Miracle-dz/UniFAR-FTUs huggingface dataset:
Essential Factors for a Healthy Microbiome: A Scoping Review

Recent discoveries of the purpose and potential of microbial interactions with humans have broad implications for our understanding of metabolism, immunity, the host–microbe genetic interactions. Bioavailability and bioaccessibility of phytonutrients in foods not only enrich microbial diversity in the lower human gastrointestinal tract (GIT) but also direct the functioning of the metagenome of the microbiota. Thus, healthy choices must include foods that contain nutrients that satisfy both the needs of humans and their microbes. Physical activity interventions at a moderate level of intensity have shown positive effects on metabolism and the microbiome, while intense training (>70% VO2max) reduces diversity in the short term. The microbiome of elite endurance athletes is a robust producer of short-chain fatty acids. A lifestyle lacking activity is associated with the development of chronic disease, and experimental conditions simulating weightlessness in humans demonstrate loss of muscle mass occurring in conjunction with a decline in gut short-chain fatty acid (SCFA) production and the microbes that produce them. This review summarizes evidence addressing the relationship between the intestinal microbiome, diet, and physical activity. Data from the studies reviewed suggest that food choices and physical fitness in developed countries promote a resource “curse” dilemma for the microbiome and our health.
Variability of pathogenicity factors representative of the human microbiome under the influence of γ-Fe2O3 nanoparticles

Biomedical applications of nanoparticles require deep understanding of their interaction with normal human microflora. Previously, the toxic and mutagenic properties of iron oxide nanoparticles as well as their effect on the growth and morphology of the microflora were extensively investigated. However, the studies related to the variability of microbial pathogenicity factors induced by iron oxide nanoparticles are very limited. Meanwhile, this characteristic of microbes is genetically determined and is important for their survival and distribution in the human body. Therefore, pathogenicity factors are significant indicators of the experimental studies. In this work, the effect of the presence of Fe2O3 nanoparticles obtained by laser target evaporation (LTE) on selected enzymes that demonstrate invasion and aggression factors was evaluated for three reference strains of Candida albicans, Staphylococcus aureus, and Escherichia coli. It was found that the presence of LTE Fe2O3 nanoparticles supplied in the form of water-based suspensions does not induce changes of the above-mentioned parameters.
Photoreduction of CO2 into CH4 Using Novel Composite of Triangular Silver Nanoplates on Graphene-BiVO4

Plasmonic photocatalysis, combing noble metal nanoparticles (NMNPs) with semiconductors, has been widely studied and proven to perform better than pure semiconductors. The plasmonic effects are mainly based on the localized surface plasmon resonance (LSPR) of NMNPs. The LSPR wavelength depends on several parameters, such as size, shape, the surrounding media, and the interdistance of the NMNPs. In this study, graphene-modified plate-like BiVO4 composites, combined with silver nanoplates (AgNPts), were successfully prepared and used as a photocatalyst for CO2 photoconversion. Triangular silver nanoplates (TAgNPts), icosahedral silver nanoparticles (I-AgNPs), and decahedra silver nanoparticles (D-AgNPs) were synthesized using photochemical methods and introduced to the nanocomposites to compare the shape-dependent plasmonic effect. Among them, T-AgNPts/graphene/BiVO4 exhibited the highest photoreduction efficiency of CO2 to CH4, at 18.1 μmolg−1h−1, which is 5.03 times higher than that of pure BiVO4 under the irradiation of a Hg lamp. A possible CO2 photoreduction mechanism was proposed to explain the synergetic effect of each component in TAgNPts/graphene/BiVO4. This high efficiency reveals the importance of considering the compositions of photocatalysts for converting CO2 to solar fuels.
Modelling nonlinear economic time series

1. Concepts, models and definitions 2. Nonlinear models in economic theory 3. Parametric nonlinear models 4. The nonparametric approach 5. Parametric linearity tests 6. Testing parameter constancy 7. Nonparametric specification tests 8. Conditional heteroskedasticity 9. State space models 10. Nonparametric models 11. Nonlinear and nonstationary models 12. Estimating parametric models 13. Basic nonparametric estimates 14. Forecasting from nonlinear models 15. Nonlinear impulse responses 16. Building nonlinear models 17. Other topics
Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.
Analysis of Vertical Wind Shear Effects on Offshore Wind Energy Prediction Accuracy Applying Rotor Equivalent Wind Speed and the Relationship with Atmospheric Stability

If the wind speed that passed through a wind turbine rotor disk area is constant, the hub height wind speed (HHWS) could be representative of the wind speed over the rotor disk area. However, this assumption cannot be applied to the large wind turbine, because of the wind shear effect by atmospheric stability. This is because the hub height wind speed cannot represent the vertical wind shear effect from the aerodynamics characteristic on the wind turbine. Using SCADA and offshore LiDAR observation data of the Anholt offshore wind farm, it is investigated whether the rotor equivalent wind speed (REWS) introduced in IEC61400-12-1 can contribute to the improvement of power output forecasting accuracy. The weighted value by separated sector area and vertical wind shear effect by difference between heights can explain the role of energy flux and atmospheric stability on the exact wind energy calculation. The commercial CFD model WindSim is used to calculate power production according to the HHWS and the REWS, and to compare them with the actual AEP of the local wind farm. The classification of atmospheric stability is carried out by Richardson number, which well represents the thermal and physical properties of the atmosphere below the atmospheric boundary layer, along with the wind shear coefficient and turbulence intensity. When atmospheric stability was classified by each stability index, the REWS-based predicted power output was sometimes more accurate than HHWS, but sometimes inferior. However, in most cases, using the REWS, it was possible to calculate an estimate closer to the actual power output. Through the results of this study, it is possible to provide a rationale for which method, REWS or HHWS, can more accurately calculate the expected power output and effectively derive the economic feasibility of the project by identifying the characteristics of local atmospheric stability before the wind farm project.
Non-Intrusive Load Monitoring: A Review

The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.
A novel operation state prediction method for servers in smart grids

In view of the difficulty in manual monitoring of the operation state of grid servers, this paper develops an operation state prediction method for severs in smart grid based on several innovative techniques. The prediction method consists of two main steps. Firstly, the warning threshold was determined by Chebyshev inequality and improved Rayleigh distribution, and then the upper bound of warning, the value of parameter ε, as well as the abnormal probability of each timepoint were calculated according to the definition of small probability event. Secondly, the back propagation neural network (BPNN) was introduced for time sequence prediction and overall analysis of the previous results, thereby obtaining the future data of CPU utilization of grid servers. The proposed method was proved valid through several experiments. The warning threshold set by our method can warn about abnormalities without sacrificing the scientific operation of the grid, and evaluate the abnormal probability of each data point. The research findings shed new light on the early warning of abnormal server states in smart grids. RÉSUMÉ. Compte tenu de la difficulté de la surveillance manuelle de l'état de fonctionnement des serveurs de réseau électrique, cet article développe une méthode de prévision de l'état de fonctionnement des serveurs dans un réseau électrique intelligent reposant sur plusieurs techniques innovantes. La méthode de prédiction comprend deux étapes principales. Tout d'abord, le seuil d'alerte a été déterminé par l'inégalité de Bienaymé-Tchebychev et l'amélioration de la distribution de Rayleigh, puis le majorant de l'alerte, la valeur du paramètre ε, ainsi que la probabilité anormale de chaque mesure dans le temps ont été calculées en fonction de la définition d'événement à faible probabilité. Deuxièmement, le réseau de neurones à rétropropagation du gradient (BPNN, le sigle de « back-propagation neural network » en anglais) a été introduit pour la prédiction de séquence temporelle et l'analyse globale des résultats précédents, obtenant ainsi les futures données d'utilisation de l’unité centrale de traitement (UCT) par les serveurs dans un réseau électrique. La méthode proposée a été validée par plusieurs expérimentations. Le seuil d'alerte défini par notre méthode peut avertir des anomalies sans sacrifier le fonctionnement scientifique du réseau électrique et évaluer la probabilité anormale de chaque point de données. Les résultats de la recherche ont 380 EJEE. Volume 20 – n° 3/2018 jeté un nouvel éclairage sur l'alerte précoce de l'état anormal des serveurs dans les réseaux intelligents.
Reduced size of the λg/4 transmission line using immitance scaling for RF device applications

This article presents the proposed reduced size of the λg/4 transmission line (TL) using immitance scaling. The proposed compact size of the TL is used for immitance scaling, which is composed of folded open stubs and series TL. It can be the design for distributed elements instead of lumped elements in a shunt capacitor, which is therefore called the folded open stubs. The proposed compact TL can be applied to a Wilkinson power divider (combiner), hybrid branch line coupler, and stub band‐pass filter in microwave applications.
Artificial Intelligence-Based Low-Terahertz Imaging for Archaeological Shards’ Classification

In order to map the migration and introduction of farming into Europe during the seventh and sixth millennia Before Common Era, archeologists have made a connection between the study of pottery and farming migration. We are interested here in the classification of pottery into coiling and spiral types based on their manufacturing techniques. To distinguish between these two techniques, we look for the lines formed by air bubbles embedded in the pottery samples. Current methods make use of bulky systems, such as computerized tomography scanners or synchrotrons. Microwave acquisition and processing offer an interesting alternative, due to the possibility to have compact and portable systems. In this article, we investigate the classification of pottery based on low-terahertz measurements in the D-band. We process the measurements with 3-D fast Fourier transform. The resulting matrix is classified with an artificial neural network, multilayer perceptron, which is optimized with the gray wolf optimizer, a bioinspired algorithm. The first results show that the accuracy reaches up to 99% using all the acquired spatial and frequency measurements. Then, we optimize the millimeter-wave (mm-Wave) measurement system with a critical criterion on accuracy in two different scenarios. In the first scenario, we reduce the spatial acquisition but maintain the wideband operation and the results show that the accuracy is between 85% and 96%. In the second one, we reduce the spatial acquisition and use a single frequency. For this second scenario, we achieve a classification accuracy, which is between 77% and 100%.
Dynamic Modeling of a Cable Suspended Parallel Robot

—Recently, Cable Suspended Parallel Robots (CSPRs) have provided considerable advances while driving in specific fields where dynamic features are required. Representative examples include moving a large cargo in fast transitions while maintaining high precision. As a result, in order to effectively handle CSPRs on complex movements with varying payloads, dynamic restrictions must be required. By real-time measuring the force applied to each cable, we can approach the payload at any point, as the manipulators act instantly when carrying distinct objects. The inverse dynamic equation is come up using the Lagrangian formulation, and the rope mass is negligible. In order to real time measurement cable tensions, a proportional-derivative(PD) model drives each motor. Finally, simulation and experiment are used to test the stability of dynamic modeling for control and to confirm the program.
Multivariate Ensemble Forecast Framework for Demand Prediction of Anomalous Days

An accurate load forecast is always important for the power industry and energy players as it enables stakeholders to make critical decisions. In addition, its importance is further increased with growing uncertainties in the generation sector due to the high penetration of renewable energy and the introduction of demand side management strategies. An incremental improvement in grid-level demand forecast of anomalous days can potentially save millions of dollars. However, due to an increasing penetration of renewable energy resources and their dependency on several meteorological and exogenous variables, accurate load forecasting of anomalous days has now become very challenging. To improve the prediction accuracy of the load forecasting, an ensemble forecast framework (ENFF) is proposed with a systematic combination of three multiple predictors, namely Elman neural network, feedforward neural network, and radial basis function neural network. These predictors are trained using global particle swarm optimization to improve their prediction capability in the ENFF. The outputs of individual predictors are combined using a trim aggregation technique by removing forecasting anomalies. Real recorded data of New England ISO grid is used for training and testing of the ENFF for anomalous days. The forecast results of the proposed ENFF indicate a significant improvement in prediction accuracy in comparison to autoregressive integrated moving average and back-propagation neural networks based benchmark models.
Analysis and Design of Improved Intelligent Search Strategy for Web Crawler

This paper mainly studies the design and implementation of the search engine's searcher Spider program, and introduces the concept and technical essentials of Spider program in detail. Network crawler is a web crawler program which can run in the background with configuration file as the initial URL crawling down with the width first algorithm and saving the target URL. The paper presents analysis and design of improved intelligent search strategy for Web crawler. Based on multi-thread web crawler, the client can access the server through socket, and the client sends its own set request to the server.
“It’s the Combination That Works”: Evaluating Student Experiences with a Multi-element Blended Design in First-year Law

This mixed method study involved twenty students enrolled in three consecutive intakes of an Australian Bachelor of Laws program’s introductory unit. Pioneering a multi-element blended design, the unit featured three key elements: summary videos, self-test online quizzes and interactive discussion boards. These elements were chosen based on evidence-based research into digital tools found effective in enhancing students’ face-to-face learning experience in blended and fully online designs. The study’s main goal was to evaluate how students utilized these elements and in what ways their previous experiences with blended designs influenced their learning process in this unit. A focus-group and online surveys were used to collect data. Based on literature review, four areas of student experience with this blended designs formed a particular focus of this study: student expectations, support, resources, and collaboration. It was found that students extensively used videos and quizzes for catch-up, revision, and clarification, while discussion boards were not perceived as useful, with students preferring to have discussions face-to-face, in and out of classroom. Findings also indicated that students’ expectations of and previous experiences with blended learning can be leveraged to strengthen blended designs.
Mediating Role of Psychological Maladjustment in Relation Between Dark Triad, Psychological Distress and Subjective Happiness of Pakistani Emerging Adults

The transition from adolescence to adulthood is fraught with challenges that might have impacts on later life and personality development. Earlier research investigated Dark Triad traits in connection to emotional problems. The current study, on the other hand, focused on investigating the mediating role of psychological maladjustment in the relation of Dark Triad traits, psychological distress, and subjective happiness in emerging adults. A sample of 546 participants aged 18–25 years (M = 21.2 years) from Pakistan have participated to complete an online survey. Standardized assessment tools were used to measure the targeted variables. Results indicated that Machiavellianism and psychopathy were positively associated with psychological distress, whereas narcissism appeared to be a non-significant predictor. Subjective happiness was positively associated with Machiavellianism and negatively associated with psychopathy. In addition, mediation analysis through Structural Equation Modeling (SEM) indicated that the Dark Triad traits (Machiavellianism and psychopathology), psychological distress, and subjective wellbeing were explained by psychological maladjustment. Implications and limitations are discussed.
An Ontological Approach to Mitigate Risk in Web Applications

Information Security (InfoSec) is becoming a high priority asset to support business activities, as organizations struggle to assure that data is available and secure in web applications. However, security is not a concern from the beginning of the development process, mainly because developers are not security specialists. Consequently, vulnerable systems are designed and when attacked can compromise organization's data and operations, enclosing high financial losses. Because most attacks targets the application layer, we propose an intelligent approach based on ontology to mitigate risks in web applications. An ontological approach can contribute to InfoSec knowledge dissemination and reduce the burden of implementing secure web applications on organizations. The ontology is based on the OWASP Top 10 Project, applied to reduce the gap between the application developer and the security knowledge. The proposed model is employed in the development's design phase; with more secure web applications as the outcome. The extensible and reusable developed ontology is evaluated in a prototype scenario of a web application named 'SMS Broadcast'. The results show that vulnerabilities can be reduced by increasing the security awareness of web developers during the application development process.
Application of AI Information Technology in the Political and Ideological Online Classroom System in the Background of Big Data

Political and ideological online teaching is a new thing integrating political and ideological theory teaching and artificial intelligence information technology. In order to effectively improve the quality of political and ideological online teaching, this paper aims to make an in-depth study on the application of artificial intelligence technology in the political and ideological online classroom system under the background of big data. This paper first expounds the overall architecture of the political and ideological online system from the system hardware architecture and software architecture and then discusses in detail the functional design of the online system and the structure of the database. This paper proposes a data mining algorithm based on scale affine transformation and curriculum data information space reconstruction, constructs the data structure model of the political and ideological online classroom system, uses scale affine transformation to fuse the curriculum data, carries out spatial reconstruction of the fused curriculum data, extracts the high-order features of the curriculum data in the political and ideological online classroom system in the reconstructed space, and completes the data mining of political and ideological courses. Experiments show that the political and ideological online classroom system under the proposed method is better than the political and ideological online classroom system under the traditional method in terms of user satisfaction and teacher operation proficiency and can effectively improve the quality of political and ideological online teaching.