Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a Non-Pharmacological Intervention Programme for Wandering in People with Dementia: Recommendations for Healthcare Providers in Nursing Homes

Background: Wandering among people with dementia (PwD) is associated with a high risk of injury and death. The stigma of dementia prevents Chinese dementia families from seeking information and support earlier, which increases the demand for long-term care facilities. Despite universal recognition of the importance of care facilities, healthcare providers in care facilities still lack the relevant nursing knowledge and skills, including non-pharmacological interventions (NPIs) that have been proven to be effective in preventing wandering. Systematic and culturally appropriate NPI programmes for healthcare providers to manage wandering among PwD in long-term care facilities are still lacking. We aimed to develop an evidence-based and culturally appropriate NPI programme for wandering in PwD to guide healthcare providers in nursing homes to prevent wandering and its adverse outcomes. Methods: The NPI programme was developed according to the framework of the Belgian Centre for Evidence-Based Medicine (CEBAM). We, (1) performed a systematic literature search to summarize the available evidence, (2) developed evidence-based recommendations for the NPI programme based on the existing evidence, and (3) carried out a validation process to revise the content of the recommendations and to determine the grades of recommendations, including group meetings with experts and a survey for end-users. Results: Based on 22 publications and validation from 7 experts and 76 end users, we developed 21 recommendations covering 4 domains: (1) caregiver education, (2) preventing excessive wandering, (3) promoting safe walking, and (4) preventing people with dementia from going missing. We created almost all recommendations of the four domains with accompanying levels of evidence and grades of recommendations. Conclusions: By combining the evidence with expert and end-user opinions, a comprehensive NPI programme was developed to support institutional healthcare providers to prevent wandering and its adverse outcomes. The benefits of this programme are currently being tested.
IDENTIFYING AND QUANTIFYING RECURRENT NOVAE MASQUERADING AS CLASSICAL NOVAE

Recurrent novae (RNe) are cataclysmic variables with two or more nova eruptions within a century. Classical novae (CNe) are similar systems with only one such eruption. Many of the so-called CNe are actually RNe for which only one eruption has been discovered. Since RNe are candidate Type Ia supernova progenitors, it is important to know whether there are enough in our Galaxy to provide the supernova rate, and therefore to know how many RNe are masquerading as CNe. To quantify this, we collected all available information on the light curves and spectra of a Galactic, time-limited sample of 237 CNe and the 10 known RNe, as well as exhaustive discovery efficiency records. We recognize RNe as having (1) outburst amplitude smaller than 14.5 − 4.5 × log (t3), (2) orbital period >0.6 days, (3) infrared colors of J − H > 0.7 mag and H − K > 0.1 mag, (4) FWHM of Hα > 2000 km s−1, (5) high excitation lines, such as Fe x or He ii near peak, (6) eruption light curves with a plateau, and (7) white dwarf mass greater than 1.2 M☉. Using these criteria, we identify V1721 Aql, DE Cir, CP Cru, KT Eri, V838 Her, V2672 Oph, V4160 Sgr, V4643 Sgr, V4739 Sgr, and V477 Sct as strong RN candidates. We evaluate the RN fraction among the known CNe using three methods to get 24% ± 4%, 12% ± 3%, and 35% ± 3%. With roughly a quarter of the 394 known Galactic novae actually being RNe, there should be approximately a hundred such systems masquerading as CNe.
Configuration-aware model predictive motion planning for Tractor–Trailer Mobile Robot

ABSTRACT This study proposes a motion planning method with strict obstacle avoidance constraints among polygonal-shaped objects for a tractor–trailer mobile robot. The conventional method generally expresses collision-free constraints by approximating a robot as one circle or several circles or ellipsoids. However, accomplishing moving tasks with minimum margins is desirable, with improved efficiency of space availability when multiple robots operate in one area such as a factory, restaurant, office, or warehouse. The target of this study is a tractor–trailer mobile robot that can transport more goods efficiently, while significantly reducing margins during movement. To control the target, multiple convex polygonal objects expressing the mobile robot and surroundings are transformed into linear simultaneous inequalities using ‘Farkas’ lemma. ' Embedding inequalities into model predictive control yields a motion planner that allows tractor–trailer mobile robot movement and possesses non-holonomic constraints. The proposed model is validated through simulation and real machine experiments. GRAPHICAL ABSTRACT
Rapid restructurization of conformationally-distinct alpha-synuclein amyloid fibrils at an elevated temperature

Protein aggregation in the form of amyloid fibrils is linked with the onset and progression of more than 30 amyloidoses, including multiple neurodegenerative disorders, such as Alzheimer’s or Parkinson’s disease. Despite countless studies and years of research, the process of such aggregate formation is still not fully understood. One peculiar aspect of amyloids is that they appear to be capable of undergoing structural rearrangements even after the fibrils have already formed. Such a phenomenon was reported to occur in the case of alpha-synuclein and amyloid beta aggregates after a long period of incubation. In this work, we examine whether incubation at an elevated temperature can induce the restructurization of four different conformation alpha-synuclein amyloid fibrils. We show that this structural alteration occurs in a relatively brief time period, when the aggregates are incubated at 60 °C. Additionally, it appears that during this process multiple conformationally-distinct alpha-synuclein fibrils all shift towards an identical secondary structure.
High power, room temperature, terahertz sources, and frequency comb based on difference-frequency generation at CQD

Quantum cascade laser (QCL) is becoming the leading laser source in the mid-infrared and terahertz range due to its rapid development in power, efficiency, and spectral covering range. Owing to its unique intersubband transition and fast carrier lifetime, QCL possesses strong nonlinear susceptibilities that makes it the ideal platform for a variety of nonlinear optical generations. Among this, terahertz (THz) source based on difference-frequency generation (DFG) and frequency comb based on four wave mixing effect are the most exciting phenomena which could potentially revolutionize spectroscopy in mid-infrared (mid-IR) and THz spectral range. In this paper, we will briefly discuss the recent progress of our research. This includes high power high efficiency QCLs, high power room temperature THz sources based on DFG-QCL, room temperature THz frequency comb, and injection locking of high-power QCL frequency combs. The developed QCLs are great candidates as next generation mid-infrared source for spectroscopy and sensing.
Wavelet-based Estimation for Heteroskedasticity and Autocorrelation Consistent Variance-Covariance Matrices

As is well-known, a heteroskedasticity and autocorrelation consistent covariance matrix is proportional to a spectral density matrix at frequency zero and can be consistently estimated by such popular kernel methods as those of Andrews-Newey-West. In practice, it is difficult to estimate the spectral density matrix if it has a peak at frequency zero, which can arise when there is strong autocorrelation, as often encountered in economic and financial time series. Kernels, as a local averaging method, tend to underestimate the peak, thus leading to strong overrejection in testing and overly narrow confidence intervals in estimation. As a new mathematical tool generalizing Fourier transform, wavelet transform is a powerful tool to investigate such local properties as peaks and spikes, and thus is suitable for estimating covariance matrices. In this paper, we propose a class of wavelet estimators for the covariance matrices of econometric parameter estimators. We show the consistency of the wavelet-based covariance estimators and derive their asymptotic mean squared errors, which provide insight into the smoothing nature of wavelet estimation. We propose a data-driven method to select the finest scale---the smoothing parameter in wavelet estimation, making the wavelet estimators operational in practice. A simulation study compares the finite sample performances of the wavelet estimators and the kernel counterparts. As expected, the wavelet method outperforms the kernel method when there exists relatively strong autocorrelation in the data.
Predicting the Costs of Forwarding Contracts Using XGBoost and a Deep Neural Network

This article presents an application of an XGBoost and deep neural network ensemble as a solution for a task assigned at the FedCSIS 2022 Challenge: Predicting the Costs of Forwarding Contracts. We demonstrate that prediction quality can be improved by combining the two approaches. We present a neural network architecture based on three independent flows. We then discuss the influence of long short-term memory units on the risk of overfitting. Finally, we show that the static XGBoost model can complement a neural network that processes dynamic data.
A multidimensionally consistent version of Hirota’s discrete KdV equation

A multidimensionally consistent generalization of Hirota’s discrete KdV equation is proposed, it is a quad equation defined by a polynomial that is quadratic in each variable. Soliton solutions and interpretation of the model as superposition principle are given. It is discussed how an important property of the defining polynomial, a factorization of discriminants, appears also in the few other known discrete integrable multi-quadratic models.
Influence of Plant Growth Regulators and Artificial Light on the Growth and Accumulation of Inulin of Dedifferentiated Chicory (Cichorium intybus L.) Callus Cells

Chicory (Chicorium intybus L.) is a perennial herb of the family Asteraceae, widely distributed in Asia and Europe, commonly used industrially as a raw material for extracting inulin because of a high content of inulin and biologically active compounds. Light conditions and plant growth regulators (PGRs) are two of many factors that affect the growth and inulin content of chicory callus. The aim of this work is to study the effect of PGRs and light conditions on proliferation and accumulation of inulin of chicory callus in vitro. In this study, we used semi-solid MS medium supplemented with different auxins (including Indole-3-acetic acid (IAA), naphthylacetic acid (NAA), and 2,4-dichlorophenoxyacetic acid (2,4-D)) at a concentration of 5.5–9.5 mg/L in combination with 2.0 mg/L 6 benzylaminopurine (BA) to determine induction and proliferation of callus. The increasing value of callus fresh weight was used to assess the growth of the callus in treatments. The results showed that a steady increase in callus fresh weight and inulin content in callus cells was obtained when they were cultured on MS medium supplemented with a combination of 2.0 mg/L BA with 7.5 mg/L IAA in lighting conditions with radiation equalized by the flux density of photosynthetic photons and ratios of radiation levels in the region of FR—far red > R—red. Increasing demand for organic inulin sources in production practice can be met by our finding.
Magnetic field evolution in giant radio relics using the example of CIZA J2242.8+5301

Giant radio relics are the arc-shaped diffuse radio emission regions observed in the outskirts of some merging galaxy clusters. They are believed to trace shock-waves in the intra-cluster medium. Recent observations demonstrated that some prominent radio relics exhibit a steepening above 2 GHz in their radio spectrum. This challenges standard theoretical models because shock acceleration is expected to accelerate electrons to very high energies with a power-law distribution in momentum. In this work we attempt to reconcile these data with the shock-acceleration scenario. We propose that the spectral steepening may be caused by the highest energy electrons emitting preferentially in lower magnetic fields than the bulk of synchrotron bright electrons in relics. Here, we focus on a model with an increasing mag- netic field behind the shock front, which quickly saturates and then declines. We derive the time-evolution of cosmic-ray electron spectra in time variable magnetic fields and an expanding medium. We then apply the formalism on the large radio relic in the cluster CIZA J2242.8+5301 (the Sausage relic). We show that under favourable circumstances of magnetic field amplification downstream, our model can explain the observed radio spectrum, the brightness profile and the spectral index profile of the relic. A possible interpretation for the required amplification of the magnetic field downstream is a dynamo acting behind the shock with an injection scale of magnetic turbulence of about 10 kpc. Our models require injection efficiencies of CRe - which are in tension with simple diffusive shock acceleration from the thermal pool. We show that this problem can likely be alleviated considering pre-existing CRe.
A Characterization of the Simple Group U 3(5)

In this note we consider a finite group G which satisfies the following conditions: (0. 1) G is a doubly transitive permutation group on a set Ω of m + 1 letters, where m is an odd integer ≥ 3, (0. 2) if H is a subgroup of G and contains all the elements of G which fix two different letters α, β, then H contains unique permutation h 0 ≠ 1 which fixes at least three letters, (0. 3) every involution of G fixes at least three letters, (0. 4) G is not isomorphic to one of the groups of Ree type.
Quantum Learning Machine

School of Mathematics and Physics, The Queen’s University of Belfast, BT7 1NN, United Kingdom(Received March 31, 2008)We propose a novel notion of a quantum learning machine for automatically controlling quantumcoherence and for developing quantum algorithms. A quantum learning machine can be trained tolearn a certain task with no a priori knowledge on its algorithm. As an example, it is demonstratedthat the quantum learning machine learns Deutsch’s task and ﬁnds itself a quantum algorithm, thatis diﬀerent from but equivalent to the original one.
CLASSROOM: synthetic high dynamic range light field dataset

Light field images provide tremendous amounts of visual information regarding the represented scenes, as they describe the light traversing in all directions for all the points of 3D space. Due to the recent technological advancements of light field visualization and its increasing relevance in research, the need for light field image datasets has risen significantly. Among the applications for which light field datasets are considered, high dynamic range light field image reconstruction has gained notable attention in the past years. When capturing a scene, either a single camera with a 2D microlense array or a 2D array of cameras is used to produce narrow-and wide-baseline light field images, respectively. Additionally, the turn-table methodology may be used as well for narrow-baseline light fields. While the majority of these methods enables the creation of plausible and reliable light field image datasets, such baseline-specific setups can be extremely expensive and may require immense computing resources for proper calibration. Furthermore, the resulting light field is commonly limited with regard to angular resolution. A suitable alternative to produce a light field dataset is to do it synthetically by rendering light field images, which may easily overcome the aforementioned issues. In this paper, we discuss our work on creating the “CLASSROOM” light field image dataset, depicting a classroom scene. The content is rendered in horizontal-only parallax and full parallax as well. The scene contains a high variety of light distribution, particularly involving under-exposed and over-exposed regions, which are essential to HDR image applications.
Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field.
Novel data clustering methods and applications

The need to interpret and extract possible inferences from high-dimensional datasets has led over the past decades to the development of dimensionality reduction and data clustering techniques. Scientific and technological applications of clustering methodologies include among others bioinformatics, biomedical image analysis and biological data mining. Current research in data clustering focuses on identifying and exploiting information on dataset geometry and on developing robust algorithms for noisy datasets. Recent approaches based on spectral graph theory have been devised to efficiently handle dataset geometries exhibiting a manifold structure, and fuzzy clustering methods have been developed that assign cluster membership probabilities to data that cannot be readily assigned to a specific cluster. In this thesis, we develop a family of new data clustering algorithms that combine the strengths of existing spectral approaches to clustering with various desirable properties of fuzzy methods. More precisely, we consider a slate of “random-walk” distances arising in the context of several weighted graphs formed from the data set, which allow to assign “fuzzy” variables to data points which respect in many ways their geometry. The developed methodology groups together data which are in a sense “well-connected”, as in spectral clustering, but also assigns to them membership values as in other commonly used fuzzy clustering approaches. This approach is very well suited for image analysis applications and, in particular, we use it to develop a novel facial recognition system that outperforms other well-established methods.
Elevated serum globulin fraction as a biomarker of multiyear disease severity in inflammatory bowel disease

Background Serum protein reflects albumin and globulin levels, both of which can be altered in inflammatory bowel disease (IBD). The implications of a high globulin fraction in IBD are unknown. We hypothesized that a high globulin fraction may function independently of albumin as a biomarker of disease severity in IBD patients over a multiyear period. Methods This was an observational study from a prospective IBD registry of a tertiary care center. High globulin fraction was defined as an elevated globulin level >4 g/dL. Data collected included patient demographics, medication exposures, quality-of-life scores, disease activity, emergency department visits, telephone calls, hospitalizations, and IBD-related surgeries over a 4-year period. Comparisons between patients with a high globulin fraction and those without were performed using Pearson’s chi-squared, Student’s and Mann-Whitney tests. Multivariate analyses were used to assess the relationship between high globulin fraction and healthcare utilization. Results A total of 1767 IBD patients with a 4-year follow up were included: 53.5% female, mean age 48.4±15.1 years, and 65.4% with Crohn’s disease. Of these patients, 446 (25.2%) presented with elevated globulin fraction. Patients with a high globulin fraction were more likely to be hospitalized during the study period. This result remained significant after multivariate analysis for both Crohn’s disease patients and those with ulcerative colitis. Conclusion A high globulin fraction is independently associated with greater disease severity and healthcare utilization in IBD patients, and may function as a routinely available biomarker of a more severe future disease trajectory.
A new hot-stage microscopy technique for measuring temperature-dependent viscosities of aerosol particles and its application to farnesene secondary organic aerosol

Abstract. The viscosity of secondary organic aerosol (SOA) is needed to improve predictions of air quality, climate, and atmospheric chemistry. Many techniques have been developed to measure the viscosity of micrometer-sized materials at room temperature; however, few techniques are able to measure viscosity as a function of temperature for these small sample sizes. SOA in the troposphere experience a wide range of temperatures, so measurement of viscosity as a function of temperature is needed. To address this need, a new method was developed based on hot-stage microscopy combined with fluid dynamics simulations. The current method can be used to determine viscosities in the range of roughly 104 to 108 Pa s at temperatures greater than room temperature. Higher viscosities may be measured if experiments are carried out over multiple days. To validate our technique, the viscosities of 1,3,5-tris(1-naphthyl)benzene and phenolphthalein dimethyl ether were measured and compared with values reported in the literature. Good agreement was found between our measurements and literature data. As an application to SOA, the viscosity as a function of temperature for lab-generated farnesene SOA material was measured, giving values ranging from 3.1×106 Pa s at 51 ∘C to 2.6×104 Pa s at 67 ∘C. We fit the temperature-dependent data to the Vogel–Fulcher–Tammann (VFT) equation and obtained a fragility parameter for the material of 7.29±0.03, which is very similar to the fragility parameter of 7 reported for α-pinene SOA by Petters and Kasparoglu (2020). These results demonstrate that the viscosity as a function of temperature can be measured for lab-generated SOA material using our hot-stage microscopy method.
Stochastically Projecting Tensor Networks

We apply a series of projection techniques on top of tensor networks to compute energies of ground state wave functions with higher accuracy than tensor networks alone with minimal additional cost. We consider both matrix product states as well as tree tensor networks in this work. Building on top of these approaches, we apply fixed-node quantum Monte Carlo, Lanczos steps, and exact projection. We demonstrate these improvements for the triangular lattice Heisenberg model, where we capture up to 57 percent of the remaining energy not captured by the tensor network alone. We conclude by discussing further ways to improve our approach.