Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning Strategies for Distributed PV-Storage Using a Distribution Network Based on Load Time Sequence Characteristics Partitioning

At present, due to the fact that large-scale distributed photovoltaics can access distribution networks and that there is a mismatch between load demand and photovoltaic output time, it is difficult for traditional distributed photovoltaic planning to meet the partition-based control of high permeability photovoltaic grid-connected operations. As a solution to this problem, this paper proposes a planning method for photovoltaic storage partitions. First of all, a partitioning method for electrical distance modularity based on voltage/active power and voltage/reactive power is presented, along with a modified AP-TD-K-medoids trilevel clustering algorithm that was designed to cluster and partition the distribution network. In addition, according to the partitioning results, a bilevel co-ordination planning model for distributed photovoltaic storage was developed. The upper level aimed to minimize the annual comprehensive cost for which the decision variables are the photovoltaic capacity, energy storage capacity, and power of each partition. The lower level aimed to minimize system network losses, and the decision variables for this are the photovoltaic installation capacity and energy storage installation location of the intrapartition node. Finally, we adopted the particle swarm algorithm with bilevel iterative adaptive weight to solve the planning model, and the simulation was carried out on the distribution system of the IEEE33 nodes. The results show the rationality of the model and the effectiveness of the solution method.
Limit theorems for Mandelbrot's multiplicative cascades

Let W ≥ 0 be a random variable with EW = 1, and let Zr� � r ≥ 2� be the limit of a Mandelbrot's martingale, defined as sums of product of independent random weights having the same distribution as W, indexed by nodes of a homogeneous r-ary tree. We study asymptotic properties of Zras r →∞ : we obtain a law of large numbers, a central limit theorem, a result for convergence of moment generating functions and a theorem of large deviations. Some results are extended to the case where the number of branches is a random variable whose distribution depends on a param- eter r.
‘Perfective paradox’: A cross-linguistic study of the aspectual functions of - guo in Mandarin Chinese

The toneless aspect mark -guo is generally viewed as a perfective marker with experiential function. It appears to be subject to a number of semantic constraints, such as discontinuity, repeatability or recurrence, reversibility, and indefinite reference. This article demonstrates that ‘experiential’ is only one of the three main local functions of -guo. Crucial to the determination of the local function of a -guo clause is the boundedness of the verb constellation: ‘experiential’ (atelic situation, typically Activity verbs), ‘deresultative’ (telic situation, typically Accomplishment and Achievement verbs), and ‘ex-habitual’ (stative verbs). We will first elucidate these three local functions and clarify various semantic constraints of -guo before examining a small corpus of 300 -guo sentences to ascertain the distribution of its local functions in authentic texts. Then we will analyze how these functions are manifested in other languages. The evidence suggests that -guo is untypical as a perfective marker; rather, cross-linguistically the lexico-grammatical exponents of the experiential, deresultative, and ex-habitual functions suggest that -guo behaves more like a perfect marker, hence the ‘perfective paradox’. This paper is intended to be a contribution to general and contrastive aspectology.
Aging and Orienting of Visual Attention: Emergence of a Rightward Attentional Bias with Aging?

ABSTRACT Although some studies described the apparition of a rightward bias of attention with aging, no study has examined if this bias could be explained by modifications based on age in the mechanisms of attention orienting and, specially, in the cueing effects. 48 participants of different ages were tested with a spatial cueing paradigm to assess mechanisms of attention orienting. Our results suggest the appearance of specific difficulties to orient and reorient attention towards the left side of space with aging. These results identified a right attentional anchoring with aging.
Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning

Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model—namely, combine-all in semi-supervised learning (CqSL)—to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured L2 similarity (SL2SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.
Improving anti-money laundering in bitcoin using evolving graph convolutions and deep neural decision forest

PurposeMoney laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. With the motive to curb these illegal activities, there exist various rules, policies and technologies collectively known as anti-money laundering (AML) tools. When properly implemented, AML restrictions reduce the negative effects of illegal economic activity while also promoting financial market integrity and stability, but these bear high costs for institutions. The purpose of this work is to motivate the opportunity to reconcile the cause of safety with that of financial inclusion, bearing in mind the limitations of the available data. The authors use the Elliptic dataset; to the best of the authors' knowledge, this is the largest labelled transaction dataset publicly available in any cryptocurrency.Design/methodology/approachAML in bitcoin can be modelled as a node classification task in dynamic networks. In this work, graph convolutional decision forest will be introduced, which combines the potentialities of evolving graph convolutional network and deep neural decision forest (DNDF). This model will be used to classify the unknown transactions in the Elliptic dataset. Additionally, the application of knowledge distillation (KD) over the proposed approach gives finest results compared to all the other experimented techniques.FindingsThe importance of utilising a concatenation between dynamic graph learning and ensemble feature learning is demonstrated in this work. The results show the superiority of the proposed model to classify the illicit transactions in the Elliptic dataset. Experiments also show that the results can be further improved when the system is fine-tuned using a KD framework.Originality/valueExisting works used either ensemble learning or dynamic graph learning to tackle the problem of AML in bitcoin. The proposed model provides a novel view to combine the power of random forest with dynamic graph learning methods. Furthermore, the work also demonstrates the advantage of KD in improving the performance of the whole system.
Analysis of Gaussian Light by Clipped Photocount Autocorrelation: The Effect of Finite Sampling Times and Incomplete Spatial Coherence

The theoretical treatment of clipped photocount autocorrelation of Gaussian light, introduced by Jakeman and Pike, has been extended to include the effects of finite‐duration sampling intervals and incomplete spatial coherence. A system of ``interval partitioning'' is used to derive theoretical expressions for 〈Nq(0)N(τ)〉 and 〈N0(0) N0(τ)〉, the single‐ and double‐clipped photocount autocorrelation functions. Here N(t) is the number of photons detected in a sampling interval centered at t and Nq(t) is the clipped count at level q. Each original interval is systematically partitioned in space and time. Multidimensional generating functions are applied to the sets of subintervals with the object of expressing g(1) (t), the first‐order field correlation function, in terms of measurable quantities. It is shown that under certain well‐defined conditions, 〈Nq(0) N(τ)〉−〈Nq〉〈N〉=γq|g(1)(τ)|2 and 〈1−N0〉−2−[1−2〈N0〉+〈N0(0)N0(τ)〉]−1=φ|g(1)(τ)|2, where, for a given set of experimental parameters, γq and φ can be taken a.
Template-free self-assembly of mesoporous ZnO nanocluster/ polymethyl methacrylate based anisotropic nanocomposite thin films with enhanced interfacial interactions and tuneable optical properties

Nature inspired biomimetic growth of anisotropic, hierarchical nanostructures could offer insight into new and exciting crystalline properties for advanced multifunctional applications. Our study demonstrates diethanolamine-mediated mesoscopic self-assembly of semiconducting ZnO Quantum Dots (3–5 nm) into lattice-aligned, symmetrical superstructures (40–50 nm) via non-classical oriented attachment (OA) crystal growth observed in various biomineralization processes. Multifunctional nanocomposite thin films of self-assembled nanostructures and polymethyl methacrylate were spin-coated onto plasma-treated Si wafers and the surface and interfacial properties were rigorously studied. HR-TEM images depicted the OA growth process with neighboring nano-crystals having perfectly aligned lattices. Preferential orientation of the thin films along <100> direction was evident from the XRD data. Quantum confinement in ZnO QDs and surface defect originating sharp green PL emission were examined through UV–Vis absorption and Photoluminescence spectra respectively. DLS and Zeta potential studies of surface-engineered colloidal superstructures established excellent long-term physico–chemical stability with no agglomeration or transparency loss observed in ZnO mesocrystal suspension even after 6 months. Diethanolamine, due to its dual functionality radically enhanced the interaction between polar ZnO and non-polar PMMA matrix resulting in highly stable thin films (Class II hybrids) with enhanced surface and interfacial properties as evident from the extremely low surface roughness and homogenous nanofiller dispersion observed in AFM and FE-SEM studies. Chemical interactions at the interface were also established quantitatively by XPS binding energy measurements which suggested hydrogen bonds and covalent bonds between organic-inorganic phases promoted via diethanolamine surface engineering.
Decentralized robust stabilization for large-scale feedforward non-linear systems

In this paper, we consider the decentralized robust stabilization problem for large-scale feedforward non-linear systems, which possess an upper-triangular structure, while in the decentralized control area the commonly studied large-scale feedback non-linear systems possess a lower-triangular structure. Our decentralized robust stabilizer for each subsystem takes a nested saturation feedback, with the saturation levels satisfying a set of equations and inequalities. Global asymptotic stability of the closed-loop large-scale system has been proved, and a simulation example is given.
Micro-Raman scattering from undoped and phosphorous-doped (111) homoepitaxial diamond films: Stress imaging of cracks

We report postgrowth micro-Raman stress imaging of cracks in (111) homoepitaxial diamond films. Undoped and phosphorous-doped diamond thin films grown by microwave plasma-enhanced chemical-vapor deposition on Ib (111)-oriented diamond substrates have been studied by confocal micro-Raman spectroscopy. For comparison purposes, a film grown on a (100) Ib substrate was also examined. Thanks to the confocal optics, the Raman signal arising from the epilayer could be discriminated from that arising from the substrate. As was already reported, the (111) films exhibited substantial tensile stress, indicated by a downshift in the Raman peak and by spontaneous cracking in films grown thicker than 5μm. The sixfold symmetry of the cracks supported that the films were homoepitaxial. A high compressive stress was also detected at the substrate near surface, and a partial stress relaxation was observed to occur in the vicinity of the cracks. Possible origins of the high tensile stress observed in the (111) homoepitaxial.
Complex Clifford analysis and domains of holomorphy

Abstract Integrals related to Cauchy's integral formula and Huygens' principle are used to establish a link between domains of holomorphy in n complex variables and cells of harmonicity in one higher dimension. These integrals enable us to determine domains to which analytic functions on real analytic surface in Rn+1 may be extended to solutions to a Dirac equation.
Exploiting Deep Matching and Underwater Terrain Images to Improve Underwater Localization Accuracy

Underwater terrain image (UTI) matching provides a new idea for terrain-aided localization technology. However, the diversity of UTIs makes it very difficult to accurately match real-time terrain images (RTTIs) with reference terrain images (RTIs). To improve the localization accuracy of UTIs, this letter describes how UTI matching can be modeled as a deep learning problem based on supervised algorithms. Specifically, we propose a hierarchical matching strategy for the diverse features of UTIs by combining the deep and shallow feature characteristics of a convolutional neural network (CNN) to improve the localization accuracy. We also propose a sample-generation strategy based on candidate area quality index classification, and develop a data enhancement method by simulating the underwater terrain environment to overcome the lack of network training data. Simulations prove that the proposed method achieves accurate and stable localization results.
Artificial intelligence biosensors for continuous glucose monitoring

Artificial intelligence (AI) algorithms in combination with continuous monitoring technologies have the potential to revolutionize chronic disease management. The recent innovations in both continuous glucose monitoring (CGM) and the closed‐loop highlight the far‐reaching potential of AI biosensors for individual healthcare. This review summarizes some of the most advanced progress made in CGM biosensing. We will focus on three main applications of AI algorithms in diabetes management: closed‐loop control algorithms, glucose predictions, and calibrations. The challenges and opportunities of AI technologies for CGM in individualized and proactive medicine will also be discussed.
Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency

This paper models and mitigates the secondary crash (SC) risk for serial tunnels on the freeway which is incurred by traffic turbulence after primary crash (PC) occurrence and location-heterogeneous lighting conditions along serial tunnels. A traffic conflict approach is developed where SC risk is quantified using a surrogate safety measure based on the simulated vehicle trajectories after PC occurs from a lighting-related microscopic traffic model with inter-lane dependency. Numerical examples are presented to validate the model, illustrate SC risk pattern over time, and evaluate the countermeasures for SC, including adaptive tunnel lighting control (ATLC) and advanced speed and lane-changing guidance (ASLG) for connected vehicles (CVs). The results demonstrate that the tail of the stretching queue on the PC occurrence lane, the adjacent lane of the PC-incurred queue, and areas near tunnel portals are high-risk locations. In serial tunnels, creating a good lighting condition for drivers is more effective than advanced warnings in CVs to mitigate SC risk. Combined ATLC and ASLG is promising since ASLG informs CVs of an immediate response to traffic turbulence on the lane where PC occurs and ATLC alleviates SC risks on adjacent lanes via smoothing the lighting condition variations and reducing inter-lane dependency.
Gap formation and guided modes in photonic bandgap fibres with high-index rods

Photonic bandgap fibres fabricated by infiltrating the holes of a microstructured optical fibre with high-index material are investigated numerically in the low- and intermediate-frequency regime. Bandgaps, transmission windows and the distribution of field energy between high- and low-index regions are investigated. It is shown that the numerical results found can be rationalized in a simple way by considering the properties of guided modes in a conventional step-index fibre.
A DROPLET MODEL OF QUIESCENT PROMINENCE DOWNFLOWS

Observations of quiescent prominences with the Solar Optical Telescope on the Hinode satellite have revealed the ubiquitous existence of downflows forming coherent thin and highly structured vertically oriented threads with velocities between 10 and 20 km s−1. Their widths range between 300 and 500 km. They are often initiated at the top of the visible prominence, but sometimes also at intermediate level. We propose that the downflows are made of plasma packets that squeeze themselves through the dominantly horizontal field under the action of gravity. Their origin is assumed to be hot plasma supplied from either inside or the immediate vicinity of the prominence and condensing at its top. Under compression and further cooling, the matter overflows to the flanks of the prominence dragging its magnetic field with it. Under the increasing action of gravity, vertical structures are forming which eventually disconnect from the field of the inflow channel thus forming finite plasma packets. This process is reminiscent of water flowing over a mountain ridge and breaking up into a multitude of droplets. Like water droplets being subject to air drag, the falling plasma droplets experience a drag force by the horizontal prominence field and assume a steady vertical velocity. This happens via the excitation of Alfvén waves. Lateral confinement by the prominence field determines their spatial extent. The small scales of the droplets and the directional balance of their internal tangled magnetic fields can explain the absence of appreciable vertical components in magnetic field measurements. On the basis of the observed width and vertical speed of the downflows and by adopting a prominence field of about 8 G, we derive central density and temperature of the droplets, which turn out to be quite consistent with known prominence characteristics. In the formulation of the drag force a dimensionless “magnetic drag coefficient” has been introduced with a value well below unity.
Recent methodological developments in data-dependent analysis and data-independent analysis workflows for exhaustive lipidome coverage

Untargeted lipidomics applied to biological samples typically involves the coupling of separation methods to high-resolution mass spectrometry (HRMS). Getting an exhaustive coverage of the lipidome with a high confidence in structure identification is still highly challenging due to the wide concentration range of lipids in complex matrices and the presence of numerous isobaric and isomeric species. The development of innovative separation methods and HRMS(/MS) acquisition workflows helped improving the situation but issues still remain regarding confident structure characterization. To overcome these issues, thoroughly optimized MS/MS acquisition methods are needed. For this purpose, different methodologies have been developed to enable MS and MS/MS acquisition in parallel. Those methodologies, derived from the proteomics, are referred to Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA). In this context, this perspective paper presents the latest developments of DDA- and DIA-based lipidomic workflows and lists available bioinformatic tools for the analysis of resulting spectral data.
Performance Comparison of Tungsten-Halogen Light and Phosphor-Converted NIR LED in Soluble Solid Content Estimation of Apple

A Tungsten-Halogen (TH) lamp is the most popular light source in NIR spectroscopy and hyperspectral imaging, which requires a warm-up to reach very high temperatures of up to 250 °C and take a long time for radiation stabilization. Consequently, it has a large enough volume to enable heat dissipation to prevent the thermal runaway of the electric circuit and turn out its power efficiency very low. These are major barriers for miniaturizing spectral systems and hyperspectral imaging devices. However, TH lamps can be replaced by pc-NIR LEDs in order to avoid high temperature and large volume. We compared the spectral emission of the available commercial pc-NIR LEDs under the same condition. As a replacement for the TH lamp, the VIS + NIR LED module was developed to combine a warm-white LED and pc-NIR LEDs. In order to feature out the availability of the VIS + NIR LED module against the TH lamp, they were used as the light source for evaluating the Soluble Solid Content (SSC) of an apple through VIS-NIR spectroscopy. The results show a remarkable feasibility in the performance of the partial least square (PLS) model using the VIS + NIR LED module; during PLS calibration, the correlation coefficient (R) values are 0.664 and 0.701, and the Mean Square Error (MSE) values are 0.681 and 0.602 for the TH lamp and VIS + NIR LED module, respectively. In VIS-NIR spectroscopy, this study indicates that the TH lamp could be replaceable with a warm-white LED and pc-NIR LEDs.