Miracle-dz/UniFAR-FTUs huggingface dataset:
Soy Protein Isolate as Emulsifier of Nanoemulsified Beverages: Rheological and Physical Evaluation

The production of biologically active molecules or the addition of new bioactive ingredients in foods, thereby producing functional foods, has been improved with nanoemulsion technology. In this sense, the aim of this work was to develop nanoemulsified beverages as potential candidates for the encapsulation of bioactive compounds, whose integrity and release across the intestinal tract are controlled by the structure and stability of the interfaces. To achieve this, firstly, a by-product rich-in protein has been evaluated as a potential candidate to act as an emulsifier (chemical content, amino acid composition, solubility, ζ-potential and surface tension were evaluated). Later, emulsions with different soy protein isolate concentrations (0.5, 1.0, 1.5 and 2.0 wt%), pH values (2, 4, 6 and 8) and homogenization pressures (100, 120 and 140 PSI) were prepared using a high-pressure homogenizer after a pre-emulsion formation. Physical (stability via Backscattering and drop size evolution) and rheological (including interfacial analysis) characterizations of emulsions were carried out to characterize their potential as delivery emulsion systems. According to the results obtained, the nanoemulsions showed the best stability when the protein concentration was 2.0 wt%, pH 2.0 and 120 PSI was applied as homogenization pressure.
Validation of Digital Twins: Challenges and Opportunities

Digital twins enjoy increasing interest in a diverse array of industrial sectors, such as manufacturing, healthcare, urban planning, etc. Their usefulness depends on the robustness of the corresponding digital twin models; however, validation of the model, as a mean to ensure models' robustness, is a difficult problem. Moreover, traditional validation approaches need to undergo significant transformation to be made applicable to digital twins. To the best of our knowledge, there has not been a systematic treatment of validating digital twin models. This paper identifies several challenges facing the model validation within digital twins. Furthermore, we propose an initial framework to define basic rules of digital twin model validation and introduce a systematic approach to validation that seamlessly combines expert knowledge and data gathered from available Internet of Things (IoT) devices.
Residual strain of a Cr film characterized by micromachined beams

Microbridge samples of length 134–368 µm and width 20 µm, composed of a Cr film of 150 nm thickness sputter-deposited on a silicon substrate, were fabricated by the MEMS (microelectromechanical systems) technique. The profile of ‘buckled’ beams was measured by the white light interference technique and only the middle and the boundary of the beams are shown in the results. From the length and amplitude, the full profile of the beams was fitted and residual strain in the film was deduced. The fitted results correspond well with the experimental results and the residual strain is about 5.826 × 10−3.
An Integrated Three-Phase AC–DC Wireless-Power-Transfer Converter With Active Power Factor Correction Using Three Transmitter Coils

Three-phase ac–dc converters are more commonly used for high-power applications compared with single-phase converters. For high-power wireless power transfer (WPT) applications, such as wireless electric vehicle chargers, usually a two-stage topology is applied, which consists of a three-phase ac–dc rectifier with power factor correction and a dc–dc converter for WPT. Recently, there are several studies on three-phase single-stage WPT solutions being proposed to reduce power conversion stages and, furtherly, increase the overall efficiency and reduce system cost. In this article, an integrated three-phase ac–dc WPT converter topology with active power factor correction is proposed. The count of power semiconductor devices is significantly reduced compared with the state-of-the-art three-phase single-stage topologies. Moreover, three transmitter coils are used to enhance the system power capacity. Topology description, operation analysis, control method, power loss analysis, and reference design guideline of the proposed topology are presented in detail. Finally, a laboratory prototype is built and evaluated. The functionalities, performances, and advantages are demonstrated by the corresponding experimental results.
The Role of Two Factors of Negative Symptoms and Cognition on Social Functioning in Male Patients with Schizophrenia: A Mediator Model

Objective: This study aims to compare the cognitive function and social functioning in male patients with deficit syndrome (DS) and non-DS, and to explore whether cognitive function serves as a mediator in the relationship between the two factors of negative symptoms (motivation and pleasure (MAP) and expressivity (EXP) deficits, and social functioning in schizophrenia patients. Methods: One hundred and fifty-six male patients with schizophrenia and 109 age- and education-matched normal controls were enrolled in the current study. The Chinese version of a Schedule for Deficit Syndrome (SDS) was used for DS and non-DS categorization. The Brief Psychiatric Rating Scale (BPRS) and the Brief Negative Symptoms Scale (BNSS) were used to assess psychotic and negative symptoms in patients. The Social-Adaptive Functioning Evaluation (SAFE) was adopted to evaluate patients’ social functioning, and a battery of classical neurocognitive tests was used to assess cognition, including sustained vigilance/attention, cognitive flexibility, ideation fluency, and visuospatial memory. Results: We found that male patients with DS performed worse in all four cognitive domains and social functioning compared to non-DS patients. Both total negative symptoms and its two factors were significantly associated with all four domains of cognition and social functioning in male patients. Interestingly, our results indicate that only cognitive flexibility mediates the relationship between negative symptoms and social functioning in schizophrenia patients, but there were no differences between EXP and MAP negative factors in this model. Conclusion: Our findings suggest that DS patients may represent a unique clinical subgroup of schizophrenia, and the integrated interventions targeting both negative symptoms and cognition, especially cognitive flexibility, may optimally improve functional outcomes in schizophrenia patients.
Distribution of substructures in clusters of galaxies

Wavelet analysis has been applied to the positional data of galaxies in the 117 Abell clusters. The calculations used allowed us to obtain coordinates of the centres substructures detected and to estimate the number of galaxies belonging to them. This study has shown that substructures are present in 45 clusters. Moreover, the analysis of the distribution of substructures within clusters shows that small substructures have a higher projected density than large substructures do and, for each applied wavelet scale a, the number of substructures increases towards the cluster centre.
Therapeutic Targeting of Inflammation and Virus Simultaneously Ameliorates Influenza Pneumonia and Protects from Morbidity and Mortality

Influenza pneumonia is a severe complication caused by inflammation of the lungs following infection with seasonal and pandemic strains of influenza A virus (IAV), that can result in lung pathology, respiratory failure, and death. There is currently no treatment for severe disease and pneumonia caused by IAV. Antivirals are available but are only effective if treatment is initiated within 48 h of onset of symptoms. Influenza complications and mortality are often associated with high viral load and an excessive lung inflammatory cytokine response. Therefore, we simultaneously targeted the virus and inflammation. We used the antiviral oseltamivir and the anti-inflammatory drug etanercept to dampen TNF signaling after the onset of clinical signs to treat pneumonia in a mouse model of respiratory IAV infection. The combined treatment down-regulated the inflammatory cytokines TNF, IL-1β, IL-6, and IL-12p40, and the chemokines CCL2, CCL5, and CXCL10. Consequently, combined treatment with oseltamivir and a signal transducer and activator of transcription 3 (STAT3) inhibitor effectively reduced clinical disease and lung pathology. Combined treatment using etanercept or STAT3 inhibitor and oseltamivir dampened an overlapping set of cytokines. Thus, combined therapy targeting a specific cytokine or cytokine signaling pathway and an antiviral drug provide an effective treatment strategy for ameliorating IAV pneumonia. This approach might apply to treating pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Italian University Students’ Resilience during the COVID-19 Lockdown—A Structural Equation Model about the Relationship between Resilience, Emotion Regulation and Well-Being

Over the past two years, the consequences of the severe restrictions imposed by the rapid spread of COVID-19 among the global population have been a central focus of scientific research. The pandemic has been a singular and unexpected event that found people unprepared and vulnerable in responding to its emergence, resulting in substantial psychological distress. Scientific evidence has highlighted that adolescents and emerging adults have been among those populations at greatest risk of adverse psychological outcomes, even in the long term. In particular, more than one-third of young adults reported high levels of loneliness, and nearly half of 18- to 24-year-olds felt lonely during the pandemic, experiencing both psychological and emotional distress. The lockdown, the consequent suspension of face-to-face academic activities and the severe restriction of social life have disrupted the daily routines of students already involved in coping with developmental tasks related to identity formation and the relational experience. Under such conditions, emotions and emotional regulation skills are crucial in adapting behavior to reach academic goals and face mounting levels of distress. Therefore, several studies have investigated resilience mechanisms and coping strategies of emerging adults during the pandemic. The present study focuses on university students and explores the impact of resilience and emotional regulation on adverse psychological outcomes related to persistent distress conditions associated with the COVID-19 pandemic. Students were administered a self-report assessment battery through an online platform at the beginning (T0) and the end of the lockdown (T1). A structural equation model (SEM) was used to explore the relationship between resilience, emotional regulation difficulties and psychological distress (depression, anxiety and stress). The findings indicate that psychological resilience and emotion regulation are protective factors that buffer the extent of possible distress resulting from an adverse condition such as the COVID-19 pandemic.
IFITMs from Naturally Infected Animal Species Exhibit Distinct Restriction Capacities against Toscana and Rift Valley Fever Viruses

Rift Valley Fever virus (RVFV) and Toscana virus (TOSV) are two pathogenic arthropod-borne viruses responsible for zoonotic infections in both humans and animals; as such, they represent a growing threat to public and veterinary health. Interferon-induced transmembrane (IFITM) proteins are broad inhibitors of a large panel of viruses belonging to various families and genera. However, little is known on the interplay between RVFV, TOSV, and the IFITM proteins derived from their naturally infected host species. In this study, we investigated the ability of human, bovine, and camel IFITMs to restrict RVFV and TOSV infection. Our results indicated that TOSV was extremely sensitive to inhibition by all the animal IFITMs tested, while RVFV was inhibited by human IFITM-2 and IFITM-3, but not IFITM-1, and exhibited a more heterogeneous resistance phenotype towards the individual bovine and camel IFITMs tested. Overall, our findings shed some light on the complex and differential interplay between two zoonotic viruses and IFITMs from their naturally infected animal species.
A Software Implementation and Research of Ensemble Algorithms for Solving the Different Types of Data Classification Problem

Many algorithms for solving classification problems have been studied and developed at the moment, but the problem of improving the quality (accuracy, stability) of classification solutions remains relevant. This problem can be solved by developing ensemble classification algorithms, as well as by performing a qualitative primary analysis and processing of the initial data. This research reports the results of research and implementation of ensemble algorithms, which are being developed in software to solve the problem of classification of different types of data. Homogeneous and heterogeneous ensemble classification models were implemented by using R language. Several classifiers were built on the basis of real agricultural data using the developed models. Comparative testing showed the advantages of using ensemble algorithms compared to single classifiers (increased classification accuracy, reduced logarithmic losses). According to the results of the study, the conclusion about the prospects of development and application of ensemble models to solve the problem of classification and software to solve the problems of analysis and data processing in various fields using modern technologies and software tools.
THEORETICAL AND EXPERIMENTAL INVESTIGATION OF A MICROSTRIP RADIATOR WITH MULTIPLE LUMPED LINEAR LOADS

ABSTRACT A simple theory is developed for the analysis of microstrip patch elements which are loaded at one or more points with lumped linear load impedances. The analysis is based on a cavity model in which the shape of the field distribution between the patch and ground plane is assumed to be well approximated by that of the resonant modes of a corresponding magnetic- and electric-walled cavity. The resonant mode of the loaded cavity is represented as an appropriate superposition of the modes of the corresponding unloaded cavity. The characteristic equation for the resonant frequencies of the loaded cavity is obtained in terms of the load impedances and the unloaded cavity multiport open-circuit parameters. An analysis of the input impedance of a rectangular microstrip element shorted at an arbitrary point has been implemented and the results show good agreement with experiment. An ancilliary result showing the equivalence between a thin strip and a circular cylinder model of a feed current distribution.
Surgical Outcomes of Lensectomy-Vitrectomy with Primary Intraocular Lens Implantation in Children with Bilateral Congenital Cataracts

In this study, we evaluated the long-term surgical outcomes of lensectomy-vitrectomy with primary intraocular lens (IOL) implantation in children with bilateral congenital cataracts (CCs) and investigated the potential risk factors for low vision. A total of 148 eyes in 74 children who underwent lensectomy-vitrectomy with primary IOL implantation were enrolled in this study. The surgery age was 44.04 ± 14.60 months, with a follow-up period of 46.66 ± 14.34 months. The final BCVA was 0.24 ± 0.32 logMAR, and low vision was found in 22 eyes (14.9%). Postoperative complications requiring additional surgeries included VAO (4 eyes, 5.4%), IOL pupillary captures (2 eyes, 2.0%), iris incarceration (1 eye, 0.7%), and glaucoma (1 eye, 0.7%). A higher incidence of VAO and larger postoperative refractive error was observed in younger children (≤2 years old) than in elder children (>2 years old) (p = 0.003, p = 0.047, respectively). Final BCVA was affected by preexisting comorbidity (p < 0.001), cataract density (p < 0.001), cataract size (p = 0.020), occurrence of postoperative complications (p = 0.011), and ASE (p = 0.008). Multivariate analysis showed that denser cataracts (OR = 9.303, p = 0.035) and preexisting comorbidity (OR = 4.712, p = 0.004) were the significant predictors of low vision. In conclusion, lensectomy-vitrectomy with primary IOL implantation is an effective and safe treatment for CC. The long-term visual outcome is encouraging in children with bilateral CC undergoing this procedure with a low rate of postoperative complications requiring surgeries. Moreover, eyes with denser cataracts and preexisting comorbidity may have a high risk of low vision.
How the Addition of Chitosan Affects the Transport and Rheological Properties of Agarose Hydrogels

Agarose hydrogels enriched by chitosan were studied from a point of view diffusion and the immobilization of metal ions. Copper was used as a model metal with a high affinity to chitosan. The influence of interactions between copper and chitosan on transport properties was investigated. Effective diffusion coefficients were determined and compared with values obtained from pure agarose hydrogel. Their values increased with the amount of chitosan added to agarose hydrogel and the lowest addition caused the decrease in diffusivity in comparison with hydrogel without chitosan. Liesegang patterns were observed in the hydrogels with higher contents of chitosan. The patterns were more distinct if the chitosan content increased. The formation of Liesegang patterns caused a local decrease in the concentration of copper ions and concentration profiles were affected by this phenomenon. Thus, the values of effective diffusion coefficient covered the influences of pore structure of hydrogels and the interactions between chitosan and metal ions, including precipitation on observed Liesegang rings. From the point of view of rheology, the addition of chitosan resulted in changes in storage and loss moduli, which can show on a “more liquid” character of enriched hydrogels. It can contribute to the increase in the effective diffusion coefficients for hydrogels with higher content of chitosan.
Sensitivity of Caspian sea‐ice to air temperature

Caspian sea ice concentration from satellite passive microwave data and surface daily air temperatures are analysed from 1978 to 2009. Relationships between mean winter air temperatures, cumulative freezing degree days (CFDD) and the sum of daily ice area (cumulative ice area) are found. These show that mean monthly air temperature of less than 5.5–9.5 °C, and a minimum CFDD of 3.6 ± 11.2 °C, are required for ice formation in the northern Caspian. Examination of climate projections from multi‐model ensembles of monthly mean air temperatures suggest that the northern Caspian may be largely ice‐free by 2100 for the highest emission scenario. An ocean–ice–atmosphere model of the Caspian shows weak sensitivities of the minimum CFDD to varied sea ice albedo and ice compressive strength. Sea level decline is found to reduce the minimum CFDD as well as promote the formation of higher concentration or ‘closed ice’.
A nonlinear model for time series prediction and signal interpolation

The approach is an extension of the method of radial basis functions. Parameter estimation for the nonlinear predictor is performed by a gradient descent over a mean squared error measure, starting from a random initialization of the parameters. Results on predicting segments of speech data and the sunspot series are presented and compared to a linear predictor. An approach to adaptive estimation of the model by means of an extended Kalman filter is presented. In terms of prediction residual, the nonlinear predictor is found to perform significantly better than a linear model with the same number of parameters. Difficulties in applying this model in speech processing are discussed. <<ETX>>
Discrete Event Simulation Using Distributional Random Forests to Model Event Outcomes

In discrete event simulation (DES), the events are random (aleatory) and typically represented by a probability distribution that fits the real phenomena that is studied. The true distributions of event outcomes, which may be multivariate, are often dependent on the values of covariates and this relationship may be complex. Due to difficulties in representing the influence of covariates within DES models, often only the averaged distribution or expected value of the conditional distribution is used. However, this can reduce modelling accuracy and prevent the model from being used to study the influence of covariates. Distributional random forests (DRF) are a machine learning technique for predicting the multivariate conditional distribution of an outcome from the values of covariates using an ensemble of decision trees. In this paper, the benefits of utilizing DRF in DES are explored through comparison with alternative approaches in a model of a powder coating industrial process.
Predicting Relationship Labels and Individual Personality Traits From Telecommunication History in Social Networks Using Hawkes Processes

Mobile phones contain a wealth of private information, so we try to keep them secure. We provide large-scale evidence that the psychological profiles of individuals and their relations with their peers can be predicted from seemingly anonymous communication traces–calling and texting logs that service providers routinely collect. Based on two extensive longitudinal studies containing more than 900 college students, we use point process modeling to describe communication patterns. We automatically predict the peer relationship type and temporal dynamics, and assess user personality based on the modeling. For some personality traits, the results are comparable to the gold-standard performances obtained from survey self-report data. Findings illustrate how information usually residing outside the control of individuals can be used to reconstruct sensitive information.
Assessing the cognition of movement trajectory visualizations: interpreting speed and direction

ABSTRACT This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variables in communicating movement parameters along trajectories, such as speed and direction. To do so, a web-based survey is conducted to evaluate the understanding of movement visualizations by a nonspecialist audience. This and future studies contribute fundamental insights into the cognition of movement visualizations and inspire new methods for the empirical evaluation of geovisualizations.
Estimating the effect of treatment in a proportional hazards model in the presence of non‐compliance and contamination

Summary. Methods for adjusting for non‐compliance and contamination, which respect the randomization, are extended from binary outcomes to time‐to‐event analyses by using a proportional hazards model. A simple non‐iterative method is developed when there are no covariates, which is a generalization of the Mantel–Haenszel estimator. More generally, a ‘partial likelihood’ is developed which accommodates covariates under the assumption that they are independent of compliance. A key feature is that the proportion of contaminators and non‐compliers in the risk set is updated at each failure time. When covariates are not independent of compliance, a full likelihood is developed and explored, but this leads to a complex estimator. Estimating equations and information matrices are derived for these estimators and they are evaluated by simulation studies.