Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitory effects of aloe vera gel aqueous extract and L. casei against E. coli in yoghurt

Chemical preservatives are usually used to reduce or eliminate pathogenic or spoilage microorganisms, So many researches have been done to substitute the chemicals with naturally occurring compounds, especially plant essential oils. In this study the growth and survival of E.coli as a pathogen agent were investigated under the synergistic effects of simultaneous presence of Aloe Vera gel aqueous extract and Lactobacillus casei. For this purpose, an amount of 108-109 cfu/ml of L. casei, 103 CFU/ml E.coli, and two different concentrations of Aloe Vera gel aqueous extract (5 and 10%) were added to yoghurt. The samples were kept for 10 days in 4°C and the survival of E.coli was evaluated. The presence E. coli was determined by culture in selective media and the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of Aloe Vera gel aqueous extract against E.coli was investigated by Micro-well dilution assay. The MIC and MBC values ranged 20% and 40%. The highest antibacterial activity was seen at the end of the storage period and in the samples containing 10% extract (2.33±0.24 log10 cfu/g). E. coli count in samples containing extract and in probiotic yogurt were significantly decreased in comparison with the control group at the end of storage period. however, there was no significant difference in E. coli count between probiotic and non-probiotic yogurt containing extract and According to the results of this study L.casei and Aloe vera gel aqueous extract could be used as natural preservative agents in the dairy products.
Interpersonal Psychotherapy (IPT) for a patient with moderate depressive episodes with somatic syndrome: a single case study

Interpersonal psychotherapy (IPT), is a short term, evidence-based treatment, has shown efficacy in treating individuals with affect dysregulation and other psychiatric condition. Interpersonal psychotherapy target helping patients to improve their interpersonal relationships or change their expectations about them. It also aims to assist patients to improve their social support network so that they can better manage their current interpersonal distress. This case study, therapeutic program consisting of 15-20 sessions based on the clinical interview and psychological evaluation. It mainly focused on interpersonal relationships as a means of bringing about interpersonal change and symptomatic recovery, with the goal of helping the individual to resolve the acute interpersonal crisis and to improve her social support system. This intervention included detailed assessment in the initial phase, interpersonal inventory, the interpersonal formulation, treatment agreement, IPT techniques (clarification, communication analysis, problem solving, use of affect, role playing, homework and other directives), conclusion of acute treatment and maintenance of treatment. The future directions of IPT involve assisting patients to improve their social support network with a view to ensure better management of their current interpersonal distress.
Trends in Using IoT with Machine Learning in Health Prediction System

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.
Output‐feedback Lyapunov redesign of uncertain systems with delayed measurements

This paper presents an output‐feedback Lyapunov redesign for uncertain systems with the delayed measurements, which recasts the state estimation and robust control into a unified framework. Instead of the traditional observer/differentiator‐based output‐feedback design, a static state estimator is constructed by the Taylor expansion of delayed measurements with the integral remainders. Then, a sliding variable is constructed according to the nominal Lyapunov function. A Lyapunov redesign approach is used to keep the system trajectory in predefined vicinity of origin, even subject to approximation errors and exogenous disturbances. The maximum value of the allowable delays for the closed‐loop stability is found via linear matrix inequalities. Finally, the effectiveness of the proposed method is verified in the magnetic suspension system.
The Black Body in Donald Pierson’s Thesis “Negroes in Brazil”

This article aims to present the way the black body is approached in Donald Pierson’s (1900-1995) doctoral thesis. The question to be investigated is: how does one of the first studies on racial issues carried out in Brazil treat the black body? The theoretical framework was defined by authors from the social sciences. The thesis was considered as a historical document. A content analysis was made based on the following codes: the work’s physical structure, notes on the second and the first introductions, and approaches about the body. The results suggest that the black body did not properly qualify as an object of study, but it reveals an element that identifies both biological aspects and the movement of black bodies. It is concluded that resuming the investigation initiated by Pierson about blacks in Bahia is relevant because it allows the understanding of black body markers in the Brazilian context.
Constructing an efficient and adaptive learning model for 3D object generation

Studying representation learning and generative modelling has been at the core of the 3D learning domain. By leveraging the generative adversarial networks and convolutional neural networks for point-cloud representations, we propose a novel framework, which can directly generate 3D objects represented by point clouds. The novelties of the proposed method are threefold. First, the generative adversarial networks are applied to 3D object generation in the point-cloud space, where the model learns object representation from point clouds independently. In this work, we propose a 3D spatial transformer network, and integrate it into a generation model, whose ability for extracting and reconstructing features for 3D objects can be improved. Second, a point-wise approach is developed to reduce the computational complexity of the proposed network. Third, an evaluation system is proposed to measure the performance of our model by employing various categories and methods, and the error, considered as the difference between synthesized objects and raw objects are quantitatively compared, is less than 2.8%. Extensive experiments on benchmark dataset show that this method has a strong ability to generate 3D objects in the point-cloud space, and the synthesized objects have slight differences with man-made 3D objects.
ROLE OF BIO-FERTILIZERS IN IMPROVEMENT ROSEMARY PRODUCTIVITY UNDER WATER-DEFICIT CONDITION

Corresponding author: K.A. Hammam drkhaled033@gmail.com ABSTRACT: This study was carried out during the two successive growing seasons of 2018/2019 and 2019/2020 at the Farm of Ali Mubarak, South Tahrir, El-Bahira Governorate, Horticulture Research Station. Egypt, aiming to study the impact of some bio-fertilizers (Mycorrhiza, Algae, and PGPR) combined with 3 levels of irrigation (800, 1200, and 1600m3/fed treatments and their interactions on vegetative growth, essential oil %, essential oil components, N, P, K%, and proline content of rosemary (Rosmarinus officinalis L.) plant grown in a sandy soil. The obtained results could be summarized as follows; a significant increase in plant height, number of branches, and fresh and dry weights per plant were recorded with irrigation at 1200 m3/fed. On the other hand, the highest oil percentages and oil yield/ plant resulted under the effect of irrigation at 800 m3/fed biofertilizer treatments also, increased vegetative growth parameters i.e., plant height, number of branches, herb fresh and dry weights/ plant and oil percentage, and yield/plant. Irrigation at the rate of 1200 m3/fed, and PGPR alone or their combination resulted in the highest values of the above-mentioned traits (plant growth, parameters). However, the combination between irrigation at 800 m3/fed and PGPR showed the highest values of oil percentage and oil yield/plant. The N, P and K% in dried herb recorded their higher values with medium irrigation, i.e., at the rate 1200 m3 in both cuts. Also, the results showed that there was a steady significant increase in proline content due to decreasing the irrigation rate. This investigation supports the application of naturallyoccurring plant growth promoting rhizobacteria (PGPR) and microorganism to advance plant development and sustainable option for farmers under deficit water to maximize the yield of rosemary plant.
Reliability and validation of an instrument for measuring problems with performance appraisal of automotive parts manufacturing employees

PurposeTo develop and validate an instrument to measure the problems associated with performance appraisal.Design/methodology/approachThe implementation was in two phases. Phase 1 involved the development and validation of an instrument to measure the problems with performance appraisal. Phase 2 involved the exploration and confirm the construct measurement. Data used in Phase 1 were collected from interviews with administrators and employees in the automotive parts manufacturing industry and five experts. In Phase 2, data were derived from questionnaires sent to 320 employees of automotive parts manufacturers in the Eastern Region of Thailand.FindingsProblems concerning performance appraisals were classified into two components as problems with the appraisal process and problems with the appraising person. The concepts, theories and interview results that were used to develop the instrument and assess problems with performance appraisals were consistent with the empirical evidence.Practical implicationsThe developed instrument may be used to measure problem levels of performance appraisals in organizations with high accuracy and reliability. Findings may be used as guidelines for management to effectively reduce problems with performance appraisals. The instrument may also be used for research measurement of organizational problems concerning performance appraisal.Social implicationsFairness, transparency and testability are aspects of effective management. Ignorance of problems in performance appraisals may have negative effects on a conducive working atmosphere and behaviors at the personal, group and organizational levels. Therefore, the findings of this study have social implications for the capability to examine fairness in employees' performance appraisals.Originality/valueThe instrument for measuring problems with performance appraisal was developed based on the combination of concepts, theories and interview and questionnaire data. This instrument facilitates human resource officers, managers and organizations in measuring the levels of problems with performance appraisals.
Investigation on the Emission and Diffusion of Hydrogen Sulfide during Landfill Operations: A Case Study in Shenzhen

This study investigated the emission and diffusion of hydrogen sulfide (H2S), as one of the odorous gases generated from landfills, in a municipal solid waste landfill of a south Chinese city. To this end, the flux of the H2S emissions in the working area of the landfill and its diffusion in the surrounding area were measured. The diffusion of the H2S was simulated at different wind speeds, wind directions, bare working areas of the landfill, heights of the landfill, and angles between the wind direction and the long side of the working area. The results indicated that the concentration of the H2S around the monitoring point ranged from 0 to 60 µg/m3, and the simulated data were consistent with the measured results. At a uniform wind direction, the pollution range of the H2S was narrow. Furthermore, with an increase in the height of the waste dump, the concentration of the H2S decreased in the working area but rose in the surrounding area. Notably, when the angle between the long side of the working area and the wind direction was 0°, the H2S largely spread along the extension cord of the long side of the working area. When the angle increased to 90°, the influence range of the H2S extended significantly. The working area in the landfill site should be regulated based on the monitored data to reduce the effect of this harmful gas on the living environment, and the health of the landfill staff and nearby residents.
A facile and one-pot synthesis of fluorescent graphitic carbon nitride quantum dots for bio-imaging applications

As a kind of metal-free polymer, graphitic carbon nitride (g-C3N4) has captivated considerable attention. In this work, an efficient one-pot, economical and green pathway was investigated to simultaneously prepare highly fluorescent g-C3N4 quantum dots (g-C3N4 QDs) and ultrathin g-C3N4 nanosheets via ethanol-thermal treatment of bulk g-C3N4 in the presence of KOH. The as-obtained g-C3N4 QDs exhibited narrow particle size distribution with an average diameter of 3.3 ± 0.3 nm. Benefiting from these alluring properties, such as excellent water solubility, bright blue fluorescence, favorable stability and good biocompatibility, the g-C3N4 QDs were successfully employed as a brand-new but promising candidate for bioimaging in vitro. Furthermore, this convenient strategy would open a new avenue for the large-scale synthesis of other layered materials.
Motion Characteristics Analysis of a Novel Spherical Two-degree-of-freedom Parallel Mechanism

Current research on spherical parallel mechanisms (SPMs) mainly focus on surgical robots, exoskeleton robots, entertainment equipment, and other fields. However, compared with the SPM, the structure types and research contents of the SPM are not abundant enough. In this paper, a novel two-degree-of-freedom (2DOF) SPM with symmetrical structure is proposed and analyzed. First, the models of forward kinematics and inverse kinematics are established based on D-H parameters, and the Jacobian matrix of the mechanism is obtained and verified. Second, the workspace of the mechanism is obtained according to inverse kinematics and link interference conditions. Next, rotational characteristics analysis shows that the end effector can achieve continuous rotation about an axis located in the mid-plane and passing through the rotation center of the mechanism. Moreover, the rotational characteristics of the mechanism are proved, and motion planning is carried out. A numerical example is given to verify the kinematics analysis and motion planning. Finally, some variant mechanisms can be synthesized. This work lays the foundation for the motion control and practical application of this 2DOF SPM.
A Novel Method of Billing System Using Deep Learning

In order to make the billing process of goods easier and quicker for the customers as well as the shopkeepers, the RFID tags and the bar-codes are used in most of the products. But for the edible products like fruits and vegetables, these tags cannot be used as each item has to be stuck with the tag and its weight must be measured individually. To overcome this, a system is designed in which the weighing and billing of the fruits and vegetables are automated. The proposed billing system consists of Raspberry pi, a microprocessor with a camera module and a load cell. Using Raspberry pi 3 camera module, images of fruits and vegetables are captured and are recognized by using the deep learning technique, ImageNet which is built on Convolutional Neural Network (CNN) architecture and a machine learning technique, K-means clustering is used for classifying the products into corresponding groups. The camera module captures the images of the fruits and vegetables placed on the tray under which the load cell is placed for the weighing purpose. The price of various items per kg is given as an input to the microprocessor. As a result, the Raspberry pi computes the total cost of the items and displays it on the monitor. The general-purpose high-level programming language, Python is used for developing the efficient deep learning algorithm.
How do we learn music? Collecting feedback from Kastamonu University music students

The study studied how students studying music at Kastamonu University learn about music and whether they receive additional help in 2020. The first study detailed in this article focused on identifying student perceptions and how they learned music, and aimed to come up with suggestions on how to better meet the needs of music students. To do this, we conducted two focus group interviews with music graduate (N = 6) and undergraduate (N = 4) students. Participants said that the timing of the study should better reflect their work and that the studies should be more disciplinary and better designed for graduate students. They also felt that they did not get enough critical feedback and there was a lack of standardization in the training and concert work. However, the participants also felt that the teachers were helpful, the programming contributed well to their work and the support increased their confidence. Two unexpected findings were that students generally access some form of programming offered by the school rather than take advantage of the diverse offerings, and students have misconceptions about the possibilities the school offers and how to use them. It is hoped that this study will help inform other student academic support services about focus group research for the purpose of collecting music program evaluation and student feedback.
A Low-Loss Integrated Circuit Breaker for HVDC Applications

Hybrid dc circuit breakers (HCBs) are recognized as suitable devices for protecting high-voltage direct-current (HVDC) systems, along with other dc circuit breakers (DCCB). However, compared to mechanical circuit breakers, HCBs exhibit higher conduction losses. Such losses are inevitable under no-fault conditions as current may flow through some of the semiconductor switches. An integrated circuit breaker (ICB) minimizing these losses is presented in this paper, and this is achieved by replacing semiconductor switches by mechanical components in the current path. For completeness, the topology design, operating sequence and a mathematical analysis for component sizing of the device are provided. In addition, an estimation of the conduction losses is quantified. It is estimated that the power losses of an ICB are 2 to 30% of an HCB only. The ICB has been implemented in PSCAD/EMTDC to demonstrate its effectiveness for isolating dc faults, with simulations conducted on a three-terminal HVDC grid.
Water Management for Improving Pearl Millet Production under Irrigated Environment: A Review

Pearl millet also called “poor man food” is known for its drought resistance, well adaptation to harsh conditions like soils with poor water holding capacity, low nutrient status, problematic soils, etc. Irrigation has been recognized as a basic necessity for sustaining high productivity of various crops. Moreover, it affects the crop yield directly as well as indirectly by increasing their response to other inputs including fertilizers and various management practices. It is well known that water deficit is one of the major abiotic factors limiting crop productivity in the semi-arid tropics. Out of the various production constraints; low productivity of pearl millet is mainly attributed to its cultivation under dry land conditions and improper water management under irrigated conditions. So application of irrigation water offers the scope for improving the quality as well as productivity of pearl millet. Therefore, to augment the productivity of the poor’s man crop, review of the research work related to irrigation scheduling and moisture conservation practices of pearl millet has been presented here for directing the future research.
Mental Health, Greenness and Nature Related Behaviours in the Adult Population of Stockholm County during Covid-19-related Restrictions

International data suggests that exposure for nature is beneficial for mental health and well-being. The restrictions related to Covid-19 pandemic have created a setting that allows us to investigate the importance of greenness exposure on mental health during a period of increased isolation and worry. Based on 2060 responses from an online survey in the Stockholm County, Sweden, we investigated: 1) weather the Covid-19 pandemic changed peoples’ life-style and nature-related habits, and 2) if peoples’ mental health differed depending on their exposure to greenness. Neighbourhood greenness levels were quantified by using the average Normalized Difference Vegetation Index (NDVI) within 50m, 100m, 300m, and 500m buffers surrounding the participant’s place of residence. We found that the number of individuals that reported that they visited natural areas “often” was significantly higher during the pandemic than before the pandemic. Higher levels of greenness surrounding one’s location of residence were in general associated with higher mental health/wellbeing and vitality scores, and less symptoms of depression, anxiety, and perceived and cognitive stress, after adjustments for demographic variables and walkability. In conclusion, the results from the present study provided support to the suggestion that contact with nature may be important for mental health in extreme circumstances.
Spectral Energy Relations of Isobilateral Leaves

Heflectance and transmittance have been studied for eucalypts in the spectral range from 4000 to 9500 A at varying angles of incidence. Spectral reflectance and spectral transmittance were observed to be high in the infrared. All leaf surfaces were found to be highly diffusing and not to reflect white light in accordance with the sine law.
Reproductive performance of early- and late-calving dairy cows artificially inseminated after ovulation synchronization and estrous resynchronization or artificially inseminated after observed estrus

Graphical Abstract Summary: Cows that calve late in the calving season are less likely to become pregnant and maintain a yearly calving interval for pasture-based dairies because they are too few days postpartum at the start of breeding (mating start date). We found that late-calving cows had poor reproductive performance compared with early-calving cows. Nonetheless, a major improvement to reproductive performance was achieved by targeting late-calving cows with a synchronization program.