Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Zizyphus spina-christi and Cinnamomum zeylanicum Essential Oils as Antimicrobial Agents on the Microbiological Characteristics of White Cheese

Quick Response Code Abstract: The purpose of this study is to assess the microbiological characteristics of white cheese supplemented with Ziziphus spina-christi and Cinnamomum zeylanicum essential oils during the storage period. The cheese was made from warmed (45°C) raw cow milk with Ziziphus spina-christi and Cinnamomum zeylanicum oils added, and after manufacture, the cheese was kept in sterile plastic bags at 4°C for 21 days, and microbiological tests were performed at 1, 7, 14 and 21-day intervals. Results showed that total viable bacteria (TVB), Staphylococcus aureus, and yeasts and moulds counts were significantly lower in cheese supplemented with both oils, according to the findings. The concentration of oil had a significant effect on the counts, with TVB, S. aureus, yeasts and moulds, and coliform bacteria counts being significantly higher in the control cheese and significantly lower in the cheese supplemented with 0.5% Ziziphus spina-christi oil. The storage period of cheese made with Ziziphus spina-christi and Cinnamomum zeylanicom oils had a significant effect on TVB, coliform bacteria, and S. aureus counts. The microbial counts were significantly affected by the concentrations of 0.3% and 0.5% Ziziphus spina-christi oil during the storage period, except for yeasts and moulds, which were unaffected by the 0.3% Ziziphus spina-christi oil. The counts of microbes in cheese supplemented with 0.3% and 0.5% Cinnamomum zeylanicom oils decreased significantly as the storage period progressed. The study concluded that essential oils, as natural antimicrobials, could be useful in preserving cheese made traditionally from raw milk without the addition of starter culture.
Tumor Vessels Fuel the Fire in Glioblastoma

Glioblastoma, a subset of aggressive brain tumors, deploy several means to increase blood vessel supply dedicated to the tumor mass. This includes typical program borrowed from embryonic development, such as vasculogenesis and sprouting angiogenesis, as well as unconventional processes, including co-option, vascular mimicry, and transdifferentiation, in which tumor cells are pro-actively engaged. However, these neo-generated vascular networks are morphologically and functionally abnormal, suggesting that the vascularization processes are rather inefficient in the tumor ecosystem. In this review, we reiterate the specificities of each neovascularization modality in glioblastoma, and, how they can be hampered mechanistically in the perspective of anti-cancer therapies.
Enabling Design Space Exploration for RISC-V Secure Compute Environments

Cycle-level architectural simulation of Trusted Execution Environments (TEEs) can enable extensive design space exploration of these secure architectures. Existing architectural simulators which support TEEs are either based on hardware-level implementations or abstract analytic models. In this paper, we describe the implementation of the gem5 models necessary to run and evaluate the RISC-V-based open source TEE, Keystone, and we discuss how this simulation environment opens new avenues for designing and studying these trusted environments. We show that the Keystone simulations on gem5 exhibit similar performance as the previous hardware evaluations of Keystone. We also describe three simple example use cases (understanding the reason of trusted execution slowdown, performance of memory encryption, and micro-architecture impact on trusted execution performance) to demonstrate how the ability to simulate TEEs can provide useful information about their behavior in the existing form and also with enhanced designs.
The Effect of Substitution of Extruded Soybean Meal (ESM) on Growth Performance, Carcass Characteristics, Immune Responses, Biochemical Variables of Blood, and Nutrient Digestibility of Ileal in Broiler Chickens

This study aimed to investigate the effect of substitution of extruded soybean meal (ESM) (DigestaTM) on growth performance, carcass characteristics, immune responses, biochemical variables of blood, and nutrient digestibility of ileal in broiler chickens (Ross 308). 225 chicks were randomly distributed among 5 trial groups (3 replicates of 15 chickens). The dietary treatments included different levels (0, 25, 50, 75, and 100%) of diet replacing ration of soybean meal with ESM which were fed to birds during a 42-day experimental period. The results showed that chickens fed diets 75% or 100% ESM had the highest (P <0.001) daily overweight rates and also had the best FCR. The results showed that the treatments had a significant effect on carcass parts. Replacement of 50% of soybean meal with ESM resulted in the decreased ventricular area and increased percentage of chest compared to the control group (P <0.05). Replacement of 75% soybean meal with ESM increased the antibody titer against the influenza virus in comparison with other experimental groups (P <0.01). The serum triglycerides, glucose, and uric acid (UA) were decreased by the replacement of soybean meal with ESM (P <0.05), but the total protein showed an increase by the increasing ESM to 100 % (P <0.01). Substitution of 75% and 100% of ESM in diets decreased the activity of enzyme aspartate aminotransferase (P <0.01) and increased triiodothyronine hormone (T3) (P <0.05). The use of ESM in all substitution levels increased the carcass protein content (P <0.001) and improved ileal digestibility of crude fat (P <0.01). Also, the highest ileal digestibility coefficients of protein were observed in the chickens fed diets 75 % or 100% ESM. In conclusion, the results of this experiment indicate that the extruding process of SBM has a beneficial effect on growth performance and nutrient digestibility in broiler chicks.
Method for Identifying Twitter Accounts that Have Changed their Opinion about Politicians

Nowadays, social networks provide an opportunity to communicate with the world and share political views. Therefore, each politician has an account and many followers. Revealing political opinions on Twitter accounts can be a good alternative to regular social surveys. Identifying those who changed their opinions will help to understand the reasons for disappointment in a particular political figure. In this paper we introduce our approach to collect data from Twitter, detect accounts political views, opinion changes and the reasons people get frustrated. The experimental results showed that such research was successful in the analysis of Ukrainian President supporters.
Factors Affecting Satisfaction of Customers in Business to Business Context

In order to gain competitive advantage, it is imperative to focus on the needs and preferences of customers. The central purpose of this study is to analyze the factors that affect satisfaction of industrial customers. Particularly, a case study approach is employed whereby the study seeks to gain information from the views of Indian customers of industrial products. The instruments of data collection involved administering structured questionnaire and also conducting interviews as deemed convenient. The sampling frame consisted of 184 industrial customers in India who procure different products for their use as raw material.
Pulsar: Efficient Sphere-based Neural Rendering

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological in-consistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) a modular, efficient differentiable projection operation, and 3) (optional) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to neural rendering.
Uneven Error Ellipsoid-Based Model Predictive Control for Planetary Safe Landings

Considering the impact of both position and velocity estimate errors on hazard avoidance, this paper proposes an uneven error ellipsoid-based model predictive control for planetary landing missions. The uneven error ellipsoid model not only takes into account system position uncertainty at the moment, but also reflects how fast the system is approaching the nearby hazard in the position space. By repetitively computing the current space margin and the most dangerous direction, the system quantifies the threat posed by the environment as the lander descends to the surface. In order to perform a safe and precise landing, we apply model predictive control during descent and incorporate a hazard avoidance performance index into the problem. Then, we validate the proposed method in a Bennu-based asteroid landing scenario and demonstrate its effectiveness of improving landing safety.
Fall Detection Based on Human Skeleton Keypoints Using GRU

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box’s width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.
Optimization of Drying Temperature and Time for Pork Jerky Using Response Surface Methodology

Response surface methodology (RSM) was applied to determine the optimum drying conditions for pork jerky. The physicochemical properties of pork jerky, such as final moisture content, water activity (Aw), pH, and shear force were investigated. In addition, sensory characteristics of pork jerky were evaluated and were used as a parameter for determining the optimum condition. Pork jerky samples were dried at different temperatures between 40 to 80℃ for the time ranged from 0 to 10 hr. The predicted values for moisture content, Aw, and shear force of dried pork samples were in good agreement with the experimental values with correlation coefficients (R²) of 0.95, 0.96, and 0.97, respectively. Both drying temperature and time significantly (p＜0.01) affected moisture content, Aw, pH, and shear force and their interactions were also significant at p＜0.01 except for Aw. RSM showed the optimum drying conditions for pork jerky, based on moisture content, shear force, and sensory evaluation to be 65-70℃ for 7-8 hr.
Factors Affecting Chinese Consumption of Imported Products in the Context of Global Purchasing

Over the past year, the opening-up measures have been basically implemented. From the pilot free trade zone to the Shanghai Stock Exchange and the new strategy for the development of the Yangtze River Delta, the implementation of the Foreign Investment Law and the comprehensive implementation of the pre-establishment national treatment plus a negative list management system have been introduced. Major progress was made in expanding imports to boost consumption and in further lowering tariffs. Open cooperation, innovation and sharing of the world economy to promote the national consumer imports, pluralism, open, and to discuss the level of imports and the quality of image and how to influence consumer perception of imported products to buy, in the face of the differences in the light of the imported products and home products, give consumers more independent options, thus it is concluded that the influence factors of the buyer. This paper discusses the will of consumers and the influencing factors, and on this basis, puts forward a reasonable development strategy for the e-commerce platform to sell overseas goods, and provides a reference for the government to formulate scientific support policies.
Human-Like Artificial Skin Sensor for Physical Human-Robot Interaction

Physical Human-Robot-Interaction (pHRI) is beneficial for communication in social interaction or to perform collaborative tasks but is also crucial for safety. While robotic devices embed sensors for this sole purpose, their design often is the results of a trade-off between technical capabilities and rarely considers human factors. We propose a novel approach to design and fabricate compliant Human-like artificial skin sensors for robots, with similar mechanical properties as human skin and capable of precisely detecting touch. Our artificial skin relies on the use of different silicone elastomers to replicate the human skin layers and comprises an embedded electrode matrix to perform mutual capacitance sensing. We present the sensor and describe its fabrication process which is scalable, low-cost and ensures flexibility, compliance and robustness. We introduce Muca, an open-source sensing development board and then evaluate the performance of the sensor.
The Effect of Museum Education Practices Integrated with Virtual Teaching Environments on Achievement Levels of Preschool Teacher Candidates during the Pandemic

In this study, the eff ectiveness of museum education practices integrated with virtual teaching environments during the pandemic on the achievement levels of pre-school teacher candidates was evaluated. The research was carried out using a mixed method. In the quantitative dimension, the success levels of teacher candidates for museum education practices were evaluated with the achievement test, and the data related to the qualitative dimension were evaluated with content analysis. Preschool teacher candidates (n = 30) participated in the study. In the research, for 5 weeks, museum education activities were carried out using virtual tour and second life museum applications in Vordun, Louvre and Art Nouveau virtual museums with the teacher candidates. The results obtained revealed that these activities increased the success level of teacher candidates regarding the practices. In addition, the results showed that the teacher candidates found the museum education practices integrated with virtual teaching environments highly eff ective. In addition, teacher candidates stated that an active teaching process can be created with virtual museum applications in museum education practices, and that learning can be embodied more easily.
Improving Learning to Branch via Reinforcement Learning

Mixed Integer Programming (MIP) has been applied widely in many real-world problems [1, 2]. Branch and Bound (B&B) is a general and widely used paradigm for solving MIP problems [3]. B&B recursively partitions the solution space into a search tree and compute relaxation bounds along the way to prune subtrees that provably can not contain an optimal solution. This iterative process requires sequential decision makings: node selection: selecting the next solution space to evaluate, variable selection: selecting the variable by which to partition the solution space [4]. In this work, we focus on learning a variable selection strategy, which is the core of the B&B algorithm [5].
Molecular Genetic Analysis of SARS-CoV-2 Lineages in Armenia

Sequencing of SARS-CoV-2 provides essential information into viral evolution, transmission, and epidemiology. Short-read next-generation sequencing platforms are currently the gold-standard approaches characterized by the highest accuracy. Meanwhile, Oxford Nanopore's long-read sequencing devices show great promise, offering comparable accuracy, fast turnaround time, and reduced cost. In this study, we performed whole-genome sequencing and molecular-genetic characterization of SARS-CoV-2 from clinical specimens using an amplicon-based nanopore sequencing approach. Lineage and phylogenetic analysis identified the most prevalent lineages at different time points (B.1.1.163, B.1.1.208, B.1.1, and since March 2021 - B.1.1.7). In addition, we evaluated the possible effect of identified mutations on the efficacy of recommended primers and probes used for PCR detection of SARS-CoV-2. In summary, a high-quality SARS-CoV-2 genome can be acquired by nanopore sequencing and it can serve as an efficient and affordable alternative to short-read next-generation sequencing and be used for epidemiologic surveillance and molecular-genetic analyses of the virus.
Emerging Terahertz Integrated Systems in Silicon

Silicon-based terahertz (THz) integrated circuits (ICs) have made rapid progress over the past decade. The demonstrated basic component performance, as well as the maturity of design tools and methodologies, have made it possible to build high-complexity THz integrated systems. Such implementations are undoubtedly highly attractive due to their low cost and high integration capability; however, their unique characteristics, both advantageous and disadvantageous, also call for research investigations into unconventional systematic architectures and novel THz applications. In this paper, we review the current status and future trend of silicon-based THz ICs, with the focus on state-of-the-art THz microsystems for emerging sensing and communication applications in the last few years, such as high-resolution imaging, high medium/long-term stability time keeping, high-speed wireline/wireless communications, and miniaturization of RF tags, as well as THz packaging technologies.
Deep Learning For RADAR Signal Processing

We address the current approaches to radar signal processing, which model radar signals with several assumptions (e.g., sparse or synchronized signals) that limit their performance and use in practical applications. We propose deep learning approaches to radar signal processing which do not make such assumptions. We present well-designed deep networks, detailed training procedures, and numerical results which show our deep networks outperform current approaches. In the ﬁrst part of this thesis, we consider synthetic aperture radar (SAR) image recovery and classiﬁcation from sub-Nyquist samples, i.e., compressive SAR. Our approach is to ﬁrst apply back-projection and then use a deep convolutional neural network (CNN) to de-alias the result. Importantly, our CNN is trained to be agnostic to the subsampling pattern. Relative to the basis pursuit (i.e., sparsity-based) approach to compressive SAR recovery, our CNN-based approach is faster and more accurate, in terms of both image recovery MSE and downstream classiﬁcation accuracy, on the MSTAR dataset. In the second part of this thesis, we consider the problem of classifying multiple overlapping phase-modulated radar waveforms given raw signal data. To do this, we design a complex-valued residual deep neural network and apply data augmentations during training to make our network robust to time synchronization, pulse width, and
Three-dimensional confocal reflectance microscopy for surface metrology

Confocal microscopy uses a confocal aperture in front of a detector to eliminate out-of-focus blur, providing optical sectioning, high spatial resolution, and high contrast. It enables precise three-dimensional (3D) reconstruction of a sample surface. Using objective lenses with high numerical aperture and short wavelength illumination, confocal microscopy offers high spatial resolution in both lateral and axial directions in a non-destructive and non-contact manner. These qualities make confocal microscopy an ideal tool for the inspection and measurement of microscopic samples. One of the limitations of confocal microscopy for 3D surface metrology, however, is its speed. Both lateral and axial scanning is needed to produce a stack of two-dimensional images along the height, and this takes more time. While high-end confocal laser scanning microscopy can acquire a 3D sample surface within a few seconds, an even faster imaging speed is required in some applications, such as in in-line inspection. Various techniques have been proposed to speed up 3D confocal measurements, such as chromatic confocal microscopy, differential confocal microscopy, dual-detection confocal microscopy, and direct-view confocal microscopy. Here, we review the basic principles, theories, scanning methods, and progress of confocal microscopy for surface metrology, and we discuss the latest progress in the field of ultrafast 3D surface measurement. We anticipate that confocal microscopy, which has become one of the standard metrology tools, will continue to evolve for 3D surface measurement.
A Research on CEO power and corporate performance model based on data analysis

Company performance can directly reflect the operation’s status, which has always been one of the central topics. Starting from the CEO power, this paper introduced the corporate innovation strategy and ownership concentration, established the corresponding research model, and studied the relationship between them. In the model design, the company performance was taken as the dependent variable, CEO power and corporate innovation strategy and ownership concentration was taken as independent variables, then the data processing software was used to establish a regression model. Through the model test, it is found that there is a positive correlation between CEO power and corporate performance, and the corporate innovation strategy can play an intermediary role in this relationship while ownership concentration can play a moderating role. This paper further improved the relevant research of corporate performance, and had certain reference significance for the operation.
Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

In order to reduce the threat cost of multi-UAV coordination, improve the effective flight time and the mission success rate, a path planning model was designed for the effectiveness and real-time performance of multi-UAV track coordination. In this paper, based on the path planning model under the condition of non-interference, the model of minimum cost flight path of multi-UAV based on time synergy is given, and a time-sequence-space coordination model is proposed to meet the requirements of electronic jamming, mission confusion and radar illumination. The decision variables of range and threat are introduced into the collaborative function of track cost, and by adjusting the cost functions and collaborative variables in real time, the path planning model was optimized for unmanned support aircraft in coordination with multiple unmanned aircraft mission, at the same time, the genetic algorithm is improved by combining the adaptive crossover rate and mutation rate, and the optimization of track collaborative solution process is realized. Finally, the effectiveness of track coordination model of multi-UAV based on decision variables and improved genetic algorithm is verified by semi-physical simulation.