Miracle-dz/UniFAR-FTUs huggingface dataset:
Peptidomics Analysis of Soy Protein Hydrolysates—Antioxidant Properties and Mechanism of their Inhibition of the Oxidation of Palm Olein during Frying Cycles

This study determined for the first time the structure of the peptides (i.e., peptidomics) in soy protein hydrolysates and elucidated their effects on an oil’s oxidative stability during frying cycles. The oil investigated was palm olein during 0, 4, 8, and 12 frying cycles of plantain banana chips. Proteins were extracted and hydrolyzed with two proteases. Trypsin hydrolysate (HTRY) exhibited higher anti-radical activity (DPPH, 70.2%) than the control (unhydrolyzed proteins, 33.49%) and pepsin hydrolysate (HPEP, 46.1%) at 200 µg/mL. HPEP however showed a 4.6-fold greater reduction of ferric ions (FRAP) while also possessing a higher peroxyl radical scavenging ability (716 ± 30 µM Trolox Eq/g) than HTRY (38.5 ± 35 µM Trolox Eq/g). During oil oxidative stability tests, HPEP improved the oxidative stability of the palm olein oil after 8 and 12 frying cycles, characterized by lower concentrations of hydroperoxides, and carbonyl and volatile compounds. HTRY however exerteda pro-oxidant activity. Structural data from SDS-PAGE and tandem mass spectrometry showed that the mechanism for the greater activity of the pepsin hydrolysate occurred due to unique structural features and a higher percentage of short-chain peptides. This was justified by a 25, 31, and 48% higher contents of tryptophan, histidine, and methionine, respectively (important amino acids with hydrogen atom transfer and electron-donating capacities) in the peptides identified in the pepsin hydrolysate.
A Conceptual Study of TOE and Organisational performance

The COVID-19 pandemic has changed everything and created a big challenge for all the enterprises including theSME’s. The use of technology becomes crucial for the survival of the business activities because the mode of payment andreceiving mostly shifted towards the digital method. However, the SME’s sector still facing the challenge of technologyadoption particularly in the native areas. The basic motive of this research is to examine previous studies to find the role oftechnology, organisation and environment (TOE) in organisational performance. This study analysed various theories oftechnological innovation while focusing the theory of TOE. The main factors studied in this research are the Technological,environmental and organisational variables. This research study will follow the qualitative research method. In this researchstudy we have followed the systematic “Meta-narrative” of literature review method introduced by Kuhn. To study and findthe relevant article we have used the key words of SMEs, TOE, technology and adoption. Moreover, the study period coveredin this research was from 2011 to 2023. This study highlights the important aspect of TOE and its application in SMEs.Moreover, this research contributes in the theoretical understanding of SMEs success factors while critically studying thetheory of TOE.
An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data

Abstract Background: Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals. Methods: The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. Results: In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative – a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. Conclusion: These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.
The new method of forestation automation with uav and through-the-air seedling delivery tech-nique based on the lidar generated land surface model optimization.

Deforestation is one of the biggest ecological challenges faced globally nowadays. From 1993 to 2020 the world lost almost 154 Mha of forests which brought about various negative conse-quences, such as an increase in greenhouse gas emissions, disruption of water cycles, increased soil erosion, disrupted livelihoods, etc. Scientists agree that artificial forestation is the only way to solve the problem on its present scale. Classic methods of artificial forestation include man-ual and automated ones or their combination. Despite its simplicity and comparably easier im-provement, classic methods also have some disadvantages that do not allow them to solve the problem, i.e., bad scalability. Automated forestation that utilizes UAVs is a new promising ap-proach that was developed in the last decades. The current paper addresses its common imple-mentations compared to classic forestation methods from the perspective of its improvement possibility based on their synthesis. Analysis of the existing experience of UAV-based foresta-tion consists of three stages: surface scanning, data processing and planting itself. The research showed that in addition to the usage of modelling and optimization algorithms at the first two stages, analogically to classic methods higher efficiency can be achieved by planting seedlings instead of seeds. The present paper suggests performing planting from the air to the defined points based on the creation and optimization of a tree situation model. Such approach requires the usage of advanced LIDAR generated land surface modelling techniques and methods of tra-jectory calculation. The article describes a step-by-step new method of tree planting automa-tion, suggesting theoretical and practical perspectives and directions for future research.
Progressively Learning Dynamic Level Set for Weakly Supervised Industrial Defect Segmentation

With continuous advancements in sensor technology and computer vision, automated surface defect detection has become an important problem in the modern manufacturing industry. Although deep-learning-based defect detection methods have proven successful in various inspection tasks, they often require extensive high-quality pixel-level annotations for model training. Industrial defects, characterized by diverse shapes and intricate textures, require manual labeling by skilled inspectors. This study introduces a weakly supervised defect segmentation framework called the progressively learning dynamic level set (PLDL) to overcome these constraints. This framework incorporates two parallel learning modules and a differentiable level set module interconnected through a progressive learning strategy facilitated by an innovative loss function. Using only image-level labels as inputs, the PLDL framework iteratively refines object boundaries, dynamically optimizes the training process, and generates pixel-level outputs. Our method is efficient and consistent, requiring no manual supervision or postprocessing steps. Experimental results on four benchmark datasets representing diverse industrial scenarios reveal that PLDL outperforms recent weakly supervised models. It achieved 76.52%, 85.23%, 85.71%, 84.43%, and 83.17% in mIoU, mPA, precision, recall, and $F$ -measure, respectively, indicating its superior effectiveness in weakly supervised defect segmentation.
Effect of lubricant infiltration into the groove-like surface texture on the friction response of the textured stainless-steel contact surface

Surface texturing is recognized as an effective solution for reducing friction on stainless-steel contact interfaces. Previous studies mainly focused on the influence of the texture parameters and lubricant-film depths on their tribology performances. Notably, whether the lubricant can penetrate texture and the lubricant infiltration capacity may significantly affect the tribology performances of the contact interfaces. They have been rarely considered, which may cause some well-designed textures to fail to perform their roles and be abandoned. Herein, a novel lubricant model is developed to explore the tribology performances of the contact interfaces with different lubricant infiltration capacities in textures and the influence factors of the lubricant infiltration capacities. Textured surfaces with different dimensions are fabricated by the femtosecond laser to explore the effects of the texture dimensions on the lubricant infiltration capacities. The results indicate that texture width, depth and lubricant viscosity could significantly affect the lubricant infiltration capacities. Moreover, the results indicate that the lubricant infiltration capacity would significantly affect the tribology performances of the contact interfaces. This work indicates that the impact of the lubricant infiltration should be considered in texture design. This work can be widely used to guide the texture design applied in plenty of fields.
Analysis of Forcing, Response, and Feedbacks in a Paleoclimate Modeling Experiment

It is often argued that paleoclimate studies are necessary to determine whether climate models and their predictions of future climate change can be trusted. An overall measure of the sensitivity of global mean surface temperature to a given radiative perturbation is provided by the global climate sensitivity parameter. In climate model experiments, this parameter appears to be moderately independent of the cause of the perturbation [see, for example, Hansen et al. (1997) and Hewitt and Mitchell (1997)], but it may differ from one model to the next by as much as a factor of three (IPCC, 1995). Moreover, there are some scientists who claim that all models are much more sensitive than the climate system itself (Lindzen, 1997). Thus it would be valuable to determine which models (if any) are consistent with the paleoclimate record and what factors are responsible for model differences in sensitivity. In an analysis of the Paleoclimate Modeling Intercomparison Project (PMIP) simulations of the Last Glacial Maximum (LGM) of 21,000 years ago, we have calculated how the ''forcing'' and feedbacks determine the climatic response. In the PMIP context, the ice sheet distribution is prescribed and the resulting increase in planetary albedo is the most important ''forcing''more » factor. Also important are radiation perturbations induced by changes in atmospheric CO{sub 2} concentration. Here we describe a new, approximate method for estimating the strength of forcing and feedback factors from commonly archived model output. We also summarize preliminary results from the PMIP experiment, which show that differences in forcing and to a lesser extent differences in feedbacks can explain differences in surface temperature response. « less
A Three-Step Calibration Method of Sensors’ Pose for Magnetic Localization System

Calibrating the pose of all magnetic sensors is a necessary prepossessing step in tracking intrabody objects such as wireless capsule endoscopy (WCE) by magnetic fields. The existing calibration methods are optimization-based and need good initial values for the pose of the sensors. Manual measurement of the initial values can be a cumbersome and sometimes challenging process. Therefore, we propose a three-step calibration method to automatically obtain the rough initial value of the sensor pose. First, the initial sensor orientation can be estimated by rotating the system. Next, the initial position is obtained using the orientation estimated earlier and restricting a strong magnet to face two specific orientations. We provide a detailed deduction process for obtaining the analytical solution for the position. Finally, bundle adjustment is used to further optimize the sensor orientation and position. Experimental results have shown that the magnetic localization system achieves higher accuracy after applying the proposed calibration method.
Estimating the impact of the Families Package changes in financial incentives

In this paper we analyse behavioural responses to changes in financial incentives associated with the 2018 Families Package. For this analysis, we followed the methods pioneered by Saez (2010) and Chetty et al. (2013), which use bunching around kink points in the income schedule to estimate the degree of behavioural response. In general, the role of financial incentives in labour supply behaviour has been the subject of investigation for many decades, and although there is considerable concern about adverse labour supply responses to increased generosity of benefits, the available evidence on labour supply responses is mixed and surprisingly muted. We find no evidence of response around the salient kink points related to the policy changes; however, in contrast to the lack of bunching around the policy points, we see clear evidence of bunching around the top two marginal tax rate (MTR) thresholds, as well as at twice these amounts by coupled parental units. This suggests the methodology is able to identify such behavioural responses if they exist. Moreover, according to the theoretical model established in Saez (2010), the degree of bunching around the MTR thresholds should be similar if not less than that around the Families Package policy points we examine. The results in that respect are surprising, though Saez (2010), Chetty et al. (2013), and others find that bunching tends to occur around high visibility, easily understood kink points which have large impacts on disposable income.
Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process

During the past two decades, higher education institutions have been experiencing challenges in transforming the traditional way of in-class teaching into blended learning formats with the support of e-learning technologies that make possible the collection and storing of considerable amounts of data on students. These data have considerable potential to bring digital technologies in education to a new level of personalized learning and data-driven management of the educational process. However, the way data are collected and stored in a typical university makes it difficult to achieve the mentioned goals, with limited examples of data being used for the purposes of learning analytics. In this work, based on the analysis of existing information systems and databases at Siberian Federal University, we propose principles of design for a university database architecture that allow for the development and implementation of a data-driven management approach. We consider various levels of detail of education data, describe the database organization and structure, and provide examples of learning analytics tools that can benefit from the proposed approach. Furthermore, we discuss various aspects of its implementation and associated questions.
Transcriptome-based analysis of human peripheral blood reveals regulators of immune response in different viral infections

Introduction There are difficulties in creating direct antiviral drugs for all viruses, including new, suddenly arising infections, such as COVID-19. Therefore, pathogenesis-directed therapy is often necessary to treat severe viral infections and comorbidities associated with them. Despite significant differences in the etiopathogenesis of viral diseases, in general, they are associated with significant dysfunction of the immune system. Study of common mechanisms of immune dysfunction caused by different viral infections can help develop novel therapeutic strategies to combat infections and associated comorbidities. Methods To identify common mechanisms of immune functions disruption during infection by nine different viruses (cytomegalovirus, Ebstein-Barr virus, human T-cell leukemia virus type 1, Hepatitis B and C viruses, human immunodeficiency virus, Dengue virus, SARS-CoV, and SARS-CoV-2), we analyzed the corresponding transcription profiles from peripheral blood mononuclear cells (PBMC) using the originally developed pipeline that include transcriptome data collection, processing, normalization, analysis and search for master regulators of several viral infections. The ten datasets containing transcription data from patients infected by nine viruses and healthy people were obtained from Gene Expression Omnibus. The analysis of the data was performed by Genome Enhancer pipeline. Results We revealed common pathways, cellular processes, and master regulators for studied viral infections. We found that all nine viral infections cause immune activation, exhaustion, cell proliferation disruption, and increased susceptibility to apoptosis. Using network analysis, we identified PBMC receptors, representing proteins at the top of signaling pathways that may be responsible for the observed transcriptional changes and maintain the current functional state of cells. Discussion The identified relationships between some of them and virus-induced alteration of immune functions are new and have not been found earlier, e.g., receptors for autocrine motility factor, insulin, prolactin, angiotensin II, and immunoglobulin epsilon. Modulation of the identified receptors can be investigated as one of therapeutic strategies for the treatment of severe viral infections.
An Easy Data Augmentation Approach for Application Reviews Event Inference

Application review event inference aims to assess the effectiveness of application problems in response to user actions, which enables application developers to promptly discover and address potential issues in various applications, thereby improving their development and maintenance efficiency. Despite the development of event inference models for app reviews, which extract them as user action and app problem events and establish a relationship model between events and inference labels, the accuracy of these models is constrained due to limitations in labeling and characterizing noise and the lack of robustness and generalization. To address this challenge, we propose a model called Easy Data Augmentation for Application Reviews Event Inference (short for EDA-AREI), which comprises a denoising component, data augmentation component, and event inference prediction component. Specifically, the denoising component identifies labels and characterizes noisy data to enhance dataset quality, the data augmentation component replaces non-stop words with synonyms to increase textual diversity, and the event inference and prediction component reconstructs the classifier using denoised and augmented data. Experimental results on six datasets of one-star app reviews in the Apple App Store demonstrate that the EDA-AREI method achieves an Accuracy of 71.19%, 79.14%, 69.05%, 69.02%, 68.24% and 68.48%, respectively, representing an improvement of 0.83%–2.09% compared to state-of-the-art models. Regarding the F1-score, EDA-AREI achieves values of 71.30%, 69.93%, and 68.76% on the threshold_0. 5, k-means_2, and random datasets, respectively, outperforming state-of-the-art models by 1.89%–4.02%. Furthermore, EDA-AREI achieves AUC values of 75.66% and 73.37% on the threshold_0. 5 and k-means_2 datasets, respectively. As a result, EDA-AREI demonstrates substantial improvements in Accuracy, as well as enhanced F1-score and AUC across most datasets, thereby enhancing the model's accuracy and robustness in identifying related action-problem pairs.
Effects of Preoperative Chronic Steroid Use on Postoperative Outcomes in Orthopedic Surgery: A Systematic Review and Meta-Analysis

Higher rates of postoperative complications have been found in preoperative chronic steroid users. However, the effects of preoperative chronic steroid use on outcomes in orthopedic surgery were unclear. We performed a systematic review of cohort studies examining the effects of chronic steroid use on postoperative outcomes following orthopedic surgery and searched PubMed, Embase, and CENTRAL through 29 April 2023. We included 17 studies with 1,546,562 patients. No increase in 30-day mortality (adjusted odds ratio (aOR) 1.40, 95% confidence interval (CI) 0.64–3.09) and composite thromboembolic events (aOR 1.61, 95% CI 0.99–2.63) but increases in 30-day overall complications (aOR 1.42, 95% CI 1.16–1.75), wound dehiscence (aOR 2.91, 95% CI 1.49–5.66), infectious complications (any infection (aOR 1.61, 95% CI 1.44–1.80), sepsis (aOR 2.07, 95% CI 1.34–3.21), superficial surgical site infection (SSI) (aOR 1.73, 95% CI 1.03–2.89) and deep SSI (aOR 1.96, 95% CI 1.26–3.05)), re-admission (aOR 1.62, 95% CI 1.48–1.77), both 30-day (aOR 1.28, 95% CI 1.03–1.59) and 1-year re-operation (aOR 1.78, 95% CI 1.09–2.92), pulmonary embolism (aOR 5.94, 95% CI 1.52–23.29), and deep vein thrombosis (aOR 2.07, 95% CI 1.24–3.46) were detected in preoperative steroid users. An increased risk of adverse outcomes following orthopedic surgery in chronic steroid users was found.
X-ray Detection of Prohibited Item Method Based on Dual Attention Mechanism

Prohibited item detection plays a significant role in ensuring public safety, as the timely and accurate identification of prohibited items ensures the safety of lives and property. X-ray transmission imaging technology is commonly employed for prohibited item detection in public spaces, producing X-ray images of luggage to visualize their internal contents. However, challenges such as multiple object overlapping, varying angles, loss of details, and small targets in X-ray transmission imaging pose significant obstacles to prohibited item detection. Therefore, a dual attention mechanism network (DAMN) for X-ray prohibited item detection is proposed. The DAMN consists of three modules, i.e., spatial attention, channel attention, and dependency relationship optimization. A long-range dependency model is achieved by employing a dual attention mechanism with spatial and channel attention, effectively extracting feature information. Meanwhile, the dependency relationship module is integrated to address the shortcomings of traditional convolutional networks in terms of short-range correlations. We conducted experiments comparing the DAMN with several existing algorithms on datasets containing 12 categories of prohibited items, including firearms and knives. The results show that the DAMN has a good performance, particularly in scenarios involving small object detection, detail loss, and target overlap under complex conditions. Specifically, the detection average precision of the DAMN reaches 63.8%, with a segmentation average precision of 54.7%.