Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication and Characterization of p-Cu2O on n-TiO2 Layer by Electrodeposition Method for Heterojunction Solar Cells Development

This study focused on the copper (I) oxide (Cu2O) that serves as an absorber layer, owing to its excellent optical properties, while titanium dioxide (TiO2) is a well-known material that has superior properties in solar cell development. In this work, the TiO2 nanorods layer was synthesised on a fluorine-doped tin oxide (FTO) glass substrate by a facile hydrothermal method followed by stacking the Cu2O layer using a low-cost electrodeposition method at different deposition times. Prior to deposition, a cyclic voltammetry (CV) measurement was performed, and the result showed that Cu2O films were successfully grown on the TiO2 nanorods layer with high uniformity. The crystallinity of the Cu2O/TiO2 film was increased when the deposition time was elevated. The strongest diffraction peak was detected in the sample deposited for 90 minutes. FE-SEM images revealed the formation of the pyramidal structure of Cu2O on the TiO2nanorod layer. The optical properties showed that the samples deposited at 60 minutes and above were red-shifted, with the estimated bandgap being slightly decreased when extending the deposition time. Meanwhile, the resistivity and sheet resistance of the as-prepared samples were increased. The performance of the solar cell was investigated, and the power energy conversion was slightly increased to 0.0267% for the heterojunction sample deposited at 90 minutes. Doi: 10.28991/HEF-2021-02-04-02 Full Text: PDF
Faith Factors, Character Strengths, and Depression following Hurricane Michael

ABSTRACT Natural disasters have increased exponentially in recent decades, imposing existential threats to humans. Yet, surprisingly little research has explored the role of religion and spirituality (R/S) and transcendence-related character strengths in post-disaster depression. Using data (N = 491) from victims of Category 5 Hurricane Michael, this study examined whether R/S factors and character strengths helped to buffer against post-disaster depressive symptoms. A hierarchical regression model revealed an inverse association of perceived spiritual support with depression, after adjusting for demographic and event-related factors; strength of faith and the use of prayer for coping were not significantly related with depression. Upon inclusion of character strengths into the model, those relationships changed dramatically. Hope and optimism negatively predicted depression, whereas strength of faith and prayer for coping did so positively. Follow-up moderation analyses found that victims reporting particularly high levels of R/S factors and particularly low levels of character strength experienced the most depressive symptoms. The findings suggest that the potential protection of R/S factors in disasters may actually be due to transcendence-related character strength factors.
Metal oxyhalides: an emerging family of nonlinear optical materials

Second-order nonlinear optical (NLO) materials have drawn enormous academic and technological attention attributable to their indispensable role in laser frequency conversion and other greatly facilitated applications. The exploration of new NLO materials with high performances thus has long been an intriguing research field for chemists and material scientists. However, an ideal NLO material should simultaneously satisfy quite a few fundamental yet rigorous criteria including a noncentrosymmetric structure, large NLO coefficients, desired transparent range, large birefringence, high laser damage threshold, and availability of a large-size single crystal. Therefore, the identification of promising compound systems, targeted design, and experience-based syntheses are crucial to discover novel NLO materials working in the spectral region of interest. As an important family of mixed-anion compounds, versatile metal oxyhalides containing metal-centered oxyhalide functional units ([MOmXn] (X = F, Cl, Br, and I)) are becoming a marvelous branch for interesting NLO materials. Especially, when the central metals are d0/d10 transition metals or heavy post-transition metals, a number of novel NLO materials with superior functionalities are expected. Our thorough review on the recent achievements of metal oxyhalides for NLO materials are divided into the fast-growing NLO metal oxyhalides with single type halogen anions and the newly identified NLO metal oxyhalides with mixed halogen anions. Here we mainly focus on the design strategy, structural chemistry, NLO-related properties, and structure–property correlation of the metal oxyhalides with relatively large NLO responses. We hope this review can provide an insight on the rational design and future development of emerging metal oxyhalides for NLO and other applications.
A Novel Double-Sided Offset Stator Axial-Flux Permanent Magnet Motor for Electric Vehicles

Axial-flux permanent magnet (AFPM) motors have been attracting great interest due to their key advantages of high-torque density and compact structure. Concentrated windings are commonly used for AFPM motors since they significantly reduce the radial length of the end windings. This paper proposes a novel double-sided stator single-rotor motor topology where one stator is offset by π radians. This arrangement can cancel significant space harmonics produced by the concentrated winding and reduce the core and permanent magnet losses. Analytical analysis and finite element analysis (FEA) are used to verify the principle and validate the topology. The simulation results demonstrate that this proposed double-sided offset stator motor can reduce the core loss and permanent magnet loss significantly at base speed compared with the conventional double-sided stator single-rotor motor. In addition, the magnetic core saturation and induced voltage for the double-sided offset stator motor are significantly reduced.
Current Practice in Climate Service Visualization: Taking the Pulse of the Providers’ Community

Climateurope Workshop on the Visualization of Climate Services Department organized the workshop in the framework of the Horizon 2020–funded Coordination and Support Action Climateurope. The workshop aimed to discuss different aspects of the state-of-the-art of visualizations used in climate services and produce a publication on the synthesis and recommendations. We invited participants from different projects linked to the Climateurope network, including EU Horizon 2020 (H2020) and European Research Area for Climate Services (ERA4CS) projects as well as a few national projects and private contracts. The workshop was attended by representatives of 22 projects.
Fairness in a Class Barring Power Control Random Access Protocol for Crowded XL-MIMO Systems

This article focuses on conceiving a random access (RA) protocol able to provide connections for a vast number of devices subject to intensive collisions in an extralarge massive multiple-input–multiple-output (XL-MIMO) system equipped with a uniform rectangular array (URA). The recently proposed strongest user collision resolution protocol for XL-MIMO (SUCRe-XL) presents a bias since devices closer to the base station (BS) are favored, while those farther ones are rejected, leading to poor connectivity and excessive access delays to most users not very close to the BS. The novel RA protocol for XL-MIMO scenarios proposed in this article is based on access class barring with power control (ACBPC). Exploring the visibility regions (VRs) intrinsic to XL-MIMO scenarios as a new degree of freedom, the proposed grant-based ACBPC-XL protocol can improve the probability of resolving pilot collisions. The protocol is able to manage a higher number of users in crowded applications, diminishing the access delay while improving connectivity fairness along the entire cell area since the access performance does not depend on the distance between users and BS. Numerical results indicate that in average $\approx $ 76% of users have improved connectivity when employing the ACBPC-XL instead of the SUCRe-XL protocol.
REVIEW ON THE DEVELOPMENTS OF EARTHQUAKE MONITORING AND EARLY DAMAGE ASSESSMENT SYSTEMS IN JAPAN

阪神・淡路大震災後, 地震防災対策の見直しが, 国や自治体それにライフライン事業者などにとって大きな課題となっている. その1つの大きな柱として, 地震計ネットワークの大幅な増設と早期被害推定システムの導入がある. 本文では, このようなリアルタイム地震防災の動きについて, 日本と米国における実際のモニタリングシステムや被害推定システムに焦点を当てて, これまでの発展の経緯, 現行のさまざまなシステムの紹介, 問題点と今後の展望などについて論ずる.
Effectiveness of Oral Probiotics in Reducing S. Mutans Count in Caries-active Children: A Comparison with Chlorhexidine and Herbal Mouthrinse (Hiora®)

Abstract Aim The purpose of this study was to compare the reduction in salivary mutans streptococci counts and to evaluate any change in the salivary pH values after the use of oral probiotics, chlorhexidine, and herbal mouthrinse. Method Oral probiotics, chlorhexidine, and herbal mouthrinse [Hiora®] were evaluated for their eﬃcacy against S. mutans in 75 caries-active children, aged 6-14 years. Results The values obtained were subjected to statistical analysis. Oral probiotics showed lesser reduction in the S. mutans count as compared to chlorhexidine but the values were statistically insigniﬁcant. Herbal mouthrinse was not as effective as the oral probiotics or chlorhexidine in reducing the S. mutans count. Conclusion Oral probiotics show a similar efficacy as the gold standard, chlorhexidine, in reduction of oral S. mutans. How to cite this article Kamble A, Jabin Z, Agarwal N, et al. Effectiveness of Oral Probiotics in Reducing S. Mutans Count in Caries-active Children: A Comparison with Chlorhexidine and Herbal Mouth-rinse (Hiora®). Int J Clin Pediatr Dent 2022;15(S-2):S207-S211.
Special Issue on Semantic Publishing with Formalization Papers1

Considering the abundance of scientific articles that are published every day, keeping up with the latest research is becoming a significant challenge for researchers in many fields. This is at least partially due to the fact that we are still holding on to an archaic paradigm of scientific publishing: the canonical way to publish scientific results is by writing them up in long English texts called articles, which are in the best case easy to read by human experts but remain mostly inaccessible to automated approaches (except on a very superficial level with text mining techniques). These articles then undergo peer reviewing, which is typically done in a way that is secretive and not standardized, with the effect that the reviewing process may lack transparency and the valuable comments from the reviewers cannot be reused or build upon. A range of approaches have been proposed to address some of these problems by making scientific texts machine-readable, allowing for automatic summarising, finding and retrieving information easier and even the ability to (partially) reason on the scientific texts themselves. Text mining approaches work reasonably well when it comes to simple entity extraction with techniques like named-entity recognition to extract the main concepts from a text (e.g. [1,14]), but accuracy dramatically drops with more complicated tasks like relation extraction or identifying links between entities [6,13,15]. The vast majority of existing approaches of making scientific texts machine-readable have one thing in common: they take the current paradigm of scientific articles for granted and therefore take them as their starting point to extract information. While it is important to try to process the vast amount of existing scientific literature that has the form of long English texts (and sometimes long texts in other languages), we should also think about how we can improve the way how we publish scientific insights in the first place. An important aspect of this is the vision of semantic publishing, which we mean here in the sense of genuine semantic publishing [9], where the machine-interpretable formal semantics cover the main scientific claims the work is making. Nanopublications [8], which are small RDF-based semantic
Interaction of Ethnicity and Arrival Method on Thrombectomy Delay: The Society of Vascular and Interventional Neurology Collaboration

Compared with non‐Hispanic White patients, non‐Hispanic Black (NHB) and Hispanic populations are less likely to receive acute treatment for ischemic stroke, and when they do, it can be delayed. We evaluated the interaction between arrival method and race or ethnicity on door‐to‐arterial puncture (DTAP) time in thrombectomy. We conducted a retrospective observational cohort study of consecutive adults who underwent endovascular thrombectomy from 14 US Comprehensive Stroke Centers (January 1, 2019–July 31, 2020). DTAP was assessed in a linear mixed model including an interaction term for race or ethnicity and arrival method. Of the 1908 included patients, 356 of whom were Hispanic, Hispanic and NHB patients experienced significant delays in DTAP compared with non‐Hispanic White patients (adjusted β Hisp =0.32, 95% CI, 0.08–0.55; β NHB =0.23; 95% CI, 0.04–0.41). More severe deficits were associated with shorter DTAP (β NIHSS per point −0.03; 95% CI −0.04 to −0.03), and arrival via emergency medical services or private vehicle versus transfer were associated with longer delays (β EMS =0.57; 95% CI, 0.41–0.74; β PV =1.27; 95% CI, 0.85–1.70). There was a significant interaction between Hispanic ethnicity and emergency medical services ( P =0.03) or private vehicle arrival ( P =0.04) in a direction favoring shorter treatment delays. Sites that treated a minority population of Hispanic patients (<50% being Hispanic) experienced a significant delay in DTAP among Hispanic and NHB versus non‐Hispanic White (median 72 minutes [interquartile range (IQR), 49–104] versus 83 minutes [IQR, 50–119] versus 58 minutes [IQR, 27–95], P <0.01), whereas sites treating a Hispanic majority showed no difference in DTAP ( P =0.39). Endovascular therapy is delayed in Hispanic and NHB patients when compared with non‐Hispanic White patients. Although arrival by emergency medical services or private vehicle also contributed to treatment delays, Hispanic ethnicity modified this effect such that there was less delay in DTAP. This may be related to communication barriers between caregivers and health care providers when a Hispanic patient is suddenly disabled from stroke.
Research of AC excited VSCF wind power generator excitation control system based on variable structure control

According to the characteristic of AC-excited variable speed constant frequency wind power generation,vector control technique is applied to excitation control of doubly fed induction generator(DFIG). The mathematic model of DFIG is a coupling nonlinear system in synchronous rotating m-t coordinate system.This paper proposes a novel control method.First,the original system model of DFIG is linearized and decoupled to be a pseudo-linear system which is made up of original system and inverse system.Then,the variable structure control laws of the pseudo-linear system is designed.Finally,the excitation control simulation model of DFIG is established.The simulation result demonstrafes that the combination of vector control and variable structure control can realize the independent decoupling control of active and reactive power in system output.Compared with PI controller,variable structure controller shows better dynamic response.
A Fast VANET-Assisted Scheme for Event Data Recorders

An event data recorder (EDR) is a device installed in a vehicle to record information. Similar to a black box in an airplane, an EDR is used in the study of automobile accidents. Many schemes have been proposed that use vehicle network technology to help record EDR data, including schemes involving storing data on roadside units or nearby vehicles and schemes leveraging blockchain technology. However, these schemes do not take into account the vehicle company’s server; with the increased use of autonomous vehicles, the data related to these vehicles are always uploaded to the vehicle company’s server. In this scenario, we classify the situation into different cases, according to whether or not it is an emergency and whether the vehicle and the server are connected. For these cases, we propose a scheme whereby a vehicle uploads the EDR data to a cloud server and sends the evidence of storage to the nearby vehicle through a vehicular ad hoc network. Our scheme offers a fast response due to the use of symmetric cryptography algorithms while also considering security requirements.
Public sector creativity: triggers, practices and ideas for public sector innovations. A longitudinal digital diary study

ABSTRACT This qualitative longitudinal digital diary study explores public sector creativity in practice, analysing public servants’ ideas, in terms of type and magnitude, and what practices public servants apply in coming up with these ideas and what initially triggered their creativity. Contrasting with extant research and assumptions, the findings indicate that public servants working in public executive agencies are creative, generating a diversity of ideas by applying a range of practices including expressions of autonomous and radical creativity. However, the findings also underscore the pragmatic nature of public sector creativity that is focused on keeping things running: incremental, practical and reactive.
Determinants of User Acceptance of the E-Government Services——the Case of Online Tax Filing and Payment System

The low adoption of e-Government service becomes the bottleneck of the e-Government's development.In order to improve the status,it is vary important to study the factors influencing adoption of e-Government service.The electronic tax-filing systems is a typical e-Government service.Using a theoretical model based on some theories such as technology acceptance model(TAM) and Bandwagon Pressure,this study examines factors influencing adoption of electronic taxfiling systems from user side.This paper reports on a survey collected 93 useful responses from business organizations in Zhejiang Province.The results indicate that perceived value,social influence and trust of government are important determinants of user adoption intention of electronic tax-filing systems,perceived easy of use,management readiness,trust of the net and external pressure are not the determinants.
Selection of Training Sample Length in Support Vector Regression Based on Information Entropy

Support vector regression(SVR) is a general machine learning method in recent years.It has been widely used in industry,economy and many other fields and has achieved good results.However,when the non-stationary data in large quantity to be studied,the accuracy of results will be influenced by the problem of larger number and the selection of training sample length.In order to eliminate the length of training sample and select the proper training sample,a method of selecting of training samples length based on information entropy was proposed.This method evaluates the stationarity of the data by the information entropy and then picks out the most stationary data for learning.According to the experiments,the method not only saves learning time,but also improves the accuracy of predictions.
A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

Lower limb dysfunction hinders the locomotive activity in individuals and compels them to use assistive devices such as prostheses or orthoses to restore the missing locomotive functions. Passive assistive devices have limitations in replacing the lost functionality. On the other hand, active devices could restore more natural locomotion using motorized joints. To make proper use of these embedded motors, a control architecture is required to translate the motion intent of the user into the intended prosthesis joint trajectories. In this article, we propose a temporal convolution-based online foot angle trajectory prediction network (FATP-N) that predicts the foot angular positions during unconstrained walking from the shank angular position measured using a single wearable motion tracker sensor. The data acquisition experiments were designed to reflect natural gait with turns, varying inclines, speeds, and mixed cadences. The trained models were prepared for real-time prediction by compressing them to reduce the storage and time complexity. The validations were performed on different motion conditions, including those that have not been used for model training, to verify the robustness of the model. The proposed FATP-N achieved high accuracy and could generate predictions in real time. The results indicate that the proposed approach offers a possible architecture for real-time control of powered intelligent prostheses.
Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identification

In person re-identification (Re-ID), the data annotation cost of supervised learning, is huge and it cannot adapt well to complex situations. Therefore, compared with supervised deep learning methods, unsupervised methods are more in line with actual needs. In unsupervised learning, a key to solving Re-ID is to find a standard that can effectively distinguish the difference (distance) between the features of images belonging to different pedestrian identities. However, there are some differences in the images captured by different cameras (such as brightness, angle, etc.). It is well known that the training of neural networks is mainly based on the distance between features, while in unsupervised learning, especially in unsupervised learning methods based on hierarchical clustering, the distance between features plays a more important role in the clustering phase. We improve the accuracy of a deep learning method based on hierarchical clustering under fully unsupervised conditions, starting from both feature and distance metrics. First, we propose to use spherical features, by normalizing the images in the feature space, to weaken the structural differences (length) between features, while saving the feature differences (direction) between different identities. Then, we use the sum of squared errors (SSE) as a regularization term to balance different cluster states. We evaluate our method on four large-scale Re-ID datasets, and experiments show that our method achieves better results than the state-of-the-art unsupervised methods.