Miracle-dz/UniFAR-FTUs huggingface dataset:
INDIVIDUAL DIFFERENCES IN TASK SWITCHING 1 Exploring Individual Differences in Task Switching

Previous research has shown that there are significant task-switching costs even when participants have time to prepare for task switching after cueing. We investigated individual differences in task switching by monitoring errors and response times of individual participants. In Experiment 1A, 58 participants were encouraged to finish the session early by completing 200 consecutive trials without making an error. In case of a mistake, they had to repeat their effort until the experimental session expired. Using this demanding procedure,16 participants managed to complete early. Among these 16 we identified 9 best performers who showed no significant switch costs. We conducted follow-up Experiment 1B on these best performers by systematically varying cue-stimulus intervals and inter-trial intervals. The results confirmed that these participants had no significant RT and ER switch costs when they had time to prepare the task between cue and target onset. However, significant switch costs emerged when cue and target stimulus were presented simultaneously. In Experiment 1C, using three classical task-switching paradigms, we compared the best performers with 9 controls who had made frequent errors in Experiment 1A. Although the best performers responded faster and made fewer errors, they only showed reduced switch costs in a pre-cued paradigm that had been extensively practiced. In two other paradigms with simultaneous presentation of cue and target stimulus, best performers had switch costs and showed considerable individual differences similar to the controls. We conclude that there are considerable individual differences in task switching and that smaller individual switch costs are mainly related to efficient task preparation. We speculate that efficient task preparation may be linked to better executive control and general intelligence.
Effect of AHL-lactonase and nisin on microbiological, chemical and sensory quality of vacuum packaged sturgeon storage at 4ºC

ABSTRACT Quorum sensing (QS) has been reported to be involved in bacterial food spoilage. Acetylated homoserine lactones (AHLs) are QS signals of Gram-negative bacteria. AHL lactonases AiiAAI96 is a QS inhibitor, and thus can theoretically be applied to control the Gram-negative bacteria caused sturgeon spoilage by inhibiting their AHL-based QS system. As Gram-negative bacteria and Gram-positive bacteria were both found in spoilage sturgeon, the combination of AiiAAI96 and nisin, which is an effective bactericide to control the Gram-positive bacteria, was used to control the bacteria spoilage of sturgeon. The results showed that AiiAAI96 at 6.6 × 10−2, 6.6 × 10−1 and 6.6 U/mL could suppress the growth of microflora and delayed the increase of TVB-N, sensory score and biogenic amine levels of sturgeon. In addition, 6.6 U/mL AiiAAI96 coupled with 2000 AU/mL nisin exhibited the strongest inhibition effect on spoilage and extended their shelf life by about 5 days. In conclusion, AiiAAI96 combined with nisin could be used as a suitable biological preservative for chilled sturgeon fillets.
Generating 3D texture models of vessel pipes using 2D texture transferred by object recognition☆

Research and development of smart vessels has progressed significantly in recent years, and ships have become high-value technology-intensive resources. These ships entail high production costs and long-life cycles. Thus, modernized technical design, professional training, and aggressive maintenance are important factors in the efficient management of ships. With the continuing digital revolution, the industrial shipbuilding applicability of augmented reality (AR) and virtual reality (VR) technologies as well as related 3D system modeling and processes has increased. However, resolving the differences between AR/VR and real-world models remains burdensome. This problem is particularly evident when mapping various texture characteristics to virtual objects. To mitigate the burden and improve the performance of such technologies, it is necessary to directly define various texture characteristics or to express them using expensive equipment. The use of deep-learning-based CycleGAN, however, has gained attention as a method of learning and automatically mapping real-object textures. Thus, we seek to use CycleGAN to improve the immersive capacities of AR/VR models and to reduce production costs for shipbuilding. However, when applying CycleGAN’s textures to pipe structures, the performance is insufficient for direct application to industrial piping networks. Therefore, this study investigates an improved CycleGAN algorithm that can be specifically applied to the shipbuilding industry by combining a modified object-recognition algorithm with a double normalization method. Thus, we demonstrate that basic knowledge on the production of AR industrial pipe models can be applied to virtual models through machine learning to deliver low-cost and high-quality textures. Our results provide an on-ramp for future CycleGAN studies related to the shipbuilding industry.
An Evaluation Of Direct Identification Of Pathogens From Blood Cultures By Maldi-Tof Mass Spectrometry

Rapid identification of the infecting organism may aid in choosing appropriate antimicrobial therapy. We used MALDI-TOF mass spectrometry to identify bacteria directly from the positive blood culture samples (n=21). 85,71 percent of these results was identified using of MALDI-TOF mass spectrometry. Identification time of bacteria directly from the blood culture takes more than 1 hour for 27,8 percent results.
Population differentiation and phylogeography in Lycianthes moziniana (Solanaceae: Capsiceae), a perennial herb endemic to the Mexican Transition Zone

Lycianthes moziniana (Solanaceae: Capsiceae) is a perennial herb with edible fruits that is endemic to Mexico. Three varieties are recognized, all known in the Mexican Transition Zone. Lycianthes moziniana var. margaretiana grows in the Sierra Madre Oriental, whereas L. moziniana var. moziniana is common along the Trans-Mexican Volcanic Belt and in the Sierra Madre Occidental. Lycianthes moziniana var. oaxacana is found exclusively in the Sierra Madre del Sur. The Mexican Transition Zone is a complex geological, climatic and biogeographical area, the result of tectonic and volcanic activity that has promoted genetic divergence and speciation. We determined the genetic variation and structure of L. moziniana. Using phylogeographical approaches, we described the demographic history and evolutionary processes leading its divergence. The intergenic spacers rpl32-trnL and ycf1 were sequenced for 133 individuals pertaining to 15 populations. The genealogical relationships were analysed using haplotype networks. Finally, based on ecological niche models, we inferred the palaeodistribution of L. moziniana during the Pleistocene. The genetic differences and the haplogroups matched the three described varieties. Geological and climatic events of the Mexican Transition Zone facilitated these results. The Trans-Mexican Volcanic Belt isolated the populations of the Sierra Madre Oriental and the Sierra Madre del Sur, while allowing the migration to the Sierra Madre Occidental, during the middle Holocene.
Energy Optimization of Electric Vehicles by Distributing Driving Power Considering System State Changes

In a battery-electric vehicle, a representative electric vehicle, there is a growing demand for performance and one-charge mileage improvement. As an alternative to such improvements, the capacity of the battery has been increased; however, due to the corresponding increase in the weight of the battery and the limited space in the vehicle, increasing the capacity of the battery also has limitations. Therefore, researches are being actively conducted to improve system operation efficiency to overcome such limitations. This paper proposes a distributing method of the driving forces to a battery-powered electric shuttle bus for last-mile mobility equipped with the decentralized driving system while taking into account voltage changes of the input terminals due to changes in the battery charge. The system operation efficiency changes were compared and evaluated by performing energy consumption analysis using ‘Manhattan Bus Driving Cycle’ at low voltage condition (SOC 20%). Various analyzes were performed and compared, such as the uniform distribution method of driving forces of the front and rear wheels (Uniform), the optimization method without considering the input terminal voltage change (Vnorm = 90 V), and the optimization method considering the input terminal voltage change (Vdclink). As a result, it shows that the proposed algorithm can improve 6.0% compared to the conventional uniform driving force distribution method (Uniform). Moreover, it shows that the real-time optimization method without considering the input voltage change (Vnorm = 90 V) can improve 5.3% compared to the uniform distribution method. The proposed method can obtain an additional 0.7% increase in total cost compared to the existing optimization method, which shows that the vehicle system has cost-effectiveness by reducing the battery capacity required to achieve the same mileage.
Eliciting Human Judgment for Prediction Algorithms

Even when human point forecasts are less accurate than data-based algorithm predictions, they can still help boost performance by being used as algorithm inputs. Assuming one uses human judgment indirectly in this manner, we propose changing the elicitation question from the traditional direct forecast (DF) to what we call the private information adjustment (PIA): how much the human thinks the algorithm should adjust its forecast to account for information the human has that is unused by the algorithm. Using stylized models with and without random error, we theoretically prove that human random error makes eliciting the PIA lead to more accurate predictions than eliciting the DF. However, this DF-PIA gap does not exist for perfectly consistent forecasters. The DF-PIA gap is increasing in the random error that people make while incorporating public information (data that the algorithm uses) but is decreasing in the random error that people make while incorporating private information (data that only the human can use). In controlled experiments with students and Amazon Mechanical Turk workers, we find support for these hypotheses. This paper was accepted by Charles Corbett, operations management.
Enhanced regeneration of bone defects using sintered porous Ti6Al4V scaffolds incorporated with mesenchymal stem cells and platelet-rich plasma

A new highly controlled powder sintering technique was used for the fabrication of a porous Ti6Al4V scaffold. The platelet-rich plasma (PRP) was prepared using whole blood. The PRP was used as a cell carrier to inject bone marrow mesenchymal stem cells (MSC) into the pores of the Ti6Al4V scaffold in the presence of calcium chloride and thrombin, and then the composite construct of porous Ti6Al4V loaded with PRP gel and MSC was obtained. The bare Ti6Al4V scaffold and the Ti6Al4V scaffold loaded with MSC were used as controls. The characteristics and mechanical properties of the scaffold, and the biological properties of the constructs were evaluated by a series of in vitro and in vivo experiments. The results show that the sintered porous Ti6Al4V has good biocompatibility, and high porosity and large pore size, which can provide sufficient space and sufficient mechanical support for the growth of cells and bones without an obvious stress shielding effect. However, Ti6Al4V/MSC/PRP showed a significantly higher cell proliferation rate, faster bone growth speed, more bone ingrowth, and higher interfacial strength. Therefore, the porous Ti6Al4V scaffolds incorporated with MSC and PRP may be more effective at enhancing bone regeneration, and is expected to be used for bone defect repair.
MAPKs and HSPs’ Activation of a Natural Daphnia magna Population in a Man-Perturbed Lake: Implications of Ecological Significance

Natural and anthropogenic pressures in inland waters induce molecular response mechanisms in organisms as a defense against such multiple stressors. We studied, for the first time, the expression of the stress proteins, heat shock proteins (HSP) and mitogen-activated proteins kinase (MAPK), in a Daphnia magna natural population as a response to environmental changes in a heavily modified water body (Lake Koronia, Northern Greece). In parallel, the water physicochemical parameters, nutrients’ concentration and phytoplankton abundance were measured. Our results showed fluctuations of the proteins’ levels (HSP70, HSP90, phospho-p38 MAPK, phospho-p44/42 MAPK) providing evidence of their expression in situ. HSP70 showed an increasing tendency while for HSP90, no tendency was recorded. The MAPKs’ members followed a reverse pattern compared to each other. The differential expression of HSP and MAPK members indicates that D. magna in Lake Koronia experienced stressors such as increasing temperature, salinity and increased nutrient concentrations, high pH values and variations in phytoplankton abundance that triggered their activation. These in situ findings suggest that HSP and MAPK expression patterns have the potential to be used as biomarkers of stress factors in D. magna, for effective biomonitoring and setting ecological restoration targets.
The Influence of Brand Logo Frame on Brand Character and Consumer’s Brand Evaluation

This paper proposes that the different border shapes of brand logos (rectangle and rounded rectangle) cause the differences in the evaluation of brand personality by consumers, which further affects the evaluation of brand by consumers. At the same time, this process is regulated by product category. If product category and brand personality are in the same direction, the border shape of brand logo will generate positive brand evaluation. In this paper, two experiments were designed to verify the hypothesis respectively and to guess the results of data analysis. However, due to the time problem, complete data has not been collected for analysis. This paper provides marketing inspiration for brand identification and deepens the related research on brand identification and brand evaluation.
Colibacillosis in poultry: A disease overview and the new perspectives for its control and prevention

Escherichia coli (E.coli) is a common bacterium that can be naturally found in the intestinal tract of birds and as a result in their environment. However, it can cause clinical disease called colibacillosis which is regarded as one of the most common and important diseases in poultry. Strains of E.coli that have the ability to cause clinical disease are described as Avian Pathogenic Escherichia Coli (APEC). Colibacillosis can affect birds of all ages and different types of poultry production including broiler and commercial layers and breeders. The ability of E.coli to cause colibacillosis is not always the same; that is why its role as primary or secondary pathogen triggered by various predisposing factors is contradictory and differs from case to case. Antibiotics have been used as the main tool against colibacillosis for many decades. However, the emergence of increased antibiotic resistance has posed the need of alternative treatment to colibacillosis as well as emphasizing on preventive measures to avoid disease. Τhe scope of this article is to assess recent scientific literature data on avian colibacillosis emphasizing on disease characteristics and recent data on prevention and control of the disease.
Spillovers and portfolio risk management of gold and stock markets: evidence from emerging Latin American markets

ABSTRACT This study examines the return and volatility transmission between gold and emerging Latin American stock markets during the full sample period, the global financial crisis, and the Chinese Stock market crash. Employing the VAR-AGARCH model to estimate spillovers, the results reveal the substantial return and volatility spillovers between the gold and emerging Latin American stock markets, but these spillovers vary across different gold-stock pairs and two crises. Lastly, we also provide the optimal weights and hedge ratios for all gold-stock pairs during all sample periods. Overall, these findings provide useful insights for portfolio diversification, asset pricing, and risk management.
Anapole Tolerance to Dissipation Losses in Thermally Tunable Water-Based Metasurfaces

We theoretically and experimentally demonstrate a thermally tunable anapole metasurface. By thermally changing the dielectric function, our studies unveil the tolerance of the nonradiating anapole source to the presence of dissipation losses in systems of all-dielectric meta-atoms. The so-called anapole state is an ac current distribution of electric and toroidal dipoles in such a relation that their exclusive destructive interference leads to the absence of far-field radiation. It has been shown to exist in variable dielectric configurations that involve assemblies of infinite dielectric rods, yet a systematic study of its tolerance to losses is still open. Here we examine two designs focusing on their tolerance to dissipation losses: one combining the toroidal mode of a peripheral four-cylinder system with the mixed toroidal mode of a single central cylinder of adjusted size, and another consisting of five rods in a regular pentagon arrangement. The first design exhibits a particularly enhanced toroidal dipole moment and high anapole precision. The second design exhibits anapole states with enhanced loss tolerance and, thus, it is the target of our experimental investigation. For the experiment, we use water-based rod metasurfaces designed for anapole operation in the microwave regime and we employ a characterization in a standard rectangular waveguide. The dielectric properties of water exhibit significant temperature dependence that allows for controlled permittivity and dissipation, leading to the dynamical modification of Mie resonances in meta-atoms. Thus, we experimentally prove the emergence of an anapole state and its tolerance to the losses in a well-controlled system; furthermore, we demonstrate and expose the potential of aqueous schemes for tunable electromagnetic applications.
A Review of Deep-Learning-Based Medical Image Segmentation Methods

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.
Automated Detection, Classification, and Tracking of Internal Wave Signatures Using X-Band Radar in the Inner Shelf

A method based on machine learning and image processing techniques has been developed to track the surface expression of internal waves in near–real time. X-band radar scans are first preprocessed and averaged to suppress surface wave clutter and enhance the signal-to-noise ratio of persistent backscatter features driven by gradients in surface currents. A machine learning algorithm utilizing a support vector machine (SVM) model is then used to classify whether or not the image contains an internal solitary wave (ISW) or internal tide bore (bore). The use of machine learning is found to allow rapid assessment of the large dataset, and provides insight on characterizing optimal environmental conditions to allow for radar illumination and detection of ISWs and bores. Radon transforms and local maxima detections are used to locate these features within images that are determined to contain an ISW or bore. The resulting time series of locations is used to create a map of propagation speed and direction that captures the spatiotemporal variability of the ISW or bore in the coastal environment. This technique is applied to 2 months of data collected near Point Sal, California, and captures ISW and bore propagation speed and direction information that currently cannot be measured with instruments such as moorings and synthetic aperture radar (SAR).
Identification of Liquid-Liquid Interface using Electrical Resistance Tomography

Finding interfaces among liquids in a process tank is crucial in some applications. Various industrial sectors have deployed expensive, invasive, and hazardous techniques to achieve this. In this paper, an Electrical Resistance Tomography (ERT) technique comprises a phantom tank surrounded by 16 copper electrodes. A 50 kHz, 5 mA AC signal was used as an input, and the output response in terms of voltages was fed to EIDORS reconstruction algorithm. The 2D images show the domain containing two poorly conducting liquids, i.e., water and oil, although not accurate, but showing ERT as a possible promising technique for analyzing liquid-liquid interfaces.
A systems-based approach to parameterise seismic hazard in regions with little historical or instrumental seismicity: active fault and seismogenic source databases for southern Malawi

Seismic hazard is commonly characterised using instrumental seismic records. However, these records are short relative to earthquake repeat times, and extrapolating to estimate seismic hazard can misrepresent the probable location, magnitude, and frequency of future large earthquakes. Although paleoseismology can address this challenge, this approach requires certain geomorphic setting, is resource intensive, and can carry large inherent uncertainties. Here, we outline how fault slip rates and recurrence intervals can be estimated by combining fault geometry, earthquake-scaling relationships, geodetically derived regional strain rates, and geological constraints of regional strain distribution. We apply this approach to southern Malawi, near the southern end of the East African Rift, and where, although no on-fault slip rate measurements exist, there are constraints on strain partitioning between border and intra-basin faults. This has led to the development of the South Malawi Active Fault Database (SMAFD), a geographical database of 23 active fault traces, and the South Malawi Seismogenic Source Database (SMSSD), in which we apply our systems-based approach to estimate earthquake magnitudes and recurrence intervals for the faults compiled in the SMAFD. We estimate earthquake magnitudes of MW 5.4–7.2 for individual fault sections in the SMSSD and MW 5.6–7.8 for whole-fault ruptures. However, low fault slip rates (intermediate estimates ∼ 0.05–0.8 mm/yr) imply long recurrence intervals between events: 102–105 years for border faults and 103–106 years for intra-basin faults. Sensitivity analysis indicates that the large range of these estimates can best be reduced with improved geodetic constraints in southern Malawi. The SMAFD and SMSSD provide a framework for using geological and geodetic information to characterise seismic hazard in regions with few on-fault slip rate measurements, and they could be adapted for use elsewhere in the East African Rift and globally.