Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational Intervention to Decrease Justification of Adolescent Dating Violence: A Comparative Quasi-Experimental Study

Adolescent dating violence has become a public health problem because of the associated high morbidity and mortality rates. Despite social awareness about dating violence, the high justification of violence among adolescents is one of the main risk factors for both perpetration and victimisation. Therefore, the objective of the present work was to evaluate the effectiveness of an educational intervention in reducing the justification of violence in adolescent dating. A quasi-experimental, longitudinal, prospective study with a control group was conducted. The study was carried out in six different schools in the Region of Murcia (Spain), and the participants were 854 students aged 14–18 years. The educational intervention was focused on reducing the justification of adolescent dating violence and consisted of 9 weekly 1 h group sessions. The Justification of Verbal/Coercive Tactics Scale (JVCT) and the Attitudes About Aggression in Dating Situations (AADS) survey were administered at baseline and at the end of the intervention in order to determine the justification of psychological and physical violence, respectively. At baseline, the justification of physical violence was at a medium-to-high level in boys (76.8%) and girls (56.7%), whereas psychological violence was much less justified. Concretely, female psychological violence was justified by 19.5% boys and 16.7% girls, while male violence was justified by 19.0% boys and 17.8% girls. After the educational intervention, a significant decrease in physical violence justification, especially in the AADS dimension of female aggression, was observed. The effect of the intervention was especially evident in psychological violence justification: a statistically significant difference was observed in the JVCT scores of boys (−6.4 and 1.3 points in the intervention and control groups, respectively; p = 0.031), but not of girls (p = 0.594). In conclusion, the educational intervention was adequate to reduce the justification of dating violence among the participants. It may provide adolescents with the skills and resources necessary to confront and resolve conflicts in relationships in a non-violent manner.
An Intelligent Prediction of the Next Highly Cited Paper Using Machine Learning

Highly cited articles capture the attention of significant contributors in the research community as an opportunity to improve knowledge, source of ideas or solutions, and advance their research in general. Typically, these articles are authored by a large number of scientists with international collaboration. However, this could not be the only reason for an article to be highly cited, there might be several other characteristics for an article to be more attractive to researchers and readers. In other words, there are a few other characteristics that help articles/papers to be more than others to appear in search engines or to grab readers’ attention. In this study, we modeled several machine-learning methods with a set of articles, and journal characteristics including authors-count, title characteristics, abstract length, international collaboration, number of keywords, funding information, journal characteristics, etc. We extracted 20 characteristics and developed multiple machine-learning models to automate highly-cited papers recognition from regular papers. In experiments conducted with an ensemble machine learning algorithm, 97% recognition accuracy was achieved. Other algorithms including a deep learning method using LSTMs also achieved high recognition accuracy. Such high performances can be utilized for a promising HCP auto-detection system in the future.
STUNNER: Radar Echo Extrapolation Model Based on Spatiotemporal Fusion Neural Network

Radar echo extrapolation based on deep learning is an important method for conducting precipitation nowcasting. Radar echo sequence data have spatiotemporal correlations and nonrigid movements of the radar echo. According to the characteristics of radar data, this study proposes a new spatiotemporal fusion neural network called STUNNER. STUNNER implements a two-stream spatiotemporal fusion strategy to extract and fuse spatial and temporal signals. Specifically, it uses a novel cross-network embedding method to achieve efficient spatiotemporal fusion; the fusion integrates a temporal differencing network (TDN) and a spatiotemporal trajectory network (STTN). The TDN models the high-order nonstationarity of the radar sequence to learn the motion trend. The STTN optimizes the convolution operator in a spatiotemporal long short-term memory model to extract the transient variations from the radar images. We compare STUNNER with the other five models on two public datasets. On the radar echo extrapolation task, STUNNER achieves the best performance.
Topology Planning Using Q-Learning for Microwave-Based Wireless Backhaul Networks

Today, a microwave-based solution is the one commonly used for wireless backhaul networks as it has high capacity and can be easily deployed. For such a microwave-based wireless backhaul network, a well-designed topology is important for efficient capacity utilization and high-quality mobile services. In an earlier work, we presented an approach for topology planning for microwave-based wireless backhaul networks, where an integer linear programming (ILP) model was formulated and a heuristic algorithm was also developed, subject to various constraints. However, the previous heuristic algorithm may merely lead to local optima, and moreover, only a single set of weight factors was considered for the optimization. This cannot guarantee joint optimization over all the relevant performance aspects. We overcome these drawbacks here by employing the Q-learning technique for this topology planning problem. This consists of (a) re-optimizing an initial topology based on a specific set of weight factors and (b) finding an optimal set of these weight factors. To accelerate the Q-learning process, we also develop a parallel Q-learning system to find the optimal set of weight factors. Simulation results indicate that the Q-learning based approach can jointly optimize multiple system objectives and outperforms our earlier heuristic algorithm. The parallel learning system can also significantly expedite the learning process.
Probabilistic interpretation of algebraic inequalities related to reliability and risk

The paper explores the probabilistic interpretations of algebraic inequalities and presents several findings. First, the inequality of the additive ratios can be used to increase the probability of an event occurring within a set of mutually exclusive and exhaustive events. The interpretation of this inequality produced a counter‐intuitive result, that for suppliers delivering the same quantity of reliable products alongside unknown numbers of unreliable products, the probability of purchasing a reliable product from a randomly selected supplier is higher than the probability of purchasing a reliable product from the market formed by all suppliers. Next, the paper discusses how averaging the reliabilities of components from different varieties can lead to a significant overestimation of the calculated system reliability, as demonstrated by another algebraic inequality interpretation. Finally, the paper derives tight bounds for the reliability of demand in a load‐strength interference model by interpreting the Hayashi's inequality. Notably, these bounds do not depend on the shape of the load distribution and only require the strength distribution to be known in relatively small vicinities of the lower and upper bound of the load.
The representations of arithmetic fact knowledge in memory and their development

The present dissertation focused on one aspect of mathematical knowledge: fact knowledge. More specifically, we focused on the relation between the memory representations of simple multiplication and division facts (both operands < 10), as this remains a debated issue. This dissertation contains an introductory chapter (Chapter 1), four empirical chapters (Chapters 2-5) and a general discussion (Chapter 6). In Chapter 2, we used the number-matching task (measuring automatic activation of fact knowledge) and we expected to find stronger interference effects for division problems (compared to inverse division problems) if an independent division network exists. This was not confirmed as both problem types led to automatic activation. In two subsequent studies, we focused on developmental changes in multiplication and division performance. In Chapter 3, we showed that the formation of a memory network of multiplication facts is a fast-developing process. This study laid out the foundations for Chapter 4, in which we investigated both multiplication and division performance from the moment a child acquires the multiplication and division tables until the end of fourth grade. All effects that have been robustly observed in adults were also observed in children, quickly after they acquired the tables. Moreover, all effects observed in multiplication performance were also present in division performance and strong developmental parallels between both operations were observed. These results are a necessary condition to exclude the existence of independent networks. In Chapter 5 we investigated adult multiplication and division performance by making use of a learning paradigm. Clear evidence for cross-operation transfer of learning was found, which is not in line with the idea of independent representations at high skill levels. Taken together, using different paradigms and different populations, the results of the present studies speak against the idea of independent memory networks for multiplication and division facts and show that it is more correct to consider multiplication and division as mutually dependent and interacting skills.
Do Industry Peers across and within Regions
Affect Corporate Environmental Responsibility?
Evidence from Chinese Listed Companies

In the context of China’s development of modern environmental governance systems, it is crucial to recognise the vital role played by businesses. The internal impetus for environmental governance will be generated by accelerating the emergence of a universal industry trend of enterprises proactively fulfilling their environmental responsibilities. Therefore, this study utilises listed A-share companies in Shanghai and Shenzhen stock exchanges from 2010 to 2020 as samples to investigate the peer impacts of corporate environmental responsibility (CER). It was discovered that industry peers across and within regions might influence CER. When companies meet their environmental duties, the peer effects of CER may be seen as convergent responsiveness to external pressure. In a cross-regional context, the combination of industry competitive pressure and severe environmental uncertainty may greatly amplify the peer implications of CER, and small businesses are more susceptible to convergence. In an intra-regional scenario, environmental regulatory pressure has dramatically diminished the peer effects of CER, and the high-level marketisation process ensures that environmental regulatory pressure will favour the independent fulfilment of CER. Moreover, this phenomenon is magnified in state-owned enterprises.
Unravelling the Impact of Cyclic Mechanical Stretch in Keratoconus—A Transcriptomic Profiling Study

Biomechanical and molecular stresses may contribute to the pathogenesis of keratoconus (KC). We aimed to profile the transcriptomic changes in healthy primary human corneal (HCF) and KC-derived cells (HKC) combined with TGFβ1 treatment and cyclic mechanical stretch (CMS), mimicking the pathophysiological condition in KC. HCFs (n = 4) and HKCs (n = 4) were cultured in flexible-bottom collagen-coated 6-well plates treated with 0, 5, and 10 ng/mL of TGFβ1 with or without 15% CMS (1 cycle/s, 24 h) using a computer-controlled Flexcell FX-6000T Tension system. We used stranded total RNA-Seq to profile expression changes in 48 HCF/HKC samples (100 bp PE, 70–90 million reads per sample), followed by bioinformatics analysis using an established pipeline with Partek Flow software. A multi-factor ANOVA model, including KC, TGFβ1 treatment, and CMS, was used to identify differentially expressed genes (DEGs, |fold change| ≥ 1.5, FDR ≤ 0.1, CPM ≥ 10 in ≥1 sample) in HKCs (n = 24) vs. HCFs (n = 24) and those responsive to TGFβ1 and/or CMS. PANTHER classification system and the DAVID bioinformatics resources were used to identify significantly enriched pathways (FDR ≤ 0.05). Using multi-factorial ANOVA analyses, 479 DEGs were identified in HKCs vs. HCFs including TGFβ1 treatment and CMS as cofactors. Among these DEGs, 199 KC-altered genes were responsive to TGFβ1, thirteen were responsive to CMS, and six were responsive to TGFβ1 and CMS. Pathway analyses using PANTHER and DAVID indicated the enrichment of genes involved in numerous KC-relevant functions, including but not limited to degradation of extracellular matrix, inflammatory response, apoptotic processes, WNT signaling, collagen fibril organization, and cytoskeletal structure organization. TGFβ1-responsive KC DEGs were also enriched in these. CMS-responsive KC-altered genes such as OBSCN, CLU, HDAC5, AK4, ITGA10, and F2RL1 were identified. Some KC-altered genes, such as CLU and F2RL1, were identified to be responsive to both TGFβ1 and CMS. For the first time, our multi-factorial RNA-Seq study has identified many KC-relevant genes and pathways in HKCs with TGFβ1 treatment under CMS, suggesting a potential role of TGFβ1 and biomechanical stretch in KC development.
Eigenvector-based Dimensionality Reduction for Human Activity Recognition and Data Classification

In the context of appearance-based human motion compression, representation, and recognition, we have proposed a robust framework based on the eigenspace technique. First, the new appearance-based template matching approach which we named “Motion Intensity Image” for compressing a human motion video into a simple and concise, yet very expressive representation. Second, a learning strategy based on the eigenspace technique is employed for dimensionality reduction using each of PCA and FDA, while providing maximum data variance and maximum class separability, respectively. Third, a new compound eigenspace is introduced for multiple directed motion recognition that takes care also of the possible changes in scale. This method extracts two more features that are used to control the recognition process. A similarity measure, based on Euclidean distance, has been employed for matching dimensionally-reduced testing templates against a projected set of known motions templates. In the stream of nonlinear classification, we have introduced a new eigenvector-based recognition model, built upon the idea of the kernel technique. A practical study on the use of the kernel technique with 18 different functions has been carried out. We have shown in this study how crucial choosing the right kernel function is, for the success of the subsequent linear discrimination in the feature space for a particular problem. Second, building upon the theory of reproducing kernels, we have proposed a new robust nonparametric discriminant analysis approach with kernels. Our proposed technique can efficiently find a nonparametric kernel representation where linear discriminants can perform better. Data classification is achieved by integrating the linear version of the NDA with the kernel mapping. Based on the kernel trick, we have provided a new formulation for Fisher's criterion, defined in terms of the Gram matrix only.
Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II‐III colon cancer

Abstract The current tumor‐node‐metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II‐III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50‐layer residual network model for predicting disease‐free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high‐risk stage II and III CC patients with high‐collagenDL classifier, rather than low‐collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II‐III CC patients.
Ultrasonographic Evaluation of Entheseal Fibrocartilage in Patients with Psoriatic Arthritis, Athletes and Healthy Controls: A Comparison Study

The aims of this study were as follows: (1) To evaluate the entheseal fibrocartilage (EF) during Achilles tendon insertion in patients with Psoriatic Arthritis (PsA) by using power Doppler ultrasound (PDUS), (2) to assess the intra and inter-reader reliability of the evaluation of EF thickness, (3) to compare the EF thickness of PsA patients, athletes and healthy controls (HCs), and (4) to evaluate the correlations between EF abnormalities, disease activity and functional indices in PsA. Methods: Consecutive PsA patients attending our unit were asked to participate. HCs and agonist athletes were enrolled as a control group. A bilateral PDUS evaluation of Achilles tendons was performed in order to evaluate the EF in all patients and controls. Results: In total, 30 PsA patients, 40 athletes and 20 HCs were enrolled. The median (IQR) EF thickness among the PsA patients, athletes and HCs was 0.035 cm (0.028–0.04) cm, 0.036 (0.025–0.043) cm and 0.030 (0.020–0.038) cm, respectively (p = 0.05 between PsA patients and HCs). The intra-reader reliability was excellent [ICC (95% CI) of 0.91 (0.88–0.95)] and the inter-reader reliability was good (0.80 (0.71–0.86). The assessment of EF was feasible, with a mean time of 2 min. No correlations were found with disease activity indices in PsA patients. Conclusion: The assessment of EF is a feasible and reproducible test and may be explored as a potential imaging biomarker.
Novel design method of quasi-orthogonal space-time block code

Since Jafarkhani gave the concept on the non-orthogonal space time block code,the constructing methods of it are always one of the hot research topics.This paper proposed a new quasi-orthogonal space-time block code(QOSTBC) for four transmits.Obtained a new rotated QOSTBC when input signal was preprossed by a rotation transformation,to improve the code performance.Adopted maximum likelihood(ML) decoding algorithm at receiver.The numerical simulation results show that the performance of the unpreprocessing QOSTBC is similar to the known QOSTBC.However,BER performance of the rotated QOSTBC is improved 2dB at least.
Multiwavelength fluorescence lidar observations of smoke plumes

Abstract. A five-channel fluorescence lidar was developed for the study of atmospheric aerosol. The fluorescence spectrum induced by 355 nm laser emission is analyzed in five spectral intervals using interference filters. Central wavelengths and the widths of these five interference filters are, respectively, as follows: 438 and 29, 472 and 32, 513 and 29, 560 and 40, and 614 and 54 nm. The relative calibration of these channels has been performed using a tungsten–halogen lamp with a color temperature of 2800 K. This new lidar system was operated during summer–autumn 2022, when strong forest fires occurred in the Moscow region and generated a series of smoke plumes analyzed in this study. Our results demonstrate that, for urban aerosol, the maximal fluorescence backscattering is observed in a 472 nm channel. For the smoke, the maximum is shifted toward longer wavelengths, and the fluorescence backscattering coefficients in 472, 513 and 560 nm channels have comparable values. Thus, from the analysis of the ratios of fluorescence backscattering in available channels, we show that it is possible to identify smoke layers. The particle classification based on single-channel fluorescence capacity (ratio of the fluorescence backscattering to the elastic one) has limitations at high relative humidity (RH). The fluorescence capacity indeed decreases when water uptake of particles enhances the elastic scattering. However, the spectral variation of fluorescence backscattering does not exhibit any dependence on RH and can be therefore applied to aerosol identification.
Local Near-Rings Of Cardinality P2

The main result of this paper is the determination of all nonisomorphic local near-rings with = which are not near-fields. Together with the fundamental paper [6] by Zassenhaus on near-fields and the corollary to Theorem 1 of [2], this 2 paper gives a complete description of all local near-rings of order p2. We recall that a unitary near-ring N is called local if the subset L of elements in N without left inverses is an (N, N)-subgroup and N ≠ J(N). (J(N) denotes the radical of N given in [1]. ) In [3] it was proved that N ≠ J(N) whenever L is an ideal of N. (For previous result s concerning local near-rings we refer the reader to [3].)
On the link between Bayesian estimators and Classical Bias Corrections in Time Series

There is a dichotomy between Bayesian-flat-prior and classical short sample analysis in the estimation of time series models. The first concludes that OLS is a very good estimator, the second concludes that OLS needs to be adjusted upward, to correct the small sample bias. We show that repeating the Sims and Uhlig (1991) helicopter tour over the land of the AR(1) model with an unknown constant term only deepens this dichotomy. However, we point out that if the initial conditions and constant term are made consistent using the Uhlig (1994a)’s assumptions, this gives suciently small weight to parameters implying huge growth rates, and a Bayesian would also conclude that OLS needs to be adjusted upward. In this sense, the two views are easily reconciled.
Study of rarefied shear flow by the discrete velocity method

The application of a simple discrete velocity model to low Mach number Couette and Rayleigh flow is investigated. In the model, the molecular velocities are restricted to a finite set and in this study only eight equal speed velocities are allowed. The Boltzmann equation is reduced by this approximation to a set of coupled differential equations which can be solved in closed form. The fluid velocity and shear stress in Couette flow are in approximate accord with those of Wang Chang & Uhlenbeck (1954) and of Lees (1959) over the complete range of Knudsen number. Similarly, the Rayleigh flow solution is remarkably like those found by other investigators using moment methods.