prediction classifier. The calculations of biomarkers are based on complex system approach and algebraic matrix analysis of ECG parameters. The classifier is based on the comparison of biomarkers behaviour in 3D images. It is demonstrated that the presented method allows us to predict arterial hypotension events 40-50 minutes ahead with a sensitivity of 81 %, specificity 94 %. This result was obtained from prospective real-time data collection in a live clinical intensive care environment.
Utilizando a RFT para a Detecção de Falhas de Microsserviços do Controlador O-RAN

A O-RAN (Open Radio Access Network) Alliance está definindo uma nova interface de comunicação (E2) de código aberto para customizar e controlar o comportamento da RAN. A plataforma RIC (RAN Intelligent Controller) permite implementar funções de controle da RAN por meio de microsserviços chamados xApps. Este trabalho descreve uma estratégia de tolerância a falhas para os microsserviços (xApps) que executam no controlador RIC. A estratégia proposta consiste de técnicas de particionamento de estado com replicação parcial em grupos de xApps e re-roteamento de mensagens com ciência de papel. Uma biblioteca chamada RFT (RIC Fault Tolerance) foi implementada e disponibilizada para o desenvolvimento de xApps tolerantes a falhas. Resultados experimentais apresentados neste artigo demonstram a detecção de falhas de microsserviços do controlador RIC com a RFT.
Data Mining Methods: A Review

The Big Data revolution is taking place due to the evolution of technology, where the technology enables firms to gather extremely huge amount of data, disseminating knowledge to their customers, partners, competitors in the marketplace [1]. The deeper we dive into technology, the more we compound the physical with the virtual world having in mind for instance the IoT (Internet of Things) as a network of physical devices connected together and able to exchange data. There are many Big Data platforms a company can choose like Hadoop and Apache Spark to analyze large sets of data.Moreover, many data mining techniques like Classification, Clustering Analysis, Correlation Analysis, Decision Tree Induction, Regression Analysis can be used to identify patterns for knowledge discovery. In this paper, there is an extent review and summary of Big Data Mining techniqueswith the most common data mining algorithms suitable to be used to handle large datasets. The review depicts the general pros and cons of these algorithms and the correspondingappropriate fields that apply, and in general acts as a guideline to data mining researchers to have an outlook on what algorithms to choose based on their needs and based on the given datasets.
Early-Age Cracking in High Performance Concrete Decks of a Curved Steel Girder Bridge

Highway flyover bridges in New Jersey experienced extensive early-age cracking on the high performance concrete deck recently. There were transverse cracks observed predominantly in the positive moment regions and spider cracks concentrated in the negative moment region over the box beam. A typical two-span curved continuous bridge with high performance concrete (HPC) deck, steel girders and integral box-beam was selected to identify factors that affect early-age HPC deck cracking so as to mitigate the cracking issues on existing concrete continuous bridges. Extensive field tests were performed and various structural responses were collected for model calibration and structural analysis. Cracking issues due to effects of vertical temperature differentials, staging, shrinkage, settlement, tilt of piers, dead load, live load, etc., were quantitatively investigated and summarized through parametric study. Of prime interest has been the identification of the contributions of various effects on deck cracks. The present study provides better understanding of the behavior of curved composite bridges, which can be efficiently used to reduce the risk of cracking at early ages.
Kinerja Pendidikan Anak Usia Dini dan Pendidikan Nonformal Berdasarkan Misi Pendidikan

Tujuan penulisan ini adalah untuk mengkaji potensi layanan pendidikan anak usia dini (PAUD) dan pendidikan nonformal (PNF), indikator pendidikan berdasarkan misi pendidikan, dan kinerja PAUD dan PNF tahun 2012. Metode yang digunakan adalah studi dokumentasi atau kepustakaan. Analisis dilakukan secara deskriptif menggunakan indikator pendidikan berdasarkan misi pendidikan, yaitu ketersediaan, kualitas, kesetaraan, kepastian layanan. Hasil kajian menunjukkan bahwa potensi 7 layanan pendidikan (pendidikan keaksaraan (PK), PAUD, TK, Paket A, Paket B, Paket C, dan kursus) telah berhasil menampung 12,9 juta peserta didik, dengan pendidik sebesar 575,6 ribu, dan pengelola sebesar 160,9 ribu. Selain itu, berdasarkan misi pendidikan dengan nilai ideal 100 maka ketersediaan sebesar 84,48, kualitas sebesar 59,92, kesetaraan sebesar 83,85, dan kepastian sebesar 73,45 sehingga kinerjanya menjadi 75,43. Simpulannya, kinerja PAUD dan PNF yang terendah adalah pada kualitas layanan dan yang tertinggi adalah pada ketersediaan layanan.
98.8% switching energy reduction in SAR ADC for bioelectronics application

An energy-efficient successive approximation register (SAR) analogue-to-digital converter (ADC) switching scheme is proposed for low-power biomedical applications. By dramatically reducing energy consumption in the first three bit-cycles, the average switching energy of the proposed scheme is reduced by 98.8% compared with the conventional architecture. The number of capacitors in the capacitor array of this proposed scheme is also reduced by 75.5% over the conventional approach.