Miracle-dz/UniFAR-FTUs huggingface dataset:
Quorum quenching by Bacillus cereus U92: a double-edged sword in biological control of plant diseases

Abstract In the present survey, quorum quenching activity was examined from a biocontrol point of view. Acyl-homoserine lactone (AHL) degrading bacteria were isolated from tomato rhizosphere using two standard bioreporter strains and different synthetic AHLs and then identified according to 16S rDNA sequences. Five isolates capable of inactivating both short and long 3oxo-substituted AHLs showed high similarity with the genera Bacillus, Microbacterium and Arthrobacter, and thereby Bacillus cereus U92 was determined as the most efficient quorum quencher strain. In the quantitative experiments, this strain remarkably inactivated all synthetic AHLs up to 80%. In the laboratory co-cultures, B. cereus U92 efficiently quenched QS-regulated phenotypes in Agrobacterium tumefaciens, Pseudomonas aeruginosa, Pseudomonas chlororaphis and Chromobacterium violaceum. The strain successfully reduced the frequency of Ti-plasmid conjugal transfer in A. tumefaciens by about 99% in the binary cultures. Meanwhile, in a more natural environment, this strain acted as a biocontrol agent, efficient in alleviating QS-regulated crown gall incidence on tomato roots (up to 90%) as well as attenuating Pectobacterium soft rot on potato tubers (up to 60%). On the other hand, reducing phenazine production in P. chlororaphis operated as a suppressor of its QS-regulated biocontrol activity and also inhibited pyocyanin production in P. aeruginosa, a plant growth-promoting bacterium, by 75%. In general, B. cereus U92 seems very promising in the biological control of pathogenic bacteria; however, its broad AHL-degrading activity has a detrimental role on beneficial microbes which should not be neglected.
Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images

We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained end-to-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumonia-type classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other methods, it achieves a state-of-the-art result. Moreover, the proposed method can be easily extended to other similar clinical applications such as computer-aided detection and diagnosis of pulmonary nodules in CT images, glaucoma lesions in Retina fundus images, etc.
Applying AR-based infographics to enhance learning of the heart and cardiac cycle in biology class

ABSTRACT This study is based on assessing the potential of augmented reality (AR) and infographics in science learning, emphasizing the synergistic effect on improving learning. The study investigates the impact of the combination of AR and infographic design on learning biology. Seventy-five 10th grade male students participated in this study, which employed a pre-test and post-test experimental design. After administering the pre-test, participants were randomly assigned to three groups; the first group used static infographics, the second group used infographics combined with AR, and the third group learned in an AR-based environment. While the results showed no significant difference between the mean of learning scores in the experimental Group 1 (static infographics) and the experimental Group 3 (AR), there was a significant difference between the experimental Group 2 (infographic method with AR combination) and other groups. According to the results, the infographic with AR had significant effects on learning. Some of the reasons for the superiority of this method compared to other methods used in other groups are as follows: visualizing the data, infographic illustration with 3D AR representation, the use of organizer images and, at the same time, reducing the complexity of AR with the help of infographic visualisation.
Detection of Dissolved Acetylene in Power Transformer Oil Based on Photonic Crystal Fiber

Dissolved gas analysis (DGA) is an important online monitoring technique for power transformers. When there are some faults in oil-immersed transformers, insulation materials will decompose into various small-molecule gases and continually dissolve in transformer oil. In order to figure out the composition and the concentration of dissolved gases, an optical gas sensing system based on photonic crystal fiber was developed. Hollow core photonic crystal fiber (HC-PCF) used in the system played the role of an absorption gas cell. The gas diffusion time was studied theoretically and experimentally, and the result showed that by using a 1.0 m length long HC-PCF, the theoretical diffusion time for acetylene was about 3.47 hours. Various acetylene gas samples got detected. Through Allan variance analysis, the sensitivity of the proposed sensing system could reach the best with the highest signal-to-noise ratio (SNR) when the average time was 29 s, and a minimum detection limit of 4.5 ppm could be achieved. The proposed sensing system showed tremendous advantages, such as low detection limit, no cross interference, high sensitivity. This gas sensing technique has a good application prospect in DGA for power transformers.
Special Need Students (Disorder) Response Level on Entrepreneurship Classroom: A Case Study of Binus University, Indonesia

Using the in-depth interview, this study aims to find out how the disorder students (disorder SNS) performed in the entrepreneurship classrooms. The findings show that the students with anxiety disorder could have the better performance in the entrepreneurship classroom, compared to the ADHD and autistic disorder SNS. These findings were found based on three themes of this paper: the types of disorder SNS in the entrepreneurship classes; the disorder SNS engagement in the entrepreneurship classroom, and disorder SNS responds in the entrepreneurship classrooms.
Research on Multi-Transactional Concurrent Electricity Transaction Operation Simulation Technology

The spot market is an important part of the electricity market system. It plays a fundamental supporting role in the orderly operation of the open, competitive electricity market, and is also the key to coordinating market transaction and power system operation. In China, the differences of power grid structure, power supply structure, and economic level in various provinces are large. How to choose the local spot market model and market rule can’t be answered through manual experience. At the same time, the electricity market operation is a long-term dynamic evolution process. It is affected by various factors such as power grid structure, supply and demand situation, and external environment. The bidding behaviour of market members is constantly adjusted with the market participation time. It is difficult to predict the market operating effect only through theoretical analysis. Therefore, it is necessary to study the multi-transaction concurrent operation energy trading simulation software that is suitable for the above-mentioned problems in order to solve the above problems, and provide technical support for simulation verification and comparison analysis for the relevant models and rules design of future sales participation in the electricity market. Before the official operation, the experimental simulation was carried out for the continuous operation effect of the market rules.
Pathogen Reduction in Closed Aquaculture Systems by UV Radiation: Fact or Artifact?

Differential equations were used to set a theoretical upper limit for the efficacy of UV radiation in 3 hypothetical aquaculture systems: (a) a plug-flow system, (b) an idealized closed system with no influx of pathogens, and (c) a conventional closed system in which the influx of pathogens is continuous. The equations demonstrate that, in a conventional closed system, the mass of pathogens never reaches zero even if the UV sterilizer is 100 % effective. This suggests that agents such a s UV radiation, which do not form persistent residuals, may be incapable of preventing the spread of waterborne pathogens in systems that are recirculated. Use of UV radiation in aquaculture is most effective in sterilization of raw water supplies and discharges lnto receiving waters, both of which are singlepass applications.
Biochemical, Genetic, and Molecular Characterization of Wheat Glutenin and Its Component Subunits

Of all the cereal grains, wheat is unique because wheat flour alone has the ability to form a dough that exhibits the rheological properties required for the production of leavened bread and for the wider diversity of foods that have been developed to take advantage of these attributes. The unique properties of the wheat grain reside primarily in the gluten-forming storage proteins of its endosperm. It is these dough-forming properties that are responsible for wheat being the most important source of protein in the human diet. The bread and durum wheats are polyploid species containing three (AABBDD) and two (AABB) related genomes, respectively. The genetic constitution of wheat is important because all quality traits result from the expression of genes and their interaction with the environment. The full spectrum of wheat-endosperm proteins has been exhibited in proteome studies involving the two-dimensional fractionation of the polypeptides (after disulfide-bond rupture), followed by dissection of the individual components for identification. This display (Fig. 1) shows that there are at least 1,300 polypeptides, over 300 of which have been identified by N-terminal amino
Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns

Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction.
Energy versus risk: costs of reproduction in free-ranging pythons in tropical Australia

Data from a 12-year field study have allowed us to quantify 'costs of reproduction' in a natural popu- lation of water pythons (Liasis fuscus) in tropical Australia. Both sexes of pythons cease feeding during the repro- ductive season. For males, this involves fasting for a 6 week period. Adult males lose weight rapidly over this period (approximately 17% of their body mass) but regain condition in the following months, and do not experience reduced survival. In contrast, reproductive adult females cease feeding for 3 months, lose an average of 44% of their body mass over this period, and experience increased mortality. A causal link between reproductive output and reduced female survival is supported by (i) a decrease in survival rates at female maturation; (ii) a correlation between survival rates and frequency of reproduction, in a comparison among different size classes of adult pythons; and (iii) a lowered survival rate for females that allocated more energy to reproduction. Hence, both sexes experi- ence substantial energy costs of reproduction, but a relatively higher energy cost translates into a survival cost only in females. Such non-linearities in the relationship between energy costs and survival costs may be widespread, and challenge the value of simple energy-based measures of 'reproductive effort'.
Collection line optimisation in wind farms using improved ant colony optimisation

Collection line optimisation plays an important role in reducing the wind farm construction cost and improving the reliability of collector system. First, the optimisation model of collection lines in wind farms is given. Based on the four-vertex-three-line inequality and Prim algorithm, an improved ant colony optimisation algorithm is introduced to minimise the total length of collection lines to reduce their cost. In addition, the K-means clustering algorithm is adopted to partition the total wind turbines into several groups for the improved ant colony optimisation algorithm to search the shortest collection lines. The improved ant colony optimisation algorithm is tested with a wind farm example and compared with the classic ant colony optimisation algorithm. Under the same preconditions, the experimental results show that compared to the classic ant colony optimisation algorithm, the improved ant colony optimisation algorithm finds shorter collection lines in radial configuration for the wind farm. In addition, the improved ant colony optimisation algorithm requires less computation cycles to find better collection lines.
Prevalence, Pattern and Outcome of Pediatric Lassa Fever Disease (LFD) in a Tertiary Hospital, Southeast Nigeria

The prevalence and case fatality rates of Pediatric Lassa fever disease (LFD) are not well documented. This study was aimed at determining the prevalence, pattern and outcome of Pediatric LFD. It was a prospective observational study. A total of 183 subjects that met the criteria for LFD suspects were recruited consecutively and subjected to Lassa virus PCR test. Structured questionnaire was used to collect information. Of the 183 children recruited, 24 tested positive to Lassa virus PCR, giving a positivity rate of 13.1%. Mean duration of illness at presentation was 8.54 ± 3.83 days. Fever, abdominal pain and vomiting were the three highest presenting complaints. Seven out of 24 children died giving a case fatality rate (CFR) of 29.2%. Subjects with bleeding, poor urine output, convulsions and unconsciousness were more likely to die of LFD. Positivity and CFR of LFD are high. Improved case finding and prompt treatment is
Study of harvesting losses for sustainable agricultural production in Southeast Sulawesi (a case study in Konawe district)

Post-harvest technologies have been produced rapidly, but not at all have been adopted by farmers. Rice harvesting techniques with a group of farmers equipped with rice threshing machines can reduce crop losses and threshing to 5.9%. Moreover, the use of a striper type of rice harvester can reduce crop losses to between 2-3%. In this era of mechanization, there is increasing use of combine harvester at the farm level. This study aims to know the performance of harvest management with combine harvester and post-harvest rice at the farm level and to find out the amount of crop loss with combine harvester and post-harvest rice. The study was conducted in the second crop season in 2017 in Konawe District which is the center of wetland rice in Southeast Sulawesi Province. The results of the study indicated that in general the farmer groups managing the Combine Harvester were not equipped with an organizational structure (73%). The average combine harvester manager is 5-6 people consisting of operators, grain harvesters, grain collectors, and tailor sacks. Unit of agricultural equipment and machinery management (UPJA) works with launchers and harvesting equipment carriers. The ability to harvest in a day is 2 ha, with a harvest time of 3 hours per ha, with fuel consumption of 34.6 liters/ha. While the ability of harvested area in annuals is 35 ha on average. The average weight of yield loss by various planting methods is not too different which is about 0.129 - 0.136 kg. However, the highest percentage of yield loss is in the method of planting tabela (direct scattering) which is equal to 2.084%, this is influenced by the amount of yield obtained from the planting method. While the yield loss due to transportation between distances of less than 500 meters and above 500 meters does not differ much, namely an average of 0.18 kg and 0.21 kg, with an average of 0.2 percent of the average weight before transporting.
Qualidade óssea de frangos alimentados com dietas com fitase e níveis reduzidos de fósforo disponível - DOI: 10.4025/actascianimsci.v30i3.496

Avaliou-se teor de minerais, densidade, peso, morfometria e resistencia a quebra de tibias de frangos alimentados com dietas contendo fitase e niveis reduzidos de fosforo disponivel (Pd). O delineamento foi inteiramente casualizado em esquema fatorial 2 x 3, com dois niveis de fitase (0 e 25 UFT kg -1 ) e tres niveis de Pd (100, 85 e 70% das exigencias), totalizando seis tratamentos e cinco repeticoes. Nao houve efeito dos niveis de Pd e/ou fitase sobre os teores de fosforo e de materia mineral, peso, comprimento e diâmetro. A interacao Pd x fitase foi significativa para teor de Ca, que foi menor nas tibias das aves que consumiram dietas com 70% de Pd sem fitase. A densidade da epifise proximal diminuiu pela reducao de Pd e aumentou com a inclusao da fitase na dieta. Houve efeito da interacao Pd x fitase para densidades da diafise, epifise distal e media. A resistencia ossea diminuiu em aves alimentadas com dietas com 70% das exigencias de Pd. Podem-se adotar dietas com 85% das exigencias de Pd, suplementadas com 25 UFT kg -1 de fitase, sem efeitos negativos sobre a qualidade ossea.
Uroleucon sonchi (Linnaeus) (Hemiptera: Aphididae) in Australia

Uroleucon sonchi, an aphid species of Palaearctic origin, is a recent adventive to Australia. It is now widespread in anthropic areas of all states and the Australian Capital Territory, principally on sowthistles (Sonchus spp.) but also on lettuce. It is occasionally parasitised by Aphidius sonchi Marshall (Hymenoptera: Braconidae). Its possible economic importance in Australia is discussed. The species is also recorded for the first time from Norfolk and Philip Islands.
Fraud, Whistleblowing System, and Behavioral Aspect in Public Sector

Whistleblowing is a form of anti-fraud strategy that can be applied in various organizations, including in the public sector. The facts show that public sector fraud in Indonesia is still going on, although it has implemented the whistleblowing system in the government sector. Fraud triangle indicates that fraud caused by pressure, opportunity and rationalization. Of these three factors, pressure and rationalization are influenced by individual factors. This indicates that the individual factor becomes one of the causes of fraud. Individual factors can be traced to behavioral aspects. This research presents a variety of whistleblowing mechanisms in the context of behavioral aspects.
Mainshock Anticipated by Intra-Sequence Ground Deformations: Insights from Multiscale Field and SAR Interferometric Measurements

The 2016 Central Italy seismic sequence was characterized by two main events: 24 August, Mw 6, and 30 October, Mw 6.5. We carried out high-resolution field sampling and DInSAR analysis of the coseismic and intra-sequence ground deformations along the Mt Vettore-Mt Bove causative fault (VBF). We found that during the intra-sequence period (24 August–30 October), the ground experienced some deformations whose final patterns seemed to be retraced and amplified by the following mainshock. We interpreted that (i) immediately after the 24 August earthquake, the deformation observed in the southern VBF expanded northwards and westwards over a Length of Deforming Ground (LDG) ranging between 28.7 and 36.3 km, and (ii) it extended to the whole portion of the hanging wall that was later affected by mainshock coseismic deformation. Assuming the LDG to be an indicator for an expected (=coseismic) surface rupture length and using known scaling functions, we obtained 6.4 ≤ Mw ≤ 6.7 for a possible incoming earthquake, which is consistent with the mainshock magnitude. We suggest that the evolution of the ground deformations after a significant seismic event might provide insights on the occurrence of new earthquakes with magnitudes comparable to or larger than the former.
Multi-condition Wear Evaluation of Tool Based on T-SNE and XGBoost

： On-line inspection of tool wear is a necessary function for future automated production. A good evaluation model can effectively improve machining quality and reduce economic loss. Based on the existing research, an optimized tool wear evaluation method is presented. Current signal and vibration signal of spindle in cutting process are used synthetically, which can effectively improve the deficiency of single signal analysis. Time domain, frequency domain and wavelet packet features are extracted from the collected signals, and the signal feature information is extracted as comprehensively as possible. T-SNE is used to reduce the dimension of features, and K-means is used to cluster various working conditions according to the similarity of features, which further improves the accuracy and generalization ability of model prediction. Finally, the integrated learning algorithm XGBoost is used as the estimator, and the model is evaluated by regression and classification. The results show that the problem of sample imbalance has less impact on the XGBoost algorithm. Compared with the traditional integrated learning algorithms such as random forest, XGBoost improves the prediction accuracy and reduces the prediction time by an order of magnitude. It is a more accurate and efficient tool detection algorithm and can be widely used in industry.