Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the Chemical Composition of Selected Varieties of Elderberry with Wild Growing Elderberry

Elderberries of wild-growing shrubs are most often used; however, various cultivated varieties of this shrub appear more and more often. The aim of this research was to compare the fruit composition of specific varieties with those grown wild in urban and ecologically clean conditions. Six varieties of elderberry grown on one experimental farm and two wild-growing samples from the city center and the landscape park were assessed. The content of vitamin C, antioxidant activity, sugar and organic acid content, triterpenes and carotenoids was marked in the tested fruits. The analyses show that there were significant differences in the content of the tested ingredients between the varieties tested, while the place of cultivation was of less importance. Apart from organic acids and triterpenes, fruits from wild-growing shrubs were more abundant in other compounds determined. The white variety of ‘Albida’ turned out to be the poorest in bioactive compounds.
Agrupamiento conceptual jerárquico basado en distancias

El presente trabajo de investigación se encuentra enmarcado en el programa internacional de cooperación Alfa-LerNet entre el grupo ELP del Departamento de Sistemas Informáticos y Computación (DSIC) de la Universidad Politécnica de Valencia, España y el Proyecto de Incentivos código 22/F822 “Ingeniería de Software: Conceptos, Métodos y Herramientas en un contexto de Ingeniería de Software en Evolución” de la Universidad Nacional de San Luis, Argentina.
A 1.23μJ/Inference, All-Digital Shuffle-Type Group-CNN Accelerator in 28nm CMOS With 85.8% Accuracy on CIFAR-10

To achieve high energy efficiency for edge processing, we implement an all-digital shuffle-type group CNN accelerator with binary weights, which is the first reported shuffle-type GCNN accelerator in CMOS as far as we know. Cross-level optimizations from architecture to hardware level are proposed. The NN architecture is optimized to obtain a hardware-friendly configuration. For hardware-level optimizations, we propose a three-level storage hierarchy with latch-based computing-near-memory to reduce the total power by 25.8%. We also design a novel shuffle hardware to reduce memory access in the original shuffle to 1/3 and a stage-by-stage gated MUX to reduce the power consumption in the original selector circuit to 29%. Fabricated in a 28nm CMOS process, the chip works at 0.48-0.9 V with the best energy efficiency of 1.23 μJ/inf at 0.552 V and 66 MHz with 85.8% accuracy on CIFAR-10 dataset. Its energy efficiency is 1.9- $34.8\mathbf {\times }$ better than state-of-the-art works.
Non-Proteinogenic Amino Acid β-N-Methylamino-L-Alanine (BMAA): Bioactivity and Ecological Significance

Research interest in a non-protein amino acid β-N-methylamino-L-alanine (BMAA) arose due to the discovery of a connection between exposure to BMAA and the occurrence of neurodegenerative diseases. Previous reviews on this topic either considered BMAA as a risk factor for neurodegenerative diseases or focused on the problems of detecting BMAA in various environmental samples. Our review is devoted to a wide range of fundamental biological problems related to BMAA, including the molecular mechanisms of biological activity of BMAA and the complex relationships between producers of BMAA and the environment in various natural ecosystems. At the beginning, we briefly recall the most important facts about the producers of BMAA (cyanobacteria, microalgae, and bacteria), the pathways of BMAA biosynthesis, and reliable methods of identification of BMAA. The main distinctive feature of our review is a detailed examination of the molecular mechanisms underlying the toxicity of BMAA to living cells. A brand new aspect, not previously discussed in any reviews, is the effect of BMAA on cyanobacterial cells. These recent studies, conducted using transcriptomics and proteomics, revealed potent regulatory effects of BMAA on the basic metabolism and cell development of these ancient photoautotrophic prokaryotes. Exogenous BMAA strongly influences cell differentiation and primary metabolic processes in cyanobacteria, such as nitrogen fixation, photosynthesis, carbon fixation, and various biosynthetic processes involving 2-oxoglutarate and glutamate. Cyanobacteria were found to be more sensitive to exogenous BMAA under nitrogen-limited growth conditions. We suggest a hypothesis that this toxic diaminoacid can be used by phytoplankton organisms as a possible allelopathic tool for controlling the population of cyanobacterial cells during a period of intense competition for nitrogen and other resources in various ecosystems.
Transformation of carbon dioxide, a greenhouse gas, into useful components and reducing global warming: A comprehensive review

CO2, a greenhouse gas, is continuously emitted by different sources due to the extensive use of fossil fuels. Hence various methods have been developed to mitigate CO2 gas from the environment. The capturing and storing of carbon is termed carbon capture and storage, a method of reducing the level of CO2 from the atmosphere emitted by industrial sites and power stations. The reduction of the atmospheric carbon or the stored carbon to different products by multiple techniques has been a current topic of research, taking the environmental concern into account. The reduction of CO2 is very challenging since the bond energy of C=O is very high. Additionally, the separation of the gas from the atmosphere or industrial flue gas is also technologically difficult. This review comprises a detailed discussion of the methods adopted for the conversion of CO2 into different carbon nano‐materials and hydrocarbons. The mechanisms associated with the conversion, along with the role of electrodes used for the conversion of the gas during electrolysis, have also been conferred extensively.
Studies of early-type variable stars. VII : The orbit and physical parameters for TV Cassiopeiae

From a new spectroscopic orbit of the Algol system TV Cas, based on Reticon observations, we find : V 0 =−4.1±0.8 km s −1 , K p =86.1±1.0 km s −1 and K s =212.9±3.6 km s −1 and m p /m s =2.47±0.05. This measurement of the mass ratio is the first reliable determination but confirms Plaskett's detection of the secondary. The velocity measures themselves are based on fitting theoretical profiles generated by a physical model of the binary, to the observed cross-correlation function. Such profiles match this function very well, much better in fact than Gaussian profiles which are generally used.
Trust in Government and COVID-19 Response Policy: A Comparative Approach

Abstract As nations work to respond to COVID-19, trust in government is critical to achieving health outcomes. Studies argue that greater trust in government is associated with increased compliance with COVID-19 public health policies. This analysis investigated predictors of public trust during COVID-19 in 16 countries grouped in four regions. The data used (n = 47,000) are taken from the Worldwide COVID-19 Attitudes and Beliefs dataset. Five hypotheses test the effects of stringency, geographic location, age, gender, income, and education levels on public trust. Findings reveal that increased stringency measures and education levels are positively associated with trust.
Water Occurrence in the Two Largest Lakes in China Based on Long-Term Landsat Images: Spatiotemporal Changes, Ecological Impacts, and Influencing Factors

Poyang Lake and Dongting Lake are the two largest freshwater lakes in China. They play a critical ecological role in regional sustainable development, which is closely related to water occurrence (WO). This study aimed to characterize the spatiotemporal changes, ecological impacts, and influencing factors of WO in the two lakes. First, this study utilized the Google Earth Engine (GEE) platform and all available Landsat images to retrieve the time series of WO in Poyang Lake and Dongting Lake on a two-year scale from 1989 to 2020. Then, the study explored the landscape variation intensity, landscape structure, and their ecological impacts on net primary production (NPP) and total nitrogen (TN) based on the landscape transfer rate (LTR), landscape indices, and geostatistical connectivity analysis. Finally, the study investigated the natural and human factors that influence the changes in WO. The major conclusions include the following: (1) the mean WO showed a significantly decreasing trend in the two lakes from 1989 to 2020. According to the LTR, the landscape variation in Dongting Lake was more intense before 2002, while after 2002, the landscape variation in Poyang Lake was more intense. (2) The landscape structure of WO gradually fragmented and diversified, along with decreasing hydrological connectivity in the two lakes. (3) The increase in NPP in Poyang Lake and the increase in TN in Dongting Lake were closely correlated with the changes in WO. (4) The upstream runoff showed a significantly positive correlation with the mean WO in Poyang Lake and the annual precipitation showed a significantly positive correlation with the mean WO in Dongting Lake; the influences of natural factors on WO changes became weaker. This study reinforces the understanding of long-term changes in the water body in Poyang Lake and Dongting Lake.
Coordinated Control of Intelligent Fuzzy Traffic Signal Based on Edge Computing Distribution

With the development of Internet of Things infrastructures and intelligent traffic systems, the traffic congestion that results from the continuous complexity of urban road networks and traffic saturation has a new solution. In this research, we propose a traffic signal control scenario based on edge computing. We also propose a chemical reaction–cooperative particle swarm optimization (CRO-CPSO) algorithm so that flexible traffic control is sunk to the edge. To implement short-term real-time vehicle waiting time prediction as a collaborative judgment of CRO-CPSO, we suggest a traffic flow prediction system based on fuzzy logic. In addition, we introduce a co-factor (collaborative factor) set based on offline learning to take into account the experiential characteristics of intersections in urban road networks for the generation of strategies by the algorithm. Furthermore, the real case of Changsha County is simulated on the SUMO simulation platform. The issue of traffic flow saturation is improved by our method. Compared with other methods, our algorithm enhances the proportions of vehicles that reach their destinations on time by 13.03%, which maximizes the driving experience for drivers. Meanwhile, our algorithm reduces the driving times of vehicles by 25.34%, thus alleviating traffic jams.
A Spatial–Temporal Multitask Collaborative Learning Model for Multistep Traffic Flow Prediction

Traffic flow prediction is a fundamental capability for successful deployment of intelligent transportation systems. Traditionally, multiple related prediction tasks are undertaken individually, without considering the relationships among the tasks. This paper presents a spatial–temporal multitask collaborative learning model for multistep traffic flow prediction. The novel approach learns multiple related prediction tasks collaboratively by extracting and utilizing appropriate shared information across tasks. First, each traffic flow prediction problem is formulated as a supervised machine-learning task. Next, the sparse features shared across multiple tasks are learned by solving a regularized optimization problem. To deal with the non-convex and non-smooth challenges, the optimization problem is then transformed into an equivalent convex problem. Finally, the global optimal solution of the convex problem is found by solving a variation of this problem using an alternating minimization algorithm. The proposed model incorporates both the spatial correlation between different observed stations and the intrinsic relationship between different traffic flow parameters, as well as the coarse-grain temporal correlation between different days in a week and the fine-grain temporal correlation between different prediction steps. Application of the proposed model to a real case study for SR180-E freeway in Fresno, California showed its effectiveness, robustness and advantages for multistep traffic flow prediction.
Transformative Online Mentoring: The Student Interns' Lenses

The implementation of distance - online learning during the COVID-19 outbreak prompted an online student internship in the Higher Education Institutions. This required an adjusted mentoring strategy suited to the training needs of the student interns amidst the academic shift. This study explored online mentoring through the lenses of the 11 student interns. The student interns' participation was based on purposive sampling and the number of participants was determined based on data saturation. This study utilized Husserlian phenomenology as a design and Colaizzi method in analyzing the data. Significant statements from the narratives were extracted and coded, and themes were formulated. The online mentoring as presented in themes includes communicating vision, putting an online system in place, adjusting online strategies, employing effective problem-solving methods, creating a strong virtual support, and transforming the student interns. These themes were utilized in creating the transformative online mentoring model. It was found out that effective online mentoring requires improved strategies that need to be implemented to ensure quality internship and pre-service education. It was recommended that teacher education programs must prepare pre-service teachers with digital and virtual skills prior to their student internship.
Analysis of Rheumatic Heart Disease Mortality in the Chinese Population: A JoinPoint and Age–Period–Cohort Study

(1) Background: We aimed to analyze rheumatic heart disease (RHD) mortality trends in China’s urban and rural areas and to determine the roles of age, period, and cohort effects. (2) Methods: Based on mortality data extracted from the China Health Statistics Yearbook, we calculated the crude mortality rate of RHD. Age–adjusted rates were computed by the direct method using the 2020 census as the standard population. The annual percentage change (APC) and average annual percentage change (AAPC) were determined by the JoinPoint regression model. The age–period–cohort model was used to estimate the effects of age, period, and cohort. (3) Results: From 2006 to 2020, the general trend in RHD standardized mortality declined. The RHD mortality rate was higher in rural than in urban areas and among females than males. The elderly (over 60 years old) were at high risk for RHD deaths in China. The age effect increased with age, and the cohort effect showed a declining trend as chronology grew, but the period effect was not significant. (4) Conclusions: China has achieved great success in RHD, but RHD mortality may increase with age. Compared with the period effect, age and cohort effects dominated the risk of RHD deaths.
Emerging Reasons and Consequences of Early School Leaving in Quezon Province

This research investigated the reasons that led school-aged children in Quezon province to leave school early. It utilized qualitative case study method. The researcher recruited four early school leavers and their parents to participate through snowball sampling technique. The data were gathered through semi-structured interviews and video recordings where transcribed data were subjected to thematic analysis to classify emerging reasons and results of early school leaving. Findings revealed that satisfaction from work and money, failure to understand English language, and children's reactions to derogatory actions and remarks influenced school-aged children to quit schooling. Consequently, the participants experienced negative consequences such as unstable jobs and feelings of regret. Hence, interplay of diverse reasons led students' decision to quit school. However, further local studies must be endeavoured to triangulate statistical reports in mitigating undocumented causes of early school leaving.
Functionalization of Bacterial Cellulose With Bacterial Pigments: Optimization Using a Full Factorial Design Approach

Pigments from natural sources, such as bacterial pigments, have gained increased attention in recent years due to their biodegradability, non-toxicity, and noncarcinogenicity. The intention to replace synthetic and oil-derived compounds is not restricted to synthetic dyes; other applications include the replacement of oilderived polymers for more environmentally friendly options, such as biopolymers. In this work, the functionalization of a bacterial cellulose (BC) biopolymer with bacterial pigments was explored using a full factorial design methodology to evaluate the best functionalization conditions to produce colored BC. From the factors and interactions evaluated, it was possible to conclude that the variable duration of the functionalization procedure could be reduced to a low level without significantly affecting the functionalization of the BC samples with bacterial pigments. Moreover, BC is a product with high industrial applicability, versatility, and sustainability. Hence, the multifunctional colored BC can be applied in the packaging, paper, and textile industries, among others. Keywords: bacterial pigments, bacterial cellulose, full-factorial design, optimization
Magnetohydrodynamic Models of Axisymmetric Protostellar Jets

We present the results of a series of axisymmetric time-dependent magnetohydrodynamic (MHD) simulations of the propagation of cooling, overdense jets. Our numerical models are motivated by the properties of outflows associated with young stellar objects. A variety of initial field strengths and configurations are explored for both steady and time-variable (pulsed) jets. For the parameters of protostellar jets adopted here, even apparently weak magnetic fields with strengths B ≳ 60 μG in the preshocked jet beam can have a significant effect on the dynamics, for example, by altering the density, width, and fragmentation of thin shells formed by cooling gas. Strong toroidal fields (≥100 μG) with a radial profile that peaks near the surface of the jet result in the accumulation of dense shocked gas in a "nose cone" at the head of jet. We suggest that this structure is unstable in three dimensions. A linear analysis predicts that axisymmetric pinch modes of the MHD Kelvin-Helmholtz instability should grow only slowly for the highly supermagnetosonic jets studied here; we find no evidence for them in our simulations. Some of our models appear unstable to current-driven pinch modes; however, the resulting pressure and density variations induced in the jet beam are not large, making this mechanism an unlikely source of emission knots in the jet beam. In the case of pulsed jets, radial hoop stresses confine shocked jet material in the pulses to the axis, resulting in a higher density in the pulses in comparison to purely hydrodynamic models. In addition, if the magnetic field strength varies with radius, significant radial structure is produced in the pulses (the density is strongly axially peaked, for example) even if the density and velocity in the jet follow a constant "top-hat" profile initially.
Protection against Marburg Virus and Sudan Virus in NHP by an Adenovector-Based Trivalent Vaccine Regimen Is Correlated to Humoral Immune Response Levels

The Marburg virus (MARV) and Sudan virus (SUDV) belong to the filovirus family. The sporadic human outbreaks occur mostly in Africa and are characterized by an aggressive disease course with high mortality. The first case of Marburg virus disease in Guinea in 2021, together with the increased frequency of outbreaks of Ebola virus (EBOV), which is also a filovirus, accelerated the interest in potential prophylactic vaccine solutions against multiple filoviruses. We previously tested a two-dose heterologous vaccine regimen (Ad26.Filo, MVA-BN-Filo) in non-human primates (NHP) and showed a fully protective immune response against both SUDV and MARV in addition to the already-reported protective effect against EBOV. The vaccine-induced glycoprotein (GP)-binding antibody levels appear to be good predictors of the NHP challenge outcome as indicated by the correlation between antibody levels and survival outcome as well as the high discriminatory capacity of the logistic model. Moreover, the elicited GP-specific binding antibody response against EBOV, SUDV, and MARV remains stable for more than 1 year. Overall, the NHP data indicate that the Ad26.Filo, MVA-BN-Filo regimen may be a good candidate for a prophylactic vaccination strategy in regions at high risk of filovirus outbreaks.
Recent Update on PCSK9 and Platelet Activation Experimental Research Methods: In Vitro and In Vivo Studies

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a crucial factor in the development and progression of cardiovascular diseases. PCSK9 has been demonstrated to modify LDL plasma levels and increase platelet activation, which promotes atherosclerosis, a defining feature of nearly all cardiovascular diseases. Platelet activation has been shown to promote and maintain the response to atherosclerosis development, from beginning to progression and exacerbation, which can lead to advanced cardiovascular events including myocardial infarction (MI) or death. Research on PCSK9 and platelet activation is currently underway with the main goal of reducing the risk of advanced cardiovascular events by preventing or slowing down atherosclerosis progression. Both in vitro and in vivo studies have been used to explore PCSK9 functions to develop new drugs targeting PCSK9. Finding the most suitable study models that represent the pathological and physiological systems found in humans is very important to achieving the goal. This review aimed to present a current and comprehensive overview of the experimental models that have been used to investigate the role of PCSK9 in platelet activation-induced atherosclerotic cardiovascular diseases.
Modelling magnetized neutron stars using resistive magnetohydrodynamics

This work presents an implementation of the resistive MHD equations for a generic algebraic Ohm's law which includes the effects of finite resistivity within full General Relativity. The implementation naturally accounts for magnetic-field-induced anisotropies and, by adopting a phenomenological current, is able to accurately describe electromagnetic fields in the star and in its magnetosphere. We illustrate the application of this approach in interesting systems with astrophysical implications; the aligned rotator solution and the collapse of a magnetized rotating neutron star to a black hole.