Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of Single-Cycle THz Pulse Generation and X-ray Emission: Water-Flow Irradiated by Two Ultra-Short Laser Pulses

The interaction of two subsequent ultra-short sub-milli-Joule laser pulses with a thin water flow results in an emission of a strong single-cycle THz pulse associated with enhanced soft X-ray emission. In this paper, a chain of processes produced in this interaction is analyzed and compared with other THz generation studies. It is demonstrated that the enhanced THz and X-ray emissions are produced by an energetic electron beam accelerated in the interaction of a main laser pulse with liquid water ejected from the surface by the pre-pulse. This scheme thus provides an efficient laser energy conversion in a THz pulse, avoiding laser self-focusing and filamentation in air.
Discrepancy Theory

Discrepancy theory is a subﬁeld of combinatorics in which one asks the following question: given a ﬁnite set system S 1 ,. , S m ⊆ {1,. , n }; color the elements {1,. , n } with two colors, say red and blue . What is the difference between red and blue elements in the most unbalanced set for the best coloring? The two main results are the Beck-Fiala Theorem and Spencer’s Theorem, which both have very elegant proofs as we will see in this lecture.
Research Progress of α-Glucosidase Inhibitors Produced by Microorganisms and Their Applications

Based on the easy cultivation of microorganisms and their short cycle time, research on α-glucosidase inhibitors (α-GIs) of microbial origin is receiving extensive attention. Raw materials used in food production, such as cereals, dairy products, fruits, and vegetables, contain various bioactive components, like flavonoids, polyphenols, and alkaloids. Fermentation with specific bacterial strains enhances the nutritional value of these raw materials and enables the creation of hypoglycemic products rich in diverse active ingredients. Additionally, conventional food processing often results in significant byproduct generation, causing resource wastage and environmental issues. However, using bacterial strains to ferment these byproducts into α-GIs presents an innovative solution. This review describes the microbial-derived α-GIs that have been identified. Moreover, the production of α-GIs using industrial food raw materials and processing byproducts as a medium in fermentation is summarized. It is worth analyzing the selection of strains and raw materials, the separation and identification of key compounds, and fermentation broth research methods. Notably, the innovative ideas in this field are described as well. This review will provide theoretical guidance for the development of microbial-derived hypoglycemic foods.
Application of Carbon Capture and Utilization (CCU) in Oil and Gas Industry to Produce Microalgae-Based Biofuels with Solvent-Captured Method

The production process in the oil and gas industry, which is a major demand, still plays a huge role in carbon emissions, especially in the refining process. The energy and industrial sectors are responsible for more than 75% of these global CO2 emissions. This condition is an important issue regarding the effort to reduce climate change due to these emissions by implementing CCU. This article aims to examine methods of carbon capture with chemical absorption by solvents and to compose a model diagram of carbon utilization with microalgae. An extensive literature search was conducted in accordance with the scoping review methodology and the PEO framework. Our search criteria were limited to article research within the last 5 years (2017–2021). Themes found from this review included the CCU method in general, carbon capture by solvent method, type of solvent used, advantages and disadvantages, and utilization of carbon in the gas and oil industries. CCU is a model that can be offered as an alternative to reduce CO2 emissions produced by industry. The scoping review result shows the best method for carbon capture is with monoethanolamine (MEA) solvent. The flue gas from post-combustion streams into the absorption column and the solvent is added. The carbon-rich solvent is regenerated by heat to produce a clean solvent to be reused in the capture cycle. Carbon that has been absorbed by the MEA in the form of gas will be channeled through pipes to the microalgae industry as utilization of captured carbon and then converted to biofuels. It was discovered that MEA is a cost-effective solvent, efficiently captures carbon, and can be used repeatedly. However, the amine emissions from MEA are considered hazardous. The conclusion is that MEA solvent has advantages and disadvantages. Further optimization research is needed to determine the preeminent capture and separation process. Thus, it is necessary to determine the best conditions for the use of captured carbon by microalgae.
Scaling Byzantine Fault-Tolerant Consensus With Optimized Shading Scheme

This article introduces a novel scalable multishard Byzantine fault tolerance (SharBFT) consensus protocol combined with a blockchain sharding optimization scheme. SharBFT builds upon the classic BFT state-machine replication approach and extends it into a hierarchical multishard prototype to enable scalable and concurrent Byzantine consensus. This prototype enhances scalability and bolsters the security of global consistency in comparison to existing protocols. Moreover, SharBFT employs a novel consensus voting mechanism based on the threshold signature scheme, resulting in linear message communication complexity and optimized consensus operations. In additional, SharBFT integrates a sharding optimization model (SOM) to enhance consensus efficiency in dynamic system environments. The proposed SOM aims to minimize the average consensus latency while ensuring security and scalability. This article presents experimental results conducted in a real-world cloud environment, illustrating significantly improved performance.
Transmission of Flower Traits and Fertility in Black Rose x Hybrid Tea Rose Cross Combination

Breeding studies of scented cut roses have gained significance recently. The Black rose, a naturalized old garden rose in Türkiye, is a valuable genetic resource for breeding programs due to its pleasant scent. This study investigated the fertility of the Black rose (Rosa odorata cv. Louis XIV, Halfeti rose) as a seed parent and its ability to transmit flower traits to offspring. Four commercial cut rose varieties with known pollen germination rates were used as pollen parents. Each combination underwent at least 33 pollinations, and the fruit set rate, the average seed number per fruit, and the seed germination rate were examined. Both parents and one-year-old F1 progeny were evaluated for petal number, scent, and flower color traits during two flowering periods. Cross-pollination resulted in fruit set rates ranging from 70.21% to 100.0%, average seed numbers per fruit ranging from 5.0 to 10.0, and seed germination rates ranging from 4.42% to 21.69%. Morphological characterization revealed that 5.05% of the progeny exhibited moderate to intense scent, 42.42% had sour cherry-colored petals, and petal numbers ranged from 9 to 54. Considering the overall efficiency of rose breeding programs, the findings demonstrate the promising fertility of the Black rose and its relative success in forming efficient combinations with hybrid tea roses. However, it also suggests that the Black rose is not as successful in transmitting the scent to its offspring as in transmitting the flower color.
Transcriptomic Analysis of Light-Induced Genes in Nasonia vitripennis: Possible Implications for Circadian Light Entrainment Pathways

Simple Summary Almost all animals on Earth, which are subject to daily environmental changes, have evolved an endogenous circadian system to maintain daily timekeeping with their environments. Light serves as the most crucial external cue for pollinating insects, such as bees and wasps, enabling them to navigate, forage, and reproduce. It is essential to understand the impact of light on circadian systems in animals, especially considering the prevalence of artificial light nowadays. In our study, we aimed to study the possible molecular mechanisms of the circadian light input pathway in the jewel wasp, Nasonia vitripennis, by utilizing a transcriptomic sequencing approach. We proposed a model for such a light input pathway that involves multiple photoreceptors, neurotransmissions, and light-induced core clock gene expressions. Our study represents the initial step in understanding the circadian light input pathway in wasps and provides interesting candidates for future testing. Abstract Circadian entrainment to the environmental day–night cycle is essential for the optimal use of environmental resources. In insects, opsin-based photoreception in the compound eye and ocelli and CRYPTOCHROME1 (CRY1) in circadian clock neurons are thought to be involved in sensing photic information, but the genetic regulation of circadian light entrainment in species without light-sensitive CRY1 remains unclear. To elucidate a possible CRY1-independent light transduction cascade, we analyzed light-induced gene expression through RNA-sequencing in Nasonia vitripennis. Entrained wasps were subjected to a light pulse in the subjective night to reset the circadian clock, and light-induced changes in gene expression were characterized at four different time points in wasp heads. We used co-expression, functional annotation, and transcription factor binding motif analyses to gain insight into the molecular pathways in response to acute light stimulus and to form hypotheses about the circadian light-resetting pathway. Maximal gene induction was found after 2 h of light stimulation (1432 genes), and this included the opsin gene opblue and the core clock genes cry2 and npas2. Pathway and cluster analyses revealed light activation of glutamatergic and GABA-ergic neurotransmission, including CREB and AP-1 transcription pathway signaling. This suggests that circadian photic entrainment in Nasonia may require pathways that are similar to those in mammals. We propose a model for hymenopteran circadian light-resetting that involves opsin-based photoreception, glutamatergic neurotransmission, and gene induction of cry2 and npas2 to reset the circadian clock.
Prevalence of the metabolic syndrome and its components in secondary school student population in the city of Douala, Cameroon

While the burden of metabolic syndrome (MetS) is still increasing in sub-Saharan Africa, there is a lack of data among young Cameroonian population. The aim of this study was to evaluate the prevalence of MetS and its components among secondary school students in Douala. This was a cross-sectional prospective study carried out on 803 students recruited from February to May 2021 in public and private secondary schools in Douala city, Cameroon. MetS was assessed according to the IDF/AHA/NHLBI 2009 consensus definition. The data collection consisted of a questionnaire on sociodemographic characteristics, measurement of anthropometric parameters (height, weight, body mass index (BMI), waist circumference) and overnight fasting blood sample. Blood pressure (BP), fasting blood glucose, HDL cholesterol and triglycerides were measured using standard methods. The mean age was 18±3 years, 73.3% female. The prevalence of MetS was 27.4%, common among participants aged ≥16 years, and higher in females compare to males (33.7% vs. 11.1%, P#x003C;0.0001). The prevalence of MetS components i.e abdominal obesity, high BP, fasting hyperglycemia, low-level HDL cholesterol and hypertriglyceridemia were 14.1, 18.1, 42.8, 51.4 and 38.6% respectively. All MetS components were significantly higher in females compared to males except for high BP which was similar among the genders. In our study population, the prevalence of MetS is high and this calls for improved monitoring to limit the evolution of associated cardiometabolic complications among young Cameroonians.
Dis-confirmatory evidence drives confidence

: Assessing one's own decisions is a crucial component of cognition, enabling adaptive behavior. According to normative models, the brain employs available evidence optimally to evaluate decisions. If decision confidence (judging the accuracy of a decision) were to be assessed, the normative strategy would involve giving equal weight to each sample. However, recent findings instead suggest that the brain tends to overweight decision-congruent information when forming confidence judgements, a finding referred to as the "positive-evidence bias" (PEB). Here, we report the re-analysis of nine datasets involving human participants who judged the average color of eight shapes and rated their confidence in this decision. Our findings suggest that participants overly relied on evidence that conflicted with their choice, contrary to both the normative model and the PEB. We furthermore fitted a log-posterior-ratio (LPR) model to our data and show that our findings can instead be explained by a robust averaging principle.
SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top–bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.
Microlens-Assisted Light-Scattering Imaging of Plasmonic Nanoparticles at the Single Particle Level

We present a microlens-assisted imaging approach to record the scattering light of plasmonic nanoparticles at the single particle level. The microlens can significantly enhance the backscattering of visible light from individual plasmonic nanoparticles by several dozen folds, and single gold nanoparticles with a diameter as low as 60 nm can be imaged under a conventional optical microscope. This can benefit from a significant increase in the scattering intensity afforded by the microlens, meaning that the imaging of gold nanoparticles at a high temporal resolution (up to 5000 Hz) can be achieved, which is fast enough to record single particle adhesion events on the substrate. This research presents a fast and efficient means of acquiring scattering light from plasmonic nanoparticles, which has great potential to develop plasmonic nanoparticle-based biosensors and investigate a wide range of plasmonic nanoparticle-based fast interaction processes.
Trends in Single-Molecule Total Internal Reflection Fluorescence Imaging and Their Biological Applications with Lab-on-a-Chip Technology

Single-molecule imaging technologies, especially those based on fluorescence, have been developed to probe both the equilibrium and dynamic properties of biomolecules at the single-molecular and quantitative levels. In this review, we provide an overview of the state-of-the-art advancements in single-molecule fluorescence imaging techniques. We systematically explore the advanced implementations of in vitro single-molecule imaging techniques using total internal reflection fluorescence (TIRF) microscopy, which is widely accessible. This includes discussions on sample preparation, passivation techniques, data collection and analysis, and biological applications. Furthermore, we delve into the compatibility of microfluidic technology for single-molecule fluorescence imaging, highlighting its potential benefits and challenges. Finally, we summarize the current challenges and prospects of fluorescence-based single-molecule imaging techniques, paving the way for further advancements in this rapidly evolving field.
Non-Contact Face Temperature Measurement by Thermopile-Based Data Fusion

Thermal imaging cameras and infrared (IR) temperature measurement devices act as state-of-the-art techniques for non-contact temperature determination of the skin surface. The former is cost-intensive in many cases for widespread application, and the latter requires manual alignment to the measuring point. Due to this background, this paper proposes a new method for automated, non-contact, and area-specific temperature measurement of the facial skin surface. It is based on the combined use of a low-cost thermopile sensor matrix and a 2D image sensor. The temperature values as well as the 2D image data are fused using a parametric affine transformation. Based on face recognition, this allows temperature values to be assigned to selected facial regions and used specifically to determine the skin surface temperature. The advantages of the proposed method are described. It is demonstrated by means of a participant study that the temperature absolute values, which are achieved without manual alignment in an automated manner, are comparable to a commercially available IR-based forehead thermometer.
Slow Freezing of Preserved Boar Sperm: Comparison of Conventional and Automated Techniques on Post-Thaw Functional Quality by a New Combination of Sperm Function Tests

Simple Summary The slow freezing of boar spermatozoa is carried out by exposure to liquid nitrogen vapors (conventional) or by using automated equipment that controls the temperature drop curve. In both cases, freezing/thawing causes a reduction in sperm functionality. Therefore, the objective of this study was to compare both freezing techniques through a panel of evaluations of sperm function by CASA and flow cytometry technologies. We observed the negative effect of post-thawing incubation time on sperm motility. In addition, there was a significant decrease in the levels of free radicals, reduced lipid peroxidation, and a loss of cholesterol and markers of apoptosis, which have an impact on the better conservation of the motility of frozen boar spermatozoa with automated equipment. For this reason, the control of the temperature drop curve allows sperm functionality to be preserved. No significant differences were observed for some parameters associated with the state and preservation of the membranes. Abstract The slow freezing of boar sperm is the only way to preserve genetic material for extended periods; this can be achieved with exposure to liquid nitrogen vapors (conventional) or by using automated freezing equipment. The aim was to compare the effect of both techniques on post-thaw functionality. Boar sperm devoid of seminal plasma and resuspended in lactose-egg yolk-glycerol medium were cryopreserved. Conventional: straws were exposed to LN2 vapors; automated: using a drop curve of −39.82 °C·min−1 for 113 s from −5 to −80 °C during the critical period; and subsequent immersion in NL2. Cell viability, cholesterol flow, mitochondrial membrane potential (MMP), lipid peroxidation, peroxynitrite, superoxide anion levels, phosphatidylserine translocation, and caspase activation were evaluated by flow cytometry. In addition, total motility (TM) and progressive motility (PM) were determined by the SCA system immediately (T0), 60 (T60), and 120 min (T120) post-thawing. Automated freezing significantly reduces cholesterol flow and free radical and lipid peroxidation levels, making it possible to preserve motility for 120 min of incubation. At the same time, viability, acrosome integrity, MMP, and caspase activation did not differ from the conventional technique. In conclusion, controlling the temperature drop curve using automated freezing equipment reduces oxidative/nitrosative stress, preserving membrane fluidity and sperm motility.
Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

Model-based image denoising methods are well suited for use as image processors in remote sensing systems such as satellites due to their well-developed mathematical theory and low computational cost, but these methods can often only deal with a single type of random noise. In this paper, based on the model-based image denoising techniques, a remote sensing image mixed noise denoising algorithm using Gaussian curvature in the image surface and shearlet transform is proposed. The Gaussian curvature filtering (GCF) is used to suppress the salt & pepper noise (SPN) in the image to get the first denoised image. The second denoised image is obtained by processing the coefficient matrices of the shearlet transform decomposition using statistical method and adaptive median filtering (AMF). Finally, the reconstructed image is again optimized by AMF to eliminate the residual SPN. Specially, we propose the goodness-of-fit (GOF) test denoising algorithm in shearlet domain based on the empirical distribution function (EDF) statistics to solve the problem of insufficient denoising by the traditional threshold function. Our method can effectively remove the noise and improve the visibility and usability of remote sensing images. Experimental results show that our proposed method has PSNR and MSE performance improvement compared with related model-based and learning-based methods.