Quantitative Comparison between Traditional and Intensive Face-to-Face Education through an Organizational Model

Face-to-face education continues to present benefits in terms of student motivation, even though in COVID-19 scenario, online education has been the model of choice. In addition to the traditional face-to-face style, the intensive face-to-face style remains, which allows greater flexibility for the student. The objective of this study was to compare both educational styles and build an organizational model to improve student satisfaction. Two-way general linear model (GLM) with educational styles and satisfaction as fixed factors and discriminant analysis was applied. The selection of the most discriminant variables was made applying the F of Snedecor, Wilks’-Lambda, and the 1-Tolerance. A discriminant model was built. The four variables with the highest discriminant power were problem-solving communication with students’ representatives and shared knowledge and goals with lectures in the intensive style and frequent communication with administrative officers in the traditional style. In addition, it was found that greater face-to-face attendance did not imply greater coordination and that intensive style students show greater satisfaction. The appropriate duration of face-to-face education can contribute to the design of an innovative hybrid system in the future.

Assessment of Oral Skills in Adolescents

There is broad consensus on the need to foster oral skills in middle school due to their inherent importance and because they serve as a tool for learning and acquiring other competences. In order to facilitate the assessment of communicative competence, we hereby propose a model which establishes five key dimensions for effective oral communication: interaction management; multimodality and prosody; textual coherence and cohesion; argumentative strategies; and lexicon and terminology. Based on this model, we developed indicators to measure the proposed dimensions, thus generating a self-report tool to assess oral communication in middle school. Following an initial study conducted with 168 students (mean age = 12.47 years, SD = 0.41), we selected 22 items with the highest discriminant power, while in a second study carried out with a sample of 960 students (mean age 14.11 years, SD = 0.97), we obtained evidence concerning factorial validity and the relationships between oral skills, emotional intelligence and metacognitive strategies related to metacomprehension. We concluded that the proposed model and its derived measure constitute an instrument with good psychometric properties for a reliable and valid assessment of students’ oral competence in middle school.

A NOVEL DEEP LEARNING NEURAL NETWORK SYSTEM FOR IMBALANCED HEART SOUNDS CLASSIFICATION

This study proposed a novel TFNNS method, which aimed to solve the imbalanced phonocardiogram (PCG) signals’ classification. TFFNS consisted of three submodules: HeartNet, 2D-Maps transformation, and TF-Mask augmentation. HeartNet, deep neural networks (CNNs), was designed to recognize the categories of PCG signals. In particular, on the basis of short-time Fourier transform and Mel filtering, 2D-Maps transformation was used to convert one-dimensional PCG into two-dimensional Savitzky-MFSC feature maps that were fed into HeartNet; TF-Mask augmentation was designed to augment the training datasets by randomly shielded Savitzky-MFSC maps in the domains of time and frequency. We trained our model on the PASCAL heart sounds’ datasets to classify three categories of heart sounds including normal, murmur, and extrasystole. We also evaluated and compared the model with the baselines on the consistent evaluation protocols. The experimental results showed that the proposed TFFNS method significantly promoted the performance of the PCG signals’ classification and exceeded the baselines by giving the mean precision of 94%, heart problem specificity of 99%, and discriminant power of 1.317.

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.

Organizational Differences among Universities in Three Socioeconomic Contexts: Finland, Spain and Ecuador. Relational Coordination Approach

The knowledge of local culture is essential to establish competitive strategies in higher education. The objective of this research was to identify the organizational differences among three universities with different international contexts and satisfaction level. An approach was made regarding Relational Coordination (RC) attributes: accurate, frequent and problem-solving communication, shared knowledge, mutual respect and shared goals, by discriminant analysis method. A random sample of 300 students, 100 belonging to each university, was surveyed on the 23 RC variables in 2017–2018. First, the RC variables were evaluated by general linear model (GLM). The three universities—Arcada University of Applied Science (ARCADA) in Finland, University of Cordoba (UCO) in Spain and Agricultural Polytechnic of Manabi “MFL” (ESPAM) in Ecuador—and the two levels of student satisfaction—Low and High—were used as fixed factors. Second, a discriminant model was built with RC variables. A higher level of RC practices concerning to accurate, frequent and problem-solving communication achieved higher levels of satisfaction, regardless of the universities’ socioeconomic context. RC differentiation among three universities showed that shared goals with lecturers and administrative officers and problem-solving communication among classmates were the variables with the highest discriminant power. Two clusters were obtained, where UCO was the most differentiated university. In conclusion, organizational practices made a difference among the three universities. Discriminant analysis can be adapted and extended to different universities to improve quality.

Discriminant Canonical Tool for Differential Biometric Characterization of Multivariety Endangered Hen Breeds

This study aimed to develop a tool to perform the morphological characterization of Sureña and Utrerana breeds, two endangered autochthonous breeds ascribed to the Mediterranean trunk of Spanish autochthonous hens and their varieties (n = 608; 473 females and 135 males). Kruskal–Wallis H test reported sex dimorphism pieces of evidence (p < 0.05 at least). Multicollinearity analysis reported (variance inflation factor (VIF) >5 variables were discarded) white nails, ocular ratio, and back length (Wilks’ lambda values of 0.191, 0.357, and 0.429, respectively) to have the highest discriminant power in female morphological characterization. For males, ocular ratio and black/corneous and white beak colors (Wilks’ lambda values of 0.180, 0.210, and 0.349, respectively) displayed the greatest discriminant potential. The first two functions explained around 90% intergroup variability. A stepwise discriminant canonical analysis (DCA) was used to determine genotype clustering patterns. Interbreed and varieties proximity was evaluated through Mahalanobis distances. Despite the adaptability capacity to alternative production systems ascribed to both avian breeds, Sureña and Utrerana morphologically differ. Breed dimorphism may evidence differential adaptability mechanisms linked to their aptitude (dual purpose/egg production). The present tool may serve as a model for the first stages of breed protection to be applicable in other endangered avian breeds worldwide.

Detection of Frozen–Thawed Duck Fatty Liver by MALDI-TOF Mass Spectrometry: A Chemometrics Study

The marketing of poultry livers is only authorized as fresh, frozen, or deep-frozen. The higher consumer demand for these products for a short period of time may lead to the marketing of frozen–thawed poultry livers: this constitutes fraud. The aim of this study was to design a method for distinguishing frozen–thawed livers from fresh livers. For this, the spectral fingerprint of liver proteins was acquired using Matrix-Assisted Laser Dissociation Ionization-Time-Of-Flight mass spectrometry. The spectra were analyzed using the chemometrics approach. First, principal component analysis studied the expected variability of commercial conditions before and after freezing–thawing. Then, the discriminant power of spectral fingerprint of liver proteins was assessed using supervised model generation. The combined approach of mass spectrometry and chemometrics successfully described the evolution of protein profile during storage time, before and after freezing-thawing, and successfully discriminated the fresh and frozen–thawed livers. These results are promising in terms of fraud detection, providing an opportunity for implementation of a reference method for agencies to fight fraud.

Psychometric Properties of the Spanish Version of Breast Cancer and Lymphedema Symptom Experience Index.

This study was designed to adapt the Breast Cancer and Lymphedema Symptoms Experience Index (BCLE-SEI) to the Spanish language (BCLE-SEI-Es) and to assess its psychometric properties in Spanish-speaking women diagnosed with breast cancer. 286 breast cancer survivors were recruited. Study measured demographic and medical data and the BCLE-SEI. Reliability was measured using Cronbach’s alpha and test-retest reliability (n = 29) after an interval of two weeks. A robust principal components analysis was conducted to explore the dimensions of each BCLE-SEI-Es subtest. Discriminant power of the BCLE-SEI was assessed through a non-parametric test evaluating score differences between non-lymphedema and lymphedema patients. A cut-off point was established via a ROC curve. Cronbach’s alpha: all scales had a value above 0.9. Test-retest reliability: Correlations between questionnaire administrations were above 0.7. The first and second subtests showed a good fit to a unidimensional and two-factor structure, respectively. Lymphedema patients score significantly higher in all BCLE-SEI scales (p < 0.001). A cut-off point was established to predict a possible lymphedema case. The BCLE-SEI-Es is a valid, reliable tool for assessing and identifying the presence of lymphedema among breast cancer survivors and assessing its impact on the physical, functional, psychological and emotional aspects.

Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors

Background: Common variants may contribute to the variation of prognosis of heart failure (HF) among individual patients, but no systematical analysis was conducted using transcriptomic and whole exome sequencing (WES) data. We aimed to construct a genetic risk score (GRS) and estimate its potential as a predictive tool for HF-related mortality risk alone and in combination with traditional risk factors (TRFs).Methods and Results: We reanalyzed the transcriptomic data of 177 failing hearts and 136 healthy donors. Differentially expressed genes (fold change &gt;1.5 or &lt;0.68 and adjusted P &lt; 0.05) were selected for prognosis analysis using our whole exome sequencing and follow-up data with 998 HF patients. Statistically significant variants in these genes were prepared for GRS construction. Traditional risk variables were in combination with GRS for the construct of the composite risk score. Kaplan–Meier curves and receiver operating characteristic (ROC) analysis were used to assess the effect of GRS and the composite risk score on the prognosis of HF and discriminant power, respectively. We found 157 upregulated and 173 downregulated genes. In these genes, 31 variants that were associated with the prognosis of HF were finally identified to develop GRS. Compared with individuals with low risk score, patients with medium- and high-risk score showed 2.78 (95%CI = 1.82–4.24, P = 2 × 10−6) and 6.54 (95%CI = 4.42–9.71, P = 6 × 10−21) -fold mortality risk, respectively. The composite risk score combining GRS and TRF predicted mortality risk with an HR = 5.41 (95% CI = 2.72–10.64, P = 1 × 10−6) for medium vs. low risk and HR = 22.72 (95% CI = 11.9–43.48, P = 5 × 10−21) for high vs. low risk. The discriminant power of GRS is excellent with a C statistic of 0.739, which is comparable to that of TRF (C statistic = 0.791). The combination of GRS and TRF could significantly increase the predictive ability (C statistic = 0.853).Conclusions: The 31-SNP GRS could well distinguish those HF patients with poor prognosis from those with better prognosis and provide clinician with reference for the intensive therapy, especially when combined with TRF.Clinical Trial Registration:https://www.clinicaltrials.gov/, identifier: NCT03461107.