Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Yeast and Lactic Acid Bacteria Strains, Isolated from Spontaneous Raw Milk Fermentation, for the Production of a Potential Probiotic Fermented Milk

Probiotic milk is a class of fermented milk that possesses health-promoting effects, not only due to the lactic acid bacteria (LAB) presence but potentially also to yeast activity. Hence, the aim of this work was to isolate and select yeasts from spontaneous milk fermentations to be used as inoculum, together with LAB, for manufacturing a potentially probiotic acidic low-alcohol fermented milk. Six yeast species were detected from the spontaneous milk fermentation. A screening of 13 yeast strains and 14 previously isolated LAB strains, based on the resistance to bile salts and to acidic conditions, was carried out. The best performing strains were successively tested for in vitro gastrointestinal tolerance. A strain of Kluyveromyces marxianus and a strain of Lactococcus lactis were selected for the manufacturing of two different fermented milk. The values of the main technological and microbiological parameters (pH, organic acids, ethanol, and microbial concentrations) of the experimental milk were in the range of those reported for this category of products. The evaluation of microorganism survival in fermented milk samples subjected to simulated gastrointestinal conditions highlighted a high resistance of both strains. In conclusion, the selected microbial starter culture enabled the setting up of potential probiotic fermented milk.
Research on joint friction identification of robot based on SVR

In the precise control of robot, the robot joint friction has periodic ripple errors when the LuGre friction model is used for joint torque calculations. An improved LuGre friction model is proposed for this problem, including the steady-state friction force represented by the LuGre friction model, and the velocity-dependent position-dependent term. The friction model is identified in steps, and the steady-state friction parameters are identified using the features of the LuGre friction model, and the position-dependent terms in the model are identified by SVM multiclass classification method, SVR and least squares method to solve the system of equations. The results of the experimental study show that the error can be reduced by more than 50% when using the improved model and the identification method to calculate the joint friction moments.
A Paradigm Shift toward Semantic Communications

The last 70 years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficiency practical systems. Classical communication systems address the capability-deficiency issue mainly by module stacking and technique densification with ever increasing complexity. From such a traditional viewpoint, classical source coding only utilizes explicit probabilistic models to compress data, regardless of the meaning of transmitted source messages. Also, channel coded transmission does not identify the source content. In this sense, state-of-the-art communication systems work merely at the technical level, as summarized by Weaver. Unlike the traditional system design philosophy, this article proposes a new route to boost the system capabilities toward intelligence-endogenous and primitive-concise communications. The communication paradigm upgrades to the semantic level, which is radically different since all the key techniques imply the use of meanings of transmitted data, thus deeply changing the design philosophy of the communication system. This paradigm shift unveils a promising direction due to its ability to offer an identical quality of service with much lower data transmission requirements. Different from similar works, this article constitutes a brief tutorial on the framework of semantic communications, its gain analyzed from the information theory perspective, a method to calculate the semantic compression bound, and an exemplary use case of semantic communications.
Rehabilitation Monitoring of Post-Laryngectomy Patients through the Extraction of Vocal Parameters

This paper deals with the analysis of substitution voices of post-laryngectomy patients with the aim of identifying a methodology to track the effectiveness of rehabilitation therapies. The available data-set includes 22 patients that had undergone Open Partial Horizontal Laryngectomy (OPHL) of type II and 10 patients that had undergone OPHL of type III. A pre-processing algorithm that relies on the spectral kurtosis has been designed to remove non-harmonic frames from the available recordings of patients, thus minimizing the number of outliers among the extracted parameters. Such an algorithm has been tuned starting form the results of a control group of 10 healthy subjects. After this preliminary step, from the harmonic frames a series of parameters have been extracted that belong to spectral domain (tilt, kurtosis, entropy and Soft Phonation Index) and cepstral domain (Mel-Frequency Cepstral Coefficients, MFCCs, and Cepstral Peak Prominence Smoothed, CPPS). Then, the patients have been subdivided into two classes according to the index I (Intelligibility) of the auditory perceptual scale INFVo and a Kolmogorov-Smirnov two-samples test has been run, which has highlighted that low-band MFCCs, spectral entropy and spectral kurtosis show the best discrimination capability of substitution voices. This outcome has been confirmed by an alternative method that is based on the performance of classification algorithms. A classification accuracy of about 81% has been obtained using a logistic regression model that was trained with median of MFCC3, range of MFCC4 and 95° percentiles of MFCC6 and MFCC9. The same accuracy has been provided by a coarse decision tree algorithm trained with skewness of MFCC1, median of MFCC3 and 95° percentile of spectral entropy.
Heavy Metal Bioaccumulation and Histopathological Studies of Fish Tissues from Ose River, Ondo State, Nigeria

The exposure of the aquatic ecosystem to heavy metal from both natural and anthropogenic activities are on the rise and have harmful health effects on all aquatic organisms and human. This study assessed the concentrations of heavy metals (Fe, Pb, Cd, Cr, As and Zn) in surface water, sediments and fish tissues of Ose River and examined the histopathological changes of the tissues of two fish species (Mormyrus rume and Clarias garienpinus). Surface water, sediments and fish samples were collected and procured from Ose River in Ondo State, Nigeria from February to April 2021. Heavy metal digestions were done in the laboratory using HNO3/HCl while the concentrations were determined using Atomic Absorption Spectrometer. Histopathological alterations in the fish tissues were also assessed. Bioaccumulation factor (BAF), ecological risk index and potential ecological risk level were used to assess the investigated metal concentrations. In the surface water, the concentration of Cd (0.010–0.030 mg/l) and As (0.010–0.05 mg/l) exceeded the WHO limits of 0.01mg/l respectively while Fe, Pb, Cr and Zn were within the limits. In the sediments, all metals exceeded the limits except Pb and As. The concentrations of Pb (0.30±0.000–4.12±0.030 mg/l), Zn (9.970±0.010–30.77±0.023 mg/l), Cd (0.026±0.002–0.331±0.004 mg/l) and Cr (ND-0.331±0.004 mg/l) in the fish tissues were seen to be higher than the permissible limits of 0.5 mg/l, 5 mg/l, 0.005 mgl and 0.05-0.15 mg/l respectively. The BAF showed no probability. The alterations observed in the kidneys of the fish were cholestasis, necrosis, loss of glomeruli structure and tubular necrosis. While the gills showed congestion of the central veins and interlamellae hyperplasia caused by the presence of a monogenean parasite. The livers however revealed loss of hepatocytes nuclei, necrosis in hepatocytes and lymphocytic infiltrate. The study showed that the study site was polluted with heavy metals and may pose serious health issues to fishers, those using the water and people feeding on fish from the river. This study therefore suggests good indicators for bio-monitoring of heavy metals in Ose River. Seeing that the site was polluted with heavy metals hence, a more comprehensive assessment of the river is recommended.
Motivational Factors for Choosing ICT as an Elective Subject in Mathematics Education

This is an era of information and communication technology. Many students are highly interested in Information and Communications Technology (ICT) all over the world. Mathematics is often viewed as a critical enabling course that’s why ICT is necessary for higher level mathematics students. For this purpose, the curriculum of mathematics education of master’s levelhas adjusted ICT subject in mathematics course and most of the students choose ICT as an elective subject. Therefore, this study intended to explore the motivational factors for choosing ICT as an elective subject in mathematics along with gender perspective. This is descriptive survey research standing on post-positivist paradigm. The central department of education, mathematics education at Tribhuvan University was taken as the study area. Sixty- five students (53 boys, 12 girls)of master's degree 4thsemester who had chosen ICT as an elective subject in mathematics education were selected as samples by simple random sampling method. The major tool of data collection was questionnaire and collected data were analyzed using simple descriptive statistics mean, percentage and inferential statistics t-test. The study found that the most popular motivational factor for choosing ICT as an elective subject in mathematics was their personal interest and the least popular reason was social factors for both boys and girls. This indicates that students were highly motivated towards ICT due to intrinsic motivation then after extrinsic motivation. In addition, there was no significant differences exist between boys and girls motivation in choosing ICT as an elective subject. Therefore, we should equally treat and encourage both boys and girls to study ICT as an elective subject in mathematics on which ICT helps to concretize mathematical concepts from abstraction.
Nutritional Values of Teff (Eragrostis tef) in Diabetic Patients: Narrative Review

Introduction Teff (Eragrostis tef) is an indigenous crop in Ethiopia and exists in white, red, and mixed varieties. Several types of research confirmed that teff is rich in many essential amino acids, slowly digesting carbohydrates, essential fatty acids, minerals, vitamins, fibers, and other components. Since teff has a low glycemic index, is enriched in essential amino acids and fatty acids, and contains nutritionally balanced minerals, vitamins, and their precursors, it could be a promising food for the prevention and management of diabetes. People with diabetes mellitus are recommended to feed on a diet having a low glycemic index and enriched in essential nutrients. Objective This review aimed to summarize the nutritional composition of teff (Eragrostis tef) and its value in diabetic patients. Methodology We searched Web of Science, PubMed, Medline, Embase, and Google Scholar for studies on the nutritional composition of teff and its value for diabetic patients published in English since 2010. Conclusion According to available data, teff is a nutritionally valuable food type for diabetic patients.
Granger Causality Changes during Rt-fMRI Neurofeedback Training of Emotion Regulation for Insomnia Patients

: The amygdala is a key brain region in the emotional network. Studies have shown that emotion regulation neurofeedback training targeting amygdala based on real-time functional magnetic resonance can effectively improve the symptoms of insomnia patients. However, the brain mechanism for this improvement remains unclear. In this paper, Granger causality model was constructed to analyze the difference of causality between different brain regions before and after neurofeedback training. Firstly, the brain regions related to emotion regulation with significant differences in ReHo before and after neurofeedback training were selected as regions of interest. Secondly, the time series of the regions of interest were extracted to establish the Granger causality model. Finally, through group level analysis, the difference of effective connection before and after neurofeedback training was used as a biomarker to evaluate the effect of emotion regulation. The results have shown that rt-fMRI neurofeedback training targeting the amygdala significantly regulated the activity of brain regions related to emotion regulation in insomnia patients. And the effective connections from the right triangle inferior frontal gyrus to the left amygdala, the left precuneus to the left middle frontal gyrus, and the right middle cingulate gyrus to the left middle frontal gyrus were significantly enhanced. While the effective connection from the left middle frontal gyrus to the left precuneus was significantly reduced. Moreover, these changes were consistent with the scale evaluation results and clinical psychiatric studies, which further demonstrated that real-time fMRI neurofeedback training can change the effective connectivity of brain regions related to emotion regulation, and these changes could be used as a potential biomarker to evaluate the effect of neurofeedback training.
A Branch-Gain-Balanced LNA Based on Voltage- Controlled Resistor Feedback and Shared CMFB Amplifier

The low-noise amplifier (LNA) determines the noise performance and linearity of the ultrasound imaging system. However, due to the characteristics of the ultrasound system, the LNA is single-ended input and double-ended output which can lead to unbalanced branch gains. To address this issue, a negative feedback mechanism based on voltage-controlled resistor and shared common-mode feedback (CMFB) amplifier is proposed to make the gains at the two branches of the LNA equal and resistant to PVT fluctuations. Test results show LNA’s equivalent input-referred noise voltage density is $1.37 \mathrm{nV} / \sqrt{} \mathrm{Hz}$ at 5MHz, and the −3dB bandwidth is 200MHz. The proposed negative feedback mechanism optimizes the gain difference between VON and VOP at the frequency of 5MHz from 2.3dB to 0.5dB.
Transcriptomic Analyses of Grapevine Leafroll-Associated Virus 3 Infection in Leaves and Berries of ‘Cabernet Franc’

Grapevine leafroll-associated virus 3 (GLRaV-3) is one of the most important viruses affecting global grape and wine production. GLRaV-3 is the chief agent associated with grapevine leafroll disease (GLRD), the most prevalent and economically destructive grapevine viral disease complex. Response of grapevine to GLRaV-3 infection at the gene expression level is poorly characterized, limiting the understanding of GLRaV-3 pathogenesis and viral-associated symptom development. In this research, we used RNA-Seq to profile the changes in global gene expression of Cabernet franc, a premium red wine grape, analyzing leaf and berry tissues at three key different developmental stages. We have identified 1457 differentially expressed genes (DEGs) in leaves and 1181 DEGs in berries. The expression profiles of a subset of DEGs were validated through RT-qPCR, including those involved in photosynthesis (VvPSBP1), carbohydrate partitioning (VvSUT2, VvHT5, VvGBSS1, and VvSUS), flavonoid biosynthesis (VvUFGT, VvLAR1, and VvFLS), defense response (VvPR-10.3, and VvPR-10.7), and mitochondrial activities (ETFB, TIM13, and NDUFA1). GLRaV-3 infection altered source–sink relationship between leaves and berries. Photosynthesis and photosynthate assimilation were inhibited in mature leaves while increased in young berries. The expression of genes involved in anthocyanin biosynthesis increased in GLRaV-3-infected leaves, correlating with interveinal tissue reddening, a hallmark of GLRD symptoms. Notably, we identified changes in gene expression that suggest a compromised sugar export and increased sugar retrieval in GLRaV-3-infected leaves. Genes associated with mitochondria were down-regulated in both leaves and berries of Cabernet franc infected with GLRaV-3. Results of the present study suggest that GLRaV-3 infection may disrupt mitochondrial function in grapevine leaves, leading to repressed sugar export and accumulation of sugar in mature leaf tissues. The excessive sugar accumulation in GLRaV-3-infected leaves may trigger downstream GLRD symptom development and negatively impact berry quality. We propose a working model to account for the molecular events underlying the pathogenesis of GLRaV-3 and symptom development.
The Effects of Structural Capital on Organisational Innovation in Australian SMEs

Despite much research on structural capital, little is currently known about the effect in SMEs, especially in Australia. Innovation is important for SMEs to survive in the market and being sustaining competitive advantage. Organisational innovation has been studied by a number of scholars. However, it fails to prove conclusive evidence on the issue. Hence, this study used Business Longitudinal Database (BLD) from the Australian Bureau of Statistics (ABS) that examined 2,154 SMEs of various industries from 2009-2011 to test the hypotheses using Poisson regression. The main aim of this study is to identify whether structural capital is associated with product and process innovation in SMEs. Our research finding shows that structural capital has a positive and significant effect on product and process innovation after two-year interval. Structural capital simplifies the flow of information within the firm and firms with poor working procedures will not be fully competitive in the market. SC will give the important data and mindfulness for new item advancement and in the meantime, it will add to the creating of new or enhanced final product. This result shows that organizations' push to systematize authoritative learning through structural capital in the long run to improve the business practices and day to day operation.
It Takes Three to Enhance: A Pilot Study of Collaboration in the Basic Course

While instruction from the communication center and library has supported students and teaching for many years, we seek a more intentional collaboration between these support services and the basic communication course. This article follows a pilot case study performed to explore student perceptions of an in-class collaborative session designed to meet the needs of students in the basic communication course (BCC). Through a mixed- methods approach, basic course students and course instructors were surveyed to understand and explore the perceptions and possible outcomes of the project. The results suggest the importance of collaboration and in-class instruction, yielding future case-specific enhancements and generalizable suggestions and precautions for other universities and colleges.
Is the linear no-threshold (LNT) model relevant for radiotherapy?

Initially considered as a kind of radiobiological law, the linear no-threshold (LNT) model, which by definition supports the absence of any threshold for cancer risk induction after irradiation, is nowadays more reasonably described as a pragmatic and prudent approach by the International Organizations. However, it remains today a dogma in radiological protection. Actually, this model had been essentially developed for the radiological protection of a general population against low, and sometimes very low, doses of irradiation. Radiation oncologists are dealing with a totally different situation since they deliver, on purpose, high doses of radiations in more or less limited volumes of the body of cancer patients, patients for whom no other alternatives do exist to get rid of their malignant tumors. Simultaneously, the radiation oncologists inevitably give low and even very low doses at distance from the so-called target volumes. In such a specific situation, what is the carcinogenicity of these low doses and the relevance of the LNT model in radiotherapy? Thus, this paper addresses three critical questions: 1) what is the risk acceptability of the radiation doses delivered by radiotherapy of malignant tumors? 2) what is the real carcinogenic risk of (very) low doses delivered at distance from the target volume? 3) are the clinical radiotherapy data, i.e., the number of second primary cancers, accumulated since more than a century, consistent with the LNT model? In conclusion, the LNT model appears to be poorly adapted to the high doses locally delivered to cancer patients and dramatically overestimates, in most cases, the risk of secondary radio-induced cancers. In fine, the real risk of the LNT model in radiotherapy would be to promote radiophobia in cancer patients and to see some of them turning away from a life-saving treatment.
Development of Data Acquisition System for Real-Time Monitoring Using a Smartphone

The main aim of this study is to develop and deploy a distributed data acquisition system to acquire different environmental parameters using sensors. The available literature has neglected to account for the role that the concept of real-time monitoring plays, a shortcoming this paper addresses. We explored to what extent, and in what ways can real-time monitoring improve the concept of data acquisition. We do so to better enable access to data from anywhere in the world using smartphones. In this paper, a distributed data acquisition system application for real-time monitoring using smartphone is presented. The system has two sensors, namely, DHT22 to measure both temperature and humidity, and BMP180 Barometer sensor which measures temperature, pressure, and altitude. For data acquisition, a Raspberry Pi is used, which acquires data from the sensors that are connected to it. The Raspberry Pi has in-built Wireless Fidelity (Wi-Fi) which enables it to communicate to the outside world. A webserver, in the form of Thingspeak is created and runs on the Raspberry Pi to allow real-time monitoring from a smartphone or any smart mobile device. The user can log into Thingspeak and monitor the temperature, humidity, air pressure and altitude of the environment. This paper gives an overview over existing literature, presenting the evolution of the real-time data acquisition in a full spectrum of different industries, and focusing on a particular subset that deals with digital-based data acquisition. In this study we explored people's conceptions of sensing and analysis capabilities of different data acquisition systems, their accuracy and reliability in big and small industries.
Benzene and Toluene Detection with a Quartz-Enhanced Photoacoustic Sensor

The analysis of Volatile Organic Compounds (VOCs) concentration in exhaled breath can be a promising approach for early and non-invasive diagnosis of different diseases. This work reports the detection of benzene and toluene molecules using a Quartz-Enhanced PhotoAcoustic Spectroscopy (QEPAS) sensor. These two molecules are VOCs commonly found in part-per-billion (ppb) level in the human exhaled breath.
Half-integer quantum Hall effect of disordered Dirac fermions at a topological insulator surface

The unconventional (half-integer) quantum Hall effect for a single species of Dirac fermions is analyzed. We discuss possible experimental measurements of the half-integer Hall conductance g x y of topological insulator surface states and explain how to reconcile Laughlin's flux insertion argument with half-integer g x y . Using a vortex state representation of Landau level wave functions, we calculate current density beyond linear response, which is in particular relevant to the topological image monopole effect. As a major result, the field theory describing the localization physics of the quantum Hall effect of a single species of Dirac fermions is derived. In this connection, the issue of (absent) parity anomaly is revisited. The renormalization group (RG) flow and the resulting phase diagram are extensively discussed. Starting values of the RG flow are given by the semiclassical conductivity tensor which is obtained from the Boltzmann transport theory of the anomalous Hall effect.