Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic fields generated by submarine power cables have a negligible effect on the swimming behavior of Atlantic lumpfish (Cyclopterus lumpus) juveniles

Submarine power cables carry electricity over long distances. Their geographic distribution, number, and areal coverage are increasing rapidly with the development of, for example, offshore wind facilities. The flow of current passing through these cables creates a magnetic field (MF) that can potentially affect marine organisms, particularly those that are magnetosensitive. The lumpfish (Cyclopterus lumpus) is a migratory species that is widely distributed in the North Atlantic Ocean and Barents Sea. It migrates between coastal spawning grounds and pelagic offshore feeding areas. We tested whether lumpfish respond to MFs of the same intensity as those emitted by high voltage direct current (HVDC) submarine power cables. Laboratory experiments were conducted by placing juvenile lumpfish in an artificial MF gradient generated by a Helmholtz coil system. The intensity of the artificial MF used (230 µT) corresponded to the field at 1 m from a high-power submarine cable. The fish were filmed for 30 min with the coil either on or off. Swimming speeds, and presence in the different parts of a raceway, were extracted from the videos and analyzed. Juvenile lumpfish activity, defined as the time that the fish spent swimming relative to stationary pauses (attached to the substrate), and the distance travelled, were unaffected by exposure to the artificial MF. The swimming speed of juvenile lumpfish was reduced (by 16%) when the coil was on indicating that the fish could either sense the MF or the induced electric field created by the movement of the fish through the magnetic field. However, it seems unlikely that a 16% decrease in swimming speed occurring within 1 m of HVDC cables would significantly affect Atlantic lumpfish migration or homing.
Shielding of a slowly moving test charge in dusty plasmas

The far‐field potential of a slowly moving test charge in dusty plasmas is calculated accounting for the dust grain charge fluctuations. It is found that the latter cause a damping, which in turn gives rise to a dipole‐like far‐field potential. The presence of the dipole‐like potential is attributed to the dust grain charge perturbations caused by the oscillating electron and ion currents induced by the motion of a test charge.
Toxicity Prediction in Pelvic Radiotherapy Using Multiple Instance Learning and Cascaded Attention Layers

Modern radiotherapy delivers treatment plans optimised on an individual patient level, using CT-based 3D models of patient anatomy. This optimisation is fundamentally based on simple assumptions about the relationship between radiation dose delivered to the cancer (increased dose will increase cancer control) and normal tissue (increased dose will increase rate of side effects). The details of these relationships are still not well understood, especially for radiation-induced toxicity. We propose a convolutional neural network based on multiple instance learning to analyse toxicity relationships for patients receiving pelvic radiotherapy. A dataset comprising of 315 patients were included in this study; with 3D dose distributions, pre-treatment CT scans with annotated abdominal structures, and patient-reported toxicity scores provided for each participant. In addition, we propose a novel mechanism for segregating the attentions over space and dose/imaging features independently for a better understanding of the anatomical distribution of toxicity. Quantitative and qualitative experiments were performed to evaluate the network performance. The proposed network could predict toxicity with 80% accuracy. Attention analysis over space demonstrated that there was a significant association between radiation dose to the anterior and right iliac of the abdomen and patient-reported toxicity. Experimental results showed that the proposed network had outstanding performance for toxicity prediction, localisation and explanation with the ability of generalisation for an unseen dataset.
An approach for the regularization of a power flow solution around the maximum loading point

A method which is capable of greatly widening the range where the power flow solution is regular is presented. This is achieved by giving some modifications to the conventional power flow solution method and eliminating the singular point or shifting it to region where the voltage is lower than that of the maximum loading point. Then, the continuous execution of V-P curves including the maximum loading point is realized. The efficiency and effectiveness of the method were tested in a practical 598-node system are compared with the conventional method. >
SPECTROSCOPIC OBSERVATIONS OF NOVA PW VULPECULAE.

The authors present optical spectrophotometry and infrared photometry of PW Vul (Nova Vul 1984 No. 1), a slow classical nova. Their analysis suggests that the helium abundance (He/H ≡ 0.12) is slightly below normal for a slow nova, but comparable to that observed in fast novae. The oxygen and neon abundances are close to solar if the electron temperature in the ejecta exceeds 15,000K, but oxygen is enhanced by ≡10 times its solar value if the electron temperature is close to 10,000K.
Coalition agreements and governments’ policy-making productivity

Abstract One of the biggest challenges parties in multiparty governments face is making policies together and overcoming the risk of a policy stalemate. Scholars have devoted much attention to the study of how various institutions in cabinet and parliament help coalition parties with conflicting policy preferences to be efficient in the policy-making process. Coalition agreements are one of many instruments coalition partners can use to facilitate policy making. However, many scholars describe such agreements’ actual role as cheap talk, due to their legally non-enforceable nature. Do coalition agreements make a difference in the policy-making productivity of multiparty governments? To address this question, this article focuses on governments’ policy output and investigates whether coalition agreements increase the policy-making productivity of multiparty cabinets. Its central argument is that written agreements between coalition partners strengthen the capacity of coalition governments to make policy reforms, even when there is a high degree of ideological conflict among partners. To evaluate this argument, the article analyzes data on economic reform measures adopted by national governments in 11 Western European countries over a 40-year period (1978–2017), based on a coding of more than 1000 periodical country reports issued by the Economist Intelligence Unit (EIU) and the Organisation for Economic Co-operation and Development (OECD). The results show that while coalition agreements foster policy productivity in minimal winning cabinets, they play a weaker role in minority and surplus governments. Coalition agreements limit the negative effect of intra-cabinet ideological conflict on reform productivity, suggesting that such contracts help parties overcome the risk of policy stalemate.
Highly-Scaled Self-Aligned GaN Complementary Technology on a GaN-on-Si Platform

This paper reports on the scaling of self-aligned GaN complementary technology on a GaN-on-Si platform to push its performance limits for circuit-level applications. The highly scaled self-aligned p-channel FinFET (fin width =20 nm) achieved $I_{D,max}$ of -300 mA/mm and RON of $27 \Omega \cdot$ mm, a record for metal organic chemical vapor deposition (MOCVD)-grown III-N p-FETs. A systematic study on impact of fin width scaling and recess depth in these transistors was conducted. A new self-aligned scaled n-channel p-GaN-gate FET process (n-FET), compatible with the p-FinFET, demonstrated enhancement-mode (E-mode) n-FETs $(L_{G}=200$ nm, $I_{D, max}=525$ mA/mm, $R_{ON}= 2.9 \Omega \cdot$ mm) on the same platform. The p-FETs and n-FETs feature competitive performance in their respective categories, and when taken together, offer a leading solution for GaN complementary technology on a GaN-on-Si platform.
High current YBa2Cu3O7−δ thick films on flexible nickel substrates with textured buffer layers

High current YBa2Cu3O7−δ (YBCO) thick films on flexible nickel substrates with textured buffer layers were fabricated. Highly textured yttria‐stabilized‐zirconia buffer layers were deposited by using ion beam assisted deposition (IBAD). Pulsed laser deposited YBCO films were not only c‐axis oriented with respect to the film surface but also strongly in‐plane textured. The in‐plane mosaic spread of YBCO films was ∼10°. A critical current density of 8×105 A/cm2 was obtained at 75 K and zero field for thin YBCO films. It was also demonstrated that thick YBCO films with a high critical current and excellent magnetic field dependence at liquid nitrogen temperature can be obtained on flexible nickel substrates by using the textured buffer layers. The result indicates that thick film technology in combination with IBAD buffer layers could be a viable method for fabricating YBCO tapes in long lengths.
The effect of attack cost on network robustness

Network robustness is one of the most central topics in the field of complex networks. In this paper, the factor of attack cost associated with network robustness is taken into account. The attack cost is assumed to be positively correlated to the degree of nodes. We found that the performance of different attack strategies is sensitive to the total attack cost. In particular, the high-degree removal strategy (HDRS) is no longer the best attack strategy unless the total attack cost is high. The effect of the assortativity coefficient on the network robustness model with attack cost is extensively investigated. For assortative networks, as the level of assortativity increases, the low-degree removal strategy (LDRS) and the random removal strategy (RRS) are more effective whatever the total attack cost is but HDRS is less (more) effective when the total attack cost is low (high). For disassortative networks, as the level of disassortativity increases LDRS and RRS are less effective whatever the total attack cost is but HDRS is more (less) effective when the total attack cost is low (high). Our work provides insight into the robustness and vulnerability of networked systems with respect to the cost of attack.
Active Impedance Network Buck-Boost Three-Level T-Type Inverter With Enhanced Voltage Gain

This paper proposes a new PWM technique for a reduced-component-count active impedance network (RCC-AIN) three-level inverter to maximize the input/output (I/O) voltage gain. Although the max boost control (MBC) technique offers the same I/O voltage gain, it suffers from the drawback of low-frequency (LF) ripples of the source current. In contrast, the proposed PWM technique is a combination of improved MBC (IMBC) and a new switching state pattern that eliminates this drawback and offers up to 79% reduction in the peak-to-peak ripple value of the source current while maintaining the same I/O voltage gain as MBC. The cumulative sum of ripple components is used to find the exact maximum and minimum peaks of the source current over the fundamental period. The proposed switching pattern doubles the switching frequency of a boosting switch to limit the current spikes of capacitors during state change-over. The RCC-AIN topology was analyzed in various voltage-boosting techniques, and the proposed PWM technique utilizes the full potential of the RCC-AIN topology in a better way. The findings have been verified using theoretical calculations, simulation, and experimental results.
Cross-Domain Few-Shot Hyperspectral Image Classification With Class-Wise Attention

Few-shot learning (FSL) is an effective method to solve the problem of hyperspectral image (HSI) classification with few labeled samples. It learns transferable knowledge from sufficient labeled auxiliary data to classify unseen classes with limited labeled samples for training. However, the distribution difference between auxiliary data and unseen classes results in the learned transferable knowledge not being well applied to the new task. Therefore, a class-wise attentive cross-domain FSL (CA-CFSL) framework is proposed in this article, in which a feature extractor is learned to extract data features with discriminability and domain invariance. The class-wise attention metric module (CAMM) introduces class-wise attention on the FSL framework to learn more discriminative features, which improves the interclass decision boundaries. Furthermore, an asymmetric domain adversarial module (ADAM) is designed to enhance the ability of extracting domain-invariant representations, which combines asymmetric adversarial training with embedded domain-specific information. Experimental results on four public HSI datasets demonstrate that the proposed method outperforms the existing methods.
Raman Spectroscopy of Bulk and Thin-Layer (Ba,Sr)TiO3 Ferroelectrics

A comparative Raman study of (Ba,Sr)TiO3 bulk ceramics, sol-gel derived polycrystalline thin film, and heteroepitaxial thin film deposited by rf sputtering onto (001) MgO substrate is reported. It is found that phase transition sequence in the heteroepitaxial thin film is markedly different from those observed in the bulk ceramics and polycrystalline thin film.
Computational Experiments on Detecting Meaning shift in Jokes

The paper describes an experimental approach to detect the meaning shift, one of the most fundamental characteristics of humor, which is studied by many scientists in different interdisciplinary theoretical methodologies. We measured cosine similarity between setups and punchlines and explained these results through the set of objective criteria such as cosine results limitations, punchline length, three groups of words in joke’s parts. We also decided to investigate the originality of obtained distribution of cosine similarity to the same distributions calculated for fiction texts. The results demonstrate crucial differences in distributions for all the verification texts. We described an automatic approach for extracting meaning concepts of setup and punchline aided by word embeddings of its top three semantically closest words. We also provide a comparative analysis of distribution of cosine similarities among jokes. The proposed approach allows obtaining embeddings for setup and punchline made on the top of the closest words in semantic space.
Phase-Dependent Modulation of Muscle Activity and Intermuscular Coupling During Walking in Patients After Stroke

Many patients experience motor and sensory impairments after stroke, leading to gait disturbances. Analysis of muscle modulation mode during walking can provide evidence for neurological changes after stroke, while how stroke affects individual muscle activity and muscular coordination in certain gait sub-phases remains unclear. The purpose of the present study is to comprehensively investigate phase-dependent ankle muscle activity and intermuscular coupling patterns in post-stroke patients. In this experiment, 10 post-stroke patients, 10 young healthy subjects and 10 elderly healthy subjects were recruited. All subjects were asked to walk at their preferred speeds on the ground, and surface electromyography (sEMG) and marker trajectory data were collected simultaneously. The gait cycle of each subject was divided into four substages according to the labeled trajectory data. On this basis, fuzzy approximate entropy (fApEn) was used to analyze the complexity of ankle muscle activity during walking. And the transfer entropy (TE) was applied to reflect directed information transmission between ankle muscles. Results found that complexity of ankle muscles activities in patients after stroke showed similar trends to that in healthy subjects. Different from healthy subjects, the complexity of ankle muscle activity in patients with stroke tends to increase in most of the gait sub-phases. While TE values between the ankle muscles in patients with stroke tend to decrease throughout the gait cycle, especially in the second double support stage. Compared with age-matched healthy subjects, patients recruit more motor units throughout their gait while increasing muscle coupling to achieve gait function. The combined application of fApEn and TE provide a more comprehensive understanding of phase-dependent muscle modulation mechanisms in post-stroke patients.
Design analysis of a high Tc superconducting microbolometer

We propose an antenna‐coupled microbolometer based on the resistive transition of a high Tc superconducting film as a detector for far infrared and millimeter waves. Such microbolometers can be mechanically stronger, more easily fabricated, and much faster than conventional bolometric infrared detectors. A design analysis shows that a noise equivalent power of 2.5×10−12 W Hz−1/2 is achievable for modulation frequencies up to 10 kHz. The superconducting film must be of high quality with narrow resistive transition and low 1/f noise.
Molecular Dynamics simulation of electrical field induced conformational transition and associated frictional performance of monomolecular films

Sparse monomolecular film assemblies with polar end groups such as carboxyl have attracted considerable interest because of their ability to undergo conformational transition under electrical fields. We report the results from molecular dynamics simulation of electrical field induced conformational transitions and associated frictional performance of carboxyl-terminated monomolecular films. Simulation results indicate that the density of the monomolecular film has a significant influence on the conformational transition under electrical fields. In the case of loose-packed monomolecular films, carboxyl-terminated chains cluster together to form a disordered film due to large interchain separation. Under the application of a positive electrical field, the chain backbone rotates and lies down on the substrate forming disordered clusters, whereas under the application of negative electrical fields, the chains stand up and cluster together to form disordered clusters. Under shallow indentation with a purely repulsive indenter, loose-packed monomolecular films subjected to a positive electrical field exhibit a lower level of frictional response compared with films subjected to negative and no electrical fields. In the case of close-packed monomolecular films, the molecular chains assemble in an ordered film and the space is not enough for backbone chains to generate large-scale conformational transition. Due to this spatial limitation, the applied electrical field was not found to have any influence on the backbone chain rotation and associated frictional response under shallow indentations with a purely repulsive indenter.
Convergence analysis of a relaxed extragradient–proximal point algorithm application to variational inequalities

In this paper we present a relaxed version of an extragradient–proximal point algorithm recently proposed by Solodov and Svaiter for finding a zero of a maximal monotone operator defined on a Hilbert space. The aim is to introduce a family of parameters in order to accelerate the rate of convergence of this algorithm. First we study the convergence and rate of convergence of the relaxed algorithms and then we apply them to the generalized variational inequality problem. For this problem, the operator is a sum of two operators: the first one is single-valued, monotone and continuous and the second one is the subdifferential of a nonsmooth lower semicontinuous proper convex function ϕ. To make the subproblems easier to solve, we consider, as in the bundle methods, piecewise linear convex approximations of ϕ. We explain how to construct these approximations and how the subproblems fall within the framework of our relaxed extragradient–proximal point algorithm. We prove the convergence of the resulting algorithm without assuming a Dunn property on the single-valued operator. Finally, we report some numerical experiences to illustrate the behavior of our implementable algorithm for different values of the relaxation factor. A comparison with another algorithm is also given.
An Online Deep Reinforcement Learning-Based Order Recommendation Framework for Rider-Centered Food Delivery System

As an important part of intelligent transportation systems, On-demand Food Delivery (OFD) becomes a prevalent logistics service in modern society. With the continuously increasing scale of transactions, rider-centered assignment manner is gaining more attraction than traditional platform-centered assignment among food delivery companies. However, problems such as dynamic arrivals of orders, uncertain rider behaviors and various false-negative feedbacks inhibit the platform to make a proper decision in the interaction process with riders. To address such issues, we propose an online Deep Reinforcement Learning-based Order Recommendation (DRLOR) framework to solve the decision-making problem in the scenario of OFD. The problem is modeled as a Markov Decision Process (MDP). The DRLOR framework mainly consists of three networks, i.e., the actor-critic network that learns an optimal order ranking policy at each interaction step, the rider behavior prediction network that predicts the grabbing behavior of riders and the feedback correlation network based on attention mechanism that identifies valid feedback information from false feedbacks and learns a high-dimensional state embedding to represent the states of riders. Extensive offline and online experiments are conducted on Meituan delivery platform and the results demonstrate that the proposed DRLOR framework can significantly shorten the length of interactions between riders and the platform, leading to a better experience of both riders and customers.
Impact of Urinary Incontinence on Quality of Life

Introduction. Urinary incontinence is the uncontrolled outflow of urine at the age of a person’s life when it should be under voluntary control. It occurs in both sexes but is more common in women. Incontinence is a major social, medical and hygienic problem. Quality of life is seriously threatened when incontinence occurs and limits the individual’s daily functioning. Aim. To identify the impact of urinary incontinence on the quality of life by reviewing research papers and professional literature. Methods. The study was focused on the analysis of data on the quality of life of patients diagnosed with urinary incontinence. Articles published between 2017 and 2022 that were recognized as relevant for the investigation were used. Seven articles were selected for investigation. Results. The results are shown in two steps. The first step consisted of finding suitable, high-quality research papers. The second step was the analysis of the relevant papers. Conclusion. Urinary incontinence is proven to have a great effect on the quality of life and brings with it a number of mental problems such as loss of self-esteem, feelings of discomfort, insecurity, withdrawal and depression. It is important to recognize urinary incontinence on time and implement appropriate treatment methods in order to maintain a level of quality of life that is acceptable for the individual.
The effectiveness of a training program in developing professional performance of social workers in dealing with the negative effects of school bullying towards autistic students

: This research is aimed at verifying the effectiveness of a training program in developing the professional performance of social workers in dealing with the negative effects of school bullying towards autistic students. The researcher adopted a quasi-experimental approach. The sample includes (22) social workers working with autistic students in Beni-Suef City (experimental group). The training program was implemented for three months through weekly sessions with the experimental group. The researcher used a professional performance scale that has four dimensions as shown: scientific knowledge and professional information, professional skills and abilities, the professional directions. and professional experiences. The results of the research training program succeeded to develop this professional performance.