Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimisation study of geographical allocation of solar PV generation capacity for grid support

Due to growing concerns regarding environmental impacts and energy security, the renewable energy sector and its role in large-scale power generation is becoming increasingly significant, both locally and internationally. While it reduces reliance on fossil fuels, however, grid-integration of renewable energy generation also introduces considerable risks at high penetration levels due to the intermittent nature of sources such as photovoltaic (PV) solar and wind energy. The development of methodologies for addressing these concerns has become a prominent field of research, which includes aspects such as smart-grid technologies and optimal siting and sizing of renewable energy plants with regard to the grid infrastructure and demand profile. This project investigates one such aspect, namely optimisation of the allocation of solar PV generation capacity in the context of grid-related metrics such as the seasonal and daily peak demand periods and energy supply variability. The main research objectives focus on determining the impacts of optimal geographical distribution of solar PV sources in the South African context, using grid support metrics defined in terms of diurnal and seasonal trends as performance criteria. The development and implementation of the optimisation strategy required the identification and specification of appropriate optimisation parameters, such as sets of potential locations that reflect the seasonal and diurnal diversity of the local solar resource, a comprehensive range of objective functions that is representative potential grid support metrics and a range of optimisation algorithms. Three groups of locations, referred to as site groups, were selected based on geographical significance with regard to diurnal and seasonal solar cycles. A set of optimisation objectives were defined based on grid-support considerations such as maximising average energy delivery, prioritising delivery during peak demand periods and minimising the day-to-day variability of the solar PV renewable energy contribution. A selection of candidate metaheuristic optimisation algorithms were identified for evaluation, namely the genetic algorithm, two standard variations of the pattern search technique and an additional pattern search variation that incorporates a genetic algorithm for a hybrid approach. The various site groups, diurnal and seasonal specifications, optimisation objectives and optimisation algorithms were consolidated to define a comprehensive set of optimisation problem cases. The evaluation and subsequent analysis of the problem cases was implemented via an integrated software platform that forms part of an ongoing software application development project. Based on the designated optimisation strategy, a solar PV optimisation module was developed and integrated with an established database-driven user interface. The correspondii Stellenbosch University https://scholar.sun.ac.za
Overview of wireline access technologies in Kosovo

This paper provides an overview of the existing wireline access technologies in the Republic of Kosovo. In addition, the market share is presented as well as the impact that competition, government and the regulatory authority have on Kosovo’s wireline market. In this market, there are three main players, followed by some local internet service providers. Copper-based networks, such as twisted-pair and coaxial cable networks, dominate Kosovo’s wireline access topology. Even though FTTH is not new to Kosovo, its deployment has been very limited and expansion is related just to the connection of business clients. In the past two years, two of the main operators (IPKO Telecommunication and Telekomi i Kosoves) have started with the deployment of FTTH, with gigabit passive optical network technology being the preferred architecture.
Application of Apriori Association Rule Mining Algorithm in University Management System

The emergence of the digital era has made a revolutionary change to the design of the management system of colleges and universities. Apriori association rule mining algorithm has been run through the whole process of the university management system. An appropriate algorithm for the management of data resources was used in fragmented and chaotic Universities，which can accelerate the standardization of local resources and improve the utilization of data management resources in Colleges and universities. Based on this, the application of Apriori Association Rule Mining Algorithm in the university management system was studied. The university digital data archives were introduced firstly and then the Apriori algorithm was introduced in this paper, and finally the application of Apriori Association Rule Mining Algorithm in the university management system was introduced in details. Test results showed that the application of Apriori Association Rule Mining Algorithm in the university management system was more practical and reasonable.
ANALISIS PENDAPATAN DAN STRATEGI PENGEMBANGAN USAHATANI JAGUNG HIBRIDA DI KECAMATAN LABUAN KABUPATEN DONGGALA

This research aimed to analyze the income and the development strategy forhybrid cornfarming system at Labuan Sub District of Donggala Regency. The research used a purposive sampling method. Secondary and primary data were collected through observation, interview, questionnaire and focus group discussion (FGD). The income analysis indicated that the farmers’ income level of the hybrid corn farming system in Labuan sub district of Donggala regency was IDR 11,896,031.15/0.92/planting season/ha. The strategy for developing the hybrid corn farming system was strength-opportunity to supportprograms as follows : 1) improvingproduction by using or adopting newagriculture technologies; 2) establishing partnership with industry or government to obtain markets or production facility supplies; 3) increasing productivity through extension. Some factors needed to be taken into consideration in the development of the hybrid corn farming system in sub districtof Donggala regencyare the availability equipments, capital, post-harvest handling, fertilizer distribution, pest and diseaseprevention, climate change and the availability of youth labor. Both government and private sectors are needed to support the programs for developing the hybrid corn farming system in Labuan district of Donggala regencyin order to improve the welfare of the farmer groups and their family. Keywords : Development of Hybrid Corn, Farm, Income, Strategy.
A review on three dimensional scaffolds for tumor engineering

Two-dimensional (2D) cell culture and in vivo cancer model systems have been used to understand cancer biology and develop drug delivery systems for cancer therapy. Although cell culture and in vivo model studies have provided critical contribution about disease mechanism, these models present important problems. 2D tissue culture models lack of three dimensional (3D) structure, while animal models are expensive, time consuming, and inadequate to reflect human tumor biology. Up to the present, scaffolds and 3D matrices have been used for many different clinical applications in regenerative medicine such as heart valves, corneal implants and artificial cartilage. While tissue engineering has focused on clinical applications in regenerative medicine, scaffolds can be used in in vitro tumor models to better understand tumor relapse and metastasis. Because 3D in vitro models can partially mimic the tumor microenvironment as follows. This review focuses on different scaffold production techniques and polymer types for tumor model applications in cancer tissue engineering and reports recent studies about in vitro 3D polymeric tumor models including breast, ewing sarcoma, pancreas, oral, prostate and brain cancers.
A low-cost sensor applied to microclimate monitoring of marathon courses in Hong Kong

Annually, more than 500 marathons with competitors ranging from elite marathon runners to recreational athletes are contested worldwide. With the increase in the popularity of marathons, more literature is now available regarding the effect of weather conditions on marathon runners. Most of this research has been based on statistical analysis of past marathon data e.g. quantifying the effect of weather conditions on performance or records of the runners. There is, however, a lack of research into the environmental dynamics of different marathon course terrains and environment conditions that may adversely impact thermo-bioclimatic conditions experienced by the marathon runners. This paper details a study of monitoring the dynamics and the microclimate along the route of the popular Hong Kong Standard Chartered Marathon. The event route is confined to the Hong Kong urban area, passes through various types of urban morphology and topography, from commercial areas to harbour tunnels, to bridges. Small and durable temperature/humidity loggers were deployed at strategic locations along the route to measuring air temperature/relative humidity in one minute intervals. With the aid of GIS, minutely collected measurements were geographically mapped to the sampled locations with terrain profile incorporated and further correlated with respective environmental conditions for analysis in the means of spatial and dynamic aspects. This empirical study, with the integration of GIS, allowed for the identification of hotspots and interrelations of various environmental attributes that may impact the microclimate of marathon courses. In addition, any challenges and unfavorable conditions that may impact marathon runners along the race route are discussed.
Develop a General Framework for Estimating Cetacean Density from Data Collected by Slow-Moving Autonomous Ocean Vehicles, Investigating Key Aspects of Survey Design, Data Collection, and Data Analysis

Abstract : In this project, which started in April 2015, we focus on cetacean density estimation using autonomous underwater vehicles such as ocean gliders. These instruments are of particular interest to the Navy and have the potential to play a key role in future marine mammal monitoring efforts. The major advantage of gliders and other autonomous vehicles over prior methods is their ability to provide both spatial and temporal coverage of an area during a survey. However the methods for estimating cetacean density from autonomous vehicles do not currently exist.
Evaluation of First Seizure and Newly Diagnosed Epilepsy

ABSTRACT PURPOSE OF REVIEW This article focuses on the evaluation of children and adults who present with new-onset seizures, with an emphasis on differential diagnosis, classification, evaluation, and management. RECENT FINDINGS New-onset seizures are a common presentation in neurologic practice, affecting approximately 8% to 10% of the population. Accurate diagnosis relies on a careful history to exclude nonepileptic paroxysmal events. A new classification system was accepted in 2017 by the International League Against Epilepsy, which evaluates seizure type(s), epilepsy type, epilepsy syndrome, etiology, and comorbidities. Accurate classification informs the choice of investigations, treatment, and prognosis. Guidelines for neuroimaging and laboratory and genetic testing are summarized. SUMMARY Accurate diagnosis and classification of first seizures and new-onset epilepsy are key to choosing optimal therapy to maximize seizure control and minimize comorbidities.
Geo-distributed Machine Learning using Parameter Servers

— Large organizations operate in datacenters across the globe to offer low latency services to users located in its vicinity. The data generated by users which interact with these services is present in the respective datacenters distributed across the globe. It is a common practice for organizations to run simple algorithms like logistic regression or even complex machine learning algorithms to get insights from the data. One way to run these algorithms is to transfer the data to single centrally located datacenter and use one from many of the available distributed machine learning methods to obtain insights. But this approach can be costly in terms of time required and the money spent to transfer huge amount of data to a central location, also regulatory restriction might prevent transfer of data in certain cases. In this work we develop and evaluate techniques to run logistic regression and neural network based supervised machine learning algorithms on geo-distributed data. Our techniques are built on top of the Parameter Server based learning approach, and we evaluate the modiﬁed versions of synchronous and asynchronous stochastic gradient descent (SGD) considering tradeoffs between data exchanged across datacenters and the rate of convergence.
With Such Friends, Who Needs Enemies? Using A "Friendly" Question Type Discriminator For Robust QA Training

Despite the proven benefits of domain adversarial learning in promoting the learning of domain-invariant representations by QA transformer models, we hypothesize that this addition might inadvertently also lead the model to become insensitive to the question types of the input data. Our original proposal is to counteract this problem with the incorporation of an additional "friendly" question type discriminator (QT discriminator for short) that is trained to predict the training data’s question types from the QA model’s hidden states. In contrast to domain adversarial training, our QA model was initially meant to try and "help" instead of fool the QT discriminator so that the latter would be forced to structure its representations more heavily around the question type of its input. Our experimentation, however, showed that the model shows the most significant OOD improvement over the baseline and vanilla adversarial training when its objective is to merely force the QT discriminator to make determinate predictions (vs random) without adding the restriction of them being correct. Given this freedom during training, we discover to our great surprise that the QA model often chooses to fool instead of help the QT discriminator by using representations that the latter indeed confidently interprets as being associated with a determinate question type - though not necessarily the correct one. In the analysis, we provide a plausible explanation for why this improves robustness and use it as justification to propose this method as a potentially generalizable tactic of easily incorporating domain knowledge about what properties of the data are robust into the in-domain training process. Finally, we acknowledge the limitations of this work in fully evaluating the effectiveness and theoretical justification of this new approach and point towards future work that could surpass these limitations.
Effects of Team Leadership Behavior of SME Organization on Teamwork and Job Satisfaction

The purpose of this study is to verify the relationship between teamwork and job satisfaction in team leadership behavior of SMEs. For this study, the survey data of 158 SME employees in the Seoul metropolitan area were analyzed by structural equation modeling. The results of the study are as follows. Team leadership behavior had a significant effect on teamwork. Teamwork was found to have a positive effect on job satisfaction. Therefore, this study identified ways to enhance competitiveness and stimulate team leadership behavior by cultivating team leadership behavior and teamwork competencies in accordance with the strategies of SMEs. It can also contribute to the development and strategy formulation and management of team leadership competencies to improve performance in SMEs.
Train Science Process Skills (SPS) Through the Development of Guided Inquiry Student Worksheets Oriented to Blended Learning on Chemical Equilibrium Materials

The purpose of this research is to produce a guided inquiry student worksheets oriented to blended learning that is appropriate to train students' Science Process Skills (SPS) on the sub-material of factors that affect the shift in the direction of chemical equilibrium. The appropriateness of student worksheets is reviewed from 4 aspects, namely validity, training, effectiveness, and practicality. This study uses a type of Research and Development (R&D) based on a 4D development model (Define, Design, Develop, and Disseminate) which has changed the 3D development model (Define, Design, and Develop) with only limited trials. A limited trial was conducted on 20 students of class XI MIPA 4 SMAN 1 Balongpanggang by applying a pre-experimental one-group pretest-posttest design. The research instrument consisted of student worksheets validation sheets, SPS training observation sheets, SPS pretest-posttest sheets, and student response questionnaire sheets. The results showed (1) The average percentage of student worksheets validity was 89%, with a very valid category. (2) The average percentage of students' SPS training for each SPS component, namely observing, formulating the problems, formulating hypotheses, designing investigations on aspects of determining tools and materials and determining variables, conducting the investigations, analyzing data, and concluding at three meetings, is 100%, 93%, 96%, 100%, 96%, 99%, 91%, and 91% with very good categories. In designing investigations aspects of making flowcharts and communicating components are 73% and 61%, respectively, with good categories. The overall average percentage is 90%, with a very good category. (3) The significance value (sig 2-tailed) of the paired sample T-test results is 0.000, and the average n-gain score of the SPS pretest-posttest is 0.47 in the medium category. (4) The average percentage of the practicality of student worksheets based on student response questionnaires is 95%, with a very practical category. Based on the study results, it can be concluded that the guided inquiry student worksheet oriented to blended is appropriate to train students' SPS on the sub-materials of factors that affect the shift in the direction of chemical equilibrium.
Exploring Question Answering on SQuAD 2.0 Using Character Embeddings, Self-Attention, and QANet

In this paper, we explore the Question Answering task on the SQuAD 2.0 dataset using two models: BiDAF (Bi-Directional Attention Flow) and QANet, which leverages local convolution and self-attention. First, we added character embed-dings and self-attention to the baseline BiDAF model, which improved our F1 and EM scores to 63.64 and 60.29. Then, we implemented the QANet model, which failed to improve performance beyond the baseline but was nonetheless rewarding to experiment with.
Efficient Multiscale Object-based Superpixel Framework

Superpixel segmentation can be used as an intermediary step in many applications, often to improve object delineation and reduce computer workload. However, classical methods do not incorporate information about the desired object. Deep-learning-based approaches consider object information, but their delineation performance depends on data annotation. Additionally, the computational time of object-based methods is usually much higher than desired. In this work, we propose a novel superpixel framework, named Superpixels through Iterative CLEarcutting (SICLE), which exploits object information being able to generate a multiscale segmentation on-the-fly. SICLE starts off from seed oversampling and repeats optimal connectivity-based superpixel delineation and object-based seed removal until a desired number of superpixels is reached. It generalizes recent superpixel methods, surpassing them and other state-of-the-art approaches in efficiency and effectiveness according to multiple delineation metrics.
End-To-End Optimization of Online Neural Network-supported Two-Stage Dereverberation for Hearing Devices

—A two-stage online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear ﬁltering approach with a single-channel single-frame post-ﬁlter. Both components rely on power spectral density (PSD) estimates provided by deep neural networks (DNNs). This contribution extends our prior work, which shows that directly optimizing for a criterion at the output of the multi-channel linear ﬁltering stage results in a more efﬁcient dereverberation, as compared to placing the criterion at the output of the DNN to optimize the PSD estimation. In the present work, we show that the dereverberation performance of the proposed ﬁrst stage particularly improves the early-to-mid reverberation ratio if trained end-to-end. We thus argue that it can be combined with a post-ﬁltering stage which beneﬁts from the early-to-mid ratio improvement and is consequently able to efﬁciently suppress the residual late reverberation. This proposed two stage procedure is shown to be both very effective in terms of dereverberation performance and computational demands. Furthermore, the proposed system can be adapted to the needs of different types of hearing-device users by controlling the amount of reduction of early reﬂections. The proposed system outperforms the previously proposed end-to-end DNN-supported linear ﬁltering algorithm, as well as other traditional approaches, based on an evaluation using the noise-free version of the WHAMR! dataset.
Debiasing the estimate of treatment effect on the treated with time-varying counfounders

With the increased availability of large health databases comes the opportunity of evaluating treatment effect on new data sources. Through these databases time-dependent outcomes can be analysed as events that can be measured using counting processes. Estimating average treatment effect on the treated (ATT) requires modelling of time-varying covariate and time-dependent treatment and outcome. Gran et al. proposed an easy-to-implement method based on additive intensity regression to estimate ATT. We introduce a debiased estimate of the ATT based on a generalization of the Gran’s model for a potentially repeated outcome and in the presence of multiple time-dependent covariates and baseline covariates. Simulation analyses show that our corrected estimator outperforms Gran’s uncorrected estimator. Our method is applied to intensive care real-life data from MIMIC-III databases to estimate vasoppressors effect on patients with sepsis.
Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.