Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial and Fermentation Potential of Himanthalia elongata in Food Applications

Himanthalia elongata is a brown oceanic seaweed rich in bioactive compounds. It could play an important role in food production because of its antimicrobial and antioxidant properties. Three strains belonging to the Lactobacillus casei group (Lactobacillus casei, Lactobacillus paracasei, and Lactobacillus rhamnosus) and a Bacillus subtilis strain were used for the solid-state fermentation of commercial seaweeds, and bacterial growth was monitored using the plate count method. High-pressure processing (HPP) was also employed (6000 bar, 5 min, 5 °C) before extraction. The antimicrobial activity of the extracts was tested in terms of the main food pathogenic bacteria (Salmonella spp., Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, and Bacillus cereus), and the phenolic content was estimated using the Folin–Ciocalteau method. In addition, targeted UHPLC-MS2 methods were used to unravel the profile of phlorotannins. H. elongata allowed the growth of the L. casei group strains and B. subtilis, showing the fermentability of this substrate. Significant antimicrobial activity toward L. monocytogenes was observed in the extracts obtained from unfermented samples, but neither fermentation nor HPP enhanced the natural antimicrobial activity of this seaweed species. The content in the phenolic compounds decreased because of the fermentation process, and the amount of phenolics in both the unfermented and fermented H. elongata extracts was very low. Despite phlorotannins being related to the natural antimicrobial activity of this brown seaweed, these results did not support this association. Even if fermentation and HPP were not proven to be effective tools for enhancing the useful compounds of H. elongata, the seaweed was shown to be a suitable substrate for L. casei group strains as well as for B. subtilis growth, and its extracts exhibited antimicrobial activity toward foodborne pathogens.
A non-randomized study investigating the effectiveness of cognitive reframing in socially disconnected widows

Objectives: Socially disconnected widows usually live a lonely and depressing life with anxiety and low self-worth. Many have contemplated suicide and others have become a victim of suicide already. Evolving an intervention to provide succor to them to take control of their lives may help a great deal. This paper presents the results of a nonrandomized study assessing the potency of cognitive reframing (CR) in reducing social disconnectedness (SD) among the widows. Methods: The pre- and post-data was collected from a non-randomized sample of 41 widows in the treatment group and 45 in the waitlist control group. The mean age of the participants was 41.383 ± 6.730 [95% CI = 39.940–42.940] (min. = 25 - max. = 56) years. The cognitive reframing administered spanned for eight weeks. Results: Analysis of the data collected suggests that cognitive reframing is significantly effective in reducing socially disconnected behavior among the widows in the study. The social disconnected behavior among the widows reduced by 40.95% compared to 8.29% observed in the waitlist control group. Conclusions: The CR technique may be helpful in reducing social disconnectedness in widows. However, further study may be required in a randomized sample to enhance generalization.
Adapting Logic to Physics: The Quantum-Like Eigenlogic Program

Considering links between logic and physics is important because of the fast development of quantum information technologies in our everyday life. This paper discusses a new method in logic inspired from quantum theory using operators, named Eigenlogic. It expresses logical propositions using linear algebra. Logical functions are represented by operators and logical truth tables correspond to the eigenvalue structure. It extends the possibilities of classical logic by changing the semantics from the Boolean binary alphabet {0,1} using projection operators to the binary alphabet {+1,−1} employing reversible involution operators. Also, many-valued logical operators are synthesized, for whatever alphabet, using operator methods based on Lagrange interpolation and on the Cayley–Hamilton theorem. Considering a superposition of logical input states one gets a fuzzy logic representation where the fuzzy membership function is the quantum probability given by the Born rule. Historical parallels from Boole, Post, Poincaré and Combinatory Logic are presented in relation to probability theory, non-commutative quaternion algebra and Turing machines. An extension to first order logic is proposed inspired by Grover’s algorithm. Eigenlogic is essentially a logic of operators and its truth-table logical semantics is provided by the eigenvalue structure which is shown to be related to the universality of logical quantum gates, a fundamental role being played by non-commutativity and entanglement.
Coded Permutation Entropy: A Measure for Dynamical Changes Based on the Secondary Partitioning of Amplitude Information

An improved permutation entropy (PE) algorithm named coded permutation entropy (CPE) is proposed in this paper to optimize the problems existing in PE based on the secondary partitioning. The principle of CPE algorithm is given, and the performance of it for dynamical change detection is analyzed using synthetic signal, logistic map and Lorenz map. The detection ability of CPE algorithm in different signal-to-noise ratios (SNR) is studied and the algorithm complexity is discussed. The results show that CPE can accurately capture minor feature information and amplify the detection results of dynamical changes compared with PE, weighted permutation entropy (WPE) and amplitude-aware permutation entropy (AAPE), but it has less robustness to noise and requires a higher computation cost than the others. Finally, we use the new algorithm to analyze the rolling bearing fault signals. The application of actual signals illustrates that CPE performs better in detecting abnormal pulse of the rolling bearing when the embedded dimension is small. From all the analyses in this paper, we find that CPE has a better performance for dynamical change detection compared with the other three algorithms when there is a larger repetition rate of permutation pattern in the position sequences.
How different Perceived Leadership Styles have an influence on Organisational Commitment on Tourism SMEs?

The objective of this study was to analyse the issues that tourism SMEs face in adopting the right perceived leadership style within the workplace and its impact on the organisational commitment of the employees. The study aimed to understand the problems related to leadership styles for SMEs and determined its impact on organisational commitment. This study helped to identify the role of the right leadership style to help SMEs gain success. This study used quantitative research and primary data. The sample was 383 employees. A self-prepared questionnaire was used with a 5-point Likert scale to measure the leadership style and commitment variables. The questionnaire was shared with HR and employees at the SMEs. The results of the study helped in answering the research questions. The study found that most of the tourism SMEs use the autocratic style in the organisation which directly negatively impacts the organisational commitment of the employees. The majority of the employees responded that they often want to leave the organisation. However, the results also showed that employees would stay given that their leaders sometimes gave them the freedom to express their opinion. Furthermore, it was found that the lack of involvement of employees has lowered the emotional attachment to the workplace of employees and as a result, their commitment and motivation to do their job have been impacted. The significant association of perceived leadership styles with organisational commitment has been proved statistically. Thus, it has been concluded that tourism SMEs must use democratic or other leadership styles that are appropriate as per the culture in their workplace where employees are given autonomy and freedom to give their opinions. Empowerment will also enhance the organisational commitment in a positive manner.
Classification of EEG Signals during Meditation and Controlled State Using PCA, ICA, LDA and Support Vector Machines

In this work, the adaptable signal processing algorithm is proposed for Electroencephalogram (EEG) signals. In this algorithm, the Discrete Wavelet transform is applied to EEG signals for decomposing it into its frequency sub-bands. Statistical analysis is applied to these sub-bands to extract the statistical features which characterize the distribution of wavelet coefficient. Several classification algorithms such as Independent Component Analysis, Principal Component Analysis and Linear Discriminant Analysis which is popularly called as ICA, PCA, and LDA respectively is applied to these features to reduce its size. Then these reduced features applied to SVM classifier which categorizes the EEG signals into Meditation state and Controlled (Normal) state. Here Performance accuracy from different classifier is evaluated and then compare to know which classifier is best suited for EEG signal classification for meditation and controlled state. The meditation method used here was Vipassana Meditation which is a type of mindfulness meditation practice. The finding from above procedure gave the best classifier for classification of EEG signals during controlled and meditation state.
-1- AN EXAMINATION OF WHETHER AND HOW RACIAL AND GENDER BIASES INFLUENCE CUSTOMER SATISFACTION RATINGS ABSTRACT Across three studies, we examine whether and how biases may influence customer satisfaction

Across three studies, we examine whether and how biases may influence customer satisfaction ratings and produce discriminatory judgments for minorities and female employees. Study 1 shows that customers are less satisfied with nonwhite and women employees than their equally-well performing white and male counterparts. Study 2 shows that raters’ non-conscious racial and gender biases influence satisfaction ratings of the contextual attributes of organizations that employ nonwhite and women employees. Our final study further confirms these disturbing findings and suggests that the presence of nonwhite and women employees may produce lower aggregated customer satisfaction ratings which may ultimately hurt individuals and organizations financially. (xx words)
Investing In CSR Pays You Back in Many Ways! The Case of Perceptual, Attitudinal and Behavioral Outcomes of Customers

Researchers and scholars have widely attributed corporate social responsibility (CSR) to enormous outcomes. However, the customer-specific outcomes are either less investigated or lack clarity. By focusing on perceptual, attitudinal and behavioral outcomes of CSR, this study entails that CSR influences customers’ citizenship behavior (behavioral outcome) both directly and indirectly (through service quality and affective commitment—perceptual and attitudinal outcomes). Survey data collected from 669 fast-food restaurant customers were analyzed through the structural equation modeling technique. The results revealed a positive and significant relationship between restaurants’ CSR efforts and customers’ behavioral responses in terms of citizenship behavior. Findings also highlight that CSR does not only have a direct relation but the sequential mediation mechanism also exists. The study extends the existing literature by focusing on the ignored causal link of CSR and customer citizenship behavior (CCB) by considering the service quality and affective commitment as an explanatory mechanism, and provides certain practical implications which could also be useful for managers of the restaurant industry to devise their socially responsible practices.
Impact of secondary salinisation on the structure and diversity of oligochaete communities

Secondary salinisation has become one of the most important factors responsible for changes in the aquatic biota. Earlier research has focused on macroinvertebrates including oligochaetes in anthropogenically saline rivers and streams, but studies on oligochaetes in anthropogenically saline stagnant waters remain scarce. Therefore, this study was conducted to assess changes in the species composition as well as the abundance and biomass of oligochaete communities along a large salinity gradient in the anthropogenic inland water bodies located in the Upper Silesian Coal Basin (Southern Poland), which is one of the largest coal basins in Europe. Herein, a total of 27 oligochaete species including five alien species were assessed, namely, Potamothrix bavaricus, Potamothrix hammoniensis, Potamothrix moldaviensis, Psammoryctides albicola, and Psammoryctides barbatus. The results confirmed that the freshwater oligochaetes could tolerate elevated water salinity and showed highest densities and taxa richness in intermediate salinity. Moreover, the waters with the highest salinity had an extremely low number of oligochaete species. A salinity level above 2800 mg L−1 led to significant loss of diversity of the oligochaetes, and consequently, these habitats were colonized by halotolerant species, especially Paranais litoralis, whose abundance increased with increasing salinity gradient.
Rey–Osterrieth Complex Figure – copy and immediate recall (3 minutes): Normative data for Spanish-speaking pediatric populations

OBJECTIVE: To generate normative data for the Rey–Osterrieth Complex Figure (ROCF) in Spanish-speaking pediatric populations. METHOD: The sample consisted of 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Each participant was administered the ROCF as part of a larger neuropsychological battery. The ROCF copy and immediate recall (3 minutes) scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the analyses. ∗Address for correspondence: Juan Carlos Arango Lasprilla, Ph.D., BioCruces Health Research Institute, Cruces University Hospital, IKERBASQUE, Basque Foundation for Science, Plaza de Cruces s/n. 48903, Barakaldo. Bizkaia, Spain. Tel. : +34 94600 6000/Ext. 7963; E-mail: jcalasprilla@gmail.com. 1053-8135/17/$35.00 © 2017 – IOS Press and the authors. All rights reserved 594 J.C. Arango-Lasprilla et al. / Rey–Osterrieth Complex Figure Test RESULTS: The final multiple linear regression models showed main effect for age on copy and immediate recall scores, such that scores increased linearly as a function of age. Age2 affected ROCF copy score for all countries, except Puerto Rico; and ROCF immediate recall scores for all countries, except Chile, Guatemala, Honduras, Paraguay, and Puerto Rico. Models indicated that children whose parent(s) had a MLPE >12 years obtained higher scores compared to children whose parent(s) had a MLPE ≤12 years for Chile, Puerto Rico, and Spain in the ROCF copy, and Paraguay and Spain for the ROCF immediate recall. Sex affected ROCF copy and immediate recall score for Chile and Puerto Rico with girls scoring higher than boys. CONCLUSIONS: This is the largest Spanish-speaking pediatric normative study in the world, and it will allow neuropsychologists from these countries to have a more accurate approach to interpret the ROCF Test in pediatric populations.
A Brief Assessment of the Impact of Nearly 40 Years of Assimilated Observations Over the Amazon Basin

Adding a Forecast Sensitivity‐based Observation Impact component to Version 2 of the Modern Era Retrospective‐analysis for Research and Applications, the present study provides an assessment of the impact of nearly 40 years of observations on short‐range (24‐hr) forecasts over the Amazon basin. Under self‐verification, forecast errors are found to slightly increase from the early data‐sparse days to the more recent years, when data dramatically increase. Throughout the reanalysis, satellite radiances dominate in volume, but only before 1999 they dominate the impacts. Beyond 1999, over 50% of forecast error reduction is associated with conventional observations (radiosondes). Atmospheric Motion Vectors are also found to be large contributors to error reduction, but their contribution reduces in dry periods. In opposition to Atmospheric Motion Vectors, satellite radiances tend to contribute more in the dry season. Results provide motivation for additional conventional observations and the use of all‐sky treatment of radiances.
COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES

Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.
A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.
Culturally Relevant Computer Science Pedagogy: From Theory to Practice

Recent initiatives to broaden participation in computing aim to provide increased computer science (CS) access and opportunity for underrepresented K-12 students. Researchers have identified challenges related to teaching CS courses in sways that ensure underrepresented student populations are engaged, interested, and motivated to participate in computing coursework, including making course content relevant to students’ lives and interests. An emerging area of scholarship proposes merging culturally relevant pedagogy with CS in order to develop engaging and rigorous computing instruction for diverse groups of students. Much of this work has been theoretical rather than practical; thus, we present case studies of three programs to describe practice-based examples of culturally relevant computer science teaching and learning. Consistent themes across the instructional practices we share include engagement and relevance; confidence and identity; and social justice. These foundational practical applications of culturally relevant CS education can inform efforts to enhance engagement, preparation, participation, and persistence in computing for underrepresented students toward successfully addressing inclusion, diversity, and equity.
Design and Modelling of a Compact Variable Stiffness Mechanism for Wearable Elbow Exoskeletons

This paper presents a compact variable stiffness mechanism (VSM) with reconfigurability. The VSM is able to achieve variable stiffness in adjustable ranges, due to its reconfigurable design. The new VSM features with a compact and light weight structure, and can be used as a stand alone module, or integrated in wearable elbow exoskeletons and other transmissions. In the paper, mechanics models are developed for VSM to reveal the stiffness performance and torque-deflection characteristics. A prototype of VSM is constructed and static testing results validate the models. The proposed VSM is finally integrated in a wearable elbow exoskeleton.
A Case for Software-Based Adaptive Routing in NUMA Systems

Memory placement in NUMA systems has a significant impact on overall performance; however, most prior work has not considered the performance impact of NUMA interconnect as a deterministic routing have been used to access a remote memory. In this work, we propose adaptive routing in NUMA interconnect to exploit path diversity in NUMA systems. In particular, we propose a software-based adaptive routing where packet routing paths are changed by modifying the routing table dynamically during runtime - thus, reducing interconnect channel contention and improving overall performance. Adaptive routing does not minimize memory contention but since the interconnect channel can be shared by multiple NUMA nodes and creates contention, adaptive routing maximizes the interconnect bandwidth by load-balancing traffic across different interconnect channels. We provide a proof-of-concept implementation of software-based adaptive routing on AMD Hypertransport-based system and demonstrate performance benefits, including how it is orthogonal to thread and memory schedulers and complements existing OS (or NUMA) schedulers.
Models of Margin: From the Mathematical Formulation to an Operational Implementation

This paper develops a mathematical formulation of a margin problem in an automotive battery sizing use case. This formulation is done thanks to theoretical models of margin. This enables to use an approach with explicit margins, which is compared to a worst-case analysis and a probabilistic modeling. The models of margin are then adapted to a numerical implementation through the definition of patterns and presets adapted to the case study.
Design Spatial-based Information System using Flexible Field-Value Database*

This study aims to design a Spatial-based information system using flexible or variable field-value database. The information system uses primary and secondary data obtained from field surveys and reports of various regional work unit in Kediri Regency. Every regional unit has their own data format in publishing their information, and every data is also has a geo-spatial data coordinates that represent physical data location. In this research, we used data from agriculture, education and health regional work unit as sample data case. The results of this study is in the form of an information system that can saved and processed various field-value data format from various regional unit of Kediri Regency.