Miracle-dz/UniFAR-FTUs huggingface dataset:
Metabolic Profiling of Bulgarian Potato Cultivars

Potatoes (Solanum tuberosum L.) are the fourth most economically important crop in the world. They have a short period of vegetation and are an excellent source of carbohydrates, amino acids, vitamins, organic acids, minerals and phenolics as antioxidant substances. Potato can be a major dietary source of various bioactive compounds. In this study, we applied gas chromatography coupled with mass spectrometry (GC-MS) metabolite profiling to classify eight Bulgarian potato cultivars bred in the Maritsa Vegetable Crops Research Institute (VCRI), Plovdiv, according to their metabolite contents. Altogether, we determine their flavonoids/phenolics to evaluate their nutritive quality for the breeding program with the target of determining strong health-promoting compounds. The “Kalina” cultivar is highlighted as the best one with the highest number of metabolites, containing 14 out of the 26 evaluated; it was selected as the highest-quality cultivar, compared with the other seven cultivars studied. According to the grouping of the cultivars in principal component analysis PCA, their positive distribution is explained mainly by them having the highest contents of aminobutyric and isocitric acids, methionine and alanine and lower levels of fumaric acid, pyroglutamic acid and glycine, in contrast to the cultivars distributed negatively, which had high contents of carbohydrates and relatively low contents of most of the amino acids. The highest number of amino acids was found in the cultivar “Kalina”, followed by “Perun” and “Bor”. The highest number of carbohydrates was found in “Pavelsko” and “Iverce”, while the prominent accumulation of organic acids was found in “Kalina”, “Bor” and “Rozhen”. The highest number of flavonoids in the flesh of the tubers was found in the cultivars “Nadezhda” and “Pavelsko”, followed by “Bor”. The highest ratio of flavonoids/phenolics in the flesh was found in “Pavelsko” and in “Nadezhda”, followed by “Iverce”.
Dor crônica em idosos residentes em São Paulo, Brasil: prevalência, características e associação com capacidade funcional e mobilidade (Estudo SABE)

The objective was to identify the prevalence and characteristics of chronic pain and the association with functional capacity. This was a cross-sectional study with a population sample of community-dwelling elderly residents in the city of Sao Paulo, Brazil, using home interviews. Statistical analysis used the RaoScott test of association with p < 0.05. Prevalence of chronic pain was 29.7% (95%CI: 25.4-33.9), most frequent in the lower back (25.4%) and lower limbs (21.9%), and moderate in 45.8% and intense in 46% of the sample. Chronic pain was associated with increased dependence and decreased mobility (p < 0.001).
The Autonomic Nervous System and Big Five Personality

Background: Covid-19 brings change to human life. Human should adapt with this condition. People with certain personality trait may respond differently to the same situation. The autonomic nervous system (ANS) is important for regulation of human body function. It plays an important role in the physiological responses that arise in response to events around humans. Objective: to identify relationship between ANS function and personality. Methodology: We studied autonomic nervous system function and personality of 64 subjects older than 18 years old. Subjects with ANS dysregulation that had been diagnosed by physician were excluded. The Schellong test and The Big Five Personality questionnaire were used to identify ANS function and personality respectively. Relationship between variables was identified using Spearman test. Results: Autonomic nervous system function of the subject were normal (79.7%), type-1 (14.1%), type-2a (3.1%) and type-3 (3.1%). Subjects which have high openness personality were 36%, high conscientiousness were 70%, high extraversion personality were 50%, high agreeableness personality were 91%, and low neuroticism were 92%. There was no relationship between ANS function and openness (p=0.49), extraversion (p=0.34), agreeableness (p=0.77), and neuroticism (p=0.90). There was a relationship between ANS function and conscientiousness (p=0.049). Conclusion: There was a relationship between ANS function and conscientiousness but not with other types of OCEAN personality.
Interaction between dairy cow physiology and milking technology

One central aspect in milk production is the removal of the milk by the milking machine. In the present study the regulatory background of milk secretion and milk removal were intensively studied. The focus of interest was the interaction between dairy cow physiology and milking technology. A study on the interaction between the teat anatomy, teat functionality and milk flow profiles showed that the teat anatomy and the vacuum required to open the teat canal could partly explain quarter specific milk flow characteristics. In various experiments the effects of milking routines on oxytocin release, milk ejection and milking performance were investigated. Minimal mechanical impulses on the teat were sufficient to induce oxytocin release and to start the milk ejection. An individually adjusted pre-stimulation according to the actual degree of udder filling improved the milk flow rate and reduced the vacuum load to the teat. At the end of milking a quarter specific removal of teat cups reduced quarter specific machine-on time by approximately 20 %. Automatic milking systems (AMS) differ considerably from conventional systems. The adaptation during the changeover from conventional to automatic milking was studied in cows with and without previous experience in AMS. The heart rate during the first visits to the AMS was remarkably elevated in the latter group and the milk yield was reduced by 15 % due to the changeover. Unexperienced cows entered the AMS voluntarily at day 12 after the changeover. The adaptation capacity between individuals differed and was related to the adrenal cortex sensitivity. Experienced cows immediately visited the AMS voluntarily without human intervention. Furthermore the heart rate was not elevated during their first AMS visits after the changeover. The milk production and the milk fat content were reduced when milking intervals were prolonged while protein and lactose contents as well as somatic cell counts were not affected. These relationships are of importance, because AMS milking results in coincidental milking intervals. The time to regain ovarian cyclicity and days open were not affected by enhanced milking frequencies in AMS as compared to conventional twice daily milking. An optimal balance between the physiological demands of the dairy cow and the milking machine can reduce negative impacts on the teat anatomy and therefore avoid negative effects on biological teat functions. AMS do not overstrain the adaptation or coping capacity of dairy cows, but during the changeover period an accurate management is essential to avoid stress load on the cow and economical losses of the farmer. The demonstrated interactions between dairy cow physiology and milking technology improve the knowledge about dairy cow husbandry and can help to enhance the resource efficiency of milk production systems.
Mayflies and long-distance runners: the effects of new business formation on industry growth

This note analyses the effects of new business formation on industry growth. Dynamic panel techniques are used to test two hypotheses. First, does hit-and-run competition secure efficiency in an industry? Second, do innovative startups lead to amplified innovations by diminishing the knowledge filter? The results illustrate how new businesses can be viewed as either mayflies or long-distance runners.
Eigenspace-based fuzzy c-means for sensing trending topics in Twitter

As the information and communication technology are developed, the fulfillment of information can be obtained through social media, like Twitter. The enormous number of internet users has triggered fast and large data flow, thus making the manual analysis is difficult or even impossible. An automated methods for data analysis is needed, one of which is the topic detection and tracking. An alternative method other than latent Dirichlet allocation (LDA) is a soft clustering approach using Fuzzy C-Means (FCM). FCM meets the assumption that a document may consist of several topics. However, FCM works well in low-dimensional data but fails in high-dimensional data. Therefore, we propose an approach where FCM works on low-dimensional data by reducing the data using singular value decomposition (SVD). Our simulations show that this approach gives better accuracies in term of topic recall than LDA for sensing trending topic in Twitter about an event.
Saint Wilgefortis: A Queer Image for Today

An increasing number of people identify outside of the sex/gender binary, many of whom are in crisis and under attack simply because of how they choose to identify. There are few opportunities for them to experience healing in liturgies, particularly as these liturgies often perpetuate a normative view of the sex/gender binary through language and art. This article offers Saint Wilgefortis as an emancipatory image that offers healing while also transforming ethical attitudes and behaviors toward those who identify outside of the sex/gender binary. First is an examination of the history of the cult of Wilgefortis. This is followed by interpretations of the medieval devotion to Wilgefortis, providing a liberating depiction of someone who blurs boundaries, who is ‘both and neither,’ who is and is not Christ (human and divine), and who is and is not ‘female’ or ‘male. ’ Next is an exploration of contemporary portrayals of Wilgefortis, providing a queer, multivalent, and prophetic image for today. Finally, there is a discussion of how Wilgefortis could be incorporated into liturgies that minister especially to those who identify outside of the sex/gender binary while also expanding the imagination of those who struggle to see sex/gender as a spectrum.
Prevalence, Antimicrobial Resistance, and Molecular Characterization of Escherichia coli Isolated from Food Contact Surfaces in Seafood Pre-Processing Plants (India)

Background: The survival of pathogens in biofilms poses a threat to food safety. The aim of this study was to determine prevalence, antimicrobial resistance, and molecular characterization of Escherichia coli strains. Methods: Swab samples (n=144) were collected from biofilm formed on food contact surfaces in seafood pre-processing plant in India. E. coli was isolated and identified using uid A gene by Polymerase Chain Reaction (PCR). The risk assessment of the isolates was carried out in terms of their drug resistance and the presence of virulence genes. Antimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method. Phylogenetic grouping was done by quadruplex PCR. Molecular typing of the strains was performed by Enterobacterial Repetitive Intergenic Consensus-PCR (ERIC-PCR). Data were statistically analyzed using SPSS version 22. Results: Enteropathogenic E. coli (EPEC) strains were the most prevalent serotype. Multiplex PCR analysis revealed the presence of shiga toxin genes (stx1, stx2), intimin (eae), and enterohemolysin genes (hlyA). Shiga toxin gene stx2 showed the highest prevalence (83.33%). Among various phylogroups, B1 (45.56%) and B2 (30%) were the most prevalent phylogroups. Resistance to ampicillin (85.56%), piperacillin (84.44%), and cefpodoxime (85.56%) was widespread among the E. coli strains. Conclusion: The presence of genetically heterogeneous multi drug resistant E. coli strains with virulence potential showed a high risk in the seafood industry.
Kex at the 2022 SBST Tool Competition

Kex is an automatic white-box test generation tool for Java programs, which is able to generate executable test suites (as JUnit test suites) aiming to satisfy the branch coverage criterion. It uses symbolic execution to analyze control flow graphs of the program under test (PUT) and produces interesting symbolic inputs for each basic block of PUT. Kex then feeds these inputs to an original backward-search based algorithm called Reanimator, which generates executable JUnit test cases satisfying the symbolic inputs. Kex-reflection is a modification of Kex that uses Java reflection library to generate test cases from symbolic inputs. This paper summarizes the results and experiences of Kex and Kex-reflection participation in the tenth edition of the Java unit testing tool competition at the International Workshop on Search-Based Software Testing (SBST) 2022.
A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits

Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.
Use of road underpasses by mammals and a monitor lizard in eastern Australia and consideration of the prey‐trap hypothesis

Abstract Road networks continue to expand globally with predictable effects on ecological systems. Research into the effectiveness of road underpasses and overpasses for wildlife has been concentrated in North America and Europe. In Australia, most studies of underpasses have been of relatively short duration and without reference sites to give context to the measured rates of use. We studied 5–7 road underpasses at two locations in eastern Australia over 2–3 years, comparing camera trap detections of animals in underpasses with those at nearby forest sites. Three species of large macropod (wallabies and kangaroos) were frequently detected in the underpasses, with some underpasses traversed 1–4 times per week, and in many cases exceeded detections in the forest. The lace monitor (Varanus varius) was detected in all underpasses, often once per week during spring and summer, and infrequently in the forest. At each location, a different small macropod species, including one regionally threatened, showed a higher probability of detection in one underpass compared with several of the forest sites. The vulnerable koala (Phascolarctos cinereus) was detected infrequently in underpasses and in the adjoining forest. The short‐beaked echidna (Tachyglossus aculeatus) had a high probability of detection in a single underpass. The “prey‐trap hypothesis” postulates that predators will exhibit increased activity at underpasses as a consequence of prey being funneled. We found the red fox (Vulpes vulpes) had high activity in some underpasses. However, its activity coincided less than expected with the activity of the mammals most at risk to it. Our results provide no consistent support for the “prey‐trap hypothesis. ” Instead, our study confirms the generic value of underpasses for a range of medium‐large mammals as well as one large reptile. Habitat adjoining underpasses exert a strong influence on their use and require greater consideration to maximize underpass use.
Masked Face Recognition by Applying SSD and ResNet Model for Attendance System

The rapid development of technology in artificial intelligence (AI) includes face recognition. Some of these developments in face recognition systems can be found easily in everyday life, such as entry access, payment, or attendance recording systems. Notably, the demand for image-based face recognition methods to record attendance increases due to its effectiveness and efficiency. Compared to conventional methods like fingerprint or RFID, facial recognition offers better results. On the other hand, with the arising needs in dealing with the novel coronavirus (COVID-19) pandemic, official health protocols require mask-wearing and maintaining a minimum distance of 1 meter between individuals to prevent the spread of the virus. Along with such a situation, a face recognition system to record attendance can reduce the occurrence of direct contact and allow each individual to maintain a safe distance, including from the attendance device. This paper presents the performance of a masked-faced recognition system that implemented SSD (Single Shot Detection) and ResNet feature extraction. The face recognition system application developed using Python and related libraries show a stable level of masked face recognition accuracy. This evaluation was made at predetermined distances between the face and the camera and measured at room lighting of 200 lux with an average accuracy of 67%. The application also has a feature to send notification emails to every employee who is unable to attend work on their scheduled workdays.
Evaluating the soil moisture retrievals for agricultural drought monitoring over Brazil

A model for monitoring agricultural drought (SIMAGRI) has been developed in Brazil. This model is based on gridded precipitation product, real evapotranspiration calculated from vegetation index data (as proposed by [1]), and soil water storage. The soil water storage is derived from the estimation of field capacity and wilting point using pedo-transfer functions (PTFs). The SIMAGRI model suggest that the soil moisture influence is unquestionably a quantitative indicator of drought. In addition, using this model, it is possible to monitor drought episodes in agricultural regions of Brazil, especially over the Northeast, where vulnerability to drought is the highest in the country due to the prevalence of rain fed agricultural practice and frequent droughts.
Engagement y satisfacción académica: rol mediador del compromiso afectivo en estudiantes de negocios

La situación actual del sistema universitario ha permitido experimentar diversos procesos de cambio, compromisos académicos y administrativos, valorando la interacción entre el engagement académico, compromiso afectivo y satisfacción académica. La finalidad de la investigación es examinar el efecto mediador del compromiso afectivo en la vinculación entre el engagement y la satisfacción académica en los estudiantes de negocios. Se ha estudiado en una muestra de 469 encuestas válidas de estudiantes universitarios de pregrado de programas de negocios recopiladas por medio de un cuestionario estructurado en línea. Además, se recurrió a la utilización de escalas de engagement académico, compromiso efectivo y satisfacción académica ampliamente utilizadas. Asimismo, para la contrastación estadística se ha utilizado SEM - Sistema de ecuaciones estructurales. Los hallazgos dan a conocer que el engagement académico tiene un efecto positivo en la satisfacción académica. Asimismo, el compromiso afectivo tiene un rol promotor en la vinculación entre el engagement académico y la satisfacción académica. Además, los resultados tienen importantes aportes teóricos y prácticos para la comunidad universitaria.
Social Network Analysis Algorithms, Techniques and Methods

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings.
LDC: Lightweight Dense CNN for Edge Detection

This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 2% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code will be available.
Convolutional Implementation and Analysis of the CFS-PML ABC for the FDTD Method Based on Wave Equation

In this letter, the convolutional implementation of the complex frequency shifted perfectly matched layer (CFS-PML) absorbing boundary condition (ABC) is proposed for the finite-difference time-domain (FDTD) method of solving wave equation. The FDTD solution time-marching formulations of the developed wave-equation convolutional CFS-PML (CPML) ABC are derived. Several numerical experiments are used to verify the capacity of the proposed algorithm. Solution results confirm that the proposed CPML ABC can obtain excellent absorption for both traveling waves and evanescent waves.
A Hybrid Design Technique for Realizing Metasurface based Wideband and Wide Dual-Band Circularly Polarized Dielectric Resonator Antennas

Compact, low-profile metasurface-based wideband and wide dual-band circularly polarized (CP) dielectric resonator antennas (DRAs) are presented in this paper. The rectangular-shaped DRs are loaded over two different types of strategic 7 × 7 metasurface unit cells including a "+" shape metasurface for single-band and square-slotted shape metasurface for dual-band performance. The DRs are fed diagonally with a single perturbed probe feed which is placed at an appropriate angle of 29°. Thus, the hybrid design combination of metasurface unit cells together with the angle of feed location from the center of DRs generates wideband CP radiation with enhanced 3-dB Axial Ratio (AR) and wide impedance matching bandwidths. Proposed antennas are successfully implemented and fabricated prototypes are measured demonstrating significant performance enhancement.