Miracle-dz/UniFAR-FTUs huggingface dataset:
Fractional Herglotz variational problems of variable order

We study fractional variational problems of Herglotz type of variable order. Necessary optimality conditions, described by fractional differential equations depending on a combined Caputo fractional derivative of variable order, are proved. Two different cases are considered: the fundamental problem, with one independent variable, and the general case, with several independent variables. We end with some illustrative examples of the results of the paper.
Design and Fabrication of a Large Area Ultrathin Vapor Chamber Based on Print-Wick-Structuring Technology

We explore a large area ultrathin vapor chamber (UTVC) with special wick structures. At present, the thickness of the UTVC applied to mobile electronic devices is usually above 0.3 mm. In this article, the UTVC with a thickness of 0.2 mm and an area of more than 5000 mm2 is designed and fabricated by the print-wick-structuring (PWS) combo technology. We screen print several types of paste on the copper-etched sheet to form the specific wick structures, which greatly influences the heat transfer performance. Thus, the limitation of the traditional copper mesh wick technology in two-phase flow transportation and circulation in a large area and ultranarrow space is solved. The wick structures formed at the sintering temperature of 780 °C enable the working fluid to flow through a 60-mm path in 6 s. The test results show that the UTVC can transfer a heat load of up to 5 W and the temperature difference between the evaporator and condenser can be controlled to 2.5 °C so that the thermal resistance can reach 0.5 °C/W. In addition, the manufacturing process introduced in this article makes the UTVC feasible for mass production.
The Visual Scanning Behavior and Mental Workload of Drivers at Prairie Highway Intersections With Different Characteristics

Highway intersections are crash prone locations, and drivers’ improper attention allocation and sudden increase of mental workload are main contributing factors. To explore the visual scanning characteristics and mental workload of drivers at prairie highway intersections with different characteristics, an on-road driving test was taken at 6 intersections scattered on a typical prairie highway with 3 different shapes and 2 different priority rules, and drivers’ eye movement and ECG data were collected. The results show that there is significant difference in fixation allocation and transferring among intersections with different shapes and priority rules. The X-shaped intersection without the right of way shows the longest average fixation duration and the most gaze shifts between the drivers’ lane and the intersection road, whereas the three-way intersection with the right of way shows the shortest fixation duration and the gaze transfer path is relatively simple and the transfers are less. The mental workload implies almost the same conclusion, with the X-shaped intersection without right of way shows the highest mental workload which is manifested by largest heart rate growth rate and the lowest RMSSD, whereas the three-way with priority of way shows the opposite trend. This study revealed that both intersection types and priority rule made differences in drivers’ scanning behavior and mental workload at prairie highway intersections, which can support the development of appropriate countermeasures regarding the applications in advanced driver assistance systems and the design of intersection constructions to warn drivers about potential critical events at different types of intersections.
More than 100-wavelength-channel picosecond optical pulse generation from single laser source using supercontinuum in optical fibres

A novel method for generating multiwavelength-channel picosecond optical pulses from a single laser source is proposed using supercontinuum in optical fibres combined with an all-fibre birefringent periodic filter. More than 100-wavelength-channel, 10 ps optical pulses are generated simultaneously over a 1.224–1.394 μm spectral range with a 1.9 nm spacing using a 7.6 ps Nd : YLF laser pulse source.
Combined Vaccination with B Cell Peptides Targeting Her-2/neu and Immune Checkpoints as Emerging Treatment Option in Cancer

Simple Summary Therapies with monoclonal antibodies (mAbs) targeting tumor-associated antigens (TAAs) or immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. Nevertheless, the inevitable development of resistance and the failure to respond are among this approach’s disadvantages, limiting the duration of disease- or progression-free and overall survival. As an alternative to therapeutically efficacious monoclonal antibodies, the concept of active immunization with vaccines has been repeatedly discussed. In particular, mimotopes, representing the B cell epitope of therapeutic mAbs, have been shown to induce immunological memory and effectively produce antibodies with similar functionality to the respective mAbs/ICIs. This review focuses on a new frontier of vaccinations directed against two cancer-relevant targets, addresses concerns about the safety of active immunization targeting PD-1 and discusses limitations and outlooks. Abstract The application of monoclonal antibodies (mAbs), targeting tumor-associated (TAAs) or tumor-specific antigens or immune checkpoints (ICs), has shown tremendous success in cancer therapy. However, the application of mAbs suffers from a series of limitations, including the necessity of frequent administration, the limited duration of clinical response and the emergence of frequently pronounced immune-related adverse events. However, the introduction of mAbs has also resulted in a multitude of novel developments for the treatment of cancers, including vaccinations against various tumor cell-associated epitopes. Here, we reviewed recent clinical trials involving combination therapies with mAbs targeting the PD-1/PD-L1 axis and Her-2/neu, which was chosen as a paradigm for a clinically highly relevant TAA. Our recent findings from murine immunizations against the PD-1 pathway and Her-2/neu with peptides representing the mimotopes/B cell peptides of therapeutic antibodies targeting these molecules are an important focus of the present review. Moreover, concerns regarding the safety of vaccination approaches targeting PD-1, in the context of the continuing immune response, as a result of induced immunological memory, are also addressed. Hence, we describe a new frontier of cancer treatment by active immunization using combined mimotopes/B cell peptides aimed at various targets relevant to cancer biology.
Exploring the Potential of Immersive Virtual Reality in the Treatment of Unilateral Spatial Neglect Due to Stroke: A Comprehensive Systematic Review

The present review aims to explore the use of Immersive Virtual Reality (IVR) in the treatment of visual perception in Unilateral Spatial Neglect (USN) after a stroke. PubMed, Scopus, Embase and Pedro databases were searched, from inception to 1 February 2022. All studies that investigated the effect of IVR on USN, such as outcome in the stroke population, have been included. The current comprehensive systematic review was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations and was registered in the PROSPERO database [CRD42022311284]. Methodological quality was assessed through JBI critical appraisal tool. A total of 436 articles were identified through the database searches. A total of 10 articles, with a heterogeneous study design, which involved 77 patients with USN with low-to-moderate methodological quality, have been selected. Five out the included studies tested usability of IVR for assessed or treated visual perception deficits in USN, comparing the results with 134 healthy subjects. In the rest of studies that tested IVR such as treatment, three showed statistical positive results (p < 0.05) in visual perception outcome. To date, the literature has suggested the potential benefits in the use of IVR for the treatment of visual perception disorders in USN. Interestingly, IVR motivates patients during the rehabilitation process improving compliance and interest. The heterogeneity in the studies’ design and in IVR treatments indicate the need of future investigations in the consideration of potentiality and low-cost of this technology.
Black hole uniqueness theorems in higher dimensional spacetimes

We review uniqueness theorems as well as other general results about higher dimensional black hole spacetimes. This includes in particular theorems about the topology of higher dimensional spacetimes, theorems about their symmetries (rigidity theorem), and the classification of supersymmetric black holes. We outline the basic ideas underlying the proofs of these statements, and we also indicate ways to generalize some of these results to more general contexts, such as more complicated theories.
Activated Eosinophils Predict Longer Progression-Free Survival under Immune Checkpoint Inhibition in Melanoma

Simple Summary Immune checkpoint inhibitors, which stimulate the patient’s own T-cells to attack tumor cells, have revolutionized the treatment of metastatic melanoma. However, not all melanoma patients respond to therapy possibly due to a lack of T-cells present in or entering tumor tissue. It is presumed that eosinophils could aid T-cell-mediated immune response against tumor cells. In order to describe the local association of eosinophils and T-cells within the tumor microenvironment we investigated specific markers for cell type and activation status using immunofluorescence. Additionally, blood measurements were performed to determine the effects of eosinophil count and their activation status on the efficacy of immune checkpoint inhibition. There was a strong correlation between activated eosinophils and T-cells in melanoma. Furthermore, patients with high blood levels of activated eosinophils showed a delayed tumor progression. In the future, eosinophils may serve as prognostic biomarkers as well as novel therapeutic targets in melanoma. Abstract Immune checkpoint inhibition (ICI) has yielded remarkable results in prolonging survival of metastatic melanoma patients but only a subset of individuals treated respond to therapy. Success of ICI treatment appears to depend on the number of tumor-infiltrating effector T-cells, which are known to be influenced by activated eosinophils. To verify the co-occurrence of activated eosinophils and T-cells in melanoma, immunofluorescence was performed in 285 primary or metastatic tumor tissue specimens from 118 patients. Moreover, eosinophil counts and activity markers such as eosinophil cationic protein (ECP) and eosinophil peroxidase (EPX) were measured in the serum before therapy start and before the 4th infusion of ICI in 45 metastatic unresected melanoma patients. We observed a positive correlation between increased tumor-infiltrating eosinophils and T-cells associated with delayed melanoma progression. High baseline levels of eosinophil count, serum ECP and EPX were linked to prolonged progression-free survival in metastatic melanoma. Our data provide first indications that activated eosinophils are related to the T-cell-inflamed tumor microenvironment and could be considered as potential future prognostic biomarkers in melanoma.
The Relevance Property For Prediction Intervals

Suppose that we have time series data which we want to use to find a prediction interval for some future value of the series. It is widely recognized by time series practitioners that, to be practically useful, a prediction interval should possess the property that it relates to what actually happened during the period that the data were collected as opposed to what might have happened during that period but did not actually happen. We call this the ‘relevance property’. Despite its obvious importance, this property has hitherto not been formulated in a mathematically rigorous way. We provide a mathematically rigorous formulation of this property for a broad class of conditionally heteroscedastic processes in the practical context that the parameters of the time series model must be estimated from the data. The importance in applications of this formulation is that it provides us with the most appropriate way of measuring the finite‐sample coverage performance of a time series prediction interval.
Quantitive Harmonic Analysis and Force Ripple Suppression of a Parallel Complementary Modular Linear Reluctance Machine

Considering the fluctuating price of permanent magnet, magnetless machine is a new trend in development. Variable reluctance linear machines (VRLM) with simple structure, good robustness, and high dynamic performance, is one of the potential candidates for linear direct-drive wave energy conversion (WEC) application replacing PM-excited linear machine. To ameliorate the fault-tolerance performance of VRLM, the researchers have come up with modular or segmented design, denoted as modular variable reluctance linear machines (MVRLM) However, suffering from large thrust ripple, the design of MVRLM still needs to be improved. To relieve the force ripple of MVRLM, a novel H-shaped modular variable-flux linear reluctance machine (HMVF-LRM) is proposed in this paper. The principle of force ripple suppression by using parallel-complementary structure is analyzed deeply. In this paper, the analytical calculation of induced voltage is developed quantitively with equivalent magnetic circuit method (EMC) and harmonics analysis. Then the design mechanism of HMVF-LRM is illustrated. Further, the performance and fault-tolerant capability of the proposed machine are simulated and evaluated by finite element analysis (FEA), and the prototype is tested to verify the effectiveness of the machine design.
An Abelian theorem with application to the conditional Gibbs principle

Let $X_1,. ,X_n$ be $n$ independent unbounded real random variables which have common, roughly speaking, light-tailed type distribution. Denote by $S_1^n$ their sum and by $\pi^{a_n}$ the tilted density of $X_1$, where $a_n \rightarrow\infty$ as $n\rightarrow \infty$. An Abelian type theorem is given, which is used to approximate the first three centered moments of the distribution $\pi^{a_n}$. Further, we provide the Edgeworth expansion of $n$-convolution of the normalized tilted density under the setting of a triangular array of row-wise independent summands, which is then applied to obtain one local limit theorem conditioned on extreme deviation event $(S_1^n/n=a_n)$ with $a_n\rightarrow \infty$.
Self-energy determination and electron–phonon coupling on Bi(110)

The electron–phonon coupling strength for two different surface states on Bi(110) close to the Fermi level is determined by measuring the energy- and temperature-dependent imaginary part of the self-energy and fitting it to different models of the phonon spectrum. For the hole pockets near the and points, the electron–phonon mass enhancement parameter λ is found to be 0.19(3) and 0.27(2), respectively.
Signal Useful Information Recovery by Overlapping Supports of Time-Frequency Representations

This paper presents a novel method for automatic extraction of useful information from time-frequency distributions of noisy signals. Signals' features are examined through the combined analysis of their time-frequency energy distributions and inverse complexity maps. The inverse complexity approach gives a new entropy-based insight into the signal structure. These two approaches result in mostly disjoint time-frequency supports, overlapping only in the proximity of the signal components. Locations of the signal components (i.e., useful information) are thus identified by low complexity and high amplitude occurring together in the time-frequency plane. Compared to existing methods using only the time-frequency energy distribution, useful information extraction by the proposed method exhibits a significantly reduced error rate.
Three types of nonlinear resonances

We analyse different types of nonlinear resonances in a weakly damped Duffing oscillator using bifurcation theory techniques. In addition to (i) odd subharmonic resonances found on the primary branch of symmetric periodic solutions with the forcing frequency and (ii) even subharmonic resonances due to symmetry-broken periodic solutions that bifurcate off the primary branch and also oscillate at the forcing frequency, we uncover (iii) novel resonance type due to isolas of periodic solutions that are not connected to the primary branch. These occur between odd and even resonances, oscillate at a fraction of the forcing frequency, and give rise to a complicated resonance `curve' with disconnected elements and high degree of multistability. We use bifurcation continuation to compute resonance tongues in the plane of the forcing frequency vs. the forcing amplitude for different but fixed values of the damping rate. In this way, we demonstrate that identified here isolated resonances explain the intriguing structure of "patchy tongues" observed for week damping and link it to a seemingly unrelated phenomenon of "bifurcation superstructure" described for moderate damping.
Advanced biosensing using simultaneous excitation of short and long range surface plasmons

We present a novel optical biosensor which exploits simultaneous excitation of two different surface plasmons in a single sensing spot. It allows compensation of cross-sensitivity to background refractive index change, as a typical surface plasmon resonance sensor is sensitive to both the absorption of a thin film on the surface (sensing event) and the refractive index change of the background (an interfering effect). The structure parameters are optimized to yield low noise and minimum cross-sensitivity. In the performed model experiment, IgE biomolecules at a concentration of 250 ng ml−1 are detected during a change in the background refractive index of 10−3.
Doob--Martin boundary of R\'emy's tree growth chain

R\'emy's algorithm is a Markov chain that iteratively generates a sequence of random trees in such a way that the $n^{\mathrm{th}}$ tree is uniformly distributed over the set of rooted, planar, binary trees with $2n+1$ vertices. We obtain a concrete characterization of the Doob--Martin boundary of this transient Markov chain and thereby delineate all the ways in which, loosely speaking, this process can be conditioned to "go to infinity" at large times. A (deterministic) sequence of finite rooted, planar, binary trees converges to a point in the boundary if for each $m$ the random rooted, planar, binary tree spanned by $m+1$ leaves chosen uniformly at random from the $n^{\mathrm{th}}$ tree in the sequence converges in distribution as $n$ tends to infinity -- a notion of convergence that is analogous to one that appears in the recently developed theory of graph limits. We show that a point in the Doob--Martin boundary may be identified with the following ensemble of objects: a complete separable $\mathbb{R}$-tree that is rooted and binary in a suitable sense, a diffuse probability measure on the $\mathbb{R}$-tree that allows us to make sense of sampling points from it, and a kernel on the $\mathbb{R}$-tree that describes the probability that the first of a given pair of points is below and to the left of their most recent common ancestor while the second is below and to the right. The Doob--Martin boundary corresponds bijectively to the set of extreme points of the closed convex set of normalized nonnegative harmonic functions, in other words, the minimal and full Doob--Martin boundaries coincide. These results are in the spirit of the identification of graphons as limit objects in the theory of graph limits.
Optimal control of storage incorporating market impact and with energy applications

Large scale electricity storage is set to play an increasingly important role in the management of future energy networks. A major aspect of the economics of such projects is captured in arbitrage, i.e. buying electricity when it is cheap and selling it when it is expensive. We consider a mathematical model which may account for nonlinear|and possibly stochastically evolving|cost functions, market impact, input and output rate constraints and both time-dependent and time-independent ineciencies or losses in the storage process. Our main concern is to develop the associated strong Lagrangian theory. The Lagrange multipliers associated with the capacity constraints in particular have important economic interpretations with regard to the dimensioning of storage|both with respect to its capacity and its rate constraints| and prove key to the ecient control of a store. We also develop an algorithm which determines, sequentially in time, both these Lagrange multipliers and the optimal control. This algorithm further identies, for each point in time, a time horizon beyond which it is not necessary to look in order to identify the optimal control at that point; this horizon is furthermore the shortest such. The algorithm is thus particularly suitable for the management of storage over extended periods of time. We give examples related to the management of real-world systems. Finally we consider a pragmatic approach to the real-time management of storage in a stochastic cost environment, which is computationally feasible, optimal under certain ideal conditions, and which may in general be expected to perform close to optimally. Our results are formulated in a general setting which permits their application to other energy management problems, and to other commodity storage problems.
Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA–SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority.