Miracle-dz/UniFAR-FTUs huggingface dataset:
Assay method validation of triamcinolone acetonide (TA) to support the investigation of TA-loaded nanoparticles

The aim of this study was to develop the valid analytical method which used for the assay of triamcinolone acetonide (TA) in the investigation of TA-loaded nanoparticle formulations. High Performance Liquid Chromatography (HPLC) method was applied in this study by using an Econosil column, C18 10 'm, 250 x 4.6 mm (Alltech Associates Inc, PA, USA) as the stationary phase. The mobile phase consisted of a composition of acetonitrile (ACN) and 20mM phosphate buffer solution (pH 4.2) in the proportion of 50:50 v/v. The HPLC assay of TA was validated for selectivity, linearity, precision, recovery (accuracy), sensitivity and stability of TA during the assay. Results showed that the concentration of TA in the samples can be determined against the standard in the concentration range of calibration curve. The system precision and level of recovery were considered to be acceptable, and the method was selective and sensitive. Key words: triamcinolone acetonide, assay, validation
Version Transitioning of Enterprise Systems in Software Ecosystems

This paper presents an emerging theory of version transitioning from an old to a new version of a pre-packaged enterprise system among consultant companies in a software ecosystem. The emerging theory proposes the key categories of Perceiving, Pushing, Implementing, and Increased experience as stages in the transition process, and the categories of Technology impact, Supplier impact, Customer impact, Strategy impact, and Market impact as key contextual categories impacting the transition process. The emerging theory proposes an iterative nature of the transition process in which each stage in the process is undergone multiple times by the consultant companies. The integration of the emerging theory with existing adoption and difusion theories provides an initial step towards a formal theory of version transitioning in software ecosystems.
Offset-Calibration With Time-Domain Comparators Using Inversion-Mode Varactors

This brief presents a differential time-domain comparator formed by two voltage controlled delay lines, one per input terminal, and a binary phase detector for comparison solving. The propagation delay through the respective lines can be adjusted with a set of digitally controlled inversion-mode varactors. These varactors provide tuning capabilities to the comparator; feature which can be exploited for offset calibration. This is demonstrated with the implementation of a differential 10-bit successive approximation register analog-to-digital converters. The design, fabricated in a 0.18- $\mu \text{m}$ CMOS process, includes an automatic mechanism for adjusting the capacitance of the varactors in order to calibrate the offset of the whole converter. Correct functionality was measured in all samples.
Hierarchical control of offshore wind farm connected by parallel diode‐rectifier‐based HVDC and HVAC links

This article investigates the operation of offshore wind farm connected by parallel diode-rectifier-based HVDC (DR-HVDC) and HVAC links. A secondary voltage control is proposed to control the offshore AC voltage amplitude by regulating the DC voltage of the DR-HVDC link. A secondary frequency control and a phase angle control are proposed to adjust the reactive power reference in the primary control, which synchronise the offshore point of common coupling (PCC) frequency and phase angle to those of the HVAC link. Such secondary voltage control, frequency control, and phase angle control enable seamless transition from DR-HVDC mode to parallel mode. A tertiary power control scheme is further proposed to control the active power flow distribution between DR-HVDC and HVAC links through the regulation of PCC phase angle. To ensure smooth transition from HVAC mode to parallel mode, a virtual DC power control is proposed to control the virtual DC power at zero prior to the connection of the DR-HVDC link. A small-signal model of the parallel system is developed, and the stability analysis is carried out for the proposed control scheme. Simulation results in PSCAD/EMTDC verify the proposed control under normal and fault conditions.
Efficiency of Corrosion Inhibitors Used for Concrete Structures

Nowadays, Corrosion of steel in concrete is one of the major causes of premature deterioration of reinforced concrete structures. The use of corrosion inhibitors is found to be one of the effective methods to control rebar corrosion. Therefore, the major challenge for structural engineers is how to achieve the goal for the investors and the architects as well. The aim of this book is to present the most advanced materials and construction techniques used in the reinforced concrete structure industry. This book is intended to serve as a guide to junior and senior engineers who work in design, construction, and maintenance of reinforced concrete structures to assist them in choosing the materials, method of construction. The first chapter's present literature review on corrosion inhibitors used for reinforcement of hardened concrete to control a project to achieve the owner target. In addition, all the materials, all methods and the measurement methods used to obtain the results. Moreover, the book presents the study of synthetic solutions simulating the concrete. In the last chapter, shows evolution of corrosion risk of reinforced concrete structures in tidal zone.
Towards comfort-optimal trajectory planning and control

ABSTRACT This paper describes a method to analyse and evaluate different trajectory planning methods and controller types for usage in automated vehicles. Its application is shown by using a novel trajectory planning approach considering comfort aspects (based on Rapidly Exploring Random Tree (RRT)), two different controllers to follow the planned path (cascade controller and flatness based controller) and a simulation method to obtain resulting lateral vehicle accelerations. The method is used to plan and drive a trajectory through a roundabout. It can be seen that the lateral accelerations of the controller-driven vehicle are in the range of the values used for planning. However, the results of both controllers show differences in lateral deviation and in smoothness of lateral accelerations. The simulation results are then compared to real-world test drives in the same roundabout. The measured lateral accelerations are in the same range as well but show a smoother progression than the two controller models.
Clinical Management of Molar Incisor Hypomineralization Affected Molars in a Pediatric Patient Including Endodontic Treatment, Case Report and Review of the Literature

Abstract The term molar incisor hypomineralization (MIH) describes the clinical manifestation of enamel hypomineralization of systemic origin affecting one or more permanent first molars and incisors. The purpose of this case report is to discuss the management of two mandibular molars with hypomineralization and to emphasize endodontic treatment for pediatric patients. An 8-year-old girl was referred to the pediatric dental clinic for restorations of multiple carious teeth. Upon clinical examination, based on the pattern of enamel hypomineralization and posteruptive breakdown presented, the diagnosis of MIH was established. As a part of the comprehensive treatment, one molar was restored with resin-based composite restorations. The other was endodontically involved and was treated with apexification and then covered with a stainless-steel crown. A two-year recall appointment revealed both molars to be free from signs and symptoms of inflammation. Management of MIH-affected molars should follow a conservative approach and endodontic treatment should be considered.
Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.
Enriching Wikidata with Linked Open Data

Large public knowledge graphs, like Wikidata, contain billions of statements about tens of millions of entities, thus inspiring various use cases to exploit such knowledge graphs. However, practice shows that much of the relevant information that fits users' needs is still missing in Wikidata, while current linked open data (LOD) tools are not suitable to enrich large graphs like Wikidata. In this paper, we investigate the potential of enriching Wikidata with structured data sources from the LOD cloud. We present a novel workflow that includes gap detection, source selection, schema alignment, and semantic validation. We evaluate our enrichment method with two complementary LOD sources: a noisy source with broad coverage, DBpedia, and a manually curated source with a narrow focus on the art domain, Getty. Our experiments show that our workflow can enrich Wikidata with millions of novel statements from external LOD sources with high quality. Property alignment and data quality are key challenges, whereas entity alignment and source selection are well-supported by existing Wikidata mechanisms. We make our code and data available to support future work.
DustPy: A Python Package for Dust Evolution in Protoplanetary Disks

Many processes during the evolution of protoplanetary disks and during planet formation are highly sensitive to the sizes of dust particles that are present in the disk: the efficiency of dust accretion in the disk and volatile transport on dust particles, gravoturbulent instabilities leading to the formation of planetesimals, or the accretion of pebbles onto large planetary embryos to form giant planets are typical examples of processes that depend on the sizes of the dust particles involved. Furthermore, radiative properties like absorption or scattering opacities depend on the particle sizes. To interpret observations of dust in protoplanetary disks, a proper estimate of the dust particle sizes is needed. We present DustPy: a Python package to simulate dust evolution in protoplanetary disks. DustPy solves gas and dust transport including viscous advection and diffusion as well as collisional growth of dust particles. DustPy is written with a modular concept, such that every aspect of the model can be easily modified or extended to allow for a multitude of research opportunities.
Superexchange and Spin-Orbit Coupling in Mono and Bilayer Chromium Trihalides

We build a microscopic model to study the intra- and inter-layer superexchange due to electrons hopping in chromium trihalides (CrX 3 , X= Cl, Br, and I). In evaluating the superexchange, we identify the relevant intermediate excitations in the hopping. In our study, we ﬁnd that the intermediate hole-pairs excitations in the p -orbitals on X ion play a crucial role in mediating various types of exchange interactions. In particular, the inter-layer antiferromagnetic exchange may be realized by the hole-pair-mediated superexchange. Interestingly, we also ﬁnd that these virtual hopping processes compete with each other leading to weak intra-layer ferromagnetic exchange. In addition, we also study the spin-orbit coupling eﬀects on the superexchange and investigate the Dzyaloshinskii-Moriya interaction. Finally, we extract the microscopic model parameters from density functional theory for analyzing the exchange interactions in a monolayer CrI 3 .
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems

The ability of machine learning (ML) models to "extrapolate" to situations outside of the range spanned by their training data is crucial for predicting the long-term behavior of non-stationary dynamical systems (e.g., prediction of terrestrial climate change), since the future trajectories of such systems may (perhaps after crossing a tipping point) explore regions of state space which were not explored in past time-series measurements used as training data. We investigate the extent to which ML methods can yield useful results by extrapolation of such training data in the task of forecasting non-stationary dynamics, as well as conditions under which such methods fail. In general, we find that ML can be surprisingly effective even in situations that might appear to be extremely challenging, but do (as one would expect) fail when "too much" extrapolation is required. For the latter case, we show that good results can potentially be obtained by combining the ML approach with an available inaccurate conventional model based on scientific knowledge.
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers—long short-term memory and gated recurrent unit—within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.
A geometric framework for outlier detection in high‐dimensional data

Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework which exploits the metric structure of a data set. Our approach rests on the manifold assumption, that is, that the observed, nominally high‐dimensional data lie on a much lower dimensional manifold and that this intrinsic structure can be inferred with manifold learning methods. We show that exploiting this structure significantly improves the detection of outlying observations in high dimensional data. We also suggest a novel, mathematically precise and widely applicable distinction between distributional and structural outliers based on the geometry and topology of the data manifold that clarifies conceptual ambiguities prevalent throughout the literature. Our experiments focus on functional data as one class of structured high‐dimensional data, but the framework we propose is completely general and we include image and graph data applications. Our results show that the outlier structure of high‐dimensional and non‐tabular data can be detected and visualized using manifold learning methods and quantified using standard outlier scoring methods applied to the manifold embedding vectors.
A Novel ANFIS-Based Islanding Detection for Inverter-Interfaced Microgrids

This paper presents a new islanding detection strategy for low-voltage inverter-interfaced microgrids based on adaptive neuro-fuzzy inference system (ANFIS). The proposed islanding detection method exploits the pattern recognition capability of ANFIS and its nonlinear mapping of relation between inputs. The ANFIS monitors seven inputs measured at point of common coupling (PCC), namely root-mean square (RMS) of voltage and current (RMS<sub><italic>U</italic></sub> and RMS<sub><italic>I</italic></sub>), total harmonic distortion (THD) of voltage and current (THD<sub><italic>U</italic></sub> and THD<sub><italic>I</italic></sub>), frequency (<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>), and active and reactive powers (<inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>) that are experimentally obtained based on practical measurement in a real-life microgrid. The proposed method is composed of passive monitoring of the mentioned inputs. It does not influence power quality (PQ) and considerably decreases non detection zones (NDZs). In order to cover as many situations as possible, minimize false tripping and still remain selective; type and number of samples are introduced. Here, one of the primary goals is reducing NDZ while keeping PQ in order. Based on the sampled frequency and number of samples, we find that the proposed method has less detection time and better accuracy when compared to the reported methods. Simulations performed in MATLAB/Simulink software environment and several tests performed based on different active load conditions and multiple distributed generation, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision- making problem. However, poor runtime safety hinders RL- based decision-making strategies from complex driving tasks in practice. To address this problem, human demonstrations are incorporated into the RL-based decision-making strategy in this paper. Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL. A complex lane change task in an off-ramp scenario is established to examine the performance of the developed strategy. Simulation results suggest that human demonstrations can effectively improve the safety of decisions of RL. And the proposed strategy surpasses other existing learning-based decision-making strategies with respect to multiple driving performances.
On Leave-One-Out Conditional Mutual Information For Generalization

We derive information theoretic generalization bounds for supervised learning algorithms based on a new measure of leave-one-out conditional mutual information (loo-CMI). Contrary to other CMI bounds, which are black-box bounds that do not exploit the structure of the problem and may be hard to evaluate in practice, our loo-CMI bounds can be computed easily and can be interpreted in connection to other notions such as classical leave-one-out cross-validation, stability of the optimization algorithm, and the geometry of the loss-landscape. It applies both to the output of training algorithms as well as their predictions. We empirically validate the quality of the bound by evaluating its predicted generalization gap in scenarios for deep learning. In particular, our bounds are non-vacuous on large-scale image-classification tasks.
Keeping Less is More: Point Sparsification for Visual SLAM

When adapting Simultaneous Mapping and Localization (SLAM) to real-world applications, such as autonomous vehicles, drones, and augmented reality devices, its memory footprint and computing cost are the two main factors limiting the performance and the range of applications. In sparse feature based SLAM algorithms, one efficient way for this problem is to limit the map point size by selecting the points potentially useful for local and global bundle adjustment (BA). This study proposes an efficient graph optimization for sparsifying map points in such SLAM systems. Specifically, we formulate a maximum pose-visibility and maximum spatial diversity problem as a minimum-cost maximum-flow graph optimization problem. The proposed method works as an additional step in existing SLAM systems, so it can be used in both conventional or learning based SLAM systems. By extensive experimental evaluations we demonstrate the proposed method achieves even more accurate camera poses with approximately 1/3 of the map points and 1/2 of the computation.
Ray-Space Motion Compensation for Lenslet Plenoptic Video Coding

Plenoptic images and videos bearing rich information demand a tremendous amount of data storage and high transmission cost. While there has been much study on plenoptic image coding, investigations into plenoptic video coding have been very limited. We investigate the motion compensation (or so-called temporal prediction) for plenoptic video coding from a slightly different perspective by looking at the problem in the ray-space domain instead of in the conventional pixel domain. Here, we develop a novel motion compensation scheme for lenslet video under two sub-cases of ray-space motion, that is, integer ray-space motion and fractional ray-space motion. The proposed new scheme of light field motion-compensated prediction is designed such that it can be easily integrated into well-known video coding techniques such as HEVC. Experimental results compared to relevant existing methods have shown remarkable compression efficiency with an average gain of 20.03% and 21.76% respectively under “Low delayed $B$ ” and “Random Access” configurations of HEVC.
Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split

Ad platforms require reliable measurement of advertising returns: what increase in performance (such as clicks or conversions) can an advertiser expect in return for additional budget on the platform? Even from the perspective of the platform, accurately measuring advertising returns is hard. Selection and omitted variable biases make estimates from observational methods unreliable, and straightforward experimentation is often costly or infeasible. We introduce Asymmetric Budget Split, a novel methodology for valid measurement of ad returns from the perspective of the platform. Asymmetric budget split creates small asymmetries in ad budget allocation across comparable partitions of the platform's userbase. By observing performance of the same ad at different budget levels while holding all other factors constant, the platform can obtain a valid measure of ad returns. The methodology is unobtrusive and cost-effective in that it does not require holdout groups or sacrifices in ad or marketplace performance. We discuss a successful deployment of asymmetric budget split to LinkedIn's Jobs Marketplace, an ad marketplace where it is used to measure returns from promotion budgets in terms of incremental job applicants. We outline operational considerations for practitioners and discuss further use cases such as budget-aware performance forecasting.