Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of High-resolution Melting Curve Analysis with Specific Target Gene Sequencing to Identify the Most Common Species of Aspergillus and Fusarium

Background: Currently, it appears that new molecular-based methods could substitute microscopic and culture assessment for the first-line detection of microorganisms isolated from clinical specimens. However, it will remain the "continual strategy" until this technology is attuned to identifying all fungi that can be isolated from biological specimens. Objectives: The present study aimed to validate a high-resolution melting (HRM) technique to identify clinical filamentous fungi. Moreover, it was attempted to compare the results with those of the target gene’s polymerase chain reaction (PCR) sequencing. Methods: A total of 54 specimens of bronchoalveolar lavage (BAL), nail, ear discharge, blood culture, and cornea were collected from patients suspected of fungal infection. All Fusarium spp. and Aspergillus spp. were recognized based on Tef-α and beta-tubulin region sequencing, as well as PCR-HRM analysis. Results: The Tef-α sequence analysis revealed the most frequent spp. to be Fusarium solani followed by F. oxysporum (n = 3), F. caucasicum (n = 3), F. coeruleum (n = 3), F. falciforme (n = 1), F. proliferatum (n = 1), F. brevicatenulatum (n = 1), F. globosom (n = 1), and F. verticillioides (n = 1). Based on the beta-tubulin sequences, Aspergillus flavus (n = 10), A. fumigatus (n = 7), A. niger (n = 2), A. terreus (n = 1), and A. orezea (n = 1) were identified in this study. Furthermore, the dataset analysis of PCR-HRM revealed that 33 isolates belonging to Fusarium spp. were F. solani (n = 24), F. oxysporum (n = 3), F. proliferatum (n = 3), F. falciforme (n = 1), F. verticillioides (n = 1), and F. brevicatenulatum (n = 1). Moreover, isolates (n = 21) belonging to Aspergillus spp. included A. flavus (n = 11), A. fumigatus (n = 7), A. niger (n = 2), and A. terreus (n = 1). Conclusions: The sequencing method has a time-consuming and costly nature, and there exists conformity between the sequence results of the Tef-α/beta-tubulin regions and PCR-HRM. The PCR-HRM method is a reliable approach in the clinical laboratory to identify Aspergillus and Fusarium spp. However, some closely related spp. show no curve algorithm differences in PCR-HRM.
Low‐profile wideband circularly polarized asymmetric dipole antenna with artificial magnetic conductor

In this article, a novel low‐profile asymmetric dipole antenna with artificial magnetic conductor (AMC) is presented for wideband circularly polarized operation. First, the asymmetric dipole is adopted to realize a wide impedance bandwidth. By modifying the shape of dipole arms and combing a rhombus‐shaped arm with a quasi‐triangle‐shaped arm, three resonances are achieved by the asymmetric dipole, leading to a wide impedance bandwidth. Second, an AMC surface with square patch units is introduced to reduce the antenna profile to 0. 11λ0, where λ0 denotes the lowest operation wavelength in free space. Finally, the presented wideband circularly polarized (CP) antenna design was fabricated and tested. Reasonable agreement between the measurements and simulations is obtained, demonstrating that this CP antenna achieves a 115% impedance bandwidth (1.45‐5.4GHz) and a 97% axial ratio bandwidth (1.69‐4.88GHz). Within the CP operation band, the presented antenna yields symmetrical and unidirectional radiation patterns with a 6.7‐dBic average gain. Such a broadband and low‐profile CP asymmetric dipole antenna can be a promising candidate for a wide variety of CP wireless applications.
High-cycle fatigue and surface layer stability of case-hardened PM-steels with graded porosity

In order to increase the fatigue performance of mechanically loaded PM-components, case hardening procedures can be used to improve the surface layer states in terms of hardness and residual stress. In this work, the effects of carbonitriding treatment on two typical PM-steels with multiple densities are investigated in terms of their surface layer state (hardness, residual stress and retained austenite) before and after fatigue testing and their resulting pulsating fatigue performance. The results confirm fatigue strengths that can be formulated linearly as a function of porosity as well as a cyclic stability of the surface layer condition at loads below the fatigue strength. Further optimisation of the fatigue strength can be expected by tailoring the surface layer with respect to residual stresses. Comparisons to other studies are difficult due to many process parameters, different load cases and limited data, but indicate comparable fatigue strengths.
Research on Seismic Signal Classification Based on Time-Frequency Map and Deep Learning

Time frequency analysis is an effective method of processing non-stable signals, and this paper attempts to introduce the combination of time-frequency graph and deep learning neural network to study the classification of seismic signals. The short-term Fourier transformation, wavelet transformation and S transformation were used to obtain the time-frequency graph in seismic signal processing, and the good performance of image feature extraction ability was used by deep learning neural network, and Resnet50 was used to classify the results. The combination of time-frequency graph and deep learning neural network can effectively classify seismic signals, and from the classification accuracy, the combination of S transformation and Resnet50 network reached 89.82%, which is more superior.
Human–wildlife conflict in the roof of the world: Understanding multidimensional perspectives through a systematic review

Abstract Human–wildlife conflicts have intensified by many folds and at different levels in recent years. The same is true in the case of the Hindu Kush Himalaya (HKH), the roof of the world, and a region known for its wealth in biodiversity. We present a systematic literature review (SLR) using the search, appraisal, synthesis, and analysis (SALSA) framework; and for spatial and network analysis, we employed the VOSviewer software. The review—covering 240 peer—articles within a span of 27 years (from 1982 to 2019)—revealed that in the last decade, there was a 57% increase in publications but with a disproportionate geographical and thematic focus. About 82% of the research concentrated on protected areas and large carnivores and mega herbivores played a big role in such conflicts. About 53% of the studies were based on questionnaires, and the main driver reported was habitat disturbance of animals due to land‐cover change, urbanization, and increase in human population. On the management front, the studies reported the use of traditional protection techniques like guarding and fencing. Our analysis of 681 keywords revealed a prominent focus on ‘human‐wildlife conflict,’ ‘Nepal,’ ‘Bhutan,’ ‘Snow Leopard,’ and ‘Leopard’ indicating the issue linked with these species and countries. The involvement of 640 authors from 36 countries indicates increasing interest, and Nepal and India are playing key roles in the region. As for the spatial analysis that was conducted, while it showed regional variations, there were conspicuous limitations in terms of having a transboundary focus. Thus, particular attention ought to be paid to building transboundary partnerships and improving management interventions; there is also a pressing need to understand the patterns of human–wildlife convergence, especially involving meso‐mammals.
Automotive Package Lead Pullback Elimination Using Monte Carlo Analysis for Determining Leadframe and Blade Design

This study focuses on eliminating the lead pullback problem in an automotive quad flat no lead (QFN) package in order to meet the non-negotiable requirement to have a solder wettable or solderable lead sidewall. It involves using a non-traditional approach of Monte Carlo tolerance analysis to determine the final leadframe and singulation blade design solution. It was found out that the zero lead pullback could be achieved by reducing the leadframe lead to lead distance from 0.275 mm to 0.225 mm and increasing the blade thickness from 0.325 mm to 0.350 mm. Actual results from 10 line stressing lots all showed zero pullback validating the effectiveness of the final design and the use of Monte Carlo tolerance analysis technique. Costly investment for a lead pullback inspection system was avoided and the 100% manual inspection eliminated.
An improved Top‐K algorithm for edge servers deployment in smart city

In smart city IoT applications, the deployment of edge servers has problems such as unbalanced servers load and low servers utilization. Therefore, we study the edge servers deployment problem in mobile edge computing environments for smart cities through an improved Top‐K algorithm in this paper. This algorithm comprehensively considering the distance between base stations and edge servers, the weight ratio of base stations in the base station cluster, the coverage of edge servers, and the upper limit of computing tasks, which aims to reduce the access delay of tasks and deployment cost of edge servers, balance the load among edge servers, improve quality of user experience (QoE), and quality of service (QoS) of the smart city. Firstly, deploy an edge server at the base station with the most tasks and divide base station clusters according to the minimum distance strategy. Then, the location of edge servers adjusted according to the cumulative sum of the weight ratio of base station tasks and distance product in each base station cluster. Finally, the simulation results show that the deployment strategy in this paper is better than other methods in terms of server utilization, load balancing and cost, and is slightly better than other algorithms in terms of delay.
Overview of the Dynamic Role of Specialty Cut Flowers in the International Cut Flower Market

The global cut flower industry has faced serious challenges over the years, but still remains an important sector of agriculture. Floriculture businesses seek new, innovative trends and niches to help increase product sales. Specialty cut flower (SCF) production has increased in the past 20 years in the US, Australia, Africa, and Europe. SCF production and sales could increase further if these new products were supported by dynamic marketing campaigns that focus on their strengths compared to the traditional cut flowers (TCF) such as roses, carnations, gerberas, and chrysanthemums. The major strength of SCF is the eco-friendly profile, which is associated to low CO2 footprints and environmental outputs. This contrasts TCF cultivation, which is associated to high energy inputs, especially at the traditional production centres (e.g., The Netherlands). It is suggested that environmental legislations, production costs, and customer demand for eco-friendly products will positively affect future SCF cultivation and sale.
Effects of Day and Night Shifts on Stress, Anxiety, Quality of Life, and Oxidative Stress Parameters in Nurses

AIM This study aimed to determine the effects of day and night shift work on stress, anxiety, quality of life, and oxidative stress parameters in nurses. METHOD This was a descriptive, cross-sectional study. The study was conducted between October 2014 and January 2015. The study sample included 60 nurses working in a university hospital who met the inclusion criteria (n=60). A questionnaire was used to evaluate stress, quality of life, satisfaction with life, and anxiety sensitivity, and blood samples were obtained. In blood samples, total oxidant status and total antioxidant status, cortisol, epinephrine, and norepinephrine levels were measured. Descriptive statistics, t test, analysis of variance, and correlation analysis were performed for statistical analyses. RESULTS These results revealed that the nurses employed in surgical clinics had a lower quality of life and higher anxiety sensitivity as compared to those employed in other clinics. In addition, significant relationships were detected between total oxidant status, total antioxidant status and cortisol levels and stress, quality of life, satisfaction with life, and anxiety sensitivity. TOS and TAS, epinephrine, norepinephrine, and cortisol levels; stress, quality of life, satisfaction with life, and anxiety sensitivity were similar between the nurses working daytime and in shifts. CONCLUSION As the stress level and anxiety sensitivity of the nurses increased, the total oxidant levels and cortisol levels increasedm and in contrast, the quality of life and overall satisfaction with life decreased.
Does Anger Toward God Moderate the Relationship Between Religiousness and Well-Being?

Most of the current research has found that highly religious people have better well-being compared to people with low religiousness. However, the former group is not immune from occasionally feeling anger toward God, which has an adverse effect on well-being. The purpose of this research is to study whether anger toward God moderates the effect of religiousness on the well-being of Christian college students. The data were derived from 228 respondents (55 male) from a religious university using the Four Basic Dimensions of Religiousness (4-BDRS), the Attitude toward God Scale (ATGS-9), and the Satisfaction with Life Scale (SWLS). The results of the moderation analysis using Process version 3 demonstrated that Anger toward God significantly moderated the effect of religiousness toward well-being (b = .01, 95% Cl [.001, .023], t = 2.14, p < .05). The higher the level of anger toward God, the lesser the effect of religiousness on well-being. Therefore, it is essential for students to resolve their divine struggles. The implications and applications of this study are discussed.
A Systematic View of Leakage Risks in Deep Neural Network Systems

As deep neural networks (DNNs) continue their reach into a wide range of application domains, the neural network architecture of DNN models becomes an increasingly sensitive subject, due to either intellectual property protection or risks of adversarial attacks. In observing the large gap between the architectural surfaces exploration and the model integrity study, this paper first presents the formulated schema of the model leakage risks. Then, we propose DeepSniffer, a learning-based model extraction framework, to obtain the complete model architecture information without any prior knowledge of the victim model. It is robust to architectural and system noises introduced by the complex memory hierarchy and diverse run-time system optimizations. Taking GPU platforms as a showcase, DeepSniffer performs model extraction by learning both the architecture-level execution features of kernels and the inter-layer temporal association information introduced by the common practice of DNN design. We demonstrate that DeepSniffer works experimentally in the context of an off-the-shelf Nvidia GPU platform running a variety of DNN models. The extracted models are directly helpful to the attempting of crafting adversarial inputs. The DeepSniffer project has been released in https://github.com/xinghu7788/DeepSniffer.
A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

The increasing availability of data, gathered by sensors and intelligent machines, is changing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear).
Biogeographical patterns of meso- and bathypelagic fish along a Northeastern Atlantic transect

The influence of oceanographic variables on assemblages of meso- and bathypelagic fish was investigated along a Northeastern Atlantic Ocean transect (Cape Verde to the Bay of Biscay) during May 2019. Fish were collected using a mrozooplankton trawl during daylight hours at ten stations. Along the transect, 17 hydrographic stations were also performed with a CTD (Conductivity, Temperature, and Depth). A total of 130 fish taxa were identified. The dominant family was Gonostomatidae, with four species (Cyclothone braueri, Cyclothone microdon, Cyclothone pseudopallida, and Cyclothone pallida) being responsible of more than 78% of the total density. The most frequent species that appeared to be ubiquitous were C. braueri and C. pseudopallida, while Myctophidae was the most diverse family. Multivariate analyses revealed two clusters related with the latitudinal gradient. The fish community in the southern stations (25–37°N) was more diverse than in the northern stations (42–48°N). Temperature from 300 to 700 m depth explained 65% of variation in terms of density and 58% in terms of biomass, both statistically significant. The investigated variation in the deep-pelagic ecosystems on a large spatial scale gives essential information to ecosystem management approhes and marine spatial planning.
Communication as Cure

Fleeing oncoming enemy forces in Southern France in the summer of 1940, Leonora Carrington passed into Spain and suffered a mental breakdown. Written in the summer of 1943 in an abandoned embassy building in Mexico City, the essay Down Below recalls this treatment in unnerving detail, anticipating later (semi-)autobiographical works such as Janet Frame’s Faces in the Water (1961) or Sylvia Plath’s The Bell Jar (1963). This essay seeks to build upon this scholarship by examining the composition of the text as a ‘treatment’ in itself, thus centring the clinical reality of Carrington’s experience, but also re-asserting her authorial agency. The intensely complex gestation of the text is, the author argues, intrinsic to its central themes and concerns, as well as constituting an essential element of the journey from illness to health. The author draws a comparison between the dehumanising effects of the sanitorium and the convulsive drug Cardiazol, whereby the isolation of madness is unbearably heightened; and the collaborative restoration of identity that occurs through narration.
Applying Fractional Strategies on Number Line among Primary School Students

The teaching and learning of fractions have been getting public attention since it is one of the most problematic topics among primary and secondary students. This study aims to investigate to what extent the primary school pupils apply fractional strategies to solve problems on number line. This study was conducted using a qualitative methodology. The data were collected from task-based clinical interviews. The subjects of this study were selected among the Year Five students in Malaysia. A total of eight students participated in this study. This study revealed three types of fractional strategies. They are (1) finding an interval in fractions on number line, (2) applying concepts of decimal and interchange with fraction, and (3) comparing values of fractions. The findings showed achieving fractions arithmetic proficiency is crucial in developing the knowledge of fraction magnitude representations on the number line.
Analysis of Amino Acids and Related Compounds by Capillary Electrophoresis

For more than two decades, capillary electrophoresis (CE) was found to be a powerful tool in the separation of low molecular weight charged compounds, such as amino acids (AA). This article provides an overview on the current status of CE for analyzing AAs and their metabolites in biological and nonbiological samples. A survey of works published during the last 20 years is presented, underlining the main advantages brought by CE for the study of AAs and their amine metabolites, such as catecholamines, indolamines, histamine and other monoamines, as well as aminothiols. Sample preparation (including derivatization and concentration) and separation protocols were covered. Most AAs lack a strong UV chromophore, so the different detectors used in CE for their detection were described, including classical UV-Vis spectrophotometry, laser-induced fluorescence, mass spectrometry, electrochemistry, conductometry and chemiluminescence. The applications in life science and biotechnology were summarized. The advantages and limitations of CE analysis for AAs and future developments in this field were tentatively discussed.
Price Prediction of Traditional Chinese Medicine Based on ARIMA and Improved Elman Neural Network

The price’s change of traditional Chinese medicine contains linear, non-linear and other miscellaneous factors. It is difficult for people to use a separate model such as neural network model to judge its price trend. Based on the background, a combined forecasting model is proposed in this paper, it consists of Autoregressive Integrated Moving Average model and Elman neural network which is improved by correlation analysis. The combined forecasting model can use its two algorithm model to deal with the linear and nonlinear factors. Meanwhile, the innovation of this paper is using correlation analysis to import extra additional parameters for the neural network, which can increase its accuracy. A large number of traditional Chinese medicine’s price data was collected to be training samples, the final results show that the combined forecasting model has advantages over stability and accuracy than ARIMA or Elman neural network.