Miracle-dz/UniFAR-FTUs huggingface dataset:
Sulfur Amino Acid Metabolism and the Role of Endogenous Cystathionine-γ-lyase/H2S in Holstein Cows with Clinical Mastitis

Simple Summary Endogenous hydrogen sulfide (H2S) has been implicated in many physiological and pathological processes, particularly in inflammatory responses and adaptive immunity. In this study, we identified the candidate differentially expressed proteins (DEPs) associated with H2S metabolism in Holstein cows with clinical mastitis (CM). The results revealed 17 DEPs included in 44 Gene Ontology (GO) terms and five Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to sulfide metabolism and indicated the important role of cystathionine-γ-lyase (CTH)/H2S in CM. Our findings can support research into the function and regulatory mechanism of CTH/H2S in Holstein cows and provide a basis for the prevention and treatment of CM. Abstract H2S plays an important role in various inflammatory diseases. However, the role of H2S and synthetic enzymes in Holstein cows with CM is unknown. The aim of this study was to identify DEPs associated with sulfide metabolism and further investigate their roles in dairy cows with CM. From 3739 DEPs generated by data-independent acquisition proteomics, we identified a total of 17 DEPs included in 44 GO terms and five KEGG pathways related to sulfide metabolism, including CTH and cystathionine-β-synthase (CBS). Immunohistochemical and immunofluorescence staining results showed that CTH and CBS proteins were present mainly in the cytoplasm of mammary epithelial cells. Endogenous H2S production in the serum of the CM group was significantly lower than that of the healthy Holstein cows. CTH and CBS mRNA and protein levels in the mammary glands of the CM group were significantly downregulated compared to those of the healthy group. These results indicate that CTH and H2S were correlated with the occurrence and development of CM in Holstein cows, which provides important insights into the function and regulatory mechanism of CTH/H2S in Holstein cows.
Adult Stem Cells Host Intracellular Symbionts: The Poriferan Archetype

Unlike vertebrates, adult stem cells (ASC) in a wide range of aquatic invertebrate phyla are morphologically diverse, exhibiting a wide range of differentiation states as well as somatic and germline physiognomies. They may arise de novo by trans-differentiation from somatic cells and above all represent phenotypes of specialized cells with multifunctionality. One unexpected phenomenon is the presence of intracellular symbionts in the ASCs of some invertebrates. Overviewing the literature on intracellular symbionts in sponge (Porifera) ASCs and in other aquatic invertebrates, we reveal that ASC intracellular prokaryotic and eukaryotic symbionts are restrictive to a single sponge class, the Demospongiae. The eukaryotic symbionts in sponges are exclusively unicellular photosynthetic algae, and are found only in pluripotent stem cells, most frequently in the archaeocytes; they are documented in five orders of Demospongiae. Bacteriocyte-like cells have been reported in sponges and three other phyla, indicative of their independent evolutionary origins. The results of this study add considerable insight into the establishment and maintenance of intracellular symbioses in ASCs of aquatic invertebrates, and provide new a understanding of the diversity of symbiotic associations across the tree of life.
Determination of Optimum Parameters for Walls with Phase Change Materials

As is known, the majority of energy consumption takes places in residences. In order to optimize insulation cost it is necessary to minimize energy consumption in buildings. Recently, phase change materials in sandwich panel have been placed into the building wall or surface instead of conventional insulation materials. Therefore, both energy is stored within the walls and insulation is made using FDM's low heat conduction coefficient. In this study, the optimum insulation thickness was determined by taking weather conditions of three different regions in Turkey into consideration. In addition, heating loads were calculated according to a single value of solar radiation absorption rate of building's exterior surface. P1P2 method which includes heating load and economical method was used when calculating the optimum insulation thickness. Accordingly, the optimum insulation thickness, energy saving and repayment periods for three different regions were also determined through using economic data, such as interest, inflation, lifetime. In addition to that, the results were compared with regards to standard insulation and without radiation condition.
Carbon Graphitization: Towards Greener Alternatives to Develop Nanomaterials for Targeted Drug Delivery

Carbon nanomaterials have attracted great interest for their unique physico-chemical properties for various applications, including medicine and, in particular, drug delivery, to solve the most challenging unmet clinical needs. Graphitization is a process that has become very popular for their production or modification. However, traditional conditions are energy-demanding; thus, recent efforts have been devoted to the development of greener routes that require lower temperatures or that use waste or byproducts as a carbon source in order to be more sustainable. In this concise review, we analyze the progress made in the last five years in this area, as well as in their development as drug delivery agents, focusing on active targeting, and conclude with a perspective on the future of the field.
Application of Genetic Algorithms Coupled with Neural Networks to Optimization of Reinforced Concrete Footings

This paper first applies genetic algorithms to optimally design reinforced concrete isolated footings subjected to concentric loading. Based on the ACI Building Code, constraints are built by considering wide-beam and punching shears, bending moment, allowable soil pressure, the development length for deformed bars and clear distance between deformed bars. Design variables consist of the width, length and thickness of the footing and the number of bars in the long and short directions, all of which are discrete. The objective function is to minimize the cost of steel and concrete in the footing. There are totally 144 cases of reinforced concrete isolated footings considered. The optimal results are randomly divided into three groups for the use of neural networks: training data, validation data and testing data. Two kinds of artificial neural networks are employed in this paper: two-layer feedforward backpropagation networks and radial basis networks. Linear regression analysis between the network outputs and targets of the testing data is performed to judge the accuracy of the neural networks. Numerical results show that the feedforward backpropagation network is very effective and has high accuracy with the correlation coefficients and the slope of the regression line being close to one and the y-intercept close to zero. Besides, it is better than the radial basis networks and needs much fewer neurons in the hidden layer.
Calculation and analysis of e ffi ciencies and annual performances of Power-toGas systems

This paper describes a generic and systematic method to calculate the efficiency and the annual performance for Power-to-Gas (PtG) systems. This approach gives the basis to analytically compare different PtG systems using different technologies under different boundary conditions. To have a comparable basis for efficiency calculations, a structured break down of the PtG system is done. Until now, there has not been a universal approach for efficiency calculations. This has resulted in a wide variety of efficiency calculations used in feasibility studies and for business-case calculations. For this, the PtG system is divided in two sub-systems: the electrolysis and the methanation. Each of the two sub-systems consists of several subsystem boundary levels. Staring from the main unit, i.e. the electrolysis stack and/or methanation reactor, further units that are required to operate complete PtG system are considered with their respective subsystem boundary conditions. The paper provides formulas how the efficiency of each level can be calculated and how efficiency deviations can be integrated which are caused by the extended energy flow calculations to and from energy users and thermal losses. By this, a sensitivity analysis of the sub-systems can be gained and comprehensive goal functions for optimizations can be defined. In a second step the annual performance of the system is calculated as the ratio of useable output and energetic input over one year. The input is the integral of the annual need of electrical and thermal energy of a PtG system, depending on the different operation states of the plant. The output is the higher heating value of the produced gas and – if applicable – heat flows that are used externally. The annual performance not only evaluates the steady-state operating efficiency under full load, but also other states of the system such as cold standby or service intervals. It is shown that for a full system operation assessment and further system concept development, the annual performance is of much higher importance than the steady-state system efficiency which is usually referred to. In a final step load profiles are defined and the annual performance is calculated for a specific system configuration. Using this example, different operation strategies are compared.
The Stability of the Systems with Command Saturation, Command Delay, and State Delay

: This article presents the study of the stability of single-input and multiple-input systems with point or distributed state delay and input delay and input saturation. By a linear transformation applied to the initial system with delay, a system is obtained without delay, but which is equivalent from the point of view of stability. Next, using sufﬁcient conditions for the global asymptotic stability of linear systems with bounded control, new sufﬁcient conditions are obtained for global asymptotic stability of the initial system with state delay and input delay and input saturation. In addition, the article presents the results on the instability and estimation of the stability region of the delay and input saturation system. The numerical simulations conﬁrming the results obtained on stability were performed in the MATLAB/Simulink environment. A method is also presented for solving a transcendental matrix equation that results from the process of equating the stability of the systems with and without delay, a method which is based on the computational intelligence, namely, the Particle Swarm Optimization (PSO) method.
CIA_A_354911 885..902

Purpose: Limited information is available concerning primary care providers’ encountered barriers and facilitators when implementing falls prevention and providing interventions in a real-life setting. This study aimed to identify barriers and facilitators when i) implementing a falls risk assessment strategy at GP practices and among community nurses and ii) providing evidence-based falls prevention interventions in a real-life setting to independently living, frail older people. Methods: A researcher’s journal was maintained during the implementation of a falls risk assessment strategy, which entailed notes from informal conversations with GPs, practice nurses and community nurses. After implementation, two online focus groups with GPs, practice and community nurses, physioand exercise therapists were conducted. Data were thematically analyzed. Results: Data were collected from 32 GPs, 13 practice nurses, eight community nurses, nine physiotherapists, and two exercise therapists. The GPs and nurses acknowledged that falls prevention is part of their job, meaningful, and that they have sufficient knowledge and skills to offer falls prevention. Collaboration, a previously implemented care program for older people, resources, reimbursement for interventions, and patients’ motivation, awareness and health issues were considered to be important factors for the implementation of falls prevention. Physioand exercise therapists described collaboration with different disciplines, receiving sufficient referrals, reimbursements, intensity and set-up of the interventions, and patients’ motivation, expectations, goals, selfconfidence, awareness, and health issues as important factors when providing falls prevention interventions. Conclusion: This study identified care provider-, context-, patient-, and innovation (strategy)-related barriers and facilitators when implementing falls prevention and providing interventions in primary care. Development of a more successful implementation strategy should focus on intensifying collaboration, reimbursement for interventions, availability of resources, and patients’ lack of motivation and health issues. Hence, falls prevention may become more structurally applied, reducing a major threat for the quality of life of independently living older people.
Modification of Convolutional Neural Network GoogLeNet Architecture with Dull Razor Filtering for Classifying Skin Cancer

— Skin is the widest external organ covering the human body. Due to a high intensity exposure to the environment, the skin can experience various health problems, one of which is skin cancer. Early detection is needed so that further medication for patients can be done immediately. In this regard, the use of artificial intelligence (AI)-based solutions in detecting skin cancer images can be used to detect skin cancer potentials. In this study, the classification of benign and malignant skin cancer types was carried out by utilizing GoogLeNet Convolutional Neural Network (CNN) method. The GoogLeNet architecture has the advantage of having an inception module, allowing the convolution and pooling processes to run in parallel terms that can reduce computing time and speed up the classification process without lowering the system accuracy. This study consisted of several stages, starting from the data acquisition of 600 skin cancer images from Kaggle.com to the uniformity of the input size that allows the system to work faster. There was also a utilization of dull razor filtering to reduce input image noise due to hair growing along the epidermis. After the preprocessing process was complete, GoogLeNet architecture processed the image input before categorizing the input into benign or malignant skin cancer. The system’s performance was then evaluated using performance parameters such as accuracy, precision, recall, and F-1 score, and it was compared to other methods. The system managed to obtain satisfactory results, including the accuracy of 97.73% and the loss of 1.7063. As for precision, recall, and F-1 score parameters, each received an average value of 0.98. The system performance proposed by the authors successfully have better accuracy compared to the previous study with much less use of datasets. The test results show that CNN method is able to detect and classify skin cancer accurately, so it is expected that this method could help medical workers in diagnosing the community.
Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter

In the field of visual tracking, discriminative correlation filter (DCF)-based trackers have made remarkable achievements with their high computational efficiency. The crucial challenge that still remains is how to construct qualified samples without boundary effects and redetect occluded targets. In this paper a feature-enhanced discriminative correlation filter (FEDCF) tracker is proposed, which utilizes the color statistical model to strengthen the texture features (like the histograms of oriented gradient of HOG) and uses the spatial-prior function to suppress the boundary effects. Then, improved correlation filters using the enhanced features are built, the optimal functions of which can be effectively solved by Gauss–Seidel iteration. In addition, the average peak-response difference (APRD) is proposed to reflect the degree of target-occlusion according to the target response, and an adaptive Kalman filter is established to support the target redetection. The proposed tracker achieved a success plot performance of 67.8% with 5.1 fps on the standard datasets OTB2013.
Tailoring 3D Mapping Frameworks for Field Robotics*

—Mapping is an essential part for the adoption of robots in agricultural and forestry environments. Providing the robot with the ability to map its surroundings, facilitates its navigation and is necessary for implementing obstacle avoidance, without human interference. Herein we present the challenges of outdoor environments, present an overview of existing mapping frameworks and then evaluate their suitability for ﬁeld applications. Two widely used mapping frameworks, OctoMap and RTAB-Map are analyzed within the Robot Operating System (ROS) ecosystem and a parametric study is carried out in order to assess their performance, under both simulated and real-world constraints. Finally this work aims to be utilized as a deployment reference guide for mobile robotic applications in outdoor environments.
NOVEL NANOCOMPOSITE INSULATION MATERIALS FOR THE ENHANCING PERFORMANCE OF POWER CABLES

This paper has investigated novel nano-metric industrial materials based on Power Low Profile PLP for enhancing the electrostatic field distribution in the insulation of power cables estimated by Charge Simulation Method CSM. The electrical insulation properties of new composite materials have been enhanced by adding certain values of spherical nanoparticles of Clay and Fumed Silica in a matrix of polymer industrial materials. The size of the spherical inclusion acts as the reinforcing phase and has a major effect on the dielectric properties of composite industrial materials. Therefore, with respect to nanoparticles as the preferred reinforcing phase for the composites, the dielectric interphase has a major effect on its dielectric properties. Theoretical and experimental results have been investigated in comparison with conventional structure materials and new nanocomposite industrial materials for AC and DC power cables. Finally, this paper explained the economical cost of the new nanocomposite insulation materials related to the conventional power cables insulation materials.
A Study on the Estimation of Multi-Object Social Distancing Using Stereo Vision and AlphaPose

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.
MimdRAID: Low Latency Secondary Storage

Disk arrays can provide scalable bandwidth with increasing number of disks, but they do not address latency. In this paper, we study how to improve secondary storage read latency by systematically increasing the ratio between disk heads and usable capacity. The most direct technique is to build \faster" disks by altering disk geometry. In addition, as an alternative to building such disks, we also describe how we can emulate these faster disks using multiple conventional disks. We show that all these techniques are governed by a common principle that the overhead-independent part of the latency improves by a factor of the square root of the amount of extra resources. We evaluate some of these latency reduction techniques on a prototype implementation which we call MimdRAID. On a 12-disk system, for random reads, we achieve a 2 (cid:2) improvement in latency and 21 (cid:2) improvement in throughput compared to a single drive. This is 80% to 95% better than the throughput achieved on a conventional mirrored system.
EVALUATION OF AROMA VOLATILES IN NATURALLY FERMENTED KVASS AND KVASS EXTRACT

Kvass is a non-alcoholic beverage produced by fermenting kvass mash with yeast; alcohol content in kvass must be less than 1.2% by volume. Kvass extracts have longer shelf-life and they are essentially free of ethanol. The aim of this research was to evaluate and compare aroma compounds in naturally fermented kvass and kvass extracts. Experiments were carried out at the Latvia University of Agriculture, Department of Food Technology from November 2014 to February 2015. Three commercially available kvass samples (Bruveris, Bauskas and Liepkalni) were used to produce kvass extracts applying vacuum evaporation. The investigation of volatile compounds in kvass and kvass extracts was performed using solid phase microextraction and gas chromatography mass spectrometry. Dry matter content in kvass extracts was 32.4 ± 0.3% (ISO 2173:2003). In all kvass and extract samples in total 25 volatile compounds were detected. Ten of them were esters, five alcohols, five acids, four aldehydes and three ketones. Such aroma compounds as ethyl acetate (fruity flavour), hexyl acetate (fruit, herb) and ethyl decanoate (grape) were found only in Bruveris kvass, 2,3-butanedione (buttery) and phenethyl butyrate (floral) were found only in Bauskas alus kvass and three volatile compounds were identified only in Liepkalni kvass – acetic acid (sour), furfuryl alcohol (burnt) and carvone (caraway). Less than a half of the main aroma volatiles in kvass were also identified in kvass extracts and total values of peak areas were significantly lower in kvass extracts compared to kvass (p = 0.01).
odeling environment for model predictive control of buildings

Model predictive control (MPC) is an advanced control that can be used for dynamic optimization of HVAC equipment. Although the benefits of this technology have been shown in numerous research papers, currently there is no commercially or publicly available software that allows the analysis of building systems that employ MPC. The lack of detailed and robust tools is preventing more accurate analysis of this technology and the identification of factors that influence its energy saving potential. The modeling environment (ME) presented here is a simulation tool for buildings that employ MPC. It enables a systematic study of primary factors influencing dynamic controls and the savings potential for a given building. The ME is highly modular to enable easy future expansion, and sufficiently fast and robust for imulation tool nergy efficiency implementation in a real building. It uses two commercially available computer programs, with no need for source code modifications or complex connections between programs. A simplified building model is used during the optimization, whereas a more complex building model is used after the optimization. It is shown that a simplified building model can adequately replace a more complex model, resulting in significantly shorter computational times for optimization than those found in the literature.