Miracle-dz/UniFAR-FTUs huggingface dataset:
Aging and Vascular Disease: A Multidisciplinary Overview

Vascular aging, i.e., the deterioration of the structure and function of the arteries over the life course, predicts cardiovascular events and mortality. Vascular degeneration can be recognized before becoming clinically symptomatic; therefore, its assessment allows the early identification of individuals at risk. This opens the possibility of minimizing disease progression. To review these issues, a search was completed using PubMed, MEDLINE, and Google Scholar from 2000 to date. As a network of clinicians and scientists involved in vascular medicine, we here describe the structural and functional age-dependent alterations of the arteries, the clinical tools for an early diagnosis of vascular aging, and the cellular and molecular events implicated. It emerges that more studies are necessary to identify the best strategy to quantify vascular aging, and to design proper physical activity programs, nutritional and pharmacological strategies, as well as social interventions to prevent, delay, and eventually revert the disease.
Development of a Conceptual Framework Integrating Entrepreneurial Intention, Start-Up Preparation, and Start-Up Decision

The fundamental goal of this study is to develop a conceptual framework that integrates entrepreneurial intention, startup preparation, and startup decision. Thus, the Systematic Literature Review (SLR) technique and article selection based on the PRISMA approach is applied in this study. Between 2000 and 2020, the study included 38 papers from well-known databases. After reviewing previous literature, this study identified seven research gaps. Eventually, it developed a conceptual framework with three contextual variables: perceived educational support, perceived relational support, perceived structural support with perceived behavioral control, entrepreneurship intention, start-up preparation, and start-up decision. This first study combines all seven variables, resulting in start-up decisions under a single framework. The essential constraints are relying on a few databases and using only 38 peer-reviewed publications published in scholarly journals and written in English. Furthermore, academic research articles published between 2000 and 2020 are excluded. This study proposes practitioners of the aspects required early in beginning a business, which supports formulating effective policies for prospective entrepreneurs to enhance economic growth and innovation at the firm and national levels.
Classification of waste materials with a smart garbage system for sustainable development: a novel model

In today’s conditions, where the human population is increasing, environmental pollution is also increasing around the world. One of the most important causes of environmental pollution is the waste materials in the garbage. Misuse of waste materials causes significant damage to both the environment and human health. With the developing technology, the recyclability of the raw material used in the production of waste materials significantly affects both the raw material needs of the countries and the energy savings. Therefore, many traditional activities are carried out in recycling facilities in order to reuse the waste materials that can be recycled in many countries. At the beginning of these activities is manual waste collection and pre-processing depending on the human workforce. This process poses a serious threat to both the environment and human health. For this reason, there is a need for a smart system that automatically detects and classifies the waste materials in the garbage. In this study, Xception, InceptionResNetV2, MobileNet, DenseNet121 and EfficientNetV2S deep learning methods based on artificial intelligence, which automatically classify the waste materials in the garbage, were used and in addition to these methods, Xception_CutLayer and InceptionResNetV2_CutLayer based on transfer learning techniques were proposed. The proposed methods and artificial intelligence-based deep learning methods were trained and tested with a dataset containing 6 different waste materials. According to the findings obtained as a result of training and testing, a classification success rate of 89.72% with the proposed Xception_CutLayer method and 85.77% with the InceptionResNetV2_CutLayer method, a better success rate was obtained than the other artificial intelligence-based methods discussed in the study.
An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification

Nowadays, news text information is exploding, and people need more and more heterogeneous news content. Therefore, news text topic identification is needed to help viewers quickly and accurately screen and filter news related to their interests to save time and energy. The Latent Dirichlet Allocation (LDA) model is the most commonly used method for text topic identification. The optimal number of topics must be specified in advance when using the LDA model to extract topics in previous studies. However, selecting the too-large or the too-small number of topics significantly impacts the final results of LDA topic models, directly determining the quality of topic extraction. Moreover, the news text datasets from social media are very time-sensitive, and the combination of temporal and semantic modeling has not been considered in past studies of news topic identification. This paper proposes an adaptive optimal topic number determination method for fusing semantic and temporal information in news datasets to address the existing problems. Semantic and temporal are first extracted in this method as two different views. Then, density peak clustering of multi-view information fusion is performed based on the two obtained feature vectors. The clustering results are used as the final optimal number of topics. To demonstrate the effectiveness of the proposed method, this paper compares the performance of four traditional methods for determining the optimal number of topics with the performance of this paper’s method on public datasets. The results show that the optimal number of topics considering semantic and temporal factors is significantly better than the other four traditional methods regarding F-value, PMI scores, and MI scores. It performs well in other indicators as well. The above experimental results show that the method proposed in this paper combines the temporal and semantic of news data to determine the optimal number of topics of news text, which can improve the accuracy of selecting the optimal number of topics in the LDA model and the effectiveness of the topic identification of news text to some extent. It can help viewers better understand and utilize the massive news text information. In addition, the method also broadens the idea of identifying and mining unique datasets from multiple perspectives.
Sperm quality parameters, fertilizing potential, metabolites, and DNA methylation in cold-stored and cryopreserved milt from Atlantic salmon (Salmo salar L.)

Cold storage and freezing/thawing of milt may affect sperm functionality and the subsequent fertilization ability of milt. This study aimed to investigate sperm quality parameters and fertilization potential of Atlantic salmon milt, stored cold and subsequently cryopreserved, using different storage conditions. The objective was also to assess if analysis of milt metabolites and sperm DNA methylation signatures could be applicable to further elucidate sperm quality and fertilization following preservation. Milt samples were collected from eight mature Atlantic salmon males and stored for 4 days at 2°C and 8°C. Samples were taken on day one of storage at 2°C and on day four of storage at 2°C and 8°C. Storage for 4 days at 8°C is expected to be detrimental to sperm quality, and was included to create contrasts. Correspondingly, aliquots of cold-stored milt were prepared for cryopreservation, resulting in a total of six experimental conditions. Samples from all six experimental conditions were used in fertilization trials and analyzed for sperm viability, motility, ATP content, DNA fragmentation index, and High DNA stainability. In addition, milt samples from four of the males were analyzed for targeted metabolites and DNA methylation signatures by reduced representation bisulfite sequencing. The fertilization trials were performed using sperm:egg ratios of 75 × 103 and 500 × 103, respectively. Storage duration, temperature, and cryopreservation of cold-stored milt influenced several sperm quality parameters, metabolites, and DNA methylation signatures. The total motility, progressive motility, ATP, and velocity parameters were the sperm parameters with the strongest correlation to fertilization rates (p < 0.01). Several metabolites were correlated with fertility rates in both cold-stored and cryopreserved samples (p < 0.05). The fertilizing capacity of cold-stored milt was significantly reduced after 4 days of storage at 8°C, while corresponding cryopreserved milt showed reduced fertilization at both storage temperatures (2°C and 8°C) (p < 0.05). The results indicate that cryopreservation of milt stored for 1 day does not compromise either fertilization ability or DNA methylation signatures.
FIB-TEM Analysis on a Wark-Lovering Rim from the Vigarano CV3 Chondrite

5374 FIB-TEM ANALYIS ON A WARK-LOVERING RIM FROM THE VIGARANO CV3 CHONDRITE T. J. Zega1, M. Cosarinsky2, G. J. MacPherson3, K. D. McKeegan4. 1Materials Science and Technology Division, Naval Research Laboratory, Washington D.C., USA. tzega@nrl.navy. mil. 2Space Research and Planetary Sciences, University of Bern, Bern, Switzerland. 3Dept. of Mineral Sciences, Smithsonian Institution, Washington D.C., USA. 4Dept. of Earth and Space Sciences, University of California-Los Angeles, Los Angeles, CA, USA.
Evaluation of Statistical Treatment of Left-Censored Contamination Data: Example Involving Deoxynivalenol Occurrence in Pasta and Pasta Substitute Products

The handling of data on food contamination frequently represents a challenge because these are often left-censored, being composed of both positive and non-detected values. The latter observations are not quantified and provide only the information that they are below a laboratory-specific threshold value. Besides deterministic approaches, which simplify the treatment through the substitution of non-detected values with fixed threshold or null values, a growing interest has been shown in the application of stochastic approaches to the treatment of unquantified values. In this study, a multiple imputation procedure was applied in order to analyze contamination data on deoxynivalenol, a mycotoxin that may be present in pasta and pasta substitute products. An application of the proposed technique to censored deoxynivalenol occurrence data is presented. The results were compared to those attained using deterministic techniques (substitution methods). In this context, the stochastic approach seemed to provide a more accurate, unbiased and realistic solution to the problem of left-censored occurrence data. The complete sample of values could then be used to estimate the exposure of the general population to deoxynivalenol based on consumption data.
Driver Drowsiness Detection Using R-R Interval of Electrocardiogram and Self-Attention Autoencoder

Drowsy driving detection is crucial for avoiding serious traffic accidents. Changes in sleep conditions affect the autonomic nervous system (ANS) and, subsequently, heart rate variability (HRV), which is fluctuation in the R-R interval (RRI) in an electrocardiogram (ECG). HRV is easy to measure with a wearable sensor, and it may be possible to use HRV to detect drowsy driving. In conventional HRV-based drowsy driving detection methods, some HRV features are extracted from RRI data for analysis, but it may result in the loss of time-series characteristics of the RRI data. This study proposes a new driver drowsiness detection method that can detect abnormal changes in the RRI data caused by drowsiness. The proposed method employs a self-attention autoencoder (SA-AE), which is a type of neural network that can utilize time series characteristics. An experiment with a driving simulator was performed to evaluate the drowsiness detection performance of the proposed method. In this experiment, RRI data were collected from 20 participants (nonprofessional drivers) while driving, whose sleep conditions were scored by a sleep specialist based on electroencephalography (EEG) data. The experimental result showed that the proposed RRI-based drowsiness detection method detected 16 out of 18 sleep-related events while driving (sensitivity of 88%), and a false-positive rate of 0.60 times per hour was achieved. In addition, we validated the proposed method employing 18 middle-aged professional drivers. The proposed drowsiness detection method would contribute to preventing accidents caused by drowsy driving in the future.
A Deep Dive into Common Open Formats for Analytical DBMSs

This paper evaluates the suitability of Apache Arrow, Parquet, and ORC as formats for subsumption in an analytical DBMS. We systematically identify and explore the high-level features that are important to support efficient querying in modern OLAP DBMSs and evaluate the ability of each format to support these features. We find that each format has trade-offs that make it more or less suitable for use as a format in a DBMS and identify opportunities to more holistically co-design a unified in-memory and on-disk data representation. Our hope is that this study can be used as a guide for system developers designing and using these formats, as well as provide the community with directions to pursue for improving these common open formats.
From Awareness to Action: Pathways to Equity in Pain Management

This commentary calls for a shift in the identification, analysis, and treatment of disparities in pain management. We provide context and research that summarize social and structural determinants that contribute to disparities across multiple levels of the pain management continuum. Informed by the evidence, we provide guideposts for mitigating disparities in the assessment, diagnosis, and care provided to those experiencing pain, with a focus on highlighting the specific needs of marginalized communities and the importance of culturally appropriate and context-specific approaches to pain management. This commentary informs efforts to promote equity by identifying areas of concern, guiding interventions, and advocating for policies that aim to eliminate disparities in pain treatment. Researchers, health care providers, and organizations can collectively work to provide equitable culturally sensitive pain management and improve overall patient outcomes.
Evaluation of high-pressure front-mounted water jets for frictional-ignition suppression. Report of Investigations/1989

The U.S. Bureau of Mines conducted a laboratory study to determine what effect use of water-jet-assisted cutting has on frictional-ignition suppression. A single bit with a steel tip, installed on a rotating drum, repeatedly made 22-in-long cuts in a block of Berea sandstone. The drum was operated in an enclosure that contained an explosive methane-air mixture. High-pressure front-mounted water jets operating at 2,000 to 5,000 psig and low-pressure rear-mounted sprays operating at 80 psig were used; the numbers of ignitions that occurred with each type of spray were compared. The rear-mounted spray was more effective for preventing frictional ignitions than the front-mounted water jet.
Co-Simulation of a Cellular Energy System

The concept of cellular energy systems of the German Association for Electrical, Electronic & Information Technologies (VDE) proposes sector coupled energy networks for energy transition based on cellular structures. Its decentralized control approach radically differs from that of existing networks. Deeply integrated information and communications technologies (ICT) open opportunities for increased resilience and optimizations. The exploration of this concept requires a comprehensive simulation tool. In this paper, we investigate simulation techniques for cellular energy systems and present a concept based on co-simulation. We combine simulation tools developed for different domains. A classical tool for studying physical aspects of energy systems (Modelica, TransiEnt library) is fused with a state-of-the-art communication networks simulator (OMNeT++) via the standardized functional mock-up interface (FMI). New components, such as cell managers, aggregators, and markets, are integrated via remote procedure calls. A special feature of our concept is that the communication simulator coordinates the co-simulation as a master and integrates other components via a proxy concept. Model consistency across different domains is achieved by a common description of the energy system. Evaluation proves the feasibility of the concept and shows simulation speeds about 20 times faster than real time for a cell with 111 households.
EEG-Based Emotion Recognition Using Spatial-Temporal Connectivity

The connectivity properties between EEG signaling channels were found to play an important role in emotion recognition. During the generation and change of emotions, the connectivity between EEG signal channels is not only manifested in the spatial domain at a specific time, but also interconnected between different time intervals, which is often overlooked. A novel approach is proposed to exploit the shapes of spatial-temporal connectivity for EEG-based emotion recognition. By quantifying the connectivity strength of EEG signal channels within and across time intervals, spatial-temporal connectivity features are extracted, and these features can be represented by shapes in the three-dimensional space of EEG signals. Through these shapes, a mapping of different emotional states and brain activity is constructed. Subsequently, a parallel multi-scale convolutional neural network is employed to extract discriminative features from these connectivity shapes, facilitating the classification and identification of emotional states. Experimental results on the DEAP dataset show that our method achieves excellent performance, with an average classification accuracy of 93.25% and 93.16% for the two emotion dimensions of valence and arousal, respectively.
Prototype of an Innovative Vacuum Dryer with an Ejector System: Comparative Drying Analysis with a Vacuum Dryer with a Vacuum Pump on Selected Fruits

The following article describes new research about the design, construction and installation of the new prototype of a vacuum dryer with an ejector system. Moreover, the testing of this new prototype involved comparing the qualities of fruit dried in a vacuum drier with an ejector system to fruit dried in a convectional vacuum drier. The data obtained were then analyzed and presented. Due to their economic relevance and highly valuable nutritional value and sensory properties, sour cherries and apricots have been chosen to be the subjects for the testing. The most appropriate quality indicators for analyzing were moisture content, aw value, share and penetration force, total phenol, flavonoid and anthocyanin content and antioxidant activity (FRAP, DPPH and ABTS test). The main results of this study were achieved by designing, constructing, installing and testing the usage of the innovative prototype of a vacuum dryer with an ejector system in the laboratory of the Technology of fruit and vegetable products of the Faculty of Technology Novi Sad, University of Novi Sad. Based on our analyses of the obtained data, it was concluded that vacuum dryer with an ejector system are similar to vacuum dryer with a vacuum pump in terms of all tested physical, chemical and biological properties of dried samples. We observed similarities in some of the most important parameters, including product safety and quality, such as the aw value and the total phenol content, respectively. For example, in dried sour cherry, the aw values ranged from 0.250 to 0.521 with the vacuum pump and from 0.232 to 0.417 with the ejector system; the total phenol content ranged from 2322 to 2765 mg GAE/100 g DW with the vacuum pump and from 2327 to 2617 mg GAE/100 g DW with the ejector system. In dried apricot, the aw ranged from 0.176 to 0.405 with the vacuum pump and from 0.166 to 0.313 with the ejector system; total phenol content ranged from 392 to 439 mg GAE/100 g DW with the vacuum pump and from 378 to 428 mg GAE/100 g DW with the ejector system.
Fractionation of Simulated Digestive Products of Bird’s Nest In Vitro: Skin Whitening Activity of Fractions

Edible bird’s nest (EBN) has been proven to exhibit whitening activity. In spite of this, the relationship between the digestive products of EBN and the whitening activity is still unclear. Hence, the B16-F10 cell model was used in this study to study the whitening activity of simulated EBN digestion products in vitro. The simulated digestive juices showed significant inhibition of tyrosinase (TYR) ( P < 0.05), and the release rate of free sialic acid was 21.87% in EBN digestive juices. After simulated gastrointestinal digestion, the relative contents of <1 kDa components, 1–3 kDa components, and >3 kDa components were 42.63%, 15.05%, and 42.32%, respectively. EBN digestive products were further fractionated, and the fraction of <1 kDa (EPP-1) exhibited the highest tyrosinase inhibitory activity with 83.12% and its peptides content was 92.44%. The EPP-1 significantly inhibited tyrosinase activity and decreased melanin content ( P > 0.05) by regulating the cAMP/MITF/TRP-1/TRP-2 tyrosinase signaling pathways in the B16-F10 cell experiment. Based on these findings, our data suggest that the digestive products of EBN may potentially be fit for developing a new generation of natural whitening health products.
Effects of di-(2-ethylhexyl) phthalate on growth, metabolism, and virulence of the plant pathogenic bacterium Acidovorax citrulli

Acidovorax citrulli is a seed-borne bacterial pathogen that causes bacterial fruit blotch in cucurbits and severely affects the production of cucumbers and watermelons globally. In this study, we investigated the effects of di-(2-ethylhexyl) phthalate (DEHP) on the growth, metabolism, and virulence of A. citrulli. Bacterial population was not affected by DEHP exposure; moreover, significant changes were not observed in lipid peroxidation, membrane permeability, and nucleic acid leakage. However, palmitoleic acid content was increased in the cell membrane of DEHP-exposed A. citrulli. Further, DEHP exposure increased the activity of TCA cycle-related enzymes, including α-ketoglutarate dehydrogenase and succinyl-CoA synthetase, along with increase in the content of glutamate, succinate, fumarate, and malate in TCA cycle. Additionally, total 270 genes were differentially expressed by the treatment, of which 28 genes were upregulated and 242 genes, including those related to translation, flagellum-dependent cell motility, and flagellum assembly, were downregulated. Regarding virulence traits, swimming activity was decreased in DEHP-exposed A. citrulli; however, biofilm formation was not affected in in vitro assay. Moreover, relative expression of pathogenicity genes, including hrpX and hrpG, were decreased in DEHP-exposed A. citrulli compared to that of unexposed A. citrulli. Therefore, these results suggest that DEHP accumulation in soil could potentially influence the metabolism and virulence traits of A. citrulli.