Miracle-dz/UniFAR-FTUs huggingface dataset:
Ice Control during Cryopreservation of Heart Valves and Maintenance of Post-Warming Cell Viability

Heart valve cryopreservation was employed as a model for the development of complex tissue preservation methods based upon vitrification and nanowarming. Porcine heart valves were loaded with cryoprotectant formulations step wise and vitrified in 1–30 mL cryoprotectant formulations ± Fe nanoparticles ± 0.6 M disaccharides, cooled to −100 °C, and stored at −135 °C. Nanowarming was performed in a single ~100 s step by inductive heating within a magnetic field. Controls consisted of fresh and convection-warmed vitrified heart valves without nanoparticles. After washing, cell viability was assessed by metabolic assay. The nanowarmed leaflets were well preserved, with a viability similar to untreated fresh leaflets over several days post warming. The convection-warmed leaflet viability was not significantly different than that of the nanowarmed leaflets immediately after rewarming; however, a significantly higher nanowarmed leaflet viability (p < 0.05) was observed over time in vitro. In contrast, the associated artery and fibrous cardiac muscle were at best 75% viable, and viability decreased over time in vitro. Supplementation of lower concentration cryoprotectant formulations with disaccharides promoted viability. Thicker tissues benefited from longer-duration cryoprotectant loading steps. The best outcomes included a post-warming incubation step with α-tocopherol and an apoptosis inhibitor, Q-VD-OPH. This work demonstrates progress in the control of ice formation and cytotoxicity hurdles for the preservation of complex tissues.
An Evaluation of the Online Social Learning Environment Instrument (OSLEI) Using Rasch Model Analysis

One of the questionnaires that will be used to evaluate social learning environments such as Facebook is the Online Social Learning Environment Instrument (OSLEI). The aim of this study was to evaluate the OSLEI using alternative method of analysis via Rasch model toward 49 undergraduate students in Malaysia. To date, no study has evaluated the OSLEI through the lens of Rasch model analysis among Malaysian samples. Firstly, the OSLEI underwent content validation by three experts. The results of the overall percent agreement on eight constructs (perceived usefulness, usage of learning groups, learning outcomes, affective outcomes, self-efficacy, trust, privacy, and teaching member presence) of the OSLEI was 86% for 48 items. Next, 10 measurement properties of Rasch model analysis were employed with the OSLEI. The results revealed that 23 respondents needed to be dropped. Subsequently, the data from the remaining 26 respondents proceeded to Rasch Model analysis of the 43 items in the OSLEI. The results of the item analysis revealed that 13 items and the construct of privacy needed to be dropped. The findings demonstrated that 35 items were robust, valid, and reliable, and could be used to measure online social learning environments.
Measuring Annual Variation in Reproductive Output Reveals a Key Role of Maternal Body Condition in Determining the Size of Eggs in Snakes

Simple Summary We measured female reproductive traits of a colubrid snake (Lycodon rufozonatus) from Zhejiang, East China in four years (1999, 2010, 2011, and 2014). After removing the influence of female size, postpartum body mass was greater in 2010 than in 2014, clutch mass was greater in 2010 than in 2011 and 2014, and egg size was greater in 2010 than in the other three years. Egg size was positively related to postpartum body condition in each year. Females laid larger eggs in 2010 than in other three years after removing the influence of maternal size and body condition. Our study does not follow the prediction that reproductive females with different amounts of resources to invest should give priority to adjusting the number rather than size of offspring. Abstract Long-term studies are especially suited for disentangling the effects of extrinsic and intrinsic factors on both total reproductive investment and reproductive allocation in offspring number versus offspring size. Female reproductive traits of the red-banded wolf snake (Lycodon rufozonatus) from Zhejiang, East China were studied in four years between 1999 and 2014. Egg-laying dates overall extended from late June to late July, and varied among years. Postpartum body mass, clutch size, clutch mass, and egg size were positively related to female size (snout vent length, SVL) in each year. Postpartum body mass, clutch mass, and egg size differed among years after accounting for female SVL, whereas clutch size did not. Setting female SVL at the same level, postpartum body mass was greater in 2010 than in 2014, clutch mass was greater in 2010 than in 2011 and 2014, and egg size was greater in 2010 than in the other three years. Females did not trade off egg size against number. Egg size was positively related to postpartum body condition in each year. Females laid larger eggs in 2010 than in other three years after removing the influence of maternal body condition. Our study provides evidence for the traditional view that reproductive output is highly linked to maternal body size in snakes, but not following Smith and Fretwell’s (1974) classic prediction that females with different amounts of resources to invest in reproduction should give priority to adjusting the number rather than size of their offspring. Maternal body size and condition both are important sources of variation in egg size, but factors other than these two variables may also affect the size of eggs produced by female L. rufozonatus.
An Intelligent system for the categorization of question time official documents of the Italian Chamber of Deputies

ABSTRACT In this work, we present an intelligent system for the automatic categorization of political documents, specifically the documents containing the parliamentary questions collected during the weekly Question Times at the Chamber of Deputies of the Italian Republic. The proposed intelligent system leverages text classification models to perform the document categorization. The system is aimed at supporting and facilitating the research activities of political science scholars, who deal with comparative and longitudinal analysis of thousands of documents. To select the best classification models for our specific task, several classical machine learning and deep learning-based text classification models have been experimentally compared.
Energy storage for photovoltaic power plants: Economic analysis for different ion‐lithium batteries

Energy storage has been identified as a strategic solution to the operation management of the electric power system to guarantee the reliability, economic feasibility, and a low carbon footprint. In this sense, this article analyzes the economic feasibility of a storage system using different Li‐ion batteries applied to a real case of the photovoltaic power plant at Alto Rodrigues, Rio Grande do Norte, Brazil. The System Advisor Model software was used to simulate the systems which allowed showing the difference between the revenue obtained from energy sales and the total generation cost. According to the results, the viability of the energy storage system can be achieved in different ways. The first way would be to reduce current investment costs in storage systems. In the second way, the energy sale price is higher than the current sale price. The third and fourth ways are a combination of cost storage reduction and the increase of energy sale price in different annual proportions.
Contrast normalization affects response time-course of visual interneurons

In natural environments, light intensities and visual contrasts vary widely, yet neurons have a limited response range for encoding them. Neurons accomplish that by flexibly adjusting their dynamic range to the statistics of the environment via contrast normalization. The effect of contrast normalization is usually measured as a reduction of neural signal amplitudes, but whether it influences response dynamics is unknown. Here, we show that contrast normalization in visual interneurons of Drosophila melanogaster not only suppresses the amplitude but also alters the dynamics of responses when a dynamic surround is present. We present a simple model that qualitatively reproduces the simultaneous effect of the visual surround on the response amplitude and temporal dynamics by altering the cells’ input resistance and, thus, their membrane time constant. In conclusion, single-cell filtering properties as derived from artificial stimulus protocols like white-noise stimulation cannot be transferred one-to-one to predict responses under natural conditions.
Forensic Trails Obfuscation and Preservation via Hard Drive Firmware

The hard disk drive stores data the user is creating, modifying, and deleting while a firmware facilitates communication between the drive and the operating system. The firmware tells the device and machine how to communicate with each other and will share useful information such as, disk size and information on any bad sectors. Current research shows that exploits exist that can manipulate these outputs. As an attacker, you can change the size of the disk displayed to the operating system to hide data in, likewise by marking an area of the disk as bad. Users may not be aware of these changes as the operating system will accept the readings from the firmware. However, although the data is not reachable via the operating system this paper looks at the traceability of manipulated data using data recovery software FTK Imager, Recuva, EaseUS and FEX Imager. This report examines the use of malicious techniques to thwart digital forensic procedures by manipulating the firmware. It is shown how this is possible and current forensic techniques or software does not easily detect a change within the firmware. However, with the use of various forensic tools, obfuscated trails are detectable. This report follows a black box testing methodology to show the validation of forensic tools or software against anti-forensic techniques. The analysis of the results showed that most tools can find the firmware changes, however, it requires an analyst to spot the subtle differences between standard and manipulated devices. The use of multiple software tools can help an analyst spot the inconsistencies.
Non-Melanoma Skin Cancer Clearance after Medical Treatment Detected with Noninvasive Skin Imaging: A Systematic Review and Meta-Analysis

Simple Summary Non-melanoma skin cancers (NMSC) represent about one-third of all malignancies. While surgery is the current gold standard treatment, many nonsurgical approaches are available for selected cases. Currently, there are no studies concerning the overall impact of dermoscopy, optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) for NMSC treatment monitoring. Therefore, we aim to review the current literature and provide an updated summary of noninvasive skin imaging in NMSC medical treatment management and the diagnostic accuracy of the most advanced technologies. Abstract Background/Objectives: Non-melanoma skin cancer (NMSC) treated with nonsurgical therapies can be monitored with noninvasive skin imaging. The precision of dermoscopy, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) in detecting clearance is unclear. We aim to report the proportion of persisting tumors identified with noninvasive technologies available in the literature. Methods: A systematic literature search was conducted on the PubMed and Cochrane Public Library Databases for articles published prior to November 2021. Statistical analyses were conducted with MedCalc 14.8. 1 software. Results: A total of eight studies (352 lesions) reporting noninvasive imaging for NMSC clearance following nonsurgical treatment were included. Most (n = 7) reported basal cell carcinoma (BCC), and one study reported squamous cell carcinoma (SCC) clearance. A meta-analysis of the BCC clearance revealed that the summary effect for RCM was higher, as compared to the other techniques. Interestingly, the sensitivity and specificity for OCT were 86.4% (95% CI: 65.1–97.1) and 100% (95% CI: 94.8–100.0), respectively, whilst, for RCM, they reached 100% (95%CI: 86.8–100) and 72.5% (95% CI: 64.4–79.7), respectively. Conclusions: Routine clinical examination and dermoscopy underperform when employed for NMSC clearance monitoring, although they represent the first approach to the patient. OCT and RCM seem to improve the detection of persistent BCC after medical treatment.
Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach

This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. In order to develop a comprehensive artificial intelligence model in this study, sixty-nine data samples were collected. To this end, the Gaussian process regression approach with four basic function kernels (Matern, squared exponential, exponential, and rational quadratic) was exploited. It was found that Matern outperformed other models with R2 = 0.987, MARE (%) = 6.048, RMSE = 0.0577, and STD = 0.0574. This precise yet simple model can be a good alternative to the complex thermodynamic, mathematical-analytical models of the past.
Fast Location and Recognition of Green Apple Based on RGB-D Image

In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.
Visualization of Robotic Sensor Data with Augmented Reality

To understand a robot’s intent and behavior, a robot engineer must analyze data at the input and output, but also at all intermediary steps. This might require looking at a specific subset of the system, or a single data node in isolation. A range of different data formats can be used in the systems, and require visualization in different mediums; some are text based, and best visualized in a terminal, while other types must be presented graphically, in 2D or 3D. This often makes understanding robots challenging for humans, as it can be hard to see the whole picture of the situation. This thesis attempts to solve this issue, by creating an augmented reality system on the virtual reality platform HTC Vive, to investigate methods for visualization of a robot’s state and world perception. It also investigates the effect augmented reality has in increasing a user’s understanding of a robot system. The visualization was achieved by projecting a robot’s sensor data into the user’s reality, presenting it in a intuitive way. Augmented reality was achieved by utilizing HTC Vive’s front facing camera, and showing the augmented video see-through in virtual reality. To test the system’s ability in increasing the user’s understanding, a user study was conducted. The study tested the users’ understanding of the robot’s perception of its environment. This was done by comparing the augmented reality system with traditional methods. The implemented augmented reality system was successfully tested on 31 subjects in the user study. Quantitative data was recorded to measure the understanding, and a questionnaire was conducted to get qualitative data about the system. The results show a significant increase in the subjects’ understanding.
Challenges and problems in the recruitment of ad hoc workers of election management body at the local level: Evidence from Banyumas

The prevailing national regulation on the recruitment of PPK, PPS, and KPPS staff sets forth that those temporary officers can be of service for only two periods of general election and cannot be involved afterwards. This regulation actually has positive aspect, but is not easy in its implementation. This article aims at examining the challenges and problems faced by the election management body at local level (Regency KPU) in preparing the human resource of ad hoc staff for the 2019 general election in Indonesia. This article was based on a qualitative research conducted in 2020 in Banyumas Regency, Central Java. This article shows that challenges that had to be faced by Regency KPU in the recruitment of ad hoc employees were the duration of screening and the way village leaders socialized the information regarding the recruitment. Whereas, the involvement of young generation and women as general election ad hoc workers in Banyumas was still low because of a number of factors, from know-how of general election to having lack information of the recruitment. This study concludes that actor’s action choice is limited by a number of matters, such as the path previously taken, rules regulating the agency, and social-political context.
Four-Channel Buck-Type LED Driver with Automatic Current Sharing and Soft Switching

A buck-type LED driver together with automatic current sharing and high step-down voltage conversion ratio but without complex control is proposed. The proposed LED driver can not only achieve zero voltage switching (ZVS) turn-on by adding only one resonant coupled inductor, which resonates with parasitic capacitors of active switches, but also can obtain lower voltage gain and better conversion efficiency. In this paper, the operating principles and design considerations of the proposed converter are discussed in detail. In addition, the number of LED strings can be extended to more than four channels. Finally, the theoretical analysis and performance of the proposed LED driver are verified by simulations and experiments using a field-programmable logic gate array (FPGA) named EP3C5E144C8N as a circuit control kernel.
Design and Application of Vocal Music Teaching Management System

This article proposes a virtual reality-based management system for vocal music instruction. Additionally, this article proposes an improved algorithm for automatic vocal main melody extraction. The pitch saliency function calculation method is improved based on the spectral characteristics of vocal music signals in order to reduce the computational complexity and time required to extract the vocal main melody. The model presented in this article has the potential to increase the recognition accuracy of the main melody model, decrease the rate of melody localization false alarms, and increase the overall accuracy of vocal main melody extraction. Additionally, this article incorporates the extraction algorithm into the management system, making it convenient for teachers to use during instruction.
Deep Transfer Learning-Based Sizing Method of Permanent Magnet Synchronous Motors Considering Axial Leakage Flux

The sizing process is necessary to analyze the electromagnetic characteristics according to the major shape parameters during the early design stage of permanent magnet synchronous motors (PMSMs). However, predicting the performance of PMSMs with 2-D finite element analysis (FEA) has errors due to axial leakage flux. Therefore, the axial leakage flux should be considered in the sizing process. The most accurate way to consider the axial leakage flux is to perform 3-D FEA, but it has a disadvantage of high computational cost. In this view, we propose a deep transfer learning-based surrogate modeling method to reduce the computational cost for calculating 3-D FEA-based motor parameters. The transfer learning is conducted using a large amount of 2-D FEA-based and small amount of 3-D FEA-based motor parameters. Using the proposed process, it is possible to accurately predict the motor characteristics according to size-related variables that satisfy the required specifications with small amount of 3-D FEA-based motor parameters. The proposed method was verified through 3-D FEA and experiments for pancake-type PMSMs, which is highly affected by axial leakage flux.
Regional Climate Effects of Irrigation Over Central Asia Using Weather Research and Forecasting Model

A dynamic irrigation scheme in which soil moisture acts as an irrigation trigger was incorporated into the Weather Research and Forecasting model coupled with the Noah‐Mosaic land surface model, in which irrigation was only considered in irrigated portions within each grid cell. Two experiments (with and without irrigation) were first designed and continuously run 21 years (1994–2014) to simulate the impacts of irrigation over Central Asia on regional climate. Soil moisture after irrigation increased greatly because of the large irrigation amount. Irrigation significantly increased latent heat flux through increasing soil evaporation and transpiration, and their contributions to increases in latent heat flux were roughly equal. The sensible heat flux was decreased because of the increased latent heat flux, which led to decreases in surface air temperature and land surface temperature. The simulations of temperature and soil moisture in croplands were improved in experiment with irrigation. The daily minimum surface air temperature was slightly affected by irrigation, even increased during nonirrigation period. During daytime, the airflows blowing from croplands to mountains would also bring parts of the increased water vapor to mountainous areas. Since the increased water vapor was transported upward in mountainous areas, irrigation obviously increased precipitation here. Multiple sensitivity experiments were conducted, and all concluded that irrigation over Central Asia led to increases in precipitation in mountainous areas, which showed that the conclusion was certain and not affected by the selected parameterization schemes and the simulated irrigation amount.
Effect of Coal Consumption on the Upgrading of Industrial Structure

This paper attempts to study the effect of China’s coal consumption on the upgrading of industrial structure based on the relevant data obtained from the China Statistical Yearbook (1992–2021). The results showed that (1) China’s coal consumption has a downward trend, but it is still more than 56.8%. Similarly, the consumption of nonpetrochemical energy is not high, but the increase with respect to time is large. (2) Percentage of China’s tertiary industry reached a maximum of 54.5% in 2020. However, there is a huge gap when compared to the global data. For example, the tertiary industries accounted for 77.4% in the US in 2017 and 71.3% in the UK in 2019. (3) In the short term, China’s coal consumption will continue to promote the upgrading of industrial structure; in the long run, it is not obvious that China’s coal consumption continues to drive the upgrading of industrial structure. (4) The intensity of the sustainable driving effect of China’s coal consumption on the upgrading of industrial structure will gradually weaken from 0.0647 to 0.00102.