Transition Field Research Articles

Large magnetocaloric effect in the frustrated antiferromagnet EuIr[formula omitted]P[formula omitted

We present a theoretical analysis of the magnetocaloric effect in the frustrated antiferromagnet EuIr2P2. Monte Carlo simulations indicate a large magnetic entropy change ΔSMmax∼5 J kg−1 K−1 at T=2.45 K for a moderate change in the external magnetic field ΔB=1.2 Tesla. The highest value of ΔSM seems to be associated with the presence of a multicritical point in the magnetic field versus temperature phase diagram (at Bm∼1.2 Tesla and Tm∼2.2 K) but it persists with similar large values for a broad range of temperatures T≲Tm. Single crystal EuIr2P2 magnetization vs magnetic field experiments allowed us to obtain the magnetocaloric effect for T>2 K. The results are consistent with the theoretical analysis and show a large magnetocaloric effect for T∼2.5 K. Finally, we present a phenomenological Landau functional analysis that provides a simplified description of the phase diagram and of the magnetocaloric effect. Although the magnetic frustration in this system leads to a rich phase diagram, we found no clear indication that it plays a significant role enhancing the magneticaloric effect, other than by reducing the transition temperatures and fields.

Onset of Nonlinear Electroosmotic Flow under an AC Electric Field.

Nonlinearity of electroosmotic flows (EOFs) is ubiquitous and plays a crucial role in ion transport, specimen mixing, electrochemistry reaction, and electric energy storage and utilization. When and how the transition from a linear regime to a nonlinear one occurs is essential for understanding, prohibiting, or utilizing nonlinear EOF. However, due to the lack of reliable experimental instruments with high spatial and temporal resolutions, the investigation of the onset of nonlinear EOF still remains in theory. Herein, we experimentally studied the velocity fluctuations of EOFs driven by an alternating current (AC) electric field via ultrasensitive fluorescent blinking tricks. The linear and nonlinear AC EOFs are successfully identified from both the time trace and energy spectra of velocity fluctuations. The transitional electric field (EA,C) is determined by both the convection velocity (U) and AC frequency (ff) as EA,C ∼ ff0.48-0.027U. We hope the current investigation could be essential in the development of both theory and applications of nonlinear EOFs.

Climate Change and SMEs: Green Transition, European Institutional Framework & Necessary Adaptations to Increase the Competitiveness of SMEs in the Region of Western Greece

Over the last decade, the climate crisis has been recognized as a great threat to humanity on a global and institutional scale. In this context, every country, with the European Union (EU) paving the way, is planning the ways and means of transitioning to a more sustainable future. Inevitably, the transition will affect citizens’ daily life and countries’ economic activity. In this framework, small and medium enterprises (SMEs) have a central role; especially in countries such as Greece, where most of the enterprises are SMEs. Thus, both the EU and the UN are promoting policies and incentives for SMEs so that they will be actively involved in promoting sustainable development goals. With the Multiannual Financial Framework 2021-2027, the EU decided to use € 401 billion to assist innovations in the fields of green transition and renewables, including SMEs to these actions. At the same time, the National Plan for Recovery and Sustainability “Greece 2.0” is moving in the same direction. Thus, the present study is an attempt to record the actions, reforms, and financial tools that “Greece 2.0” offers to SMEs, focusing on the region of Western Greece. At the same time, we will try to capture the impacts of natural gas introduction in the region. The study aims to outline the future of SMEs in Western Greece based on the current situation and the possibilities that the green transition will bring.

Crystal vs. melt compositional effects on the partitioning of the first-row transition and high field strength elements between clinopyroxene and silicic, alkaline, aluminous melts

Abstract The first-row transition element (FRTE) and high field strength element (HFSE) systematics are powerful tools for tracking the source and evolution of mantle-derived magmas. Clinopyroxene is generally considered a key fractionating mineral controlling the partitioning of trace elements between melt and residual solid during mantle melting. Although partitioning of FRTE and HFSE between clinopyroxene and basaltic melts has been well-studied, experimental constraints on their partitioning behavior in the presence of siliceous, aluminous, and alkali-rich melts are still lacking. Here we present clinopyroxene-silicic melt (67–69 wt% SiO2) partitioning experiments at 1 bar pressure and 1070–1100 °C for Co, Mn, Ni, Cu, Zn, Fe, Sc, Cr, V, Ti, Zr, Hf, Nb, and Ta. Run products consist of diopsidic clinopyroxene coexisting with various melt compositions with non-bridging oxygen to tetrahedral cation ratio (NBO/T) ranging from 0.10 to 0.22. Using our new partition coefficients (Ds) and combined with literature data, we assess some of the effects of crystal chemistry and the melt composition on the partitioning of FRTE and HFSE in this simple system. We show that partitioning of FRTE varies from mildly incompatible (e.g., D = ~0.1−1 for V, Cu, and Zn) to highly compatible (e.g., D > 10 for Cr and Ni), with the highest compatibilities observed for Ni (DNi = 13−34). The partitioning of HFSE varies from highly incompatible (D = 0.01−0.08) for Nb and Ta to mildly incompatible (D = 0.18−0.82) for Zr, Hf, and Ti. Our measured clinopyroxene-melt Ds are consistent with the theoretical predictions of the lattice strain model. Ds data for most tri-, tetra-, and pentavalent elements tend to increase with increasing tetrahedrally coordinated Al content, in agreement with those anticipated from crystal-chemical considerations. In contrast to ivAl concentrations, the clinopyroxene Na concentration has very little effect on trace element partitioning due to its low concentrations in clinopyroxene at relatively low-pressure conditions. These data further support a significant control of melt composition/structure on partitioning for highly polymerized melts. In general, measured Ds roughly increase to different extents with increasing polymerization of the melt (i.e., lower NBO/T or higher ASI). For our equilibrium melt compositions, Ds for several FRTE, such as Co and Ni, correlate well with the melt molar Mg2+/(M+ + M2+), whereas Ds for HFSE vary as a function of the melt alkali concentration. These well-defined trends support the role of melt NBO species (e.g., Mg2+) or complexing ligands (e.g., Na+ and K+) in controlling the partitioning of these elements. Overall, our new Ds data demonstrate that even very small changes in melt major-element compositions can greatly affect element partitioning in strongly polymerized silicic systems. These findings have important implications relevant to petrogenetic studies of the interaction between silicic melt and peridotite that occurs at shallow mantle conditions in various tectonic settings.

Secondary Students Receiving Special Education and English Learner Services: Identity Informed Transitions

ABSTRACT Secondary students’ identity development is multidimensional, informed by distinct sociohistorical, cultural, structural, and individual forces and factors. For students who receive both special education and English learner services, identity development may also be connected to being categorized as needing both types of support services. Understanding how students receiving both services perceive themselves, and how they leverage identity while planning their futures could inform the field of transition, yet this is an infrequent subject of inquiry in special education transition research. We interviewed 26 students, all of whom received both services in a high school in a large school district in the northeastern United States. The development of language and learner dimensions of identity emerged from the data as a key factor informing participants’ perceptions and experiences as they prepared for the transition into adulthood. Implications and recommendations for researchers and practitioners supporting students’ language and learner dimensions of identity during postschool transition are shared.

Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network

Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy.

A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model

As a crucial component in the transmission system, a planetary gearbox has a relatively complicated structure and usually operates under complex working conditions and a severe noisy environment, making it challenging to achieve precise and efficient fault diagnosis. Along with the development of artificial intelligence techniques, end-to-end fault diagnosis frameworks have been widely studied, among which convolutional and recurrent neural networks are the mainstream backbone networks. However, these networks have shortcomings in computational efficiency and feature extraction, which lead to the application of a self-attention mechanism. This paper presents a fault diagnosis method based on frequency domain Gramian angular field (GAF) and Markov transition field (MTF) features for planetary gearboxes by combining the characteristics of vibration signal fault diagnosis and transformer network structure. The experiments show that the frequency domain GAF-MTF features can effectively reduce the influence of time shifting between samples and improve diagnostic accuracy. Furthermore, comparisons with other mainstream models indicate that the proposed method can obtain competitive results and achieve more accurate and robust performance under noisy conditions.

Dipole-exchange spin waves in unsaturated ferromagnetic nanorings with interfacial Dzyaloshinski–Moriya interactions

A theoretical analysis is made for the quantized spin waves in single-layered ferromagnetic nanorings with the added effect of interfacial Dzyaloshinski–Moriya interactions (DMI). A microscopic, or Hamiltonian-based, formalism is employed that includes competing terms for the symmetric (bilinear) exchange interactions, the antisymmetric DMI, the magnetic dipole–dipole interactions, and applied magnetic field. It is found that, in our model, the transition field value between vortex and onion states is shifted by the inclusion of DMI effects. Significantly, the spin-wave frequencies are also modified with the effects being largest in the onion state close to the transition field. We present combined analytical and numerical results obtained for the static and dynamical magnetization, including the frequencies and amplitudes (with relative phase) of the spin waves when interfacial DMI is present.

A Review of the Application and Development of Flywheel Energy Storage

High power density, high efficiency and low loss are the characteristics of flywheel energy storage, which has broad application prospects in the field of rail transit. This paper introduces the basic structure and principle of flywheel energy storage, analyzes the energy storage density of the rotor in both metal and composite materials, and points out that composite materials such as T1000 fiber/resin have higher strength and lower density. The highest energy density. Finally, the development status of flywheel energy storage in rail transit, civil vehicles and other fields is summarized, and the future development prospects of power grid frequency regulation and uninterruptible power supply are prospected. This paper can provide a reference for in-depth research on flywheel energy storage materials.

Field-Induced Transition in (Nd,Dy)2Fe14B in Ultrahigh Magnetic Fields

We demonstrate the peculiarities of the magnetization process in the ferrimagnetic intermetallic compound (Nd0.5Dy0.5)2Fe14B, which has been studied theoretically and experimentally using ultrahigh magnetic fields. We observe phase transition induced by external ultrahigh magnetic fields (up to 170 T) and also describe the magnetization process analytically in terms of critical transition fields. In this work, the first and second critical fields of the field-induced magnetic transitions, Hc1 and Hc2, were estimated, and the results were verified against experimental data for Hc1. Critical field Hc2 predicting the place of transition to the forced-ferromagnetic state was estimated for the first time for (Nd0.5Dy0.5)2Fe14B compound. A comparison of the magnetization behavior for (Nd0.5Dy0.5)2Fe14B with the basic systems Nd2Fe14B and Dy2Fe14B is also performed. We demonstrate that, in the Dy2Fe14B compound, the field-induced transition type is changed from the first to the second order due to the replacement of the Nd atom by Dy one.

Correlating hidden local structural distortion with antiferroelectric-ferroelectric transition in PbZrO3-based perovskites

Externally stimulated antiferroelectric-ferroelectric (AFE-FE) transition makes AFE materials attractive for many applications ranging from energy storage to sensing. This transition-mediated macroscopic electrical characteristics have been extensively investigated, yet the underlying structural evolution remains poorly understood. Herein, we study the local- and long-range structural evolutions in the Nb-doped Pb(Zr,Sn,Ti)O3 perovskites by in-situ X-ray total scattering and diffraction, which show three different types of AFE-FE transitions. The evolution of long-range structure from diffraction and local structure identified by atomic pair distribution function (PDF) are consistent well with the phase transition behavior indicated by macroscopic polarization/strain measurements, while discrepancies exist. Interestingly, frequently employed long-range structure picture that the uniform AFE pseudo-tetragonal to FE rhombohedral transformation is insufficient to understand the distinct composition-dependent AFE-FE transition behaviours. Instead, the reversibility of this transition correlates with the chemically sensitive local atomic displacements revealed by newly developed in-situ PDF. The critical electric field of transition is associated with the local structural differences between the AFE and FE state. The macroscopic electrostrains can be well estimated by the phase transition associated lattice strain and domain alignment from in-situ synchrotron XRD data, while the local strains calculated from in-situ PDFs are found to be much lower. These results provide a fresh insight into the AFE-FE transition, and advance the understanding of structure-property relationships in AFE materials.

Influence of TiO2 Space layer on plasmon enhanced Luminescence of Ag/TiO2/NaGdF4:Er3+/Yb3+/Al3+ composite Films

ABSTRACT Lanthanide-doped upconversion nanoparticles (UCNPs)have potential applications in optical and optoelectronic devices due to their novel optical properties. The surface plasmon is an effective method in improving the upconversion luminescence (UCL) properties, and it is essential to tunee the distance between the metal NPs and UCNPs for improving UCL. In this work, the Ag/TiO2/NaGdF4:Er3+/Yb3+/Al3+ composite films were fabricated, and the effect of the TiO2 layer thickness on spectral characteristics in the composite films was investigated. It is shown that the thickness of TiO2 film has a significant effect on the Ag NPs plasma resonance and fluorescence intensity. The maximum upconversion emission enhancement factors of the red and green emission were obtained for the sample with 15-nm-thickness TiO2 under the 980 nm excitation. That is mainly originated from the enhancement of the resonate energy transfer (Yb3+→Er3+) rate under the competition between non-radiative transition and local field effects.

Evaluation of Program Costs for Implementing a Self-Determination Intervention Using the Ingredients Method

In this study, we estimated the cost of implementing the Self-Determined Learning Model of Instruction (SDLMI), an evidence-based practice in secondary transition, at scale in 17 U.S. school districts to enhance self-determination and transition goal attainment outcomes among 340 transition-age adolescents with intellectual disability. An advantage of the SDLMI is that it can be overlaid upon any curriculum and, therefore, can minimize opportunity costs for self-determination instruction. Using the ingredients method, the current study: (a) identified and described the essential ingredients of SDLMI implementation in typical high school settings, (b) assigned and described the cost of needed ingredients, and (c) calculated a generalizable estimate of the average cost of using the SDLMI with 340 students for one school year across 17 school districts to be $40,221.26 (or $118.30 per student). We discuss how decision-makers considering using the SDLMI in their context can minimize costs while maximizing effects on transition goal attainment outcomes. Several implications of these findings for the field of transition are also described.

Multicritical phase diagram of PrMn1−xSb2 with two interacting magnetic elements

We carried out systematic magnetotransport measurements to study the magnetic phase diagram of ${\mathrm{PrMn}}_{1\ensuremath{-}x}{\mathrm{Sb}}_{2}$, where two antiferromagnetic orders associated with the moments of Pr and Mn coexist. When the magnetic field ($H$) was applied parallel to the crystallographic $c$ axis, we found a tricritical point where a first-to-second order changeover takes place as in ordinary metamagnets. On the other hand, the metamagnetic transition field decreased unexpectedly with decreasing the temperature at low temperatures for $H\ensuremath{\parallel}[110]$. Further, the transition line associated with the metamagnetic transition terminated at low temperature, where a multicritical point was formed. Starting from a three-dimensional phase diagram predicted from a simple model and modifying it to fit our experimental results, we argue that the magnetic interaction between the two magnetic elements is responsible for forming the new critical point and influences the shape of the phase boundaries.

Magnetic field induced Fréedericksz transition in a system of ultrafine spherical [formula omitted] nanoparticles doped 7CB media

In this work, experimental studies of electrically and magnetically induced re-orientational transitions in suspensions of cobalt ferrite (CoFe2O4) nanoparticles in 7CB (4-n-heptyl-4'-cyanobiphenyl) liquid crystal (LC) are presented. Two concentrations are studied (i.e 0.033wt% or sample A and 0.044wt% or sample B). The results obtained are compared with the pristine liquid crystal. Low threshold voltages and magnetic fields for the re-orientational transition are observed in the doped cases. The nanoparticles bonded to the LC in close proximity are orienting in a parallel manner with the bulk liquid crystal media and causing these changes. Effect of the polarity and position of the magnet on the re-orientational mechanism in comparison to the pristine sample is also studied.

Unsupervised expectation-maximization algorithm initialization for mixture models: A complex network-driven approach for modeling financial time series

An efficient initialization of the Expectation-Maximization algorithm to estimate mixture models via maximum likelihood is proposed. A fully unsupervised network-based initialization technique is provided by mapping time series to complex networks using as adjacency matrix the Markov Transition Field associated to the time series. In this way, the optimal number of mixture model components and the vector of initial parameters can be directly obtained. An experiment conducted on financial times series with very different characteristics shows that our approach produces significantly better results if compared to conventional methods of initialization, such as K-means and Random, thus demonstrating the effectiveness of the proposed method.

Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network

Battery-related faults have become the most intractable problem hindering the further prosperity of fields like electric vehicle and grid energy storage. This paper is devoted to constructing a novel diagnostic framework for the faults in series battery packs, resorting to signal imaging and convolutional neural network (CNN) techniques. First, the voltage synchronicity between adjacent cells in a pack is quantified using the recursive correlation coefficient which can percept system anomalies sensitively. Then, reliant on the Gramian Angular Field (GAF) and Markov Transition Field (MTF) transformations, the correlation coefficient series is converted into pseudo images, the textures of which are full of informative details regarding system state. Finally, CNN models are employed to analyze the images for fault symptoms, thereby detecting fault occurrence, inferring fault type and evaluating fault grade. To obtain realistic dataset, different types and severities of faults are physically triggered on a li-ion battery pack. Experimental verification results indicate that the proposed framework can give accurate and reliable judgements on fault specifics, with the accuracy rates of fault type isolating and severity grading as 99.63% and 63.6% on GAF images, and as 99.75% and 58.7% on MTF images, respectively.

Transitional Justice and Corporate Accountability: Introducing New Players and New Theoretical Challenges

Corporate accountability for complicity in past and ongoing gross violations of human rights and economic crimes is a rapidly growing area of interest in the transitional justice field. Addressing such violations and their role in fueling conflict and repression is now recognized as a key component of societies’ efforts to come to terms with the past, and, perhaps even more importantly, creating the conditions for non-recurrence of violent conflict and human rights abuses. Truth commissions have increasingly included such economic violations in their mandates, and corporate actors have been the focus of a number of high-profile court cases. Such civil claims and criminal cases appear to offer limited redress thus far, but the normative landscape of transitional justice is shifting, both at national and international levels. Scholarly interest has spiked, as illustrated in the review essay in this volume by Evelyne Owiye Asaala, which highlights three new volumes that unpack recent developments. We also see increased civil society mobilization to demand that corporate actors are held to account and contribute to reparations. In some cases, the amounts that corporate actors can or will contribute to compensate survivors dwarf the financial contribution to reparations funds by governments, demonstrating not just their potential contribution to transitional justice, but the scope of their liability for past abuses.1

Classification of image encoded SSVEP-based EEG signals using Convolutional Neural Networks

Brain–Computer Interfaces (BCI) systems based on electroencephalography (EEG) signals are experiencing a rapid development, counting with a number of methods, mainly from signal processing and machine learning areas. Although important results have been achieved, a robust performance is still a very challenging task, mainly considering high intra- and inter-subject variability in EEG data and long acquisition time intervals. Recently, Deep Learning methods, such as the Convolutional Neural Networks (CNNs), are being used in BCI systems in search of a performance improvement. However, the straightforward use of EEG data, without any processing step, may limit the full potential of 2D-kernels in CNNs. In light of this, in this work, we consider for classification with 2D-kernel-based CNNs the problem of encoding EEG data to images as a pre-processing stage, which includes the Gramian Angular Difference and Summation Fields, Markov Transition Fields and Recurrence Plots. Additionally, a comparative analysis using a selection of CNNs is performed. Results show a favorable performance for the proposed method, pointing towards a robust BCI system using cross-subject data, with short acquisition time interval.

Automatic Vehicle Location and Computer-Aided Dispatch Systems: Design and Application Considerations

Automatic Vehicle Location (AVL) systems can track transit vehicles in real time. Computer-Aided Dispatch (CAD) software is used to monitor transit operations and assist management of transit operations. Together with AVL systems, CAD software can be used to replace a disabled vehicle by dispatching another vehicle, or meet fluctuating travel demand by adjusting transit headways, schedules, and routes. AVL and CAD technologies can vitalize transit by directly improving on-time performance, increasing transit efficiency through providing dispatchers with location, direction and status information, and reducing operating costs through reducing dependence on transit field supervisory personnel. Direct benefits to travelers can include higher reliability of travel times and reduced stress in dealing with transit unreliability. This study explores the development, availability, and impacts of AVL/CAD technologies as reported by AVL vendors and transit implementers. The study defines the key features, functions, and performance characteristics of AVL/CAD technologies that can influence the level of benefits realized. The AVL/CAD implementation context is explored by examining where, when, and for what users these systems are being implemented. The results of two surveys are reported. To explore the availability of AVU CAD systems, technology suppliers were surveyed. Suppliers identified the features, functions, and performance of available AVUCAD technologies. To determine the extent of AVL/CAD deployment, transit operators were surveyed regarding their experiences with AVL/CAD technologies and the subsequent impacts on travelers and transit agency performance. This research provides a systematic method for evaluation of AVL/CAD systems and reports the perceptions of AVL/CAD vendors and transit implementers regarding available products and their impacts. The results suggest a need for better tools to characterize and quantify the impacts and benefits of AVU CAD systems.