Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformational school leadership and the COVID-19 pandemic: Perceptions of teachers in Cyprus

The role of contextual factors has been highlighted in the school leadership literature. This points to the importance of examining the extent to which leadership models apply to, or are useful in, specific contexts. The paper investigates the extent to which school leaders adopted transformational leadership behaviours and practices during the pandemic crisis based on the perceptions of teachers. Qualitative research was conducted with 30 primary school teachers in Cyprus. The findings of the research show that overall, school leaders used behaviours and practices associated with transformational leadership to a great extent. Of the five transformational leadership dimensions proposed by Bass and Avolio, teachers were more likely to report weaknesses in the area of individualised consideration. Moreover, the perceptions of teachers suggest that the behaviours and practices of school leaders during the pandemic crisis can be linked to different leadership conceptualisations and/or models. The paper provides implications regarding educational policy and practice.
ICASENSE : une application de l'Analyse en Composantes Indépendantes à l'IRM parallèle

Les recentes methodes d'imagerie IRM parallele (SENSE et SMASH) necessitent une bonne connaissance de la sensibilite des antennes. Celle ci est generalement mesuree lors d'une phase de calibration, ou estimee par filtrage adaptatif spatio-temporel si l'on dispose d'une serie d'acquisitions (TSENSE). Nous proposons une formulation du probleme en termes de separation aveugle de sources : une analyse en composantes independantes (ICA) permet alors d'extraire les informations de sensibilite des antennes a partir d'une seule acquisition. Des resultats de simulations sont presentes, demontrant l'interet de cette nouvelle methode que nous nommerons ICASENSE.
Numerical approximation for two-dimensional neutral parabolic differential equations with delay

In this article, an effective compact difference scheme is proposed to solve two-dimensional neutral delay parabolic differential equations (NDPDEs). Concrete and detailed derivation of the scheme is given. The unique solvability and unconditional stability are obtained. Several numerical experiments indicate that the scheme has second-order accuracy in temporal direction and fourth-order accuracy in spatial direction in the sense of -norms and -norms. All procedures given in the paper can be extended to NDPDEs in three-dimensional case.
Multispectral image fusion based on diffusion morphology for enhanced vision applications

Existing image fusion methods based on morphological image analysis, that expresses the geometrical idea of image shape as a label image, are quite sensitive to the quality of image segmentation and, therefore, not sufficiently robust to noise and high frequency distortions. On the other hand, there are a number of methods in the field of dimensionality reduction and data comparison that give possibility of avoiding an image segmentation step by using diffusion maps techniques. The paper proposes a new approach for multispectral image fusion based on the combination of morphological image analysis and diffusion maps theory (i.e. Diffusion Morphology). A new image fusion algorithm is described that uses a matched diffusion filtering procedure instead of morphological projection. The algorithm is implemented for a three channels Enhanced Vision System prototype. The comparative results of image fusion are shown on real images acquired in flight experiments.
Numerical Simulations of Hydrodynamics of Nematic Liquid Crystals: Effects of Kinematic Transports

In this paper, we investigate the effects of kinematic transports on the nematic liquid crystal system numerically and theoretically. The model we used is a “1+2” elastic continuum model simplified from the Ericksen-Leslie system. The numerical experiments are carried out by using a Legendre-Galerkin spectral method which can preserve the energy law in the discrete form. Based on this highly accurate numerical approach we find some interesting and important relationships between the kinematic transports and the characteristics of the flow. We make some analysis to explain these results. Several significant scaling properties are also verified by our simulations.
Prognostic Performance of Bedside Lung Ultrasound Score (LUSS) and ROX Index in Hypoxemic Respiratory Failure Due to COVID-19

Background: Noninvasive ventilation, mainly helmet CPAP, was widely used during the COVID-19 pandemic, even outside of intensive care units. Both the ROX index and the LUS score (LUSS) have been proposed as tools to predict negative outcomes in patients with hypoxemia treated with noninvasive ventilation (NIV) outside of ICUs. We aim to evaluate whether the combination of LUSS with the ROX index improves the predictive performance of these indices in patients with hypoxemia due to COVID-19 pneumonia, treated with NIV outside of ICUs. Methods: This is a monocentric prospective observational study conducted at the university teaching hospital Fondazione IRCCS San Gerardo dei Tintori (Monza, Italy) from February to April 2021. LUSS and ROX were collected at the same time in noninvasively ventilated patients outside of the ICU. An LUS exam was performed by 3 emergency medicine attending physicians with at least 5 years’ experience in point-of-care ultrasonography using a 12-zone system. To evaluate the accuracy of the prognostic indices in predicting a composite outcome (endotracheal intubation and mortality), ROC curves were used. A logistic multivariable model was used to explore the predictors of the composite outcome of endotracheal intubation and in-hospital mortality. An unadjusted Kaplan–Meier analysis was used to explore the association with the composite outcome of survival without invasive mechanical ventilation at the 30-day follow-up by stratifying the 3 indices by their best cut-offs. Results: A total of 79 patients were included in the statistical analysis and stratified into 2 groups based on the presence of a negative outcome, which was reported in 24 patients out of 79 (30%). A great proportion of patients (66 patients—84%) were treated with helmet CPAP. All three indices (LUSS, ROX and LUSS/ROX) were independently associated with negative outcomes in the multivariable analyses. Although the comparison between the AUROC of LUSS or ROX versus LUSS/ROX did not reveal a statistically significant difference, we observed a trend toward a higher accuracy for predicting negative outcomes using the LUSS/ROX index as compared to using LUSS. With the Kaplan–Maier approach, all three indices stratified by the best cut-off reported a significant association with the outcome of 30-day survival without mechanical ventilation. Conclusions: A multimodal noninvasive approach that combines ultrasound (i.e., LUSS) and a bedside clinical evaluation (i.e., the ROX index) may help clinicians to predict outcomes and to identify patients who would benefit the most from invasive respiratory support.
Modelling Success: Articulating Program Impact Theory

Abstract: This study was undertaken to articulate program impact theory for the Comprehensive Home Option of Integrated Care for the Elderly (CHOICE) program. The study showed that CHOICE combines elements found in a traditional health maintenance organization with elements and process components drawn from primary care and case management to deliver a broad range of home support, day program, and social and health services to its participants and their informal caregivers. In doing so, the program provides participants with a level of comprehensive, coordinated care not possible within the traditional communitybased health and social service delivery system.
Histogram specification with generalised extreme value distribution to enhance retinal images

Histogram specification usually deals with greyscale image enhancements, although, it can apply to colour images; however, generally manipulating only the luminance channel. A method for automatic enhancement of digital colour retina images by specifying all colour bands of the histograms is designed to adjust brightness, contrast, and colour balance for visual diagnosis of age-related macular degeneration. The proposed algorithm employs a generalised extreme value distribution consisting of three parameters to adjust brightness and colour balance by the location parameter, contrast and tone tuned by the scale, and shape parameters, respectively. The algorithm performance is evaluated with two image datasets: structured analysis of the retina and automated retinal image analysis. The improved results show that the specified histograms based on the generalised extreme value have been provided with important features to characterise the symptoms of a macular degeneration.
Key-layered normal distributions transform for point cloud registration

A new scan matching algorithm is proposed using the concept of key layers. In the conventional multi-layered normal distributions transform (MLNDT), the number of layers and iterations per layer are fixed and mismatches in point clouds occur due to the limited number of optimising iterations per layer. Moreover, the accuracy of registration is low and the number of layers is heuristically determined in MLNDT. The proposed key-layered normal distributions transform (KLNDT) works well with both enhanced success rate and accuracy. It is also possible for KLNDT to register in higher layers than the traditional MLNDT.
Transformational policies and strategies framework accelerating green transition – The case of agriculture in Vietnam

Green transition is an urgent need for all countries, but there are many challenges in its implementation, especially in developing countries like Vietnam. This study was carried out through an analysis of relevant policies and a case study of conversion from rice monoculture to an integrated rice‐shrimp farming system in the Vietnamese Mekong Delta. The results showed that Vietnam has many policies, strategies, and plans to promote economic transformation toward sustainable, green, low emission, and climate change adaptation. However, the implementation of these policies in practice still faces some difficulties. The case study's result revealed that the rice‐shrimp model is suitable and brings many benefits to society, economy, and environment. The success of the transition depends on many factors including initiative, creativity, and consensus at the local level. A theoretical framework was built to promote the transition toward green and sustainable development in the future.
Compound deferrable options for the valuation of multi-stage infrastructure investment projects

Abstract Multi-stage planning is common for expanding infrastructure complexes or networks. Previous real-option studies developed a sequential compound call option (SCCO) for evaluating multi-stage infrastructure investment projects, including evaluating abandonment options during individual pre-scheduled investment stages. In practice, however, investment schedules involve risk and uncertainties forcing investors to change plans, raising an important issue regarding investors having more options at each commencement date. This study introduces a new model, the sequential compound deferrable call option (SCDCO), incorporating deferment options for each fold in an n-fold setting and generalizing the exercise of each deferment option into mn periods. A closed-form solution to the valuation of SCDCO is derived accordingly. A real-world case demonstrates that introducing deferment options increases project value, but the marginal benefit of increasing deferment time diminishes. The presence of dedicated assets for the usage of future expansions can also increase project value, but this increase is restricted by deferment options. Furthermore, the investment cost growth with an increase of deferment time rapidly decreases project value and vice versa. Inflation or deflation is therefore important to consider in deferment decisions. Finally, although deferment options only produce limited project value, they have the flexibility to manage risks, changes, and budgetary constraints.
Symmetric Functionals over Tensor Product Spaces in the Context of Quantum Information Theory

This thesis consists of three parts. We begin our investigation with a brief introduction into open quantum systems. Then we explain different measures of distinguishability of density operators, especially the Uhlmann Fidelity, which will be the basis for the model function we investigate in this work. We continue by explaining the time evolution of open quantum systems in more detail, and introducing the important notion of quantum channels as a concept in quantum information theory. This allows us to state a central problem of quantum information theory: the characterization of the quantum information capacity of a given quantum channel. The major challenge is the maximization of the coherent information, a function defined on a n-fold tensor product of a Hilbert space, which is non-additive and thus has to be considered in the limit as n→∞. To gain an insight into this unsolved problem, we study the channel fidelity of a quantum channel, which is a simpler function, also defined on n-fold tensor product spaces. It shares an essential feature with the coherent information in being non-additive. However, in contrast to the coherent information it is mathematically accessible. We establish the channel fidelity as a model for the coherent information and study its properties. In the second part, a short introduction to the representation theory of symmetric and unitary groups is followed by concrete instructions for Collins' and \'{S}niady's formula for the integration of functions of matrix elements of unitary groups with respect to the Haar measure. This exposition culminates in a simplification of the general formula that is optimal for investigating the channel fidelity. In the third part, we calculate channel fidelity moments for arbitrary n. In order to obtain more concrete results, we restrict ourselves to the study of Pauli channels. For these we discuss the transition of the average and variance from small n to the limit n→∞ and give an explicit formula for both in this limit. In particular, we find that for a large number n of Pauli channels, the channel fidelity distribution is peaked very strongly. Additionally, under certain restrictions, the simplified formula from part two also allows us to give concrete expressions for higher moments in the limit n→∞. We conclude by comparing our new results with results from a former work, where, in the search for maximizing states of the channel fidelity, we found states that maximize the channel fidelity of Pauli channels, at least locally. Because these local maxima have a much higher fidelity than the average of the very strongly peaked distribution, we infer that these states would not be found by a standard numerical maximization procedure. If the channel fidelity models the coherent information accurately in this regard, its maximization thus poses a very hard problem.
Digital Transformation and Corporate Environmental Green Innovation Nexus: An Approach towards Green Innovation Improvement

The impact of digital transformation on green innovation is widely discussed. However, existing studies mainly focus on the impact of the digital transformation of enterprises and fintech company development on environmental green innovation, while ignoring the effect of the digital transformation of commercial banks (DTCB) on corporate green innovation. Therefore, to fill the research gap, this paper explores the impact of DTCB on environmental green innovation in companies based on the data of listed companies from 2010 to 2019. This study finds that DTCB has significantly promoted enterprises’ environmental green innovation. Mechanism analysis shows that DTCB can promote green environmental innovation by increasing R&D expenditures and reducing agency costs. The heterogeneity analysis indicates that DTCB can only promote the green environmental innovation of private enterprises and enterprises with a high degree of digital transformation, but it cannot promote the green environmental innovation of state-owned enterprises and enterprises with a low degree of digital transformation. From the perspective of DTCB, this paper enriches the research on the relationship between digital finance and enterprise environmental green innovation. The government should promote the digital transformation of enterprises to utilize the green innovation effect of DTCB.
Improved Local Anesthesia at Inflamed Tissue Using the Association of Articaine and Copaiba Oil in Avocado Butter Nanostructured Lipid Carriers

Unsuccessful anesthesia often occurs under an inflammatory tissue environment, making dentistry treatment extremely painful and challenging. Articaine (ATC) is a local anesthetic used at high (4%) concentrations. Since nanopharmaceutical formulations may improve the pharmacokinetics and pharmacodynamics of drugs, we encapsulated ATC in nanostructured lipid carriers (NLCs) aiming to increase the anesthetic effect on the inflamed tissue. Moreover, the lipid nanoparticles were prepared with natural lipids (copaiba (Copaifera langsdorffii) oil and avocado (Persia gratissima) butter) that added functional activity to the nanosystem. NLC-CO-A particles (~217 nm) showed an amorphous lipid core structure according to DSC and XDR. In an inflammatory pain model induced by λ-carrageenan in rats, NLC-CO-A improved (30%) the anesthetic efficacy and prolonged anesthesia (3 h) in relation to free ATC. In a PGE2-induced pain model, the natural lipid formulation significantly reduced (~20%) the mechanical pain when compared to synthetic lipid NLC. Opioid receptors were involved in the detected analgesia effect since their blockage resulted in pain restoration. The pharmacokinetic evaluation of the inflamed tissue showed that NLC-CO-A decreased tissue ATC elimination rate (ke) by half and doubled ATC’s half-life. These results present NLC-CO-A as an innovative system to break the impasse of anesthesia failure in inflamed tissue by preventing ATC accelerated systemic removal by the inflammatory process and improving anesthesia by its association with copaiba oil.
TEAD Inhibitors Sensitize KRASG12C Inhibitors via Dual Cell Cycle Arrest in KRASG12C-Mutant NSCLC

KRASG12C is one of the most common mutations detected in non-small cell lung cancer (NSCLC) patients, and it is a marker of poor prognosis. The first FDA-approved KRASG12C inhibitors, sotorasib and adagrasib, have been an enormous breakthrough for patients with KRASG12C mutant NSCLC; however, resistance to therapy is emerging. The transcriptional coactivators YAP1/TAZ and the family of transcription factors TEAD1-4 are the downstream effectors of the Hippo pathway and regulate essential cellular processes such as cell proliferation and cell survival. YAP1/TAZ-TEAD activity has further been implicated as a mechanism of resistance to targeted therapies. Here, we investigate the effect of combining TEAD inhibitors with KRASG12C inhibitors in KRASG12C mutant NSCLC tumor models. We show that TEAD inhibitors, while being inactive as single agents in KRASG12C-driven NSCLC cells, enhance KRASG12C inhibitor-mediated anti-tumor efficacy in vitro and in vivo. Mechanistically, the dual inhibition of KRASG12C and TEAD results in the downregulation of MYC and E2F signatures and in the alteration of the G2/M checkpoint, converging in an increase in G1 and a decrease in G2/M cell cycle phases. Our data suggest that the co-inhibition of KRASG12C and TEAD leads to a specific dual cell cycle arrest in KRASG12C NSCLC cells.
Reproducibility and Reliability of Computing Models in Segmentation and Volumetric Measurement of Brain

Background Segmentation and morphometric measurement of brain tissue and regions from non-invasive magnetic resonance images have clinical and research applications. Several software tools and models have been developed by different research groups which are increasingly used for segmentation and morphometric measurements. Variability in results has been observed in the imaging data processed with different neuroimaging pipelines which have increased the focus on standardization. Purpose The availability of several tools and models for brain morphometry poses challenges as an analysis done on the same set of data using different sets of tools and pipelines may result in different results and interpretations and there is a need for understanding the reliability and accuracy of such models. Methods T1-weighted (T1-w) brain volumes from the publicly available OASIS3 dataset have been analysed using recent versions of FreeSurfer, FSL-FAST, CAT12, and ANTs pipelines. grey matter (GM), white matter (WM), and estimated total intracranial volume (eTIV) have been extracted and compared for inter-method variability and accuracy. Results All four methods are consistent and strongly reproducible in their measurement across subjects however there is a significant degree of variability between these methods. Conclusion CAT12 and FreeSurfer methods have the highest degree of agreement in tissue class segmentation and are most reproducible compared to others.
Estimation of Heart Rate from Vocal Frequency Based on Support Vector Machine

Heart rate (HR) is one of the vital signs used to assess our physical condition; it would be beneficial if HR could easily be obtained without special medical instruments. In this study, a feature of vocal frequency was used to estimate HR, because it can easily be recorded with a common device such as a smartphone. Previous studies proposed that a support vector machine (SVM) that adopted the inner product as the kernel function was efficient for estimating HR to a certain extent. However, these studies did not present the effectiveness of other kernel functions, such as the hyperbolic tangent function. Therefore, this study identified a combination of kernel functions of the kernel ridge regression (KRR). In addition, features of vocal frequency to effectively estimate HR were investigated. To evaluate the effectiveness, experiments were conducted with two subjects. In the experiment, 60 sets of HRs and voice data were measured per subject. To identify the most effective kernel function, four kernel functions (the inner function, Gaussian function, polynomial function, and hyperbolic tangent function) were compared. Moreover, effective features of vocal frequency were selected with the sequential feature selection (SFS) method. As a consequence, the hyperbolic tangent function worked best, and high-frequency components of voice were efficient. However, results of this research indicated that effective vocal spectrum components to estimate HR differ depending on prediction models.