Miracle-dz/UniFAR-FTUs huggingface dataset:
Het leerlandschap van organisaties

Als we in het ontwikkelen van een organisatie met het leren niet verder komen dan een goed opleidingenplan, Is dat een verloren investering. Om organisatieontwikkeling vorm te geven zal het Leren met deze ontwikkeling in lijn moeten worden gebracht. Het kiezen voor het goed organiseren Van opleidingen is daarbij onvoldoende. Leren behelst meer,veel meer. Het type leerinterventie moet Ook passen in de lijn van de organisatieontwikkeling. Het bewust uitlijnen van leerinterventies, daar Gaat dit artikel over.
Deep Learning-Based Real-Time Building Occupancy Detection Using AMI Data

Building occupancy patterns facilitate successful development of the smart grid by enhancing building-to-grid integration efficiencies. Current occupancy detection is limited by the lack of widely deployed non-intrusive sensors and the insufficient learning power of shallow machine learning algorithms. This paper seeks to detect real-time building occupancy from Advanced Metering Infrastructure (AMI) data based on a deep learning architecture. The developed deep learning model consists of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Specifically, a CNN with convolutional and max-pooling layers extracts spatial features in the AMI data. Then, the forward and backward dependencies within the CNN feature maps are learned by a bidirectional LSTM (BiLSTM) structure with three hidden layers. Case studies based on a publicly available dataset show that the developed CNN-BiLSTM model consistently and robustly outperforms the state-of-the-art machine learning classifiers and other advanced deep learning architectures with around 90% occupancy detection accuracy and high detection confidence.
Description and characterization of place properties using topic modeling on georeferenced tags

ABSTRACT User-Generated Content (UGC) provides a potential data source which can help us to better describe and understand how places are conceptualized, and in turn better represent the places in Geographic Information Science (GIScience). In this article, we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place. Our focus is on the metadata of Flickr images, in the form of locations and tags. We use topic modeling to identify regions associated with shared meanings. We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation. We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value. Using a resolution of 500 m and with 40 topics, we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.
Rethinking ‘Organizational Effectiveness’ as a Core Premise of Organization Development: Beyond Narrow Organizational Interests and Towards Wider Soulful Interventions

ABSTRACT We contend that the dominant focus within the OD discourse is on organizations and their success (i.e. a preoccupation with ‘organizational effectiveness’) at the expense of wider responsibilities and obligations, and that the framing of ‘organizational health’ is overly narrow and partial (i.e. what is healthy for the organization rather than what is ‘healthy’ at an individual or societal level). We offer some support for our assertion that OD initiatives focus too much on organizational effectiveness and organizational health and we discuss the scope for embracing a wider set of change imperatives. This involves distinguishing between ‘self-ish’ and ‘self-less’ behaviour and drawing a distinction between distributive and generative forms of change activity Finally, we conclude by briefly sketching out how this could take shape in terms of what we have chosen to call ‘soulful interventions’.
Microsatellite Markers Reveal Genetic Diversity and Relationships within a Melon Collection Mainly Comprising Asian Cultivated and Wild Germplasms

Melon, Cucumis melo L. , is an important horticultural crop with abundant morphological variability, but the genetic diversity and relationships within wild and cultivated melons remain unclear to date. In this study, thick-skinned (TC) (cultivated subspecies melo), thin-skinned (TN) (cultivated subspecies agrestis), and wild accessions were analyzed for genetic diversity and relationships using 36 microsatellite markers. A total of 314 alleles were detected with a mean allelic number of 8.72 and polymorphism information content of 0.67. Cluster analysis of the accessions resulted in four distinct clusters (I, II, III, and IV) broadly matching with the TC, TN, and wild groups. Cluster I contained only two Indian wild accessions. Cluster II was consisted of 49 South Asian accessions, 34 wild accessions, and 15 TN accessions. Cluster III was a typical TC group including 51 multiorigin TC accessions and one wild accession. The remaining 88 accessions, including 75 TN accessions, 6 wild accessions, and 7 TC accessions, formed the cluster IV, and all the TN and wild accessions in this cluster were from China. These findings were also confirmed by Principal component analysis and STRUCTURE analysis. The South Asian subspecies agrestis accessions, wild and cultivated, had close genetic relationships with a distinctive genetic background. Chinese wild melons showed closeness to cultivated subspecies agrestis landraces and could be a return from the indigenous cultivated melons. The AMOVA and pairwise F statistics (FST) presented genetic differentiation among the three groups, with the strongest differentiation (FST = 0.380) between TC and TN melons. These results offer overall information on genetic diversity and affiliations within a variety of melon germplasms and favor efficient organization and utilization of these resources for the current breeding purpose.
The Limits of Empirical Electron Density Modeling: Examining the Capacity of E‐CHAIM and the IRI for Modeling Intermediate (1‐ to 30‐Day) Timescales at High Latitudes

The Empirical Canadian High Arctic Ionospheric Model (E‐CHAIM) is a new empirical 3‐D electron density model intended as an alternative to the use of conventional standards, such as the International Reference Ionosphere (IRI), at high latitudes (above 50°N). In this study, we have manually scaled a year of data from two Canadian High Arctic Ionospheric Network (CHAIN) ionosondes. Using this high‐quality data, we examine the behavior of the polar cap ionosphere under disturbed geomagnetic conditions and assess the capacity of E‐CHAIM to model polar cap F2‐peak electron density variability on “weather‐like,” intermediate timescales (1–30 days). This is a particularly challenging environment for monthly median empirical models due to the regular occurrence of variations about the monthly mean of up to 2 MHz. We demonstrate in this study that E‐CHAIM's storm model is capable of explaining 4 to 25% of polar cap foF2 variance at 1‐ to 30‐day timescales and 5 to 50% of the amplitude of that variability, while the IRI's Storm‐Time Ionospheric Correction Model (STORM) only explains 0.2 to 9% of the variance at these timescales and no more than 5% of their amplitude. While the IRI's STORM model provided no measurable improvement over the monthly median, E‐CHAIM's storm parameterization was able to improve overall root‐mean‐square errors by 0.05 to 0.1 MHz over its quiet time model. The overall improvement through the use of storm foF2 parameterizations is found to be limited, but measurable, particularly during storm periods, where an average improvement in root‐mean‐square error of 20% is observed.
Implicit Community Discovery Based on Microblog Theme Homogeneity

With the rapid development of various social platforms, social networks have accumulated more and more data. These huge and complex data contain valuable information in many fields. It is of far-reaching significance to conduct statistical analysis of these data and dig out valuable information and apply it in real life. Based on the homogeneity of social networks, our paper proposes a framework of discovery algorithms based on the homogeneity of microblogs to realize the theme community discovery of microblog social networks. Firstly, the data set is preprocessed, the invalid data is removed and classified by microblog users. Secondly, the LDA model is used to extract the topic of microblog user blogs to describe the characteristics of microblog users. Thirdly, two users are calculated based on the theme interest. The homogeneity measure between them is used to represent the relationship of social networks. Finally, the community of the implicit social network is realized through unsupervised algorithms to build communities with the same interest as the theme. In order to verify the validity of our proposed algorithm framework, this paper uses data sets from microblog network to conduct experiments. The experimental results show that the algorithm effectively combines the homogeneity measure of the network and has achieved very good results in the implicit community discovery.
Motion, deformation, and coalescence of ferrofluid droplets subjected to a uniform magnetic field

In this article, we present the motion, deformation, and coalescence of ferrofluid droplets suspended in a nonmagnetic fluid, subjected to a uniform magnetic field in both vertical and horizontal directions. A coupling between the simplified multiphase lattice Boltzmann method and the self‐correcting scheme is constructed to numerically solve the two‐dimensional flow field and the magnetostatics equations, respectively. The Cahn‐Hilliard equation is employed to seize the diffuse interface between magnetic and nonmagnetic fluids. In order to validate the model, deformation of a ferrofluid droplet suspended in nonmagnetic fluid is simulated as a test case and the results are compared with numerical and experimental results. Furthermore, a detailed analysis on the behavior of falling ferrofluid droplets and the coalescence between a pair of ferrofluid droplets under the effect of different magnetic fields and different droplets configurations are also presented in this article. The results provide significant insight and a better understanding of these phenomena. It is found that for higher values of magnetic bond number and susceptibility, the droplet deformation is significant and the falling process is faster while a reverse behavior is observed for higher values of Eötvös number. Moreover, the magnetic energy density exhibits an interesting behavior in the vicinity of the droplets. It is concentrated between the droplets with a nonuniform distribution when the droplets are close to each other.
MM-evolver: A Multi-model Evolution Management Tool

There is a new generation of databases specifically addressing Big Data Variety: Multi-model databases store and process structurally heterogeneous data, managing several data models in one integrated backend. Yet one of the many challenges these systems face is evolution management. In our demonstration, we present our prototype implementation of a tool called MMevolver. MM-evolver carries out user-triggered schema modification operations over a multi-model database, and propagates them across all models. As a novelty, MM-evolver supports both interand intra-model schema modification operators. To the best of our knowledge, ours is the first tool addressing evolution management in the world of multi-model databases.
MultiK: A Framework for Orchestrating Multiple Specialized Kernels

We present, MultiK, a Linux-based framework 1 that reduces the attack surface for operating system kernels by reducing code bloat. MultiK "orchestrates" multiple kernels that are specialized for individual applications in a transparent manner. This framework is flexible to accommodate different kernel code reduction techniques and, most importantly, run the specialized kernels with near-zero additional runtime overheads. MultiK avoids the overheads of virtualization and runs natively on the system. For instance, an Apache instance is shown to run on a kernel that has (a) 93.68% of its code reduced, (b) 19 of 23 known kernel vulnerabilities eliminated and (c) with negligible performance overheads (0.19%). MultiK is a framework that can integrate with existing code reduction and OS security techniques. We demonstrate this by using D-KUT and S-KUT -- two methods to profile and eliminate unwanted kernel code. The whole process is transparent to the user applications because MultiK does not require a recompilation of the application.
Morphine in acute coronary syndrome: systematic review and meta-analysis

Objective Morphine is frequently used in acute coronary syndrome (ACS) due to its analgesic effect, it being recommended in the main cardiology guidelines in Europe and the USA. However, controversy exists regarding its routine use due to potential safety concerns. We conducted a systematic review of randomised-controlled trials (RCTs) and observational studies to synthesise the available evidence. Design Systematic review and meta-analysis. Data sources CENTRAL, MEDLINE, EMBASE and trial registries. Eligibility criteria for selecting studies We included RCTs and observational studies evaluating the impact of morphine in cardiovascular outcomes or platelet reactivity measures. Data extraction and synthesis Data were screened, extracted and appraised by two independent reviewers. The data were pooled results using a random-effects model. Outcomes included in-hospital mortality, major adverse cardiovascular events (MACE), platelet reactivity (using VerifyNow) and bleeding, reported as relative risk (RR) with 95% CI. We assessed the confidence in the evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. We followed the Meta-analysis Of Observational Studies in Epidemiology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results Five RCTs and 12 observational studies were included, enrolling 69 993 participants. Pooled results showed an increased risk of in-hospital mortality (RR 1.45 [95% CI 1.10 to 1.91], low GRADE confidence), MACE (RR 1.21, 95% CI 1.02 to 1.45) and an increased platelet reactivity at 1 and 2 hours (59.37 platelet reactivity units [PRU], 95% CI 36.04 to 82.71; 68.28 PRU, 95% CI 37.01 to 99.55, high GRADE confidence) associated with morphine. We found no significant difference in the risk of bleeding. We found no differences in subgroup analyses based on study design and ACS subtype. Conclusions Morphine was associated with an increased risk of in-hospital mortality and MACE but the high risk of bias leads to low result confidence. There is high confidence that morphine decreases the antiplatelet effect of P2Y12 inhibitors. PROSPERO registration number CRD42016036357.
Is the energy balance in a tropical lowland rice paddy perfectly closed?

A two-year (2015 and 2016) field experiment was carried out to study the surface energy budget and energy balance closure (EBC) in tropical low land rice paddy in Cuttack, India. Maintenance of a standing water layer in lowland irrigated rice ecosystem makes it unique and this strongly influences the surface energy balance which may alter the surface runoff, ground water storage, water cycle, surface energy budget, and possibly microclimate of the region. To study this, an experiment was conducted using eddy covariance system to measure the surface energy balance components during two cropping seasons (dry season, DS and wet season, WS) and two consecutive fallow periods (dry fallow, DF and wet fallow, WF). The rice was grown in puddled wet lands in DS and WS and the ground was left fallow (DF and WF) during rest of the year. Results displayed that daily average latent heat flux at surface (LE) and canopy height (LEc) dominated over sensible heat flux at surface (H) and canopy height (Hc), respectively due to the presence of water source coming from the standing water in the rice field. The EBC was evaluated by ordinary least square (OLS), energy balance ratio (EBR) and residual heat flux (RHF). In OLS, the slope ranged 0.38-0.89 (2015) and 0.28-0.99 (2016) during the study period. Average RHF was 10.3-12.0% higher in WS as compared to DS. It was concluded that the EBC estimated using RHF is the most suitable way to calculate closure for lowland rice paddy since it can distinguish different seasons distinctively, followed by OLS. Much variation was not observed in EBR after inclusion of storage terms (water, soil, photosynthesis, canopy) to the classical EBR.
Analysis and Design of Capacitive Power Transfer Systems Based on Induced Voltage Source Model

Capacitive power transfer is a promising mid-range wireless charging technique. The existing induced current source model suffers from the coupling variation issue, which causes troubles in resonance design and compensation development. This article explores an induced voltage source model for the capacitive coupler. Using this model, the six coupling-dependent capacitors between each pair of plates are equivalently represented by the self-capacitors and induced voltage sources. The self-capacitance is verified to be coupling independent. Based on this model, the power transfer capability, the efficiency, and the influence of adding external shunt capacitors are easily analyzed. This model is very helpful in investigating the existing topology and developing new compensations, such as the CLL-L compensation. Finally, a capacitive coupler is built to verify the model, based on which a CLL-L compensation is implemented for constant output voltage application. The achieved system peak efficiency is 88%.
EARLY STAGE DETECTION OF GLAUCOMA AND ITS LEVELS USING FUNDUS IMAGES OF EYE

Glaucoma is a condition that can cause blindness of eye. It causes the damage of eyes optic nerve. The glaucoma results in the loss of vision. The fundus image can be helpful in detection of glaucoma. In past some experiments were carried out for the detection of this disease such as fast optic Disc segmentation in Retina, Discrete and empirical wavelet transform from fundus images, Intra ocular pressure detection etc. In this paper, an ideal framework is proposed which aims at developing a smart solution that helps in identification of glaucoma using MATLAB software tools. We have collected different fundus images of eyes as a dataset. The pre-processing of the fundus image takes place followed by segmentation and classification. The pre-processing module takes the help of some filters like Gaussian filters and guided image filters for removing the noise. Classification of healthy and diseased eye will help us to proceed for further medication.
Variation in Documenting Diagnosable Chronic Kidney Disease in General Medical Practice: Implications for Quality Improvement and Research

Background: National health surveys indicate that chronic kidney disease (CKD) is an increasingly prevalent condition in Australia, placing a significant burden on the health budget and on the affected individuals themselves. Yet, there are relatively limited data on the prevalence of CKD within Australian general practice patients. In part, this could be due to variation in the terminology used by general practitioners (GPs) to identify and document a diagnosis of CKD. This project sought to investigate the variation in terms used when recording a diagnosis of CKD in general practice. Methods: A search of routinely collected de-identified Australian general practice patient data (NPS MedicineWise MedicineInsight from January 1, 2013, to June 1, 2016; collected from 329 general practices) was conducted to determine the terms used. Manual searches were conducted on coded and on “free-text” or narrative information in the medical history, reason for encounter, and reason for prescription data fields. Results: From this data set, 61 102 patients were potentially diagnosable with CKD on the basis of pathology results, but only 14 172 (23.2%) of these had a term representing CKD in their electronic record. Younger patients with pathology evidence of CKD were more likely to have documented CKD compared with older patients. There were a total of 2090 unique recorded documentation terms used by the GPs for CKD. The most commonly used terms tended to be those included as “pick-list” options within the various general practice software packages’ standard “classifications,” accounting for 84% of use. Conclusions: A diagnosis of CKD was often not documented and, when recorded, it was in a variety of ways. While recording CKD with various terms and in free-text fields may allow GPs to flexibly document disease qualifiers and enter patient specific information, it might inadvertently decrease the quality of data collected from general practice records for clinical audit or research purposes.
Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network

The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.
Current Advancements in the Development and Characterization of Full-Thickness Adult Neuroretina Organotypic Culture Systems

Retinal degenerative diseases such as macular degeneration, glaucoma, and diabetic retinopathy constitute the leading cause of blindness in the industrialized world. There is a continuous demand in investigative ophthalmic research for the development of new treatment modalities for retinal therapy. Unfortunately, efforts to identify novel neuroprotective and neuroregenerative agents have often been hindered by an experimental model gap that exists between high-throughput methods via dissociated cells and preclinical animal models. Even though dissociated cell culture is rapid and high-throughput, it is limited in its ability to reproduce the in vivo conditions. In contrast, preclinical animal models may offer greater fidelity, albeit they lack efficiency and experimental control. Retina explant cultures provide an ideal bridge to close this gap and have been used to study an array of biological processes such as retinal development and neurodegeneration. However, it is often difficult to interpret findings from these studies due to the wide variety of experimental species and culture methods used. This review provides a comprehensive overview of current ex vivo neuroretina culture methods and assessments, with a focus on their suitability, advantages, and disadvantages, along with novel insights and perspectives on the organotypic culture model as a high-throughput platform for screening promising molecules for retinal regeneration.