bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measuring Unit for Synchronously Collecting Air Dose Rate and Measurement Position
Authors: Kawabata Kuniaki , Imabuchi Takashi , Shirasaki Norihito , Suzuki Soichiro , Ito Rintaro , Aoki Yuto , Omori Takazumi 
Published: February 2024

Abstract

This paper describes a measuring unit to realize the synchronous collection of air dose rate and measurement position for efficient dosimetry survey and data logging in a working space. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and a dosimetry part, which are integrated into a single measurement unit through an embedded computer that installs a ROS (Robot Operating System) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and the measurement position. In this paper, we describe the prototype system configuration and the experimental results obtained in the mockup test space and nuclear facility to discuss its performance.
Construction of bioactive electrospun microfibers with VEGF to facilitate the repair of infected wounds
Authors: Lin Xunxun , jiashaochuan Shaochuan , Zhou Boya , Liu Haibo , Li Xixi , Ling Yu , Wei Yanlin , Jiang Xiaoyang , Rong Yongxian , Li Hongmian 
Published: February 2024

Abstract
Biomimetic dressings are considered as one of the ideal medical dressings to combat bacterial infections, shorten the tissue regeneration process, and facilitate wound repair. Researchers are committed to developing a new generation of wound dressings with unique functions to promote wound repair and healing. Here, a strategy for composite nanofiber membrane construction based on natural biopolymers HA, Gel and PLGA with carrying vascular endothelial growth factor (VEGF) was provided for pro-angiogenic wound repair. The composite nanofiber membrane prepared by electrospinning technology can maintain its biomimetic properties and biochemical properties of the functional factors. The enhanced mechanical properties and flexibility enable it to fit the wound and have great biocompatibility. In addition, the highly efficiently loaded VEGF can be controllable released and targeted to the wound area, thereby promoting the exchange of nutrients and oxygen, proliferation and migration of endothelial cells, and the formation of vascular lumen, effectively accelerating tissue repair and wound healing while avoiding unnecessary drug diffusion. Therefore, composite nanofiber dressing of VEGF@PAG can break through the limitations of traditional patches and become ideal candidates for wound healing and related biomedical applications.
CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules
Authors: Sun Jing-Xi , Zhou Xuan-Xuan , Yu Yan-Jin , Wei Ya-Ming , Xu Qing-Song , Chen Shuang-Shuang 
Published: February 2024

Abstract

Background
At present, computed tomography (CT) radiomics-based models capable of evaluating small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. Accordingly, the present study sought to develop a CT radiomics-based model capable of differentiating between benign and malignant small SPNs.
Methods
Between January 2019 and November 2021, this study enrolled consecutive patients presenting with small SPNs, randomly assigning these individuals to training and testing cohorts at an 8:2 ratio. CT images were processed to extract radiomics features, with a radiomics scoring model being developed based on the features selected in the training group through univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model.
Results
In total, this study included 210 patients in the training (n = 168) and testing (n = 42) cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Traditional CT and clinical risk factors associated with malignancy in SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model consisted of the following formula: X = -6.773 + 12.0705×radiomics score + 2.5313×lobulation + 3.1761×spiculation + 0.3253×diameter. The CT radiomics-based model, CT radiomics score, and clinicoradiological score were associated with area under the curve (AUC) values of 0.957, 0.945, and 0.853, respectively, in the training cohort, while the testing cohort exhibited corresponding AUC values of 0.943, 0.916, and 0.816.
Conclusions
The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy when employed to distinguish between benign and malignant SPNs.
Resiliency-Constrained Placement and Sizing of Virtual Power Plants in the Distribution Network Considering Extreme Weather Events
Authors: Zadehbagheri Mahmoud , Pirouzi Sasan , Dehghan Mohammad , Kiani Mohammadjavad 
Published: February 2024

Abstract

The placement and scale of virtual power plants (VPPs) in distribution networks are the only topics covered in this article that pertain to the resilience of the grid to severe weather. This problem is framed as a two-objective optimization, where the predicted energy that the network would not deliver in the case of an earthquake or flood, and the annual planning cost of the VPP, are the two objective functions to be reduced. The constraints include the formula for VPP planning, limitations on network operation and resilience, and equations for AC power flow. Uncertainties about demand, renewable power, energy prices, and the supply of network hardware and VPP components are all taken into account in stochastic programming. The proposed technique achieves a single-objective formulation in the subsequent stage by the use of a Pareto optimization strategy based on the ε-constraint method. This article uses a solver based on a hybrid of Crow search algorithm (CSA) and sine cosine algorithm (SCA) to achieve the trustworthy optimal solution with lowest dispersion in the final response. In order to tackle the problem, the proposed system looks at how the VPP affects network resilience, scales it, and combines it with the hybrid evolutionary algorithm. In the end, the numerical findings verify that the optimal placement and dimensions of VPPs help to improve the operational, financial, and resilience status of the distribution network by applying the proposed problem to a 69-bus distribution network.
Lycopene Alleviates Deoxynivalenol-induced Toxicity in Porcine Intestinal Epithelial Cells by Mediating Mitochondrial Function
Authors: Wang Jie , Lu Fan , Gu Shanshen · , Dai Chaohui · , Xiao Yeyi · , Bao Wenbin · , Wang Haifei · 
Published: February 2024

Abstract

Deoxynivalenol (DON) is widely found in food and feed, posing a threat to human and animal health. Lycopene (Lyc) is a natural plant extracts with significant antioxidant properties. This study was conducted to investigate the protective effects of Lyc on IPEC-J2 cells upon DON exposure. The detection of cell viability and trypan blue staining showed that Lyc alleviated cell damage and decreased cell apoptotic rate induced by DON. The analysis of reactive oxygen species (ROS) level and antioxidant parameter measurements showed that Lyc significantly down-regulated the content of ROS and restored antioxidant enzyme activity. Furthermore, mitochondrial membrane potential (ΔΨm) detection, mitochondrial DNA copy number (mtDNAcn) assay and adenosine triphosphate (ATP) concentration detection showed Lyc improved mitochondrial function after DON exposure. The results of transcriptome analysis, ROS detection and CCK8 assay suggested that Lyc may activated the oxidative phosphorylation (OXPHOS) to improve mitochondrial function. Conclusively, our results suggested that Lyc alleviated DON-induced oxidative stress by improving mitochondrial function through OXPHOS signaling pathway.
Recycling of Broken Glasswares Into an Adsorbate for Cationic and Anionic Dyes for Waste Water Treatment
Authors: Angayarkanny S. , Prabha J. , Sri M. 
Published: February 2024

Abstract
Chemically modified broken laboratory glass wares were proved to act as an efficient adsorbent for both anionic and cationic dyes from waste water. The glassware wastes were chemically modified with sodium hydroxide as silica gel, which are used for cationic dyes adsorption from wastewater. The said modified silica gel is again modified with cetyl trimethyl ammonium bromide (CTAB), which is a cationic amphiphile for anionic dye adsorption from wastewater. The both silica gel and modified silica gel were characterized with Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Analysis (EDAX) and Fourier Transform Infrared Spectroscopy (FTIR). The adsorptions of the dyes were confirmed visually as well as using UV- visible spectroscopy. The adsorption capacities of the modified silica gel were quantified using model dyes like methylene blue (MB) (cationic dye) and methyl orange (MO) (anionic dye). The adsorption was found to be 97.84 mg/g for methylene blue and 196.14 mg/g for methyl orange by the modified silica gel. The efficiency of the adsorption was quantified at various dosages, pH, initial concentrations and contact time. The mechanism of the adsorption is predicted by applying mathematical models. This study demonstrated an efficient way of recycling the non-biodegradable broken glassware waste from laboratories for the waste water treatment by modifying as an efficient adsorbate for both anionic and cationic dyes. This study doubly contributes towards the environmental protection by recycling hazardous broken glass wares by modifying that as an adsorbate towards clean water.
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Authors: Pfahler Simon , Georg Peter , Schill Rudolf , Klever Maren , Grasedyck Lars , Spang Rainer , Wettig Tilo 
Published: February 2024

Abstract

The Kullback-Leibler (KL) divergence is frequently used in data science. For discrete distributions on large state spaces, approximations of probability vectors may result in a few small negative entries, rendering the KL divergence undefined. We address this problem by introducing a parameterized family of substitute divergence measures, the shifted KL (sKL) divergence measures. Our approach is generic and does not increase the computational overhead. We show that the sKL divergence shares important theoretical properties with the KL divergence and discuss how its shift parameters should be chosen. If Gaussian noise is added to a probability vector, we prove that the average sKL divergence converges to the KL divergence for small enough noise. We also show that our method solves the problem of negative entries in an application from computational oncology, the optimization of Mutual Hazard Networks for cancer progression using tensor-train approximations.
Community Pharmacists’ Perceptions and Experiences of Medicine Shortages in Disruptive Situations: A Qualitative Study
Authors: Bachoolall Rivana , Suleman Fatima 
Published: February 2024

Abstract

Background: Medicine shortages are a challenge in upper, lower and middle-income countries, including South Africa. In recent years, community pharmacists in Durban, South Africa, have experienced disruptions such as the COVID-19 pandemic, flooding, civil unrest and electricity disruptions. Little is known about the impact of these disruptive situations on medicine shortages in community pharmacies.
Aim: Exploring the perceptions of community pharmacists and their experiences with medicine shortages during the COVID-19 pandemic and other disruptive situations.
Method: Convenience and snowball sampling were used to recruit community pharmacists in Durban, South Africa. Semi-structured interviews were conducted in person or via an online video conferencing platform. All interviews were audio-recorded and transcribed verbatim. The transcripts were analysed thematically on NVivo 14 software, using the Framework Method.
Results:A total of 15 community pharmacists were interviewed. Five major themes emerged from thematic analysis, viz. the perceptions of medicine shortages, the impact of disruptive situations, the consequences of medicine shortages, mitigation strategies; and further suggestions and resources. Perceptions were that shortages were exacerbated by the disruptive situations. Participants perceived a negative financial impact on patients and pharmacies, with out-of-pocket costs affecting the former and loss of income affecting the latter. The mitigation strategies used were contacting stakeholders, medicine substitution and stock management.
Conclusion: Community pharmacists felt that medicine shortages required improved communication, collaboration, policies, notification systems and guidelines to mitigate the problem further.
Does the history of option quality affect nest site choice in the acorn ant?
Authors: Tyler Kelsi , Vassie Dominique , Sasaki Takao 
Published: February 2024

Abstract

During decision−making, animals consider not only the current but also the past quality of options. For example, when humans evaluate performance (e.g. sales) of employees, they do not only consider the average performance but also the trend of performance— ascending performance is often viewed as more favorable than descending performance. In our study, we test if non-human animals have a similar bias when they are evaluating options using house-hunting by the acorn ant, Temnothorax curvispinosus, as our model system. Our data show that when nest-site quality is static over time, ant colonies tend to prefer the nest site which was better (i.e. darker) between two nest options. However, when the nest quality changes—one improves and the other worsens—over time, more colonies choose the low-quality, but improving, nest than the high-quality, but worsening, nest. These results suggest that a continuous change of option quality may influence evaluation. We discuss alternative explanations for our results, possible mechanisms and potential ecological benefits for keeping track of the nest-site quality.
A Study on Stock Forecasting Using Deep Learning and Statistical Models
Authors: Jaiswal Aditya 
Published: February 2024

Abstract

Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the stock price. Machine learning, deep learning and statistical analysis techniques are used here to get the accurate result so the investors can see the future trend and maximize the return of investment in stock trading. This paper will review many deep learning algorithms for stock price forecasting. We use a record of s&p 500 index data for training and testing. The survey motive is to check various deep learning and statistical model techniques for stock price forecasting that are Moving Averages, ARIMA which are statistical techniques and LSTM, RNN, CNN, and FULL CNN which are deep learning models. It will discuss various models, including the Auto regression integration moving average model, the Recurrent neural network model, the long short-term model which is the type of RNN used for long dependency for data, the convolutional neural network model, and the full convolutional neural network model, in terms of error calculation or percentage of accuracy that how much it is accurate which measures by the function like Root mean square error, mean absolute error, mean squared error. The model can be used to predict the stock price by checking the low MAE value as lower the MAE value the difference between the predicting and the actual value will be less and this model will predict the price more accurately than other models.
Semantic segmentation of point clouds of ancient buildings based on weak supervision
Authors: Zhao Jianghong , Yu Haiquan , Hua Xinnan , Wang Xin , Yang Jia , Zhao Jifu , Xu Ailin 
Published: February 2024

Abstract
Semantic segmentation of point cloud of ancient buildings plays an important role in heritage building information modeling (HBIM). Since the point cloud annotation task of ancient architecture point cloud is characterized by strong specialization and large workload, which greatly restricts the application of point cloud semantic segmentation technology in the field of ancient architecture, this paper researches on the semantic segmentation method based on weak supervision for ancient architecture point cloud. Aiming at the problem of small differences between classes of ancient architectural components, this paper introduces a self-attention mechanism, which can effectively distinguish similar components. We also explore the insufficiency of position encoding in baseline to construct a high-precision point cloud semantic segmentation network model for ancient buildings. We call it SQN-DLA. using only 0.1% of the annotations in our homemade dataset and the public dataset ArCH, the mIoU reaches 66.02% and 58.03%, respectively, which is improved by 3.51% and 3.91% compared with baseline, respectively.
Cytogenetic determination of two species of lacewings (Neuroptera: Chrysopidae)
Authors: Acevedo Victoria Fernández , Gil Sergio Rodríguez , Schneider Marcela 
Published: February 2024

Abstract
The Chrysopidae family is the second most important family in the order Neuroptera. Chrysoperla externa and Chrysoperla argentina are two of the four chrysopid species of economic importance in Argentina. Meiotic studies of the species have indicated that have chromosome numbers of 2n = 12. The objective of this study was to analyze and describe the biology of C. externa and C. argentina chromosomes in terms of the cell cycle and karyotype because of the lack of exhaustive details of the karyotype or mitotic cycle of both species of lacewings. Chromosomes of C. externa were measured at the metaphase stage, C. argentina chromosomes were measured at the end of the mitotic prophase. Chromosomes that exhibited natural banding were paired together. Chromosomes that did not show marked differentiation were grouped according to the criterion of a similar chromosome contribution. The chromosomes are rod shaped without primary constriction with a gradual variation in size. In both species, sex chromosomes exhibited marked dimorphism. The X chromosome is among the largest chromosome pairs and the Y chromosome among the smallest. Chrysoperla externa shows natural chromosome banding that allows three of the six pairs that make up its genome to be distinguished. Chrysoperla argentina only two chromosome pairs exhibit natural banding. In conclusion, this study presents, for the first time, a proposal for nomenclature, identification of the chromosomes and kariotype of C. externa and C. argentina, accompanied by detailed information on their cell cycle.
The prognostic value of venous return system in patients with constrictive pericarditis after pericardial stripping: a retrospective cohort study
Authors: Chen Rongping , DU Wei , Liu Dawei 
Published: February 2024

Abstract
This study aimed to explore changes in the venous return system in patients with Constrictive pericarditis (CP) after pericardial stripping and examine their value in predicting mortality. An 8-year single-center retrospective cohort study including patients with CP after pericardial stripping surgery. Hemodynamic parameters were analyzed in 90 patients at 11 time points including before and after surgery and every 4 to 9 hours in the first 48 hours in the ICU (0h, t1-t3, 24h, t4-t6, 48h). Mean systemic filling pressure (Pmsf) and resistance to venous return (RVr) were significantly higher in patients who died (non-survival group) than survivors (P = 0.016 and 0.022, respectively). Pmsf at 0h, Pmsf at 24h, Pmsf − CVP at 0h, RVr before surgery, RVr at 24h, APACHE II score, and SOFA score were evaluated as predictors of 28-day mortality. The area under the receiver operating characteristic curve was largest for RVr before surgery (0.771; 95% confidence interval, 0.583–0.958; P = 0.034). SOFA combined with RVr before surgery had the best performance (area under the receiver operating characteristic curve, 0.883; 95% confidence interval, 0.758–1.000; P = 0.003). Pmsf, Pmsf − CVP, and RVr can be used as valid indicators for prognostic assessment in patients with constrictive pericarditis after pericardial stripping.
A Block Cipher Recognition Scheme Based on Deep Learning Transformer Algorithm
Authors: Yuan Ke , Zhang Bowen , Zhou Yuwei , Sun Hanlin , Yang Wei , Jia Chunfu 
Published: February 2024

Abstract
Cryptographic algorithms are the core of data encryption, and their identification is a prerequisite for in-depth analysis of cryptography. Cryptographic algorithm identification is the process of distinguishing or identifying encryption methods by analyzing potentially distinctive information in the ciphertext when the ciphertext is known. It serves as the foundation for cryptanalysis work. The machine learning-based approach for identifying cryptographic algorithms extracts ciphertext features and trains the machine learning algorithm to build a cryptographic system identification classifier. It has the characteristics of high accuracy, concise operation process, and strong practicability. The ciphertext's complexity and the inter-data interference both rise with the number of cryptographic algorithms. This will lead to a reduction in the recognition rate of traditional machine learning cryptographic algorithm recognition solutions, poor recognition stability, and huge challenges to recognition capabilities. Deep learning algorithms have the characteristics of strong learning ability, a large amount of data processing, wide-coverage, good adaptability, and portability, etc., and have become a hot spot in the realm of cryptographic algorithm identification. In this work, a cryptographic algorithm identification methodology based on the Transformer algorithm is proposed, and the ciphertext feature extraction approach in the NIST randomness test is improved. In the research on cryptographic algorithm identification, we selected five block cipher algorithms: AES, 3DES, Blowfish, CAST, and RC2 as the research objects, and conducted two-class and five-class recognition experiments. According to experimental data, the approach suggested in this article has superior accuracy and stability than the classic machine learning model when the ciphertext size and other experimental variables are the same. Among them, on ciphertext files ranging from 1kb to 512kb, compared to the accuracy of the conventional classic machine learning algorithms SVM, GNB, KNN, RF, and LR, the average recognition accuracy of the two categories is 0.855, which is 29% higher. 33.9%, 28.5%, 34.2%, and 29.9%. Compared with the MLP accuracy of the same deep learning algorithm, the accuracy is also improved by 9%. When conducting the five-category experiment, the recognition accuracy of the scheme put forward in this piece is not less than 0.4. When the ciphertext file is 512KB, the recognition accuracy is as high as 0.42, and the average recognition accuracy of the five categories is 0.412. It is far greater than the other five classic machine learning algorithms and significantly better than the 20% random classification accuracy. Other algorithms' recognition rates fluctuate as ciphertext file sizes change. Among them, the Transformer algorithm has the smallest fluctuation and the best overall effect.