Miracle-dz/UniFAR-FTUs huggingface dataset:
The Changing Face of Journalistic Autonomy: A Case Study of De Standaard (1976–2020)

ABSTRACT Autonomy is considered a prerequisite for the press to function in service of the public. Over the past few decades, both journalism and society at large have been subjected to significant change and instability, potentially affecting journalists’ independent modes of production. We, therefore, argue that a re-evaluation of journalism’s institutional strength to perform the societal functions bestowed upon it by the public is imperative. This paper attempts to contribute to this re-evaluation by examining to what extent legacy media journalists act according to their own professional logic rather than being historically conditioned to act according to external logics imposed on the field of journalism. We adopt a qualitative historical approach grounded in the theoretical framework of field theory for our examination of the development of journalistic autonomy at Flemish newspaper De Standaard. Based on a rich body of source material that emphasizes the rarely considered perspective of chief editors, we reconstruct journalistic autonomy as a dynamic concept shaped by an intricate web of interconnected macro-, meso- and micro-level developments and interactions.
An iterative algorithm for vector equilibrium problems

A projection iterative algorithm for solving scalar equilibrium problems was generalized to solve a vector equilibrium problem defined on Euclidean space.By using nonlinear scalarization function,the vector optimization problems were changed into the scalar optimization problems.In this way,a convergence analysis of the algorithm for solving the vector equilibrium problem was given,with the result that the algorithm was converged under some appropriate conditions.
Methods for Predicting Abnormal Formation Pressure and Their Applications

The causes of creating abnormal formation pressure was analyzed. It was pointed out that empirical equation method and equivalent depth method were the major methods for seismic prediction of abnormal formation pressure. These are used for deriving empirical coefficient of blocks of Tuha Basin and the empirical equation method is employed to predict and contrast tens of exploration wells, with relative error within 10%. The equivalent depth method is used to establish normal compaction trend line for pressure prediction in Xiaocaohu and Shengbei Areas, its error is within 10%. It can meet the engineering requirements, it shows that the empirical equations and equivalent depth methods have good application effects.
Scale to Measure Medical, Nursing and Midwifery Students’ Engagement in an E-learning Histology Course

SUMMARY: E-learning courses become increasingly important and relevant in medicine and health sciences over the last decade. However, there are few teaching experiences of e-learning histology courses published in the literature worldwide. Moreover, most of these studies focus on the didactic aspects of the course without exploring student participation. The study presented below aimed to validate a scale to measure student participation in an e-learning histology course. We provide evidence of validity of the instrument based on its internal structure for use with medical, nursing, and midwifery students. The participants in this study were a group of 426 Chilean medical, nursing and midwifery students from a public university who completed the questionnaire in two consecutive semesters (2020-2021). Data from the first group of students were used to perform an exploratory factor analysis (EFA), while data from the second group of participants were used to perform a confirmatory factor analysis (CFA). The three factors identified according to the CFA were: “Habits of online,” “Motivation for online learning,” and “Interaction of online”. After eliminating one of the initial items of the instrument, the scale showed acceptable psychometric properties suggesting that it is a useful instrument to measure students’ perception of their participation in e-learning histology courses. The factors identified through the validation of the instrument provide relevant information for teachers and curriculum developers to create and implement different ways of encouraging student participation in e-learning histology courses to support online learning.
Education Research: Junior Neurology Residents Achieve Competency but Not Mastery After a Brief Acute Ischemic Stroke Simulation Course

Simulation bootcamps are used to onboard neurology trainees. It is not known whether these bootcamps result in competency for acute ischemic stroke (AIS). For this prospective, single-center pre-post educational intervention study, the Angoff standard setting method was used to determine a Minimum Passing Score (MPS) and Mastery Score for 2 AIS simulations. Junior neurology residents completed precourse knowledge and confidence assessments and had traditional didactic teaching. A week later, each resident completed the first scored AIS simulation. Each resident then practiced stroke care in an unscored simulation. Two to 8 weeks later, each resident was evaluated in an unannounced AIS simulation (the post-test). Postgraduate year (PGY)-3 adult neurology senior residents also completed a knowledge and confidence assessment and were scored on just the AIS post-test case. Using independent and pairedttests, respectively, we compared the junior residents' retention test performance to their baseline assessment and to senior residents' performance.Thirteen junior residents (9 PGY-2 adult neurology residents and 4 PGY-3 child neurology residents) participated in the course. Only 3 junior residents (23%) initially achieved the MPS in the first AIS simulation. After the simulation course, 9 junior residents (69%) achieved the MPS threshold. Although none achieved mastery, junior residents' mean performance score in the simulation improved (mean score preintervention [SD] = 10.3 [2.8] vs mean score postintervention [SD] = 15.7 [2.6],p< 0.001) and their confidence increased (mean score preintervention [SD] = 3.3 [1.9] vs mean score postintervention [SD] = 4.9 [1.2],p< 0.001,d= 1.7). Eight PGY-3 adult neurology residents were scored on the AIS post-test. Five reached MPS (63%), and 1 demonstrated mastery. The simulation scores of the postcourse juniors and seniors were similar (junior resident mean score [SD] = 15.7 [2.6] vs senior resident mean score [SD] = 16.0 [2.5],p= 0.793). A brief AIS simulation course may improve junior residents' performance and confidence to a level comparable with senior residents, although not to mastery.
AEC_GAN: Unbalanced Data Processing Decision-Making in Network Attacks Based on ACGAN and Machine Learning

Using deep learning and machine learning techniques for network intrusion detection is of great significance for enhancing the defense capability of network security systems. Given the characteristics of generative adversarial networks, such as the approximate consistency of generated samples with the input data distribution but with a random distribution within a certain bounded interval, and in response to the problem of insufficient classification performance and detection omission caused by the imbalance of different degrees of data categories and quantities in network intrusion traffic, and in light of the fact that the effectiveness of existing classification algorithms based on unbalanced traffic data still has some room for improvement, this paper proposes a network intrusion detection strategy based on auxiliary classifier generative adversarial networks. The data expansion experiments are conducted with the intrusion detection dataset NSL-KDD. The data are classified into twenty-three categories before and after the expansion by binary classification validation. The results show that the expansion of the generated samples for unbalanced network traffic data improve the subsequent recognition effect significantly. Finally, five classification performance index verification experiments are conducted. The results prove that the strategy of this paper performs better in accuracy, precision, recall rate and F-value indexes, and is capable of obtaining a large number of features from limited samples and inferring complete data distribution based on fewer features. The model as a whole has stronger generalization ability and defense effect.
Quaternionic Stockwell transform

ABSTRACT In this article, we extend the Stockwell transform to the space of all square integrable quaternion-valued functions on the real line, using the convolution of quaternion-valued functions. We prove that the extended Stockwel transform satisfies the Parseval's formula, inversion formula, and uncertainty principle. We also characterize the range of the Stockwell transform on and prove a convolution theorem for the extended Stockwell transform. Applying the convolution theorem, we extend the transform to a suitable Boehmian space of quaternion-valued functions.
Search for Supersymmetry in Hadronic Final States Using MT2 in pp Collisions at sqrt(s) = 7 TeV and Discovery Potential of Top Partners in Composite Higgs Models

The standard model of particle physics has been extraordinarily successful in describing the vast majority of collider data of the past decades. However, a number of outstanding problems remain. In an attempt to overcome the deficiencies of the standard model, a variety of new physics models have been developed, among which composite Higgs and supersymmetric models are promising candidates. In this thesis, a search for supersymmetry is presented using a data sample of 4.73 fb−1 of proton-proton collisions collected at √ s = 7 TeV with the CMS detector at the Large Hadron Collider. Fully hadronic final states are selected based on the variable MT2, an extension of the transverse mass in events with two invisible particles. The salient properties of the MT2 kinematic variable are presented and its ability to separate potential signal events from the standard model backgrounds is discussed. Two complementary searches are performed. The first targets the region of parameter space with medium to high squark and gluino masses, whereas the second is optimized to be sensitive to events with a light gluino and heavy squarks. In both analyses, the backgrounds arising from standard model processes are estimated using data control regions as well as simulation. No significant excess of events over the standard model expectations is observed. Exclusion limits are derived for the parameter space of the constrained minimal supersymmetric extension of the standard model, as well as on a variety of simplified model spectra. Composite Higgs models are attractive, non-supersymmetric extensions of the standard model, in which a naturally light Higgs boson emerges from the spontaneous breaking of the global symmetry of a new strongly interacting sector. In these models, one or more sets of heavy top partners are typically introduced with masses well within the LHC reach. The collider signatures that these new quarks can produce are analyzed in detail. The final states with two (same-sign) or three leptons are found to be the most promising discovery channels. Exotic quarks of charge 5/3 are a distinctive feature of these models. A new method is presented to reconstruct the mass of such exotic quarks from their leptonic decay, without relying on jets in the final state.
Efficient weighted least-squares algorithm for the design of FIR filters

The weighted least-squares (WLS) technique has been widely used for the design of digital FIR filters. In the conventional WLS, the filter coefficients are obtained by performing a matrix inverse operation, which needs computation of O(N3). The authors present a new WLS algorithin that introduces an extra frequency response including implicitly the weight function. In the new algorithm, the filter coefficients can be solved just by a matrix vector multiplication. It reduces the computational complexity from O(N3) to O(N2).
Reduced Nodal Admittance Matrix Method for Probabilistic GIC Analysis in Power Grids

Efficient probabilistic geomagnetically induced current (GIC) analysis in power grids provides tools for assessing and mitigating small-probability tail risks of geomagnetic disturbances, especially in early warning and real-time scenarios. This letter employs the reduced nodal admittance matrix (RNAM) to speed up GIC calculation based on Kron reduction. Moreover, the proposed RNAM method is used to achieve a more efficient analysis of probabilistic GICs, which considers the uncertainty of the substation grounding resistances. The novel method is compared with the classical algorithms including the nodal admittance matrix method, the Lehtinen-Pirjola method, and the bus admittance matrix method, and its efficiency improvement is illustrated with several power grid test cases.
The Effect of Oxygen Vacancies on the Diffusion Characteristics of Zn(II) Ions in the Perovskite SrTiO3 Layer: A Computational Study

A highly polar perovskite SrTiO3 (STO) layer is considered as one of the promising artificial protective layers for the Zn metal anode of aqueous zinc-ion batteries (AZIBs). Although it has been reported that oxygen vacancies tend to promote Zn(II) ion migration in the STO layer and thereby effectively suppress Zn dendrite growth, there is still a lack of a basic understanding of the quantitative effects of oxygen vacancies on the diffusion characteristics of Zn(II) ions. In this regard, we comprehensively studied the structural features of charge imbalances caused by oxygen vacancies and how these charge imbalances affect the diffusion dynamics of Zn(II) ions by utilizing density functional theory and molecular dynamics simulations. It was found that the charge imbalances are typically localized close to vacancy sites and those Ti atoms that are closest to them, whereas differential charge densities close to Sr atoms are essentially non-existent. We also demonstrated that there is virtually no difference in structural stability between the different locations of oxygen vacancies by analyzing the electronic total energies of STO crystals with the different vacancy locations. As a result, although the structural aspects of charge distribution strongly rely on the relative vacancy locations within the STO crystal, Zn(II) diffusion characteristics stay almost consistent with changing vacancy locations. No preference for vacancy locations causes isotropic Zn(II) ion transport inside the STO layer, which subsequently inhibits the formation of Zn dendrites. Due to the promoted dynamics of Zn(II) ions induced by charge imbalance near the oxygen vacancies, the Zn(II) ion diffusivity in the STO layer monotonously increases with the increasing vacancy concentration ranging from 0% to 16%. However, the growth rate of Zn(II) ion diffusivity tends to slow down at relatively high vacancy concentrations as the imbalance points become saturated across the entire STO domain. The atomic-level understanding of the characteristics of Zn(II) ion diffusion demonstrated in this study is expected to contribute to developing new long-life anode systems for AZIBs.
An Algorithm for Image Copy-Paste Forgery Detection by Pixel Matching

Copy-paste is an efficient type of image forgery,in which a part of a digital image is copied and pasted to another part of the same image in order to forge the image. Based on image pixel matching,a new image forensics approach nas proposed for detecting copy-paste forgery. First,discrete wavelet transform(DWT)was applied to the input image to yield a reduced dimension representation. Then the phase correlation was computed to estimate the spatial offset between the copied part and the pasted part. The copy-paste regions can be easily located by virtue of pixel matching,which shifted the input image by the spatial offset to calculate the difference between the image and its shifted version. At last,the mathematical morphological operations were used to improve the detection. Experimental results show that the method is easily implemented;it can achieve an effective and accurate location for tampered areas and is reasonably robust to various types of copypaste post processing such as filtering,lossy JPEG,JPEG2000 compression and the combination of these operations.
Semi-Supervised Bolt Anomaly Detection Based on Local Feature Reconstruction

During the process of high-speed railway online maintenance, anomaly detection of targets in grease injection facilities is extremely challenging. Environmental uncertainty (noise disturbance, various illumination conditions, changeable camera perspective, etc.) limits the performance of existing unsupervised anomaly detection methods, which tend to struggle in such complex backgrounds without prior knowledge of anomalies. Therefore, we propose a novel semi-supervised anomaly detection algorithm that only learns from anomaly-free samples and annotations while can recognize both anomaly-free and anomaly targets (key areas) and detecting anomalous targets simultaneously. We first propose a key-area recognition module (KRM) to segment anomaly-free targets and predict coordinates of both anomaly-free and anomalous key areas’ center points (key points). By using the predicted coordinates, we generated a circular mask to erase features within the key areas. Based on the erased features, a local feature reconstruction and segmentation module (LFRSM) is proposed to restore the latent embeddings within the key areas and segment both anomaly-free and anomalous key areas. The final anomaly results are obtained by analyzing the original segmentation results of KRM and the reconstructed segmentation results of LFRSM. To validate the effectiveness of the proposed algorithm, we develop a dataset, called BOLT-DET, for bolt anomaly detection in complex scenes. The proposed algorithm outperforms other existing state-of-the-art (SOTA) anomaly detection algorithms (SPADE, DFR, CFLOW, Student-Teacher, PatchCore, and DRAEM) with mean intersection over union (mIoU) of 0.66, area under the receiver operating characteristic curve (AUROC) of 0.92, and F1-Measure of 0.69 on BOLT-DET. It can accurately detect anomalies and meet the requirement of practical industrial applications.
Uncovering Phytotoxic Compounds Produced by Colletotrichum spp. Involved in Legume Diseases Using an OSMAC–Metabolomics Approach

Different fungal species belonging to the Colletotrichum genus cause anthracnose disease in a range of major crops, resulting in huge economic losses worldwide. Typical symptoms include dark, sunken lesions on leaves, stems, or fruits. Colletotrichum spp. have synthesized, in vitro, a number of biologically active and structurally unusual metabolites that are involved in their host’s infection process. In this study, we applied a one strain many compounds (OSMAC) approach, integrated with targeted and non-targeted metabolomics profiling, to shed light on the secondary phytotoxic metabolite panels produced by pathogenic isolates of Colletotrichum truncatum and Colletotrichum trifolii. The phytotoxicity of the fungal crude extracts was also assessed on their primary hosts and related legumes, and the results correlated with the metabolite profile that arose from the different cultural conditions. To the best of our knowledge, this is the first time that the OSMAC strategy integrated with metabolomics approaches has been applied to Colletotrichum species involved in legume diseases.
RESCUE - Reduction of MRI SNR Degradation by Using an MR-Synchronous Low-Interference PET Acquisition Technique

Devices aiming at combined Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) to enable simultaneous PET/MR image acquisition have to fulfill demanding requirements to avoid mutual magnetic- as well as electromagnetic-field-related interferences which lead to image quality degradation. Particularly Radio-Frequency (RF)-field-related interferences between PET and MRI may lead to MRI SNR reduction, thereby deteriorating MR image quality. RF shielding of PET electronics is therefore commonly applied to reduce RF emission and lower the potential coupling into MRI RF coil(s). However, shields introduce eddy-current-induced MRI field distortions and should thus be minimized or ideally omitted. Although the MRI noise floor increase caused by a PET system might be acceptable for many MRI applications, some MRI protocols, such as fast or high-resolution MRI scans, typically suffer from low SNR and might need more attention regarding RF silence to preserve the intrinsic MRI SNR. For such cases, we propose RESCUE, an MRI-synchronously-gated PET data acquisition technique: By interrupting the PET acquisition during MR signal receive phases, PET-related RF emission may be minimized, leading to MRI SNR preservation. Our PET insert $Hyperion\ II^D$ using Philips Digital Photon Counting (DPC) sensors serves as the platform to demonstrate RESCUE. To make the DPC sensor suitable for RESCUE to be applied for many MRI sequences with acquisition time windows in the range of a few milliseconds, we present in this paper a new technique which enables rapid DPC sensor operation interruption by dramatically lowering the overhead time to interrupt and restart the sensor operation. Procedures to enter and leave gated PET data acquisition may imply sensitivity losses which add to the ones occurring during MRI RF acquisition. For the case of our PET insert, the new DPC quick-interruption technique yields a PET sensitivity loss reduction by a factor of 78 when compared to the loss introduced with the standard start/stop procedure. For instance, PET sensitivity losses related to overhead time are 2.9% in addition to the loss related to PET gating being equal to the MRI RF acquisition duty cycle (14.7%) for an exemplary T1-weighted 3D-FFE MRI sequence. MRI SNR measurement results obtained with one $Hyperion\ II^D$ Singles Detection Module (SDM) using no RF shield demonstrate a noise floor reduction by a factor of 2.1, getting close to the noise floor level of the SNR reference scan (SDM off-powered) when RESCUE was active.
Neutrophil Gelatinase-Associated Lipocalin (NGAL) in Patients with ST-Elevation Myocardial Infarction and Its Association with Acute Kidney Injury and Mortality

Neutrophil gelatinase-associated lipocalin (NGAL) is an inflammatory biomarker related to acute kidney injury (AKI). Including 1892 consecutive patients with ST-elevation myocardial infarction (STEMI), in which NGAL was measured in 1624 (86%) on admission and in a consecutive subgroup at 6–12 h (n = 163) and 12–24 h (n = 222) after admission, this study aimed to evaluate the prognostic value of NGAL in predicting AKI and mortality. Patients were stratified based on whether their admission NGAL plasma concentration was greater than or equal to/less than the median. The primary endpoint was a composite of the first occurrence of AKI or all-cause death within 30 days. AKI was classified by the maximal plasma creatinine increase from baseline during index admission as KDIGO1 (<200% increase) or KDIGO23 (≥200% increase) according to the Kidney Disease Improving Global Outcomes (KDIGO) system. Admission NGAL > the median was independently associated with a higher risk of severe AKI (KDIGO2-3) and 30-day all-cause mortality when adjusted for age, admission systolic blood pressure and high-sensitivity C-reactive protein, left-ventricular ejection fraction, known kidney dysfunction, and cardiogenic shock with an odds ratio (95% confidence interval) of 2.26 (1.18–4.51), p = 0.014. Finally, we observed increasing predictive values in a subgroup during the first day of hospitalization suggesting that assessment of NGAL should be delayed for optimal prognostic purposes.