Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Analysis of Different Snow Grain Sizes Based on Field Measurement

Using the spectrum reflection curves of different grain size of snow measured in Binggou basin,the spectral reflection characteristics of snow are analyzed.It is found that the bands near 1030 nm are sensitive to snow grain size.The relationships between snow grain size and the spectral reflection near the band are gained quantitatively.The bands near 1 030 nm and 1 250 nm are two obvious spectral absorption peaks of snow,with rather strong reflectance signal.The spectral features are analyzed in detail through 1) the differences of spectral reflection value near 1 030 nm bands,2) the differences of spectral reflection value near 1 250 nm bands,3) absorption band depth near 1 030 nm bands and 4) absorption band area near 1 030 nm bands.By comparison,it is revealed that the absorption band area has best linear and exponential relationship with the grain size.Again,it is found that near 1 030 nm,there is less effect of the atmospheric scatting and attenuation.The method can be applied directly to hyperspectral image.
The Revisions of the First Autobiography of AT Still, the Founder of Osteopathy, as a Step towards Integration in the American Healthcare System: A Comparative and Historiographic Review

Introduction: Osteopathy was originally introduced in rural America in 1874 as a comprehensive therapeutic approach aimed at promoting health. This approach was distinct and often conflicting with conventional/allopathic therapeutic methods available at that time to fight disease. We argue that, in struggling to achieve recognition within the American healthcare system and within the educational academic field that was about to be structured, the American osteopathic profession tried to protect itself from the charges of sectarism by starting to embrace principles of the biomedical paradigm. Methods: A comparative and historiographic review of the second version of the autobiography of AT Still (1908), the founder of osteopathy, against the first (1897) was chosen as an example of the adaptation of the American osteopathic profession to its evolving academic environment. Results: Although there were only a few substantial variations, we argue that they aimed to dampen the non-biological components of osteopathy, namely, its philosophical, spiritual, religious, emotional, and Native American roots, in an effort to gain respect and recognition within the emerging gold standard of the Western medical system. The shift towards a distinct, fully integrated profession within regulated Western healthcare systems was perceived by many professionals as a threat to AT Still’s original ideas, and the trend started when he was alive. Conclusion: Our findings suggest that a crucial conversation regarding the future of the professional identity must take place within the osteopathic community.
Earth science as a philosophical background to medicine: an essay based on the autobiography of Dr Otto Sperling (1602–81)

Abstract Earth science was not taught in schools in the seventeenth century. Geology began in principle with Steno's Prodromus of 1669, which reflected tendencies in the European curiosity about nature, but had little contemporary impact. As a child in busy Protestant Hamburg, Otto Sperling was influenced by Renaissance ideas in the early 1600s and investigated plants with his family's encouragement; he continued in pharmacy and later in medicine in Leiden. As a student he visited the relatively strong medicine milieu in Copenhagen and had the opportunity to sail as a participant in King Christian IV's official visit to Bergen. Venturing into southern Norway's mountainous landscapes, he studied plants, animals and rocks. Sperling's nature studies continued on journeys in northern and southern Europe and he graduated as Doctor medicinae in Padova in 1627. En route towards England, his ship was driven to Norway, where the locals persuaded him to settle as a physician. An encounter with the Danish nobleman Corfitz Ulfeldt (1606–64) took Sperling to some of the highest medical posts and later, during Denmark–Norway's decline, dragged him into the political maelstrom. Sperling's autobiography, written in his late sixties in a Copenhagen prison, is testimony to the importance of Earth science to Sperling's philosophy and performance as a doctor.
Atomic-scale environment of niobium in ore minerals as revealed by XANES and EXAFS at the Nb K-edge

Abstract. The mineralogy of niobium (Nb) is characterized by multicomponent oxides such as AB2O6, A2B2O7, ABO4, and ABO3 in which Nb is incorporated in the B site. Such complex crystal-chemistry prevents their unambiguous identification in ore deposits such as hydrothermal rocks and laterites which exhibit complex and fine-grained textures. The understanding of the processes controlling Nb ore deposit formation in various geological settings is therefore limited, although Nb is a critical element. In this study, we use X-ray absorption spectroscopy (XAS) at the Nb K-edge to investigate the local atomic-scale structure around Nb in a large set of natural and synthetic minerals of geological and technological importance. Our X-ray absorption near-edge structure (XANES) data at the Nb K-edge show three major features of variable position and intensity and then can be related to the local distortion and coordination number of the Nb site. Shell-by-shell fits of the extended X-ray absorption fine structure (EXAFS) data reveal that the NbO6 octahedra are distorted in a variety of pyrochlore species. At least two distinct first shells of O atoms are present while reported crystallographic data yield regular octahedra in the same minerals. Next-nearest Nb–Nb distances in pyrochlore and Nb-bearing perovskite mirror a corner-sharing NbO6 network, whereas the two Nb–Nb distances in columbite are typical of edge- and corner-sharing NbO6 octahedra. Such a resolution on the Nb site geometry and the intersite relationships between the next-nearest NbO6 octahedra is made possible by collecting EXAFS data under optimal conditions at 20 K and up to 16 Å−1. The local structure around substituted Nb5+ in Fe3+, Ti4+, and Ce4+ oxides suffers major changes relative to the unsubstituted structures. The substitution of Nb5+ for Ti4+ in anatase leads to the increase in the interatomic distances between Nb and its first and second Ti4+ neighbors. The substitution of Nb5+ for Ce4+ in cerianite reduces the coordination number of the cation from eight to four, and the Nb–O bonds are shortened compared to Ce–O ones. In hematite, Nb5+ occupies a regular site, whereas the Fe3+ site is strongly distorted suggesting major site relaxation due to charge mismatch. The sensitivity of XANES and EXAFS spectroscopies at the Nb K-edge to the local site geometry and next-nearest neighbors demonstrated in this study would help decipher Nb speciation and investigate mineralogical reactions of Nb minerals in deposit-related contexts such as hydrothermal and lateritic deposits.
Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm

The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.
Instruction-Level Loop Perforation

Approximate computing (AC) offers techniques ranging from application to circuit levels. AC techniques offer better performance at the cost of inaccurate results. A widely used software AC technique is loop perforation (LP). This paper presents an Instruction-Level LP (ILLP) approach that relies on approximate hardware instructions. We extended the ACCEPT compiler and SPIKE simulator workflows to generate and simulate applications with ILLP. We evaluated the technique comparing the results of precision, number of instructions, cycles, and energy consumption. ILLP achieves a 74.61% reduction in the number of instructions for the PI application, a 51.40% reduction in the number of cycles for the FFT, and an energy saving of 74.49% for the PI.
Filarial nematodes in domestic dogs and mosquitoes (Diptera: Culicidae) from semi-rural areas in Central Chile

Climate change, competent vectors, and reservoir animals are the main factors for developing vector-borne zoonotic diseases. These diseases encompass a significant and widespread category of pathogens (e.g., viruses, bacteria, protozoa, and helminths) transmitted by blood-feeding arthropods, including ticks, fleas, lice, triatomines, mosquitoes, sandflies, and blackflies. In Chile, several studies have explored the role of dogs as reservoirs of vector-borne pathogens; however, there is a lack of research investigating the presence of pathogens in arthropods. Specifically, within the order Diptera, limited knowledge exists regarding their roles as carriers of pathogens. This study aimed to examine the presence of zoonotic filarial nematodes in mosquitoes and dogs within a previously unstudied semi-rural area of Central Chile. Two hundred samples of dog blood and seven hundred and twenty-four mosquitoes were collected during 2021–2022 and studied for filarial nematodes by PCR. The prevalence of microfilaremic dogs detected by Knott’s test was 7.5%, with Acanthocheilonema reconditum being the only species identified. Aedes (Ochlerotatus) albifasciatus was the most abundant mosquito species collected, and 15 out of 65 pools were positive for filarial nematodes. Among these pools, 13 tested positive for Acanthocheilonema reconditum, and two tested positive for Setaria equina through PCR. Additionally, five Culex pipiens specimens were positive for Acanthocheilonema reconditum. Despite the absence of zoonotic filarial species, these findings underscore the significance of monitoring pathogens in mosquitoes and animal hosts and continued research into the dynamics of vector-borne diseases, particularly in unexplored regions.
SIMULATION STUDY OF HIERARCHICAL BAYESIAN APPROACH FOR SMALL AREA ESTIMATION WITH MEASUREMENT ERROR

In small area estimation (SAE), the auxiliary variables used are commonly derived from registration data such as census and administrative data. It is assumed that the auxiliary variables are available for all areas. The limited availability of auxiliary variables can be an obstacle in SAE. The additional information from the survey can be alternative data, but it is assumed that the auxiliary variables will contain measurement errors. This study conducted a simulation of data that aims to handle when auxiliary variables are measured with errors. Two simulations were studied with some scenarios to the percentage area where the auxiliary variable is measured with error and scenarios to the generated auxiliary variables. Compare four methods: direct estimation, Fay-Herriot Empirical Best Linear Unbiased Prediction (EBLUP-FH), Ybarra-Lohr SAE with measurement error (SaeME), and Hierarchical Bayesian SaeME. The results show that, in both the simulation study, the Hierarchical Bayesian SaeME method gives a smaller the EMSE value than the other two methods when auxiliary information is measured with error.
Top Lights-Bright Spots and their Contribution to Economic Development

Satellite data on nighttime luminosity are an increasingly popular proxy for economic activity, but their utility for analyzing comparative development across the globe is severely limited by top-coding. The commonly used satellites do not accurately capture the brightness of large and densely populated cities. As a result, they underestimate differences between urban and rural regions, as well as developed and developing countries. Our main contribution is to propose a new and easy-to-use procedure to correct for top-coding of the lights data. We show that, like top incomes, top lights are Pareto distributed. On this basis, we derive simple formulas for the top-adjusted mean and spatial Gini coefficient. Furthermore, we develop simulation methods to correct the data at the pixel level. Our top-coding correction raises the worldwide Gini coefficient of spatial inequality in lights by about 9 percentage points. This rather large increase underlines the importance of big cities for global economic activity. We present further applications to show that top-coding affects estimates of the income elasticity of light, urban-rural differences, and regional or ethnic inequalities. JEL Classification: D3, O1, O18, C34
Optoelectronic Properties of Cold Plasma-Deposited, Oxidized Sn–C Thin Films

We report on investigating the structural and electronic properties of semiconducting and insulating layers produced in a process resembling percolation in a unique cold plasma fabrication method (plasma-enhanced chemical vapor deposition—PECVD). Amorphous carbon–tin films (Sn–C) produced from tetramethyl tin (TMT) with an acoustic-frequency glow discharge in a three-electrode reactor were investigated. The layers, after air exposure, oxidized to SnO2/Sn–C. Depending on the coupling capacitance applied to the plasma reactor, the films could be obtained in the form of an amorphous semiconductor or an amorphous insulator. We assume that the semiconductor consists of an internal network of channels auto-organized during deposition. The insulator does not demonstrate any internal structure features. An investigation on conductive filaments creating low-dimensional (LD) nanojunctions in the semiconductor and the location of energetic levels in the insulator was performed. The main parameters of the electronic band structure of the insulating film, such as the transport gap EG (5.2 eV), optical gap Eopt (3.1 eV), electron affinity Χ (2.1 eV), and ionization potential J (7.3 eV), were determined. We have demonstrated a simple approach for developing a catalyst candidate consisting of amorphous semiconductor–insulator nanojunctions for (photo)catalytic hydrogen evolution or CO2 reduction.
Haplotype of the Lipoprotein(a) Gene Variants rs10455872 and rs3798220 Is Associated with Parameters of Coagulation, Fibrinolysis, and Inflammation in Patients after Myocardial Infarction and Highly Elevated Lipoprotein(a) Values

Lipoprotein(a) (Lp(a)) is an independent risk factor for future coronary events. Variants rs10455872 and rs3798220 in the gene encoding Lp(a) are associated with an increased Lp(a) concentration and risk of coronary artery disease. We aimed to determine whether in high-risk coronary artery disease patients these two genetic variants and the kringle IV type 2 (KIV-2) repeats are associated with impairment of inflammatory and hemostatic parameters. Patients after myocardial infarction with elevated Lp(a) levels were included. Blood samples underwent biochemical and genetic analyses. In carriers of the AC haplotype, the concentrations of tumor necrosis factor (TNF)-α (4.46 vs. 3.91 ng/L, p = 0.046) and plasminogen activator inhibitor-1 (PAI-1) (p = 0.026) were significantly higher compared to non-carriers. The number of KIV-2 repeats was significantly associated with the concentration of high-sensitivity C-reactive protein (ρ = 0.251, p = 0.038) and overall fibrinolytic potential (r = −0.253, p = 0.038). In our patients, a direct association between the AC haplotype and both TNF-α and PAI-1 levels was observed. Our study shows that the number of KIV-2 repeats not only affects proatherosclerotic and proinflammatory effects of Lp(a) but is also associated with its antifibrinolytic properties.
A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2–120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38–100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26–1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.
Domain-specific word embeddings for patent classification

Purpose Patent offices and other stakeholders in the patent domain need to classify patent applications according to a standardized classification scheme. The purpose of this paper is to examine the novelty of an application it can then be compared to previously granted patents in the same class. Automatic classification would be highly beneficial, because of the large volume of patents and the domain-specific knowledge needed to accomplish this costly manual task. However, a challenge for the automation is patent-specific language use, such as special vocabulary and phrases. Design/methodology/approach To account for this language use, the authors present domain-specific pre-trained word embeddings for the patent domain. The authors train the model on a very large data set of more than 5m patents and evaluate it at the task of patent classification. To this end, the authors propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings. Findings Experiments on a standardized evaluation data set show that the approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches. In this paper, the authors further investigate the model’s strengths and weaknesses. An extensive error analysis reveals that the learned embeddings indeed mirror patent-specific language use. The imbalanced training data and underrepresented classes are the most difficult remaining challenge. Originality/value The proposed approach fulfills the need for domain-specific word embeddings for downstream tasks in the patent domain, such as patent classification or patent analysis.
Multi-label classification of retinal disease via a novel vision transformer model

Introduction The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously. Methods This study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function. Results and discussion The experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG.
Temporal Stability of Resting Respiratory Sinus Arrhythmia: Choosing the Quantification Method

Respiratory sinus arrhythmia (RSA) is defined as heart rate variability (HRV) synchronised with respiration, characterized by heart rate increase during inspiration and heart rate decrease during expiration. Accurate and robust quantification of resting RSA is crucial, as it serves as a reliable and noninvasive biomarker of emotion regulation capacity. This is important for its application in prediction and diagnosis of various psychopathological conditions, as well as objectivized assessment of protective traits like stress resilience. In this paper, we describe and compare several different methods for quantifying resting RSA. Our evaluation, conducted on a sample of university students (N=34) across two test sessions, separated by a minimum of one week, revealed that a novel method based on the time-domain concordance correlation coefficient (TCCC) outperformed others. This method demonstrated highest temporal stability, with an intraclass correlation coefficient (ICC) of 0.79, and was less susceptible to changes in respiratory parameters. The proposed method and experimental evaluation contribute to the establishment of a standardized approach for resting RSA quantification, with potential application in diverse fields.