Miracle-dz/UniFAR-FTUs huggingface dataset:
Perturbatively charged holographic disorder

Within the framework of holography applied to condensed matter physics, we study a model of perturbatively charged disorder in D=4 dimensions. Starting from initially uncharged AdS_4, a randomly fluctuating boundary chemical potential is introduced by turning on a bulk gauge field parameterized by a disorder strength and a characteristic scale k_0. Accounting for gravitational backreaction, we construct an asymptotically AdS solution perturbatively in the disorder strength. The disorder averaged geometry displays unphysical divergences in the deep interior. We explain how to remove these divergences and arrive at a well behaved solution. The disorder averaged DC conductivity is calculated and is found to contain a correction to the AdS result. The correction appears at second order in the disorder strength and scales inversely with k_0. We discuss the extension to a system with a finite initial charge density. The disorder averaged DC conductivity may be calculated by adopting a technique developed for holographic lattices.
Isolation and Purification of Actinides Using N,O-Hybrid Donor Ligands for Closing the Nuclear Fuel Cycle

Despite the fact that in the mass consciousness nuclear power is associated with increased environmental risks, this type of energy today remains one of the cleanest, most efficient and carbon neutral. Further development of nuclear energy is hampered by the problem of appropriate handling of spent nuclear fuel (SNF). A very attractive concept of the closed nuclear cycle has been developed to solve it. A real-life implementation of this concept requires the development of technological processes for the efficient separation of minor actinides from the rest of the SNF components. Recent progress in this area has been closely associated with N,O-hybrid donor ligands for liquid–liquid extraction. This work systematically reviews the available literature on the topic and provides detailed explanations on the behavior of the most perspective ligands. Notably, another important aspect of the application of N,O-hybrid donor ligands in nuclear cycle analytics through their incorporation in chemical sensors is also discussed.
Novel Strategy for Non-Aqueous Bioconjugation of Substituted Phenyl-1,2,4-triazole-3,5-dione Analogues

A novel 4-[4-(pentafluoro-λ⁶-sulfanyl)phenyl]-1,2,4-triazole-3,5-dione (5a) was synthesised as a potential [18F]radio-prosthetic group for radiolabelling peptides and proteins via selective bioconjugation with the phenolic side chains of tyrosine residues. Preliminary conjugation tests revealed the rapid hydrolysis of 5a under semi-aqueous conditions; these results led to further investigation into the electronic substituent effects of PTAD derivatives and corresponding hydrolytic stabilities. Five derivatives of 5a with para substituents of varying electron donating and withdrawing effects were synthesised for the investigation. The bioconjugation of these derivatives with model tyrosine was monitored in both aqueous and organic media in the presence of a variety of catalysts. From these investigations, we have found HFIP to be an effective catalyst when used in tandem with DCM as a solvent to give PTAD-tyrosine conjugate products (6a–f) in satisfactory to good yields (54–79%), whereas analogous reactions performed in acetonitrile were unsuccessful. The discovery of this system has allowed for the successful conjugation of electron-deficient PTAD derivatives to tyrosine, which would otherwise be unachievable under aqueous reaction conditions. The inclusion of these electron-deficient, fluorinated PTAD derivatives for use in the PTAD-tyrosine conjugation will hopefully broaden their applicability within fields such as 19F-MRI and PET imaging.
A Review on the Development of Pneumatic Artificial Muscle Actuators: Force Model and Application

Pneumatic artificial muscles (PAMs) are soft and flexible linear pneumatic actuators which produce human muscle like actuation. Due to these properties, the muscle actuators have an adaptable compliance for various robotic platforms as well as medical applications. While a variety of possible actuation schemes are present, there is still a need for the development of a soft actuator that is very light-weight, compact, and flexible with high power-to-weight ratio. To achieve this, the development of the PAM actuators has become an interesting topic for many researchers. In this review, the development of the different kinds of PAM available to date are presented along with manufacturing process and the operating principle. The various force models for artificial muscle presented in the literature are broadly reviewed with the constraints. Furthermore, the applications of PAM are included and classified based on the fields of biorobotics, medicine, and industry, along with advanced medical instrumentation. Finally, the needful improvements in terms of the dynamics of the muscle are discussed for the precise control of the PAMs as per the requirements for the applications. This review will be helpful for researchers working in the field of robotics and for designers to develop new type of artificial muscle depending on the applications.
Research on Bending and Ballistic Performance of Three-Dimensional Ply-to-Ply Angle Interlock Kevlar/EP Armor Material

The three-dimensional (3D) shallow cross-bending composite material has many advantages in thickness and in-plane direction, such as high strength, high modulus, inter-layer shear strength, as well as large-area area bearing, energy absorption, etc., which has great application potential in the field of bulletproof armor. To prepare a protective material with both excellent bending performance and good ballistic performance, the effects of weft density and layering method on the bending performance and ballistic performance of three-dimensional ply-to-ply angle interlock (3DPPAI) Kevlar/EP armor materials were studied. The results showed that when the weft density of the material was 33 pieces/cm, its bending performance and ballistic resistance were the best. The 3DPPAI Kevlar/EP armor material prepared by orthogonal layup had more advantages in bending performance, and the unidirectional layup had better anti-ballistic performance. The research results will lay the foundation for structural optimization and engineering applications of such materials.
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We have carried out a comprehensive study of the intrinsic anomalous Hall effect and spin Hall effect of several chiral antiferromagnetic compounds ${\mathrm{Mn}}_{3}X$ ($X$ = Ge, Sn, Ga, Ir, Rh and Pt) by ab initio band structure and Berry phase calculations. These studies reveal large and anisotropic values of both the intrinsic anomalous Hall effect and spin Hall effect. The ${\mathrm{Mn}}_{3}X$ materials exhibit a noncollinear antiferromagnetic order which, to avoid geometrical frustration, forms planes of Mn moments that are arranged in a Kagome-type lattice. With respect to these Kagome planes, we find that both the anomalous Hall conductivity (AHC) and the spin Hall conductivity (SHC) are quite anisotropic for any of these materials. Based on our calculations, we propose how to maximize AHC and SHC for different materials. The band structures and corresponding electron filling, that we show are essential to determine the AHC and SHC, are compared for these different compounds. We point out that ${\mathrm{Mn}}_{3}\mathrm{Ga}$ shows a large SHC of about 600 $(\ensuremath{\hbar}/e){(\mathrm{\ensuremath{\Omega}}\phantom{\rule{0.16em}{0ex}}\text{cm})}^{\ensuremath{-}1}$. Our work provides insights into the realization of strong anomalous Hall effects and spin Hall effects in chiral antiferromagnetic materials.
Optimal power management in a grid-connected PV-based Electric Vehicle Charging Station: The AGOA Approach

Optimal power management in a grid-connected PV-based electric vehicle charging station is achieved by designing a robust power system model. The proposed model is designed by combining an integrated converter, a maximum PowerPoint tracker (MPPT), and an efficient controller with an effective control scheme. The proposed controller is designed using the adaptive grasshopper optimization algorithm (AGOA). The proposed controller delivers precise control signals for optimal switching in the system and creates a database of control signals for optimal power flow in the system. The proposed model is run in the MATLAB/Simulink environment, and the efficiency of the system is compared with other existing methods.
Ne ii FINE-STRUCTURE LINE EMISSION FROM THE OUTFLOWS OF YOUNG STELLAR OBJECTS

The flux and line shape of the fine-structure transitions of Ne ii and Ne iii at 12.8 and 15.55 μm and of the forbidden transitions of O i λ6300 are calculated for young stellar objects with a range of mass-loss rates and X-ray luminosities using the X-wind model of jets and the associated wide-angle winds. For moderate and high accretion rates, the calculated Ne ii line luminosity is comparable to or much larger than produced in X-ray irradiated disk models. All of the line luminosities correlate well with the main parameter in the X-wind model, the mass-loss rate, and also with the assumed X-ray luminosity—and with one another. The line shapes of an approaching jet are broad and have strong blue-shifted peaks near the effective terminal velocity of the jet. They serve as a characteristic and testable aspect of jet production of the neon fine-structure lines and the O i forbidden transitions.
nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation

Cardiovascular disease (CVD) is a disease with high mortality in modern times. The segmentation task for MRI to extract the related organs for CVD is essential for diagnosis. Currently, a large number of deep learning methods are designed for medical image segmentation tasks. However, the design of segmentation algorithms tends to have more focus on deepening the network architectures and tuning the parameters and hyperparameters manually, which not only leads to a high time and effort consumption, but also causes the problem that the architectures and setting designed for a single task only performs well in a single dataset, but have low performance in other cases. In this paper, nn-TransUNet, an automatic deep learning pipeline for MRI segmentation of the heart is proposed to combine the experiment planning of nnU-net and the network architecture of TransUNet. nn-TransUNet uses vision transformers and convolution layers in the design of the encoder and takes up convolution layers as decoder. With the adaptive preprocessing and network training plan generated by the proposed automatic experiment planning pipeline, nn-TransUNet is able to fulfill the target of medical image segmentation in heart MRI tasks. nn-TransUNet achieved state-of-the-art level in heart MRI segmentation task on Automatic Cardiac Diagnosis Challenge (ACDC) Dataset. It also saves the effort and time to manually tune the parameters and hyperparameters, which can reduce the burden on researchers.
Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).
XMM-NEWTON OBSERVATIONS OF THE TeV γ-RAY SOURCE HESS J1804-216

We have analyzed three XMM-Newton observations of the central part of the unidentified TeV γ-ray source HESS J1804-216. We focus on two X-ray sources, 2XMMi J180442.0-214221 (Src 1) and 2XMMi J180432.5-214009 (Src 2), which were suggested to be the possible X-ray counterparts to the TeV source. We discover a 2.93 hr X-ray periodicity from Src 1, with the pulse profile explained with a self-eclipsing pole in an eclipsing polar. Src 2 exhibits a strong Fe emission line (FWHM ∼ 0.3 keV and equivalent width ∼0.8 keV) and large X-ray variability on timescales of hours and is probably an intermediate polar. Thus Src 1 and Src 2 are probably two field sources not responsible for the TeV emission. The observations were contaminated by strong stray light from a nearby bright source, and we see no clear extended X-ray emission that can be attributed to the supernova remnant G8.7-0.1, a popular possible association with the TeV source. The other possible association, the pulsar wind nebula candidate PSR J1803-2137, shows little long-term variability compared with a previous Chandra observation. Many point sources were serendipitously detected, but most of them are probably normal stars. Three new candidate compact object systems (other than Src 1, Src 2, and PSR J1803-2137) are also found. They are far away from the TeV source and are probably also magnetic cataclysmic variables, thus unlikely to be responsible for the TeV emission.
Gapped fermions in top-down holographic superconductors

We use holography to compute spectral functions of certain fermionic operators in three different finite-density, zero-temperature states of Aharony-Bergman-Jafferis-Maldacena (ABJM) theory with a broken $U(1)$ symmetry. In each of the three states, dual to previously studied domain wall solutions of four-dimensional gauged supergravity, we find that the fermionic operators have gapped spectra. In one case the gap can be traced to the small charge of the fermions, while in the other cases it is due to a particular interaction that mixes particles and holes.
Thermal modeling of the convective heat transfer in the large air cavities of the 3D concrete printed walls

Abstract The cavity walls are a widely used construction system. They became popular in traditional masonry construction for their capacity to reduce the passage of moisture and improve the walls’ thermal performance. However, the latter only applied to narrow cavities with restricted internal air movement. Cavities are also present in emerging technologies, such as 3D concrete printed walls. However, the large cavities of the 3D printed concrete walls have high convective heat transfers that affect the envelope’s thermal performance. Therefore, the authors developed a conjugate heat transfer finite element model to study the large cavities in 3D printed concrete walls and determine the effect on the convective heat transfer of subdividing large cavities. The results show it is possible to reduce the heat flux four times, from 40.4 W/m2 to 9.1 W/m2, subdividing a large cavity into sixteen small ones. This reduction might be higher, increasing the number of cavity subdivisions. However, it is infeasible to restrict the air movements in unfilled air cavities over 25 mm wide for the Rayleigh numbers ≥ 105. Therefore, the practicality of minimizing heat transfer by subdividing large air cavities in 3D printed walls is limited.
The role of social capital in achieving preferred customer status with manufacturing and service suppliers

PurposeThis study uses social capital theory to analyze how social capital and supplier development support achieving supplier satisfaction and preferred customer status. The resulting model is compared between manufacturing and service suppliers.Design/methodology/approachA survey receiving 482 supplier responses from manufacturing and service suppliers was utilized and analyzed using partial least squares (PLS) path modeling and multi-group comparison tests.FindingsThe paper adds new explanations for preferred customer status through empirical evidence of relationships between supplier development, social capital, supplier satisfaction, and preferred customer status. Cognitive and relational capital directly support achieving preferred customer status. The role of supplier satisfaction in achieving preferred customer status is lower for manufacturing suppliers.Research limitations/implicationsBoth service and manufacturing suppliers could also be studied in their specific industry settings. A more in-depth investigation of other business relationship dynamics, such as power, is needed in a future study.Practical implicationsService and manufacturing suppliers need different strategies to obtain the benefits from supplier development and social capital building. For service suppliers, more intangible factors are relevant in comparison to manufacturing suppliers.Originality/valueThis study advances the literature in two main ways. First, it elaborates the role of supplier development and social capital in the path toward supplier satisfaction and preferred customer status as perceived by suppliers. Second, this study answers the calls for a better understanding of the contextual characteristics underlying potential differences in how preferred customer status is formed.
Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification

The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel principal component analysis (PCA). The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.
Tide Level Prediction Using NARX-based Recurrent Neural Networks

Tide level forecasting is one of major components in oceanography and marine research. The future prediction of tides is an important issue for human and environment. This study proposes new tide level prediction methods with multiple input multiple output (MIMO) approach based on nonlinear autoregressive with exogenous inputs (NARX) model and three types of recurrent neural networks (RNNs), namely, long short-term memory (LSTM), bi-directional long short-term memory (biLSTM) and gated recurrent unit (GRU) networks. We develop three types of NARX models: NARX-LSTM, NARX-biLSTM and NARX-GRU. We evaluate the three models and compare their performances with LSTM, biLSTM and GRU network models. It turns out that the proposed MIMO NARX based RNN models, especially NARX-LSTM and NARX-GRU have better performance in comparison with their RNN counterparts (i.e., LSTM, biLSTM, and GRU). The NARX-GRU model shows the best performance for one step and three step ahead prediction in terms of RMSE in each location and average RMSE, while NARX-LSTM performs better for two steps ahead prediction in terms average RMSE. The results also show that the mixture of RNN and NARX improves the performance of RNN model for all the four tasks at four locations in the UK (Wick, Workington, Ullapol and New Heaven) and for the three time horizons (i.e., 1, 2 and 3 steps ahead).
Associations between Emotion Regulation, Feeding Practices, and Preschoolers’ Food Consumption

Previous research identified emotion dysregulation, non-responsive feeding practices, and unhealthy food consumption as risk factors for childhood obesity. However, little is known about the relationships between these factors. This study examined associations between children’s emotion regulation, parental feeding practices, and children’s food consumption. The sample consisted of 163 mothers of children aged 3–5 years. Mothers completed the Emotion Regulation Checklist, the Child Feeding Questionnaire, and the Child Health Section from the Parent Interview of the Early Childhood Longitudinal Study-B to assess model variables. Results showed that healthy food consumption was associated with higher emotion regulation abilities, higher monitoring, and lower pressure to eat. For unhealthy food consumption, the associations were in opposite directions. Higher emotion regulation abilities were also associated with higher monitoring, lower pressure to eat, and lower restriction. For lability, the associations were in opposite directions. Regression analyses revealed that children’s lability, pressure to eat, and monitoring were significant predictors of children’s food consumption. These findings suggest that children’s emotion regulation and feeding practices are important determinants of children’s food consumption. Future longitudinal studies that examine bidirectional associations between children’s emotion regulation, parental feeding practices, children’s food consumption, and potential mechanisms accounting for these associations are needed.