Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of In Situ Measurement for Site Remediation and Final Status Survey of Decommissioning KRR Site

Background: In situ gamma spectrometry has been used to measure environmental radiation, assumptions are usually made about the depth distribution of the radionuclides of interest in the soil. The main limitation of in situ gamma spectrometry lies in determining the depth distribution of radionuclides. The objective of this study is to develop a method for subsurface characterization by in situ measurement. Materials and Methods: The peak to valley method based on the ratio of counting rate between the photoelectric peak and Compton region was applied to identify the depth distribution. The peak to valley method could be applied to establish the relation between the spectrally derived coefficients (Q) with relaxation mass per unit area (β) for various depth distribution in soil. The in situ measurement results were verified by MCNP simulation and calculated correlation equation. In order to compare the depth distributions and contamination levels in decommissioning KRR site, in situ measurement and sampling results were compared. Results and Discussion: The in situ measurement results and MCNP simulation results show a good correlation for laboratory measurement. The simulation relationship between Q and source burial for the source layers have exponential relationship for a variety depth distributions. We applied the peak to valley method to contaminated decommissioning KRR site to determine a depth distribution and initial activity without sampling. The observed results has a good correlation, relative error between in situ measurement with sampling result is around 7% for depth distribution and 4% for initial activity. Conclusion: In this study, the vertical activity distribution and initial activity of Cs could be identifying directly through in situ measurement. Therefore, the peak to valley method demonstrated good potential for assessment of the residual radioactivity for site remediation in decommissioning and contaminated site.
Neutralization of Different Variants of SARS-CoV-2 by a F(ab′)2 Preparation from Sera of Horses Immunized with the Viral Receptor Binding Domain

The Receptor Binding Domain (RBD) of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, is the functional region of the viral Spike protein (S), which is involved in cell attachment to target cells. The virus has accumulated progressively mutations in its genome, particularly in the RBD region, many of them associated with immune evasion of the host neutralizing antibodies. Some of the viral lineages derived from this evolution have been classified as Variant of Interest (VOI) or Concern (VOC). The neutralizing capacity of a F(ab′)2 preparation from sera of horses immunized with viral RBD was evaluated by lytic plaque reduction assay against different SARS-CoV-2 variants. A F(ab′)2 preparation of a hyperimmune serum after nine immunizations with RBD exhibited a high titer of neutralizing antibodies against the ancestral-like strain (1/18,528). A reduction in the titer of the F(ab’)2 preparation was observed against the different variants tested compared to the neutralizing activity against the ancestral-like strain. The highest reduction in the neutralization titer was observed for the Omicron VOC (4.7-fold), followed by the Mu VOI (2.6), Delta VOC (1.8-fold), and Gamma VOC (1.5). Even if a progressive reduction in the neutralizing antibodies titer against the different variants evaluated was observed, the serum still exhibited a neutralizing titer against the Mu VOI and the Omicron VOC (1/7113 and 1/3918, respectively), the evaluated strains most resistant to neutralization. Therefore, the preparation retained neutralizing activity against all the strains tested.
SACO: un algoritmo de clustering espacial con hormigas inteligentes

Bio-inspired algorithms have received much attention in recent years, because they allow to discover, quickly and efficiently, knowledge and patterns in large databases. In this work, a new algorithm based on the behaviour of ant colonies to discover clusters in spatial databases is presented. The algorithm proposed was evaluated using several well-known test instances and compared its performance considering other proposals from the literature.
Financial Banking Dataset for Supervised Machine Learning Classification

Social media has opened new avenues and opportunities for financial banking institutions to improve the quality of their products and services and to understand and to adapt to their customers' needs. By directly analyzing the feedback of its customers, financial banking institutions can provide personalized products and services tailored to their customer needs. This paper presents a research framework for creation of a financial banking dataset in order to be used for Sentiment Classification using various Machine Learning methods and techniques. The dataset contains 2234 financial banking comments from Romanian financial banking social media collected via web scraping technique.
Nonlinear Aerodynamic Modeling of Hansa-3 Aircraft Using Neural-Gauss-Newton Method

The paper presents the nonlinear longitudinal aerodynamic modeling using Neural-GaussNewton (NGN) method from real flight data of Hansa-3 aircraft. The NGN method is an algorithm that utilizes Feed Forward Neural Network and Gauss-Newton optimization to estimate the parameters and it does not require a priori postulation of mathematical model or solution of equations of motion. The Kirchhoff’s quasi-steady stall model was used to include the nonlinearity in the aerodynamic model used for parameter estimation. Before application to the flight data at high angles of attack, the method was validated on flight data at moderate angles of attack. The results obtained in terms of stall characteristics and aerodynamic parameters were encouraging and reasonably accurate to establish NGN as a method for modeling nonlinear aerodynamics using flight data at high angles of attack. The supremacy of NGN was established by comparing the NGN estimates to that of Maximum Likelihood.
CDS: Coupled Data Storage to Enhance Read Performance of 3D TLC NAND Flash Memory

Due to the strong demand of massive storage capacity, the density of flash memory has been improved in terms of technology node scaling, multi-bit per cell technique, and 3D stacking. However, these techniques also degrade read performance and reliability. The long read latency comes from increased data sensing time and time-consuming ECC decoding time. Storing multiple bits per cell results in more read reference voltages and increased latency of identifying appropriate threshold voltages. To deal with error correction, LDPC is widely used in flash memory to provide stronger ECC capability. However, LDPC incurs a long decoding latency when bit errors are numerous. In this work, we propose coupled data storage (CDS) to improve the read performance of 3D NAND flash-memory storage devices. CDS supports two modes to improve read latency: The high read-speed mode is designed to improve data sensing time with reduced voltage states, while the data correction mode is designed to mitigate bit errors and LDPC overhead. Experiment results showed that CDS could reduce 50<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="hsieh-ieq1-3338474.gif"/></alternatives></inline-formula>66.6% read latency and 25.7<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="hsieh-ieq2-3338474.gif"/></alternatives></inline-formula>27.5% write latency under the high read-speed mode. For the data correction mode, RBER could be decreased by 37<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="hsieh-ieq3-3338474.gif"/></alternatives></inline-formula>52% and the lifetime could be prolonged to 1.6 to 3 times.
Proving Search Techniques Do Not Provide Acceptable Performance for Realistic Retrieval Tasks in Data Mining

To search some information on the web still today we use paradigm to relational data has been an active area of research within the database . however this is not a best approach to retrieve or to mine data on the web. The ubiquitous search text box has transformed the way people interact with information. The ubiquitous search text box has transformed the way people interact with information. The following results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes many search techniques from scaling beyond small datasets with tens of thousands of vertices. Keywords—component; formatting; style; styling; insert (key words)
A fuzzy sets approach to the representation of vegetation continua from remotely sensed data : an example from lowland health

Conventional image classification routines are often inappropriate for the mapping of continuous phenomena suchy as heathland vegetation. To allow for the natural fuzziness of such an environment, a fuzzy sets algorithm may be used to model the heathland vegetation more appropriately than a classification. The results of this study show that the fuzzy c-means algorithm can be used to discriminate accurately between the end points of a set of continua, and that class membership functions derived from the analysis are sensitive to the botanical composition of the vegetation canopy. Mapping the fuzzy membership functions will therefore enable a more realistic portrayal of the healthland vegetation than a conventional classification.
Numerical and Experimental Analysis to Predict the Compressive Strength of Pristine Composite Laminates

In this work, the numerical and experimental work has been carried out to verify the compressive strength of pristine composite laminates. Theoretical computations have been carried out using MATLAB to compute the strength and buckling load. Finite Element Analysis has been carried out for the composite laminate using two and three dimensional elements. PATRAN as the Pre and Post processing package and NASTRAN as the solver have been used in this work. The various failure theories considered in this article are Tsai-Wu, Hill, Hoffman, Maximum Stress and Quadratic failure theory (YSFT). All the first four failure theories are available as a standard criterion in the PATRAN package. The fifth quadratic failure criterion has been implemented using PATRAN Command Language. Static analysis along with failure theories has been carried out to compute the strength of the laminate. Static and buckling analysis has been presented for both in two-and three-dimensional models. Experiments have been carried out on number of pristine laminates and the strain values obtained from FEA have been compared with the experimental values. The theoretical computation provides a load value which is about the elastic limit of the specimen. It is generally observed that quadratic failure theory (YSFT) provides least failure indices and may be employed for conservative design.
A New Method for Short Text Compression

Short texts cannot be compressed effectively with general-purpose compression methods. Methods developed to compress short texts often use static dictionaries. In order to achieve high compression ratios, using a static dictionary suitable for the text to be compressed is an important problem that needs to be solved. In this study, a method called WSDC (Word-based Static Dictionary Compression), which can compress short texts at a high ratio, and a model that uses iterative clustering to create static dictionaries used in this method are proposed. The number of static dictionaries to be created can vary by running the k-Means clustering algorithm iteratively according to some rules. A method called DSWF (Dictionary Selection by Word Frequency) is also presented to determine which of the created dictionaries can compress the source text at the best ratio. Wikipedia article abstracts consisting of 6 different languages were used as the dataset in the experiments. The developed WSDC method is compared with both general-purpose compression methods (Gzip, Bzip2, PPMd, Brotli and Zstd) and special methods used for compression of short texts (shoco, b64pack and smaz). According to the test results, although WSDC is slower than some other methods, it achieves the best compression ratios for short texts smaller than 200 bytes and better than other methods except Zstd for short texts smaller than 1000 bytes.
The relationship between music training and cognitive flexibility: an ERP study

Background Music training involves several cognitive functions in the brain, particularly executive function. Numerous studies have proven a link between the two. Cognitive flexibility is an important component of executive function, however, there has been little study investigating the association between music training and cognitive flexibility. Method Music training (N = 22) and the control groups (N = 26) were included in the present study. A tone-related oddball task was used to investigate the tone-related inhibition and the switch task was to investigate cognitive flexibility. During the switch task, EEG data were collected. Results The behavioral results of the oddball task showed that the individuals in the music training group had a shorter reaction time and higher accuracy in both the between-tone and within-tone categories compared to the controls. The behavioral results of the switch task showed similar results, with the music training group exhibiting better reaction time and accuracy performance than the controls. ERP results showed that the music training group had smaller P3 amplitudes and greater N2 and N450 amplitudes than the control group. Discussion The findings further supported the link between music training and enhanced cognitive function.
Immunologic barriers in liver transplantation: a single-cell analysis of the role of mesenchymal stem cells

Background This study aimed to analyze the biomarkers that may reliably indicate rejection or tolerance and the mechanism that underlie the induction and maintenance of liver transplantation (LT) tolerance related to immunosuppressant or mesenchymal stem cells (MSCs). Methods LT models of Lewis-Lewis and F344-Lewis rats were established. Lewis-Lewis rats model served as a control (Syn). F344-Lewis rats were treated with immunosuppressant alone (Allo+IS) or in combination with MSCs (Allo+IS+MSCs). Intrahepatic cell composition particularly immune cells was compared between the groups by single-cell sequencing. Analysis of subclusters, KEGG pathway analysis, and pseudotime trajectory analysis were performed to explore the potential immunoregulatory mechanisms of immunosuppressant alone or combined with MSCs. Results Immunosuppressants alone or combined with MSCs increases the liver tolerance, to a certain extent. Single-cell sequencing identified intrahepatic cell composition signature, including cell subpopulations of B cells, cholangiocytes, endothelial cells, erythrocytes, hepatic stellate cells, hepatocytes, mononuclear phagocytes, neutrophils, T cells, and plasmacytoid dendritic cells. Immunosuppressant particularly its combination with MSCs altered the landscape of intrahepatic cells in transplanted livers, as well as gene expression patterns in immune cells. MSCs may be included in the differentiation of T cells, classical monocytes, and non-classical monocytes. Conclusion These findings provided novel insights for better understanding the heterogeneity and biological functions of intrahepatic immune cells after LT treated by IS alone or in combination with MSCs. The identified markers of immune cells may serve as the immunotherapeutic targets for MSC treatment of liver transplant rejection.
Complex use of cycleand power-ergometry in determining the physical working capacity of young athletes

The aim of this study is to scientifically substantiate the possibilities of complex use of cycle- and power-ergometry in determining aerobic and anaerobic-aerobic performance of power nature. Materials and methods. Young athletes (boys) aged 15–16 (n = 91) of the Brovary Higher School of Physical Education (Kyiv region) (experimental group), who participated in the study, were divided into two groups. Group А (n = 47) – speed-power sports (boxing, freestyle wrestling); group B (n = 44) – endurance sports (track and field athletics: 800, 1500, 3000 and 5000-meters race, bicycle racing 50 and 75 km). Sports experience was 3–5 years and more. The control group (group К) consisted of students aged 15–16, who did not play sports (n = 25). Physical working capacity was determined using two methods: submaximal cycle ergometric test PWC170 and submaximal power ergometric test PWC170. Method of power ergometry (patent of Ukraine No 49417) has no analogues in countries of near and far abroad. Results. The fact of the specific influence of different kinds of dosed physical exercises (strength and power exercises) on PWC170 indicators was established. Thus, no probable differences in aerobic performance indicators were found in wrestlers and boxers during the cycle ergometric test. And conversely, under the conditions of power ergometric testing, we find a significant increase in their PWC170 indicators in relation to athletes of endurance sports. The opposite character of changes was observed when testing the track and field athletes and bike riders. According to the results of the conducted cycle ergometry, these athletes had a significant increase in aerobic performance indicators in comparison with the athletes of speed-power sports. However, the changes of anaerobic-aerobic performance were not significant. It was observed that the adolescents, who did not play sports, had statistically insignificant changes in PWC170 indicators both in the conditions of cycle ergometric and power ergometric testing. Conclusions. The fact of specific influence of different types of training exercises on the body functions of adolescents was confirmed. The possibility of introducing the methods of complex use of aerobic (cycle ergometry) and anaerobic-aerobic loads (power-ergometry) into the practice of medico-biological control of young athletes is shown. Diagnostics of various types of physical working capacity (PWC170) will provide the coach with operational information about the functional state of the athletes’ body, which will allow to effectively manage a training process.
[The fossil record of the Eurasian Neogene insectivores (Erinaceomorpha, Soricomorpha, Mammalia) : Part I / L.W. van den Hoek Ostende, C.S. Doukas and J.W.F. Reumer (editors)]: WINE: putting the fossil insectivores on record

Introduction In their report of a meeting at Reisensburg, the RCMNS working group on fossil mammals presented ranges of Neogene mammal genera (De Bruijn et al, 1992). However, no erinaceid, talpid or shrew can be found in the tables. The working group concluded (p. 70); "The omission of all the insectivores is regretted, but the participants of the Reisensburg meeting consider this order insufficiently known at this moment. " In 1990, this position was justifiable if not justified. Mammal studies up until then had focused on the stratigraphical position of the various faunas. The insectivores, far less numerous than the rodents, usually played a minor role in these studies and were either identified provisionally or not included at all. Detailed descriptions of insectivores were usually furnished in monographs (e.g. Doben-Florin, 1964; Muller, 1967; Repenning, 1967; Engesser, 1975; Ziegler, 1983; Reumer, 1984; Rumke, 1985). However, already in the 1970s and 1980s, some palaeontologists were specializing in insectivores, resulting, for example, in a complete series on the Pliocene and Pleistocene insectivores from Poland (Rzebik-Kowalska, 1971, 1975, 1976, 1981, 1989, 1990a, b, 1991), and overviews from insectivores from Spain (Gibert, 1974, 1975) and Anatolia (Engesser, 1980). Around the time of the Reisenburg conference, a change was taking place in mammal palaeontology. As it became clear that our climate was changing, palaeontologists focused more and more on environmental changes in the past. In mammal palaeontology, this implied that not just taxa that were interesting as stratigraphical markers needed
Dynamic Debiasing Network for Visual Commonsense Generation

The task of Visual Commonsense Generation (VCG) delves into the deeper narrative behind a static image, aiming to comprehend not just its immediate content but also the surrounding context. The VCG model generates three types of captions for each image: 1) the events preceding the image, 2) the characters’ current intents, and 3) the anticipated subsequent events. However, a significant challenge in VCG research is the prevalent yet under-addressed issue of dataset bias, which can result in spurious correlations during model training. This occurs when a model, influenced by biased data, infers associations that frequently appear in the dataset but may not provide accurate or contextually appropriate interpretations. The issue becomes even more complex in multimodal tasks, where different types of data, such as text and image, bring their unique biases. When these modalities are combined as inputs to a model, one modality might exhibit a stronger bias than others. To address this, we introduce the Dynamic Debiasing Network (DDNet) for Visual Commonsense Generation. DDNet is designed to identify the biased modality and dynamically counteract modality-specific biases using causal relationship. By considering biases from multiple modalities, DDNet avoids over-focusing on any single modality and effectively combines information from all modalities. The experimental results on the VisualCOMET dataset demonstrate that our proposed network fosters more accurate commonsense inferences. This emphasizes the critical need for debiasing in multimodal tasks and enhances the reliability of machine-generated commonsense narratives.
Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions

This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998–2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, K+, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson’s Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems.