•PLS-PM has been subject to many improvements in last years.•Prior PLS guidelines have not covered the entire recent developments.•We explain how to perform and report an up-to-date empirical ...analysis with PLS.•We provide a fictive illustrative example on business value of social media.
Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Soft biological tissues are complex materials with a large structural variety, with differences in behavior, but with some common characteristics. Skin is an archetypal soft tissue which presents ...many common characteristics to other soft biological tissues, like being a multilayer collagen-reinforced structure, with nonlinear behavior, anisotropy, viscosity, preconditioning effects, internal stresses and tissue growth and adaptation. Departing from a detailed description of the structures of the skin and the experimental evidence, we herein analyze the different modeling approaches in the literature for the distinct aspects of the skin behavior, with attention to the implementation in finite element codes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
With the advent of large‐scale problems, feature selection has become a fundamental preprocessing step to reduce input dimensionality. The minimum‐redundancy‐maximum‐relevance (mRMR) selector is ...considered one of the most relevant methods for dimensionality reduction due to its high accuracy. However, it is a computationally expensive technique, sharply affected by the number of features. This paper presents fast‐mRMR, an extension of mRMR, which tries to overcome this computational burden. Associated with fast‐mRMR, we include a package with three implementations of this algorithm in several platforms, namely, CPU for sequential execution, GPU (graphics processing units) for parallel computing, and Apache Spark for distributed computing using big data technologies.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In this age, big data applications are increasingly becoming the main focus of attention because of the enormous increment of data generation and storage that has taken place in the last years. This ...situation becomes a challenge when huge amounts of data are processed to extract knowledge because the data mining techniques are not adapted to the new space and time requirements. Furthermore, real-world data applications usually present a class distribution where the samples that belong to one class, which is precisely the main interest, are hugely outnumbered by the samples of the other classes. This circumstance, known as the class imbalance problem, complicates the learning process as the standard learning techniques do not correctly address this situation.
In this work, we analyse the performance of several techniques used to deal with imbalanced datasets in the big data scenario using the Random Forest classifier. Specifically, oversampling, undersampling and cost-sensitive learning have been adapted to big data using MapReduce so that these techniques are able to manage datasets as large as needed providing the necessary support to correctly identify the underrepresented class. The Random Forest classifier provides a solid basis for the comparison because of its performance, robustness and versatility.
An experimental study is carried out to evaluate the performance of the diverse algorithms considered. The results obtained show that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest. Moreover, even for the same type of problem, the best performing method is dependent on the number of mappers selected to run the experiments. In most of the cases, when the number of splits is increased, an improvement in the running times can be observed, however, this progress in times is obtained at the expense of a slight drop in the accuracy performance obtained. This decrement in the performance is related to the lack of density problem, which is evaluated in this work from the imbalanced data point of view, as this issue degrades the performance of classifiers in the imbalanced scenario more severely than in standard learning.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Removal and photodegradation of methylene blue on the surface of three distinct carbonaceous materials, pure and nitrogen-doped multi-walled carbon nanotubes, and graphitic nanoribbons, were ...examined. Carbon nanostructures were characterized by scanning electron microscopy, transmission electron microscopy, thermogravimetric analysis, Raman spectroscopy, X-ray photoelectron spectroscopy, and N2 adsorption/desorption isotherms. Removal and photocatalytic activity were evaluated by static adsorption under UV irradiation. Photocatalytic activity on carbon nanostructure exhibited a pseudo-first-order kinetic model. The adsorption capacity and photocatalytic activity of carbon nanostructures were mainly attributed to their defective and reactive surface. Nitrogen-doped multi-walled carbon nanotubes showed the best adsorption capacity and photocatalytic activity, which can be attributed to the highly reactive sites due to nitrogen doping, also by π–π electron donor-acceptor interaction between sp2 lattice of carbon nanostructure surface and negatively charged sites at methylene blue molecules.
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•Carbon nanotubes and nanoribbons were used to adsorb and photo-degradation of methylene blue.•N-doped nanotubes showed higher photocatalytic degradation of methylene blue than pure carbons.•Carbonaceous materials removed methylene blue by two mechanisms, adsorption and photocatalysis.•The photocatalytic degradation of dye occurred by the reaction with OH, O2−, and HO2.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We aimed to determine whether immune checkpoint inhibitors (ICI) time-of-day infusion might influence the survival of patients with advanced non-small cell lung cancer (NSCLC).
We retrospectively ...analysed patients who received single-agent anti-PD-(L)1 therapy in any line between 2016 and 2021. We calculated by Cox regression models the association between the proportion of ICI infusions received after 16:30h and overall survival (OS) and progression-free survival (PFS).
180 patients were included, 77% received ICI as second- or further-line (median of 12 infusions/patient). The median age was 65 years (IQR 57–70), 112 patients (62%) were male, 165 (92%) were current or former tobacco smokers, 140 (78%) had performance status (PS) 0 or 1, 26 (14%) were on steroid therapy at ICI initiation. Histology was non-squamous for 139 (77%), the median number of metastatic sites was 3, and 33% had brain metastases. Patients who received at least 20% of ICI infusions after 16:30h (65 out of 180, 36%) had a statistically significant shorter median PFS as compared with patients receiving less than 20% of infusions in the evening (4.9 vs 9.4 months, log-rank p = 0.020), while numerical but not statistical shorter OS was observed (14.0 vs 26.2 months, log-rank p = 0.090). In the multivariate analysis, receiving at least 20% of evening infusions did not significantly increase the risk of death, while PS and line of treatment were significantly correlated with the OS. On the contrary, a proportion of ICI administration after 16:30h ≥20% conferred an HR for the PFS of 1.44 (95% CI: 1.01–2.05, p = 0.043), but this prognostic effect was not found when including in the model the total number of ICI infusions received (HR 1.20, 95% CI: 0.83–1.75, p = 0.329).
Time-of-day infusion of ICI may impact the survival of patients with advanced NSCLC. Underlying prognostic characteristics and the number of infusions received could represent conceivable confounding factors, linked to increased variance related to ICI infusion timing. Nonetheless, further studies may unravel chronobiological mechanisms modulating ICI efficacy.
•Does immune checkpoint inhibitors (ICI) time-of-day infusion impact on survival?•Retrospective study, advanced NSCLC patients receiving single-agent ICI in any line.•Non-significant shorter OS in patients receiving ≥20% ICI infusions in the evening.•Significant shorter PFS in patients receiving ≥20% ICI infusion in the evening.•Chronobiological mechanisms modulating ICI efficacy should be investigated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A background and exhaustive survey on fingerprint matching methods in the literature is presented.•A taxonomy of fingerprint minutiae-based methods is proposed.•An extensive experimental study shows ...the performance of the state-of-the-art.
Fingerprint recognition has found a reliable application for verification or identification of people in biometrics. Globally, fingerprints can be viewed as valuable traits due to several perceptions observed by the experts; such as the distinctiveness and the permanence on humans and the performance in real applications. Among the main stages of fingerprint recognition, the automated matching phase has received much attention from the early years up to nowadays. This paper is devoted to review and categorize the vast number of fingerprint matching methods proposed in the specialized literature. In particular, we focus on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency. We identify the main properties and differences of existing methods. Then, we include an experimental evaluation involving the most representative local minutiae-based matching models in both verification and evaluation tasks. The results obtained will be discussed in detail, supporting the description of future directions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Over the last decade, immune checkpoint inhibitors (ICI) have completely changed the treatment strategy and the prognosis of several solid cancer types. There is a lack of biomarkers to differ ...between responders and non-responders to these therapies. The development of biomarkers for immunotherapy has been mainly focused on tumor-related factors. The role of PD-L1 expression or tumor mutational burden (TMB) as potential predictive biomarkers for ICI efficacy is not universal and remains controversial. Moreover, leukocyte and neutrophil counts in blood samples have been used to develop clinical indicators of systemic inflammation like the neutrophil to lymphocyte ratio (NLR) and derived neutrophil to lymphocyte ratio (dNLR) based on the host immunologic status to respond against cancer cells by the immune-effectors. The Lung Immune Prognostic Index (LIPI) score have been developed as a reliable tool to assess the risk stratification of patients with cancer and to guide treatment decisions in the era of personalized cancer treatments. We review the clinical evidence supporting the use of the LIPI index as a clinically valuable biomarker for patients with NSCLC and other solid tumor types, treated with immunotherapy.
The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and its resulting model. For this reason, many methods of automatic feature ...selection have been developed. By using a modularization of feature selection process, this paper evaluates a wide spectrum of these methods. The methods considered are created by combination of different selection criteria and individual feature evaluation modules. These methods are commonly used because of their low running time. After carrying out a thorough empirical study the most interesting methods are identified and some recommendations about which feature selection method should be used under different conditions are provided.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input ...fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low‐quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state‐of‐the‐art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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