Miracle-dz/UniFAR-FTUs huggingface dataset:
NH4F-assisted one-pot solution synthesis of hexagonal ZnO microdiscs for efficient ultraviolet photodetection

One-pot solution method to grow large hexagonal ZnO microdiscs with the aid of ammonium fluoride (NH4F) mineralizer has been realized. The size, morphology, crystallinity and optical properties of the synthesized ZnO microdiscs can be efficiently modulated by the concentration of NH4F. X-ray diffraction and scanning electron microscopy analyses illustrate that hexagonal ZnO microdiscs achieved at 0.03 M NH4F concentration have larger disc size and narrower full-width value at half maximum of (002) peak. It implies better crystal quality compared with those from other additive concentrations. Photoluminescence results also demonstrate the same trend. These results indicate that with proper addition of NH4F, the crystal quality of ZnO microdiscs has been improved and defects have been suppressed. Furthermore, a UV photodetector has been fabricated by simply transferring the ZnO microdiscs grown with 0.03 M NH4F onto a p-type silicon substrate. The device exhibits photosensitive behaviour at 365 nm UV light illuminating when −0.6 V is applied. The response time as well as recovery time is less than 0.1 s. The relatively large photoresponsivity of 1.19 A W−1 with power consumption less than 10 nW makes it possible in application field of highly efficient low power consumption UV detection.
Predictability and Performance Aware Replacement Policy PVISAM for Unified Shared Caches in Real-time Multicores

Missing the deadline of an application task can be catastrophic in real-time systems. Therefore, to ensure timely completion of tasks, offline worst-case execution time and schedulability analysis is often performed for such real-time systems. One of the important inputs to this analysis is a safe upper bound of misses in each processor cache memory used by the system. Cache miss prediction techniques have matured significantly for private caches in single-core processors; however, remained as a challenge for unified, shared caches in multicore processors. According to prior studies, a task’s miss upper bound on a shared cache can be predicted using available private cache prediction techniques only if the shared cache maintains core-based independent static partitions. The problem is, such partitions require the use of infeasible “write-update consistency protocol” and wastes valuable cache space by duplicate caching. In this regard, this paper presents a novel cache replacement policy called “predictable variable isolation in shared antipodal memory (PVISAM). ” Its replacement decisions generate virtual core-based partitions that support demand-based runtime size adjustment and line sharing to better utilize space. Moreover, these partitions require no consistency protocol. Trace-driven experimental results for Parsec benchmark applications reveal that performance of a unified shared cache memory improves by <inline-formula> <tex-math notation="LaTeX">$101.68{\boldsymbol \times }$ </tex-math></inline-formula> on average (minimum <inline-formula> <tex-math notation="LaTeX">$1.09{\boldsymbol \times }$ </tex-math></inline-formula> and maximum <inline-formula> <tex-math notation="LaTeX">$1138.50{\boldsymbol \times }$ </tex-math></inline-formula>) when PVISAM is used instead of either the aforementioned write-update protocol-based predictable partitioning or the widely used write-invalidate consistency protocol-based partitioning. PVISAM can improve cache performance by <inline-formula> <tex-math notation="LaTeX">$0.74{\boldsymbol \times }$ </tex-math></inline-formula> on average (minimum <inline-formula> <tex-math notation="LaTeX">$0.02{\boldsymbol \times }$ </tex-math></inline-formula> and maximum <inline-formula> <tex-math notation="LaTeX">$1.12{\boldsymbol \times }$ </tex-math></inline-formula>) compared to having no partitions at all. Both predictable partitioning and PVISAM improve unified, shared cache predictability by 63.44% (minimum 26.89% and maximum 99.99%) and 19.36% (minimum 1.58% and maximum 72.51%) on average compared to no partitions and write-invalidate protocol-based partitioning, respectively. Experimental results for synthetic traces show that PVISAM remarkably improves cache performance and predictability when compared to its three competitors even in scenarios that stress the cache.
Context-Aware Plug-Load Identification Towards Enhanced Energy Efficiency in the Built Environment

It is not uncommon to employ energy labeling schemes on appliances or plug-loads that provide information regarding their expected energy usages and efficiencies. But this often does not include a follow-up action of checking whether the expectations on energy consumption are achieved or not for a particular appliance item being used. Automation of this task requires a mechanism that is context-aware regarding meta-data of the appliances (electrical ratings, energy-label information, etc.), and also aware of their operational-data consisting of realtime electrical measurements. In this work, a novel socket hardware is proposed which can access the meta-data and operational-data of appliances in an automated fashion, such that a supervisory control system can contextually identify the plug-loads for intelligent demand side management. The proposed smart cyber-physical system would enhance energy efficiency in future buildings, and would also reduce electronic waste.
The Relationship Between the Form of Employment and Employee Training and Developing Opportunities in Organizations

In modern organizations, the importance of employee training and development is increasingly emphasized because as it allows them to improve their qualifications and develop new competencies and attitudes useful for the organization. On the other hand, the flexible forms of employment, which are less permanent than indefinite period employment contracts, are increasingly used in organizations. The aim of the paper is to present the relationship between the form of employment and employee training and developing opportunities in organizations. The paper presents the results of research conducted by means of a diagnostic survey, with the use of a questionnaire among 569 employees of the Province of Silesia. A research process was conducted on the basis of grounded theory, and statistical analysis was conducted using the SPSS for Windows, assuming the level of significance at 0.05. In order to compare people with different forms of employment, the Kruskal-Wallis test was used. The research results indicated that there are certain relationships between the form of employment and employee training and development opportunities in the organization. Statistically significant differences were shown between the form of employment and the assessment of employee development opportunities, the form of employment and the assessment of employee promotion opportunities in the organization and the form of employment and the assessment of employee professional development opportunities.
Multiple Chinese Vehicle License Plate Localization in Complex Scenes

License plate localization is an essential part of automated vehicle license plate recognition (ALPR). This paper proposes a two-step approach of the vehicle license plate (LP) localization which includes a novel attempt for LP detection and a new method for LP skew correction. The proposed approach begins with detecting the LP region while classifying the LP type in the image using single shot multi-box detector (SSD) and then further precisely determining the LP corner points in detected region by fitting LP borderlines according to the character contours based on the multi-level thresholding binarization. The experiment evaluated the performance of the proposed method on 600 vehicle images (~800*800) captured under realistic complex environment. The result shows that LPs in 579 images can be localized exactly (96.50%accuracy) and processing speed is up to 27 FPS on average.
External Load Characteristic of Synchronous Generator with Wound Excitation and Permanent-Magnet Exciting Cores-Steady State Condition

The aim of this study is to develop a generator with field regulating capabilities in the rotor. This paper investigates the external load characteristics of a synchronous generator with axially arranged excitation and permanent magnet cores, using experiments and analyses. From these results, the validity of the magnetic circuit is confirmed. The experiments are carried out and measured using a prototype generator. The measured efficiency curve indicated, as a result of the prototype synchronous generator, has 86.4% efficiency at a field current of 1.0A. Characteristics of the generator are analyzed with 3D finite element (FE) analysis. The analytical and measured characteristics of the generator are compared and agree with each other. The synchronous generator, with the permanent magnet core's field capability, is obtained by supplying an axially arranged excitation core in the field current.
Increase in Nucleation Field of Nanocrystalline Nd(Fe,Co)B Magnets Due to Strengthening of Exchange Interaction—Computer Simulation

The effect of the strength of exchange interaction on the nucleation field <inline-formula> <tex-math notation="LaTeX">$H_{N}$ </tex-math></inline-formula> was studied by computer simulation with varying temperature as well as the size of the non-magnetic phase for Nd(Fe,Co)B magnets composed of fine grains. It was clarified that the spatial distribution of demagnetizing field <inline-formula> <tex-math notation="LaTeX">$H_{d}$ </tex-math></inline-formula> is widened by an increase in the size of the non-magnetic phase and that the ratio of the spatial size of <inline-formula> <tex-math notation="LaTeX">$H_{d}$ </tex-math></inline-formula> to the exchange length <inline-formula> <tex-math notation="LaTeX">$L_{\mathrm {ex}}$ </tex-math></inline-formula> is an important factor of determining <inline-formula> <tex-math notation="LaTeX">$H_{N}$ </tex-math></inline-formula>. Resultantly, the increase in the exchange strength increased <inline-formula> <tex-math notation="LaTeX">$H_{N}$ </tex-math></inline-formula>. Based on this calculation result, magnetic properties of Nd(Fe,Co)B magnets were simulated and it was found that a small amount of substitution of Co for Fe increases <inline-formula> <tex-math notation="LaTeX">$L_{\mathrm {ex}}$ </tex-math></inline-formula> and resultantly improves <inline-formula> <tex-math notation="LaTeX">$H_{N}$ </tex-math></inline-formula> at high temperatures.
Scene Recognition with Convolutional Residual Features via Deep Forest

Convolutional Neural Networks (CNNs) have made remarkable progress on image classification and other relative computer vision filed, which need large-scale data for training. In this paper, a method named DFCRF (Deep Forest with Convolutional Residual Features) is proposed. It is based on the gcForest proposed by Zhou and Feng. And we use a recent released AI Challenger dataset, containing around only 50,000 images mainly captured in China. Different from utilizing only CNNs, we use convolutional residual features for further recognition, followed by gradient-based XGBoost and cascade deep forest. Then, we conduct extensive experiments on the AI Challenger dataset and reconstructed Places2 dataset to show the effectiveness of our method.
Global Quantities Computation Using Mesh-Based Generated Reluctance Networks

This aim of this paper is to assess the efficacy of mesh-based generated reluctance network (MGRN) modeling for the performance evaluation of a permanent magnet linear machine under load conditions. In the previous publications related to the use of this modeling technique, only approach implementation under an open-circuit performance has been assessed. The MGRN approach is based on the discretization of the geometry using elementary reluctance blocks (reluctance elements). This modeling technique has been implemented in a software tool that allows the automated processing of an arbitrary geometry providing a gain in model development durations. Validation of the MGRN model has been realized by comparison with a finite element analysis (FEA). Comparisons showed that both local and global quantities evaluated by MGRN and FEA are in excellent agreement.
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice

Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning algorithm. Inspired by various philosophies about what constitutes a good criteria, different algorithms perform well on different datasets. This has motivated research into ensembles of active learners that learn what constitutes a good criteria in a given scenario, typically via multi-armed bandit algorithms. Though algorithm ensembles can lead to better results, they overlook the fact that not only does algorithm efficacy vary across datasets, but also during a single active learning session. That is, the best criteria is non-stationary. This breaks existing algorithms' guarantees and hampers their performance in practice. In this paper, we propose dynamic ensemble active learning as a more general and promising research direction. We develop a dynamic ensemble active learner based on a non-stationary multi-armed bandit with expert advice algorithm. Our dynamic ensemble selects the right criteria at each step of active learning. It has theoretical guarantees, and shows encouraging results on 13 popular datasets.
Control Enhancement of Dipole Emission Using Hybrid Metal-Dielectric Nanoantenna

A novel hybrid nanoantenna comprising a stack of two cylinders is proposed to control the enhancement of the dipole emission. The bottom cylinder is made of plasmonic metal to generate large field enhancement to boost the excitation rate of the dipole. The top cylinder is made of high permittivity low loss dielectric to enhance the far field emission intensity of the dipole while maintaining high quantum efficiency. The design principle revealed here can be extended to other geometries and is beneficial to the research fields such as fluorescence enhancement. , Raman spectroscopy. , and single photon source. , among others.
Low-SNR Asymptotic Capacity of MIMO Optical Intensity Channels With Peak and Average Constraints

The low-SNR asymptotic capacity of the multiple-input multiple-output (MIMO) optical intensity channel is studied under both average and peak intensity constraints. We focus on low SNR, which can be modeled as the scenario where both constraints proportionally vanish, or where the peak constraint is held constant while the average constraint vanishes. A capacity upper bound is derived and is shown to be tight at low SNR under both scenarios. The capacity achieving input distribution at low SNR is shown to be a maximally correlated vector-binary input distribution. Consequently, the low-SNR capacity of the channel is characterized. As a byproduct, it is shown that for a channel with peak intensity constraints only, or with peak intensity constraints and individual (per aperture) average intensity constraints, a simple scheme composed of coded ON–OFF keying, spatial repetition, and maximum-ratio combining is optimal at low SNR.
Performance Comparison of Using ACSR and HTLS Conductors for Current Uprating of 230-kV Overhead Transmission Lines

This work involves performance evaluation of current uprating of overhead transmission lines by using Aluminum Conductor Steel Reinforced (ACSR) and High Temperature Low Sag (HTLS) conductors. The test cases are 230-kV, double-circuit, single-bundle and twin-bundle, overhead transmission lines. The existing conductors are 1272 MCM ACSR, while the candidate conductors are five types of HTLS with comparable sizes. The performance comparison is carried out under a twofold ampacity condition. It can be concluded from numerical simulations that HTLS conductors are attractive for replacing ACSR conductors in current uprating. By using HTLS conductors, more clearance to ground is gained and it is feasible to reduce right of way. However, it is mentioned that the line loadability of HTLS conductors is slightly less than that of ACSR conductors so that steady-state stability limit is of concern.
A Novel Feature Selection Approach Based on Document Frequency of Segmented Term Frequency

Feature selection is a very important process in text classification. It can effectively eliminate redundant features and retain feature words with strong class distinguishing ability. In this paper, we propose a feature selection algorithm based on document frequency of segmented term frequency (STF-DF). In the algorithm, we also present two new concepts of “segmented term frequency” and “STF-DF. ” Then, we compare STF-DF with six commonly used feature selection algorithms (document frequency, information gain, chi-square, CMFS, NDM, and t-test) on three popular datasets (20 Newsgroups, Classic3, and WebKB). Experimental results show that our proposed algorithm can improve the accuracy of text classification and make the classification more effective.
Vascular Protection of Hydrogen Sulfide on Cerebral Ischemia/Reperfusion Injury in Rats

This study was undertaken to demonstrate the vascular protection of exogenous and endogenous hydrogen sulfide (H2S) on cerebral ischemia/reperfusion (I/R) injury. The effect of H2S on cerebrovascular dysfunction in middle cerebral artery (MCA) and neuronal damage were measured after cerebral I/R induced by transient middle cerebral artery occlusion (MCAO) in cystathionine c-lyase (CSE) knockdown and wild-type rats. The effect of sodium hydrosulfide (NaHS, donor of exogenous H2S), L-cysteine (L-Cys, substrate of endogenous H2S), and endothelium cells on the responses of isolated MCA derived from non-ischemic rats was also evaluated to assess the underlying mechanism of H2S-mediate cerebral vasodilation. The results revealed that the contraction and dilation of MCA profoundly decreased after cerebral I/R. The vascular dysfunction became more grievous in CSE knockdown rats than in wild-type rats. Interestingly, this vascular dysfunction was significantly alleviated by NaHS supplementation. Moreover, both NaHS and L-cysteine could induce remarkable relaxation in the isolated MCA, which was eliminated by co-application of potassium channel blockers ChTx and Apamin, or endothelial removal. By contrast, adding endothelium cells cultured in vitro together with ACh into the luminal perfusate could mimic non-NO and non-PGI2 relaxation in endothelium-denuded MCA, once CSE was knocked down from endothelium cells, and its effect on vasorelaxation was abolished. Furthermore, the indexes of neuronal injury were measured after cerebral I/R to confirm the neuroprotection of H2S, and we found that the neurological scores, cerebral infarction volume, brain water content, malondialdehyde content, and serum lactate dehydrogenase activity (a marker of cellular membrane integrity) were significantly higher in CSE knockdown rats than in normal control rats. It is not surprising that NaHS could alleviate the cerebral injury. These findings revealed that H2S has a protective effect on cerebral I/R injury via its upregulation of the endothelium-dependent contraction and dilation function of cerebral vessels, which may be related to activating potassium channel.
Polarization-Switchable Magneto-Electric Dipole

A polarization reconfigurable magneto-electric (ME) dipole antenna is presented. The ME dipole is fed by a pair of dipoles crossed through a 90° phase delay line. By controlling the state of the four p-i-n diodes that inserted in the crossed dipoles, the antenna can switch the polarization between LHCP and RHCP. A wide band and good radiation patterns can be realized by the proposed simple structure.
The Preliminary Development and Validation of a Trauma-Related Safety-Seeking Behavior Measure for Youth: The Child Safety Behavior Scale (CSBS).

Safety-seeking behaviors (SSBs) may be employed after exposure to a traumatic event in an effort to prevent a feared outcome. Cognitive models of posttraumatic stress disorder propose SSBs contribute to maintaining this disorder by preventing disconfirmation of maladaptive beliefs and preserving a sense of current threat. Recent research has found that SSBs impact children's posttraumatic stress symptoms (PTSS) and recovery. In this paper, we sought to develop and validate a novel 22-item Child Safety Behavior Scale (CSBS) in a school-based sample of 391 pupils (age 12-15 years) who completed a battery of questionnaires as well as 68 youths (age 8-17 years) who were recently exposed to a trauma. Of the sample, 93.1% (N = 426) completed the new questionnaire. The sample was split (n = 213), and we utilized principal components analysis alongside parallel analysis, which revealed that 13 items loaded well onto a two-factor structure. This structure was superior to a one-factor model and overall demonstrated a moderately good model of fit across indices, based upon a confirmatory factory analysis with the other half of the sample. The CSBS showed excellent internal consistency, r = . 90; good test-retest reliability, r = . 64; and good discriminant validity and specificity. In a multiple linear regression, SSBs, negative appraisals, and number of trauma types each accounted for unique variance in a model of PTSS. This study provides initial support for the use of the CSBS in trauma-exposed youth as a valuable tool for further research, clinical assessment, and targeted intervention.
Study on a GMDH-PID Controller Design Method Based on LASSO

This paper proposes a group method of data handling based PID (GMDH-PID) controller using the least absolute shrinkage and selection operator (LASSO) that is one of the sparse modeling methods. A database-driven PID (DD-PID) controller is one of the effective nonlinear PID controller design methods. However, some electrical control units in industrial systems cannot implement the algorithm because the DD-PID control scheme requires a large amount of memory capacity and high-performance processor. The GMDH-PID controller that approximately expresses the behavior of the DD-PID controller by combinations of simple nonlinear functions has been proposed to solve the above problem. Although the GMDH-PID method can reduce the amount of memory capacity and calculation costs, calculated coefficients in nonlinear functions may not stable due to the multicollinearity of input signals. In this research, instead of the least squares method, the LASSO is employed as a coefficient calculation method of the GMDH in which some coefficients can be calculated as zero in order to avoid the influence of the multicollinearity. The effectiveness of the proposed method is evaluated by simulation examples.
Cybersecurity of Connected Autonomous Vehicles : A ranking based approach

The concordant vision of the future automotive landscape foresees vehicles that are always connected to infrastructure and Cloud services, and that are equipped with autonomous driving or advanced driver assistance systems. It is clear that in a similar scenario cybersecurity of modern and future vehicles is paramount. With connected autonomous vehicles the protection from external attack will be an essential requirement, motivated by the outstanding safety implications of an autonomous vehicles remotely controlled by an attacker or a "malware". However, the automotive industry still lacks reliable and repeatable methods to assess the cybersecurity level of modern cars. This paper has a twofold contribution. First, it describes the ongoing effort of regulatory bodies within the European Union toward the definition of cybersecurity certification schemes. Second, it outlines the main requirements of a cybersecurity ranking approach that is suitable for certifying the security level of connected vehicles. Since improved cybersecurity guarantees will come at the expense of increased complexity and costs, the proposed ranking approach allows to assess whether the cybersecurity level is appropriate by considering the potential safety risks of a successful attack to the ranked system or subsystem.