Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation.
Glacier Sliding from Space: Multi-Scale Remote Sensing, Geodesy, and Numerical Modeling to Understand Glacier Mechanics

Armstrong, William Henry (Ph.D., Department of Geological Sciences) Glacier sliding from space: multi-scale remote sensing, geodesy, and numerical modeling to understand glacier mechanics Thesis directed by Professor Robert S. Anderson Glacier basal sliding is a poorly understood aspect of glacier mechanics, and its spatial and temporal distribution affects glacier change and the evolution of alpine landscapes. In these studies, we use on-glacier GPS, moderateand high-resolution optical satellite imagery, and numerical ice flow modeling to investigate the mechanics of glacier sliding across a variety of scales. First, we employ on-glacier GPS to investigate the intimate link between subglacial water pressure and the rate of glacier sliding in response to the onset of spring melting on Kennicott Glacier, Alaska. We find large diurnal glacier velocity fluctuations during times of high and rising water level on a well-connected ice-marginal lake. The ice surface speedup at an upglacier station is first driven by longitudinal coupling to down-glacier ice, but then evolves to respond to local basal conditions. We then utilize high-resolution WorldView satellite imagery to document the spatial pattern of the seasonal evolution of ice surface velocity over the 45 km terminal reach of Kennicott Glacier. We develop a numerical ice flow model to explore the distribution of basal sliding required to explain the observed surface speedup. We find the ice surface speedup is insensitive to the exact distribution of basal sliding, which may allow for simpler sliding parameterizations in glacier models. Finally, we employ Landsat 8 satellite imagery to characterize the spatial patterns of glacier sliding over a 45,000 km area covering 64 glacier longitudinal profiles from ice divide to terminus. We find the entire ablation area of glaciers speeds up in a uniform manner, with the speedup magnitude insensitive to winter surface speeds. These patterns of sliding may drive patterns of glacier erosion that leads to the formation of icefalls.
Comparing 1-D sediment transport modeling with field observations: Simkins Dam removal case study

ABSTRACT We present a sediment transport modelling study for the 2010 removal of the 3.3-m tall Simkins Dam on the Patapsco River, MD that released more than 56,000 m3 of sediment downstream. Our objectives are to validate the pre-removal model forecasts with detailed post-removal monitoring data, and through hindcast modelling, examine the effects of using approximate channel geometry data or more accurate data on model results. Comparisons of DREAM-1 model predictions using approximate data and field observations indicate that reach-scale model predictions were generally accurate, but some discrepancies between predicted and observed magnitudes of sediment deposition at specific locations occurred. A refined model, developed post-dam removal with more accurate channel geometry as model input, produced slightly improved results in reaches where input data were significantly improved. However, more accurate input data did not change the general conclusions nor substantially improve the model performance for the entire study reach. In conjunction with two previous studies, our results support a simplified data collection approach that enables timely predictions for decision making and minimizes study costs.
Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.
The Effect of Composition Variation of Pineapple, Squeezed Orange (Citrus sinensis), and Tomato on The Electrical Properties of Voltaic Cells as an Electrolyte Solution

This study aims to demonstrate the electrolyte solution from the fruits to be used as electrical source energy. We combined and varied the composition of the pineapple, squeezed orange, and tomato. The novelties of this research are (1) investigation of the composition of three electrolyte solutions, such as pineapple, squeezed orange, and tomato; (2) the use of these electrolytes. Experiments were done by designing the experimental apparatus and mixing fruits with different compositions. The fruits were mashed using a saw-milling apparatus. We made a series of electrical circuits to test the electrolyte solution. Data were collected every hour for 5 h. We tested the voltage, current strength, and pH. In the circuit, we used a small LED light (2 volts) for ensuring the electrical ability. The results of this study showed the presence of a voltage and current generated, turning on the LED. The more volume of the squeezed orange increased the citric acid, resulting the electrolyte solution to be more acidic (less pH). The utilization of fruits such as pineapple, squeezed orange, and tomato is effective as an electrolyte solution.
A sensitivity study of the LIdar-Radiometer Inversion Code (LIRIC) using selected cases from Thessaloniki, Greece database

ABSTRACT We investigate the uncertainty introduced to the optical and microphysical properties estimated with the lidar radiometer inversion code (LIRIC) by user-defined input parameters based on measurements carried out with a multi-wavelength Raman lidar and a sun photometer located at Thessaloniki, Greece (40.6° N, 22.9° E, 60 m above sea level). The sensitivity study involves three tests. We first evaluate the selection of the regularization parameters needed for the algorithm to initialize the iteration process. The latter two tests consider the impact of the boundary limits at the top/bottom (upper/lower limit) of the signal to the derived concentration profiles. The aforementioned tests were applied to two different cases, a Saharan dust event and a continental pollution case. We concluded that the largest uncertainties are introduced when varying the lower limit (more than 35%) regardless of the aerosol type or mode (fine/coarse). Varying the regularization parameters resulted in an uncertainty of 20%, and the selection of upper limit led to discrepancies of less than 3%. In conclusion, this sensitivity study indicates that future LIRIC users should apply an overlap function to the lidar signals before applying the methodology for minimizing the uncertainties in the near range.
Neoadjuvant systemic therapy for hepatocellular carcinoma

Surgical resection and liver transplant remain the only curative therapies for most patients with hepatocellular carcinoma (HCC). Systemic therapy options have typically been ineffective, but recent advances, such as the combination of immune checkpoint inhibitors and targeted therapies, have shown great promise. Neoadjuvant systemic therapy in resectable or locally advanced HCC is under active investigation with encouraging results in small, early-phase trials. Many of these completed and ongoing trials include combinations of systemic therapy (e.g. immune checkpoint inhibitors, tyrosine kinase inhibitors), transarterial therapies, and radiation. Despite early successes, larger trials with evaluation of long-term oncologic outcomes are needed to determine the role of neoadjuvant systemic therapy in patients with HCC who may be eligible for curative intent surgery or transplant.
Maltodextrin from Sweet Cassava: A Promising Endurance Enhancer

The effects of maltodextrin and crude extract from sweet cassava on exercise endurance were examined in the male Wistar rat. The rats were randomly assigned to either an exercise training group or a non-exercise training group. Both groups were further divided into subgroups that received either a control, crude extract (250 or 500 mg/kg), or maltodextrin (250 or 500 mg/kg) orally once daily for 16 days. The time to the point of exhaustion after weight-loaded forced swimming was measured on day 16. Body weight gain, relative organ weight, biochemical parameters, and liver and gastrocnemius muscle glycogen content were also determined. Maltodextrin at a dose of 500 mg/kg significantly increased the time to the point of exhaustion compared to all other groups. Maltodextrin and crude extract with both doses significantly increased liver and gastrocnemius muscle glycogen content compared to the control group. There were no significant differences in glucose, BUN, triglyceride, or insulin levels between the groups. Crude extract at a dose of 250 mg/kg significantly increased AST and ALT levels, and LDH levels significantly increased in the exercise training group. Creatinine levels were significantly higher in the exercise training group compared to the non-exercise training group. Exercise boosted antioxidant enzymes, glycogen, and reduced damaging free radicals in the rats. Maltodextrin and crude extract further amplified this effect by activating AMPK and PGC-1α, suggesting that they combat fatigue through an antioxidant pathway linked to AMPK. These findings suggest that maltodextrin and crude extract from sweet cassava may have the potential to enhance exercise endurance. They may increase glycogen storage in the liver and gastrocnemius muscle, potentially through improved glycogen reserves and glycogen sparing effects. Further studies are needed to elucidate the mechanisms underlying these effects.
Analysis of Noise Level Generated by Stone Cutter Machine

Marble, one of natural stone, has been widely produced since the last decade. In South Aceh, Marble stone is fabricated at Marble Production Unit that is located around Polytechnic of Aceh Selatan. The using of large-scale stone-cutting machines in Marble Production process tends to be a major noise source in Polytechnic of Aceh Selatan environment. The aim of this study is to analyze the noise level generated by Marble Cutting Machine in Marble Production Unit. The noise levels were analyzed by measuring Background Noise Level (BNL) and Sound Pressure Level (SPL). Sound Level Meter Type SL-814 was employed in the measurement. The results show that Background Noise Level measured is 53.03 dB on average. The highest Sound Pressure Level measured when the marble cutting machine was operated without workpiece is 94dB. In addition, the highest sound pressure level measured when marble cutting machine was operated with the workpiece is 96 dB. The values have generally exceeded the Threshold Noise Level allowed for education area, 55 dB. The noisy condition in campus environment would have an impact on teaching and learning processes within the Polytechnic of South Aceh.
Quantifying spatial and architectural relationships from fluvial outcrops

We gratefully acknowledge the financial support from Total S.A. for funding this work. We would like to thank W. Head, R. McAllister, and A. Newton for their assistance with the field data collection, and G. Heldreich for her valuable geological discussions in and out of the field. We would also like to thank Gary Nichols for his invaluable insight and discussions about the Huesca fluvial system and the Ebro Basin. We kindly thank Jerome Bellian, Peter Flaig, and Steve Hubbard for providing helpful suggestions and feedback that led to significant improvements to this manuscript.
The Effect of Fair Value Accounting on Firm Public Debt – Evidence from Business Combinations Under Common Control

We analyze the choice allowed to parent ﬁrms under IFRS of how to account for a business combination under common control (BCUCC), and provide evidence on the motivation to select fair values and the economic implications of this choice. A BCUCC is a merger of two ﬁrms owned by the same parent. Under IFRS, parent ﬁrms can use the acquisition method (fair values) to record the BCUCC or use assets’ historical cost. We show that parents are likely to choose fair values when they desire to increase the transparency of their ﬁnancial reports and when they likely need to raise capital. Using propensity-score matching, we ﬁnd that ﬁrms that used fair values are more likely to issue new public debt following the transaction. We also ﬁnd that the cost of issuing new debt for these ﬁrms is 55 basis points lower than that of comparable ﬁrms that did not do BCUCCs. Our results suggest that using fair values in BCUCCs can increase transparency and lower ﬁrms’ cost of debt.
57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude)

Abstract. In this paper, we introduce and provide access to daily (1960–2017) and hourly (1993–2017) datasets of snow and meteorological data measured at the Col de Porte site, 1325 m a.s. l. , Chartreuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to March 2018. A detailed study of the uncertainties originating from both measurement errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse-to-total shortwave broadband irradiance is affected by an uncertainty of ±0.21 (no unit). The estimated root mean square deviation, which mainly represents spatial variability, is ±10 cm for snow depth, ±25 kg m−2 for the water equivalent of snow cover (SWE), and ±1 K for soil temperature (±0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site, with a decrease of the mean snow depth (1 December to 30 April) of 39 cm from the 1960–1990 period to the 1990–2017 period (40 % of the mean snow depth for 1960–1990) and an increase in temperature of +0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data centre: https://doi.org/10.17178/CRYOBSCLIM.CDP. 2018.
HLA Gene Polymorphisms in Romanian Patients with Chronic Lymphocytic Leukemia

Materials and Methods This study included 66 patients with CLL, diagnosed between 2020 and 2022, and 100 healthy controls. HLA class I and class II genes (HLA-A/B/C, HLA-DQA1/DQB1/DPA1/DPB1, and HLA-DRB1/3/4/5) were investigated using next-generation sequencing technology. Results Several HLA alleles were strongly associated with CLL. The most important finding was that HLA-DRB1∗04:02:01 (p=0.001, OR = 1.05) and HLA-DRB3∗02:01:01 (p=0.009, OR = 1.03) have a predisposing role in CLL development. Moreover, we identified that HLA-A∗24:02:01 0.01 (p=0.01, OR = 0.38), HLA-DQA1∗05:05:01 (p=0.01, OR = 0.56), HLA-DQB1∗03:02:01 (p=0.03, OR = 0.40), and HLA-DRB4∗01:03:01 (p=0.03, OR = 0.54 alleles have protective roles. Correlations between HLA expression and gender showed that women had a higher expression of protective HLA alleles when compared to men. Conclusions Our data are the first to indicate that in Romanian patients with CLL, the HLA-A∗24:02:01 and HLA-DQA1∗05:05:01 alleles have a protective role against CLL development, whereas HLA-DRB1∗04:02:01 and HLA-DRB3∗02:01:01alleles are positively associated with CLL.
Diverse and durophagous: Early Carboniferous chondrichthyans from the Scottish Borders

ABSTRACT Chondrichthyan teeth from a new locality in the Scottish Borders supply additional evidence of Early Carboniferous chondrichthyans in the UK. The interbedded dolostones and siltstones of the Ballagan Formation exposed along Whitrope Burn are interpreted as representing a restricted lagoonal environment that received significant amounts of land-derived sediment. This site is palynologically dated to the latest Tournaisian–early Viséan. The diverse dental fauna documented here is dominated by large crushing holocephalan toothplates, with very few, small non-crushing chondrichthyan teeth. Two new taxa are named and described. Our samples are consistent with worldwide evidence that chondrichthyan crushing faunas are common following the Hangenberg extinction event. The lagoonal habitat represented by Whitrope Burn may represent a temporary refugium that was host to a near-relict fauna dominated by large holocephalan chondrichthyans with crushing dentitions. Many of these had already become scarce in other localities by the Viséan and become extinct later in the Carboniferous. This fauna provides evidence of early endemism or niche separation within European chondrichthyan faunas at this time. This evidence points to a complex picture in which the diversity of durophagous chondrichthyans is controlled by narrow spatial shifts in niche availability over time.
Navigating the landscape of clinical genetic testing: insights and challenges in rare disease diagnostics

With the rapid evolution of diagnostic tools, particularly next-generation sequencing, the identification of genetic diseases, predominantly those with pediatric-onset, has significantly advanced. However, this progress presents challenges that span from selecting appropriate tests to the final interpretation of results. This review examines various genetic testing methodologies, each with specific indications and characteristics, emphasizing the importance of selecting the appropriate genetic test in clinical practice, taking into account factors like detection range, cost, turnaround time, and specificity of the clinical diagnosis. Interpretation of variants has become more challenging, often requiring further validation and significant resource allocation. Laboratories primarily classify variants based on the American College of Medical Genetics and Genomics and the Association for Clinical Genomic Science guidelines, however, this process has limitations. This review underscores the critical role of clinicians in matching patient phenotypes with reported genes/variants and considering additional factors such as variable expressivity, disease pleiotropy, and incomplete penetrance. These considerations should be aligned with specific gene-disease characteristics and segregation results based on an extended pedigree. In conclusion, this review aims to enhance understanding of the complexities of clinical genetic testing, advocating for a multidisciplinary approach to ensure accurate diagnosis and effective management of rare genetic diseases.