Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the influence of the CSRR resonator parameters on the characteristics of a Ku-band microstrip SIW antenna

The paper presents the results of simulation of a Kuband microstrip substrate integrated waveguide antenna (SIW antenna) with a complementary split ring resonator (CSRR) using the computational electromagnetics code FEKO. Structural synthesis of the antenna design is used to study the influence of geometrical parameters of a resonator on the main antenna characteristics. The frequency dependences of the return loss, gain and voltage standing wave ratio in 9.5 - 13.5 GHz band are analyzed. The field distribution inside the antenna, the distribution of surface currents and the parameters of radiation patterns at resonant frequencies are also discussed. The research shows that the combination of substrate integrated waveguide technology and CSRR structure allows reducing the resonant frequency by 12.7% and obtaining the second resonant frequency, which is 8.5% higher in comparison with a SIW antenna without CSRR. It also leads to the improvement of the antenna parameters at the second resonant frequency, except for the operating band, which narrows by almost 1.5 times.
THE INTEGRAL/SPI VIEW OF A0535+26 DURING THE GIANT OUTBURST OF 2011 FEBRUARY

A0535+26 is a slowly rotating pulsar accreting from the wind of a massive Be star and exhibits two cyclotron absorption lines in its X-ray spectrum, at about 45 and 100 keV, respectively. Unlike similar sources, no significant variations of the energy of its cyclotron lines with flux have been observed to date. The bright outburst of 2011 February thus offers a unique occasion to probe this peculiar behavior at flux levels not yet observed with present-day instruments. Here we report on the spectral and timing analysis of the data from the spectrometer SPI on board INTEGRAL collected during the outburst. At the peak of the outburst the estimated luminosity is ∼4.9 × 1037 erg s−1. The fundamental cyclotron feature is detected at all flux levels, and its centroid energy is positively correlated with the flux of the source, confirming that A0535+26 is accreting at a sub-critical regime. The correlation seems to fall off at ∼1037 erg s−1, suggesting a transition from a Coulomb-stopping regime to a gas-mediated shock regime. From the timing analysis we found that the pulsar was spinning up during most of the outburst and that the spin-up rate correlates with the flux of the source, although the correlation is steeper than the one expected from the standard disk accretion theory. Finally, we show that the pulse profile of the source changes dramatically as the flux increases. At high luminosity the profile is highly asymmetric, implying an asymmetry in the geometry of the accretion flow.
Asymptotic inference in poverty indices: an empirical processes approach

ABSTRACT We derive an empirical poverty index containing most of those proposed in the literature. Then, we study its asymptotic behavior by using empirical processes theory. From the results obtained, we derive a Wald-type test for comparing a vector of theoretical poverty indices to a vector of reference indices. We study the test statistic under the null hypothesis of equality, fixed alternatives, and a sequence of local alternatives. A simulation experiment conducted shows that our test performs well for exponential and Pareto data.
Alternating Direction Implicit Orthogonal Spline Collocation on Non-Rectangular Regions

The alternating direction implicit (ADI) method is a highly efficient technique for solving multi-dimensional time dependent initial-boundary value problems on rectangles. When the ADI technique is coupled with orthogonal spline collocation (OSC) for discretization in space we not only obtain the global solution efficiently but the discretization error with respect to space variables can be of an arbitrarily high order. In [2], we used a Crank Nicolson ADI OSC method for solving general nonlinear parabolic problems with Robin’s boundary conditions on rectangular polygons and demonstrated numerically the accuracy in various norms. A natural question that arises is: Does this method have an extension to non-rectangular regions? In this paper, we present a simple idea of how the ADI OSC technique can be extended to some such regions. Our approach depends on the transfer of Dirichlet boundary conditions in the solution of a two-point boundary value problem (TPBVP). We illustrate our idea for the solution of the heat equation on the unit disc using piecewise Hermite cubics.
From Interaction to Intervention: How Mesenchymal Stem Cells Affect and Target Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) lacks estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expressions, making targeted therapies ineffective. Mesenchymal stem cells (MSCs) have emerged as a promising approach for TNBC treatment by modulating the tumor microenvironment (TME) and interacting with cancer cells. This review aims to comprehensively overview the role of MSCs in TNBC treatment, including their mechanisms of action and application strategies. We analyze the interactions between MSC and TNBC cells, including the impact of MSCs on TNBC cell proliferation, migration, invasion, metastasis, angiogenesis, and drug resistance, along with the signaling pathways and molecular mechanisms involved. We also explore the impact of MSCs on other components of the TME, such as immune and stromal cells, and the underlying mechanisms. The review discusses the application strategies of MSCs in TNBC treatment, including their use as cell or drug carriers and the advantages and limitations of different types and sources of MSCs in terms of safety and efficacy. Finally, we discuss the challenges and prospects of MSCs in TNBC treatment and propose potential solutions or improvement methods. Overall, this review provides valuable insights into the potential of MSCs as a novel therapeutic approach for TNBC treatment.
Point for Enrichment, Point for Welfare—Testing Use of a Laser Pointer with Arapaima gigas

Simple Summary Environmental enrichments are essential tools for providing appropriate welfare conditions for animals under human care by stimulating mental and physical activity. Enrichment programmes in zoos and aquariums have developed rapidly over the past two decades, especially for charismatic mammal and bird species. However, when it comes to lesser-known species such as the arapaima (Arapaima gigas), there is still room for improvement. The arapaima is one of the largest freshwater fish species. It is fished and farmed for its meat but also kept on display in Asian aquariums. Despite its agricultural and ecological importance, there is limited information on the behaviour of the species. Its conservation status on the IUCN Red List continues to be “Data Deficient”. In this study, we investigate the use of a green laser pointer as a potential visual enrichment for arapaimas. Without displaying any aggression or territorial behaviour, the fish were more active and utilized more of their habitat in the presence of the novel stimulus. We believe that our findings support the use of the laser pointer as environmental enrichment for arapaimas and provide a basis for further behavioural and welfare research with the species. Abstract The arapaima (Arapaima gigas) is one of the largest freshwater fish species, known to exceed 3 m in total length. It is listed as Data Deficient by the IUCN. A. gigas is native to the Amazon River basin where they are an important food source. Arapaimas are also farmed for meat and for live specimens in various South American and Asian countries. Despite decades of keeping the species in public aquariums, little is known of its behaviour and cognitive abilities. This pilot study provides baseline data on using a green laser pointer as environmental enrichment for this species under human care. The data collection included 18 observations before the use of the laser pointer (baseline) and 18 observations during the use of the laser pointer (test). Ten behaviours were monitored, investigating physical contact, activity pattern and habitat use by the fish. During the test, the fish significantly increased their presence in the tank, their level of activity and their use of the habitat. This pilot study provides valuable baseline data for further investigations demonstrating the value of a laser pointer as environmental enrichment for A. gigas under human care.
High-Resolution Alpha Spectrometry Using 4H-SiC Detectors: A Review of the State-of-the-Art

Silicon Carbide (SiC) semiconductor radiation detectors were first demonstrated in 1957, however, meaningful progress was delayed until the mid-1990s when high-quality epitaxially-grown SiC became available. Rapid progress ensued, and the detection of alpha particles, gamma and X-rays, protons, fission fragments, heavy ions, beta particles, minimum-ionizing particles, and neutrons was all demonstrated. In the case of alpha-particle detection and spectrometry, the obtainable energy resolution is dependent on the design characteristics of the SiC detector as well as on the material and electronic properties of the SiC material. In this article, we review the history of SiC alpha-particle detector development as well as the factors affecting detector performance and the prospects for further improvements. Detector design factors, including active-volume and entrance-window effects, are discussed in detail. Materials and electronic factors that influence the quality of alpha-particle detection are also discussed. These factors include SiC lattice defects, charge-carrier mobility, charge trapping, and the energy required to produce an electron-hole pair. Since the first demonstration of alpha-particle spectrometry, SiC detectors have evolved to the point that an energy resolution of 0.29% (15.9 keV full width at half maximum (FWHM) for 5485.7-keV 241Am alpha particles) has been reported. This resolution is comparable to the best obtainable with silicon alpha-particle detectors. The prospects for further improvements in alpha spectrometry with SiC detectors are also discussed.
Impact of Maternal Weight Gain on the Newborn Metabolome

Pre-pregnancy obesity and excessive gestational weight gain (GWG) appear to affect birth weight and the offspring’s risk of obesity and disease later in life. However, the identification of the mediators of this relationship, could be of clinical interest, taking into account the presence of other confounding factors, such as genetics and other shared influences. The aim of this study was to evaluate the metabolomic profiles of infants at birth (cord blood) and 6 and 12 months after birth to identify offspring metabolites associated with maternal GWG. Nuclear Magnetic Resonance (NMR) metabolic profiles were measured in 154 plasma samples from newborns (82 cord blood samples) and in 46 and 26 of these samples at 6 months and 12 months of age, respectively. The levels of relative abundance of 73 metabolomic parameters were determined in all the samples. We performed univariate and machine-learning analysis of the association between the metabolic levels and maternal weight gain adjusted for mother‘s age, Body Mass Index (BMI), diabetes, diet adherence and infant sex. Overall, our results showed differences, both at the univariate level and in the machine-learning models, between the offspring, according to the tertiles of maternal weight gain. Some of these differences were resolved at 6 and 12 months of age, whereas some others remained. Lactate and leucine were the metabolites with the strongest and longest association with maternal weight gain during pregnancy. Leucine, as well as other significant metabolites, have been associated in the past with metabolic wellness in both general and obese populations. Our results suggest that the metabolic changes associated to excessive GWG are present in children from early life.
Deep learning and support vector machines for transcription start site identification

Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.
Caring for a Relative With Dementia in Long-Term Care During the COVID-19 Pandemic: A Prospective Longitudinal Study

Abstract Background and Objectives The coronavirus disease 2019 (COVID-19) pandemic introduced unprecedented threats and disruptions for caregivers of people with dementia living in residential long-term care (LTC) facilities. Qualitative and cross-sectional studies have reported substantial negative effects of the pandemic on dementia caregivers’ well-being, but little to no prospective research has examined the impact of COVID-19 on caregiver well-being using pre-pandemic assessments. The present study used longitudinal data from an ongoing randomized controlled trial of a psychosocial intervention to support family caregivers whose relatives had entered LTC. Research Design and Methods Data collection began in 2016 and continued through 2021. Caregivers (N = 132) completed up to 7 assessments measuring their depressive symptoms, self-efficacy, and burden. Results Latent growth curve models testing preregistered hypotheses revealed no significant effects of the pandemic on caregiver outcomes on average, though caregivers varied in terms of individual intercepts and slopes. Furthermore, factors such as caregiver–care recipient relationship closeness, care recipient’s COVID-19 infection status, and caregivers’ ratings of LTC facilities’ COVID-19 policies did not significantly moderate trajectories of well-being. Discussion and Implications Findings highlight the heterogeneity of caregivers’ experiences during the pandemic, and suggest caution when interpreting cross-sectional findings on the impacts of the COVID-19 pandemic on caregiver well-being and distress.
TARGET MOTION ANALYSIS WITH BEARINGS AND FREQUENCIES MEASUREMENTS

In TMA, when bearing and frequency measurements are concurrently processed, no more ownship manoeuver is necessary to make the target trajectory observable (as in conventionnal Bearing Only Tracking). Firstly, using a new linearization method for frequency measurements, we rigorously establish this result about observability. Secondly, we derive two fast Batch Maximum Likelihood Estimation algorithms whose statistical efficiency is proved (by computer simulations). Experimental results demonstrate that, if a few at sea necessary conditions are fulfilled and several frequencies are concurrently processed, DBT yields proper estimates of target trajectory
A Brief Survey of Recent Advances and Methodologies for the Security Control of Complex Cyber–Physical Networks

Complex cyber–physical networks combine the prominent features of complex networks and cyber–physical systems (CPSs), and the interconnections between the cyber layer and physical layer usually pose significant impacts on its normal operation. Many vital infrastructures, such as electrical power grids, can be effectively modeled as complex cyber–physical networks. Given the growing importance of complex cyber–physical networks, the issue of their cybersecurity has become a significant concern in both industry and academic fields. This survey is focused on some recent developments and methodologies for secure control of complex cyber–physical networks. Besides the single type of cyberattack, hybrid cyberattacks are also surveyed. The examination encompasses both cyber-only hybrid attacks and coordinated cyber–physical attacks that leverage the strengths of both physical and cyber attacks. Then, special focus will be paid to proactive secure control. Reviewing existing defense strategies from topology and control perspectives aims to proactively enhance security. The topological design allows the defender to resist potential attacks in advance, while the reconstruction process can aid in reasonable and practical recovery from unavoidable attacks. In addition, the defense can adopt active switching-based control and moving target defense strategies to reduce stealthiness, increase the cost of attacks, and limit the attack impacts. Finally, conclusions are drawn and some potential research topics are suggested.
Merger Guidelines for the Labor Market

While the labor market implications of mergers have been historically ignored as “out of market” effects, recent actions by the Department of Justice (DOJ) place buyer market power (i.e., monopsony) at the forefront of antitrust policy. We develop a theory of multi-plant ownership and monopsony to help guide this new policy focus. We estimate the model using U.S. Census data and demonstrate the model’s ability to replicate empirically documented paths of employment and wages following mergers. We then simulate a representative set of U.S. mergers in order to evaluate merger review thresholds. Our main exercise applies the DOJ and FTC’s product market concentration thresholds to local labor markets. Assuming mergers generate efficiency gains of 5 percent, our simulations suggest that workers are harmed, on average, under the enforcement of the more lenient 2010 merger guidelines and unharmed, on average, under enforcement of the more stringent 1982 merger guidelines. We also provide a framework for further research evaluating alternative concentration thresholds based on assumptions about the efficiency effects of mergers and the resource constraints of regulators. Finally, we provide guidance for using the Gross Downward Wage Pressure method for evaluating the impact of mergers on labor markets. David W. Berger Department of Economics Duke University 419 Chapel Drive Social Science Building 231 Durham, NC 27708 and NBER david.berger@duke.edu Thomas Hasenzagl University of Minnesota Department of Economics 4-101 Hanson Hall 1925 Fourth Street South Minneapolis, MN 55455 thomas.hasenzagl@gmail.com Kyle F. Herkenhoff University of Minnesota Department of Economics 4-101 Hanson Hall 1925 Fourth Street South Minneapolis, MN 55455 and IZA and also NBER kfh@umn.edu Simon Mongey Kenneth C. Griffin Department of Economics University of Chicago 1126 E. 59th Street Chicago, IL 60637 and NBER mongey@uchicago.edu Eric A. Posner University of Chicago Law School 1111 E. 60th Street Chicago, IL 60637 eposner@uchicago.edu
A Novel Immune Genetic Algorithm for Distribution Network Reconfiguration

On the basis of analyzing the insufficiency of genetic algorithm in the solution of distribution network reconfiguration, a mathematical model for distribution network reconfiguration in which the network loss minimization is taken as objective function is built and a novel immune genetic algorithm is proposed. The key of the proposed algorithm lies in the construction of vaccine pool and the design of immune operator. The vaccine pool can be created and updated automatically, and the immune operator consists of vaccination and immunoassay. In addition, the coding method based on the basic loops, hyper-mutation and tournament selection operator with big selection pressure are adopted. Simulation results of IEEE 33-bus system and IEEE 69-bus system show that the proposed algorithm conforms to the feature of distribution network reconfiguration and can effectively suppress both degeneration and fluctuation during the evolution; it possesses fast convergence speed while the quantity of the solution is ensured. Comparing with traditional genetic algorithm and allied algorithms in related literatures, the proposed algorithm is superior in both efficiency and performance.
Precise qubit control beyond the rotating wave approximation

Fast and accurate quantum operations of a single spin in room-temperature solids are required in many modern scientific areas, for instance in quantum information, quantum metrology, and magnetometry. However, the accuracy is limited if the Rabi frequency of the control is comparable with the transition frequency of the qubit due to the breakdown of the rotating wave approximation (RWA). We report here an experimental implementation of a control method based on quantum optimal control theory which does not suffer from such restriction. We demonstrate the most commonly used single qubit rotations, i.e. &pgr; / 2 ? > - and π-pulses, beyond the RWA regime with high fidelity F &pgr; / 2 exp = 0.95 ± 0.01 ? > and F &pgr; exp = 0.99 ± 0.016 ? > , respectively. They are in excellent agreement with the theoretical predictions, F &pgr; / 2 theory = 0.9545 ? > and F &pgr; theory = 0.9986 ? > . Furthermore, we perform two basic magnetic resonance experiments both in the rotating and the laboratory frames, where we are able to deliberately ‘switch’ between the frames, to confirm the robustness of our control method. Our method is general, hence it may immediately find its wide applications in magnetic resonance, quantum computing, quantum optics, and broadband magnetometry.