Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term Differential InSAR monitoring of the Lampur Sidoarjo mud volcano (Java, Indonesia) using ALOS PALSAR imagery

The Lampur Sidoarjo mud volcano (Java, Indonesia), colloquially called LUSI, first appeared in May 2006. Its cause, whether the result of natural or anthropogenic activities (or a combination of both), is still being debated within the academic, engineering and political communities. The mud volcano has expelled up to 150 000 m 3 of mud per day; and over time, this large volume of mud has had a major environmental and economic impact on the region. As of October 2009 the mud flow from LUSI had covered ~6 km 2 to depths of some tens of metres, displacing approximately 30 000 residents; and continues to threaten local communities, businesses and industry. With such a large volume of mud being expelled each day it is inevitable (as with onshore oil and gas production fields) that there will be some ground surface movement and ground surface instability issues at the mud source (the main vent), and in the vicinity of the mud volcano footprint. Due to the dynamic ground surface conditions, engineers and academics alike have found it difficult to reliably monitor ground surface movements within the effected region using conventional surveying techniques. Consequently, the Atkins engineering geologists and geotechnical engineers responsible for the risk assessment of ground surface instabilities within the proximity of LUSI have called upon the use of satellite interferometry to monitor the hazard on a six-month frequency. The Advanced Land Observing Satellite (ALOS), launched 24 January 2006, carries onboard an L-band Synthetic Aperture Radar (SAR) instrument called PALSAR (Phased Array type L-band Synthetic Aperture Radar). In contrast to established C-band (5.6 cm wavelength) SAR instruments onboard ERS-1 and ERS-2, Envisat, Radarsat-1, and the recently launched Radarsat-2 satellite, PALSAR's (L-band/23.8 cm wavelength) instrument presents a number of advantages, including the ability to map larger-scale ground motions, over relatively short timeframes, in tropical environments, without suffering as significantly from signal decorrelation associated with C-band imagery. This paper presents the results of a long-term ALOS PALSAR, differential interferometric (DifSAR) monitoring campaign across the LUSI mud volcano. DifSAR processing was applied to a sequence of single- and dual-polarisation SAR images acquired on a 3- to 6-month basis since May 2006. The results highlight the capability of ALOS PALSAR in detecting decimetres of coherent ground subsidence to assist geologists and engineers in their analysis of the structure, dynamics and overall stability of the mud volcano and its surrounding region.
Scaling relation between earthquake magnitude and the departure time from P wave similar growth

We introduce a new scaling relation between earthquake magnitude (M) and a characteristic of initial P wave displacement. By examining Japanese K‐NET data averaged in bins partitioned by Mw and hypocentral distance, we demonstrate that the P wave displacement briefly displays similar growth at the onset of rupture and that the departure time (Tdp), which is defined as the time of departure from similarity of the absolute displacement after applying a band‐pass filter, correlates with the final M in a range of 4.5 ≤ Mw ≤ 7. The scaling relation between Mw and Tdp implies that useful information on the final M can be derived while the event is still in progress because Tdp occurs before the completion of rupture. We conclude that the scaling relation is important not only for earthquake early warning but also for the source physics of earthquakes.
Uncovering Patterns of SQL Errors in Student Assignments: A Comparative Analysis of Different Assignment Types

Structured Query Language (SQL) is an essential skill to acquire for those who interact with databases, such as researchers, developers, and people involved in businesses. However, the challenges that these users face while learning SQL requires further research. In particular, the types of errors that students encounter on various assignment types or under exam conditions are an area that we are interested in to determine an optimal arrangement of coursework materials for improved learning. In this paper, we analyze 156,513 student SQL submissions to homework assignments, collaborative assignments, and exams of the Database Systems course available to 730 upper-level undergraduate and graduate students offered in the Fall 2022 semester at the University of Illinois Urbana-Champaign. We look at the ratio of syntax and semantic errors, and correct submissions for each of these assignment problem types as well as the most frequent syntax error codes. We visualize our data findings and draw recommendations for future coursework arrangements from the comparisons between the assignment types for a more effective acquisition of SQL as a skill. We found that although students most commonly encountered syntax error codes 1064 and 1054 regardless of the assignment type, they made more syntax errors (and fewer semantic errors) on exam problems compared with homework and collaborative assignment problems. We recommend instructors place a higher emphasis on non-timed SQL programming problems, targeted syntax drills during instruction, and syntax support during exams.
Design and Measurement of a New Superbend-Magnet for WALS

In order to provide hard X-rays with a 1.5 GeV electron ring, a new superbend-magnet will be used in middle of each standard cell at Wuhan Advanced Light Source (WALS). Design, assembly, and detailed magnetic measurement of the prototype have been finished. The results of magnetic measurement show that central magnetic field reaches 3.67 T in a gap of 14.72 mm, and the range of high field region (>3.5 T) is larger than 40 mm in the longitudinal direction. The uniformity of the magnetic field integrals is below 5 × 10−4 within the good field region.
A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there has not been any satisfactory solution for thermal comfort measurements until now. In this paper, a contactless measuring method based on a skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named the skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, a two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.62% and 52.25% error values (NISDL method I, II) are scattered at (0 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.39%.
Erratum: Relationship between aerosol oxidation level and hygroscopic properties of laboratory generated secondary organic aerosol (SOA) particles (Geophysical Research Letters (2010) 38 (L03805))

Copyright (2011) American Geophysical Union. These materials are made available for use in research, teaching and private study, pursuant to U.S. Copyright Law. The user must assume full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used for academic research or otherwise should be fully credited with the source. Erratum: Relationship between aerosol oxidation level and hygroscopic properties of laboratory generated secondary organic aerosol (SOA) particles (Geophysical Research Letters (2010) 38 (L03805))
An Improved Multi-Vector Model Predictive Torque Control Based on Virtual Voltage Vector

This paper proposes an improved multi-vector model predictive torque control (MPTC) method based on virtual voltage vector (VVV) for dual three-phase permanent magnet synchronous motor (DTP-PMSM), which optimizes the determination of the reference voltage vector (RVV) and expands the vector range by applying multiple voltage vectors. Firstly, RVV is calculated based on the o-p stator flux coordinate system, thereby achieving accurate vector calculation and high torque tracking performance. Furthermore, the number of candidates is brought down from 12 to 5. Secondly, two VVVs and a null vector are chosen to synthesize each candidate voltage vector, extending the synthesis range to the whole plane, which realizes the flexible adjustment of the amplitude and phase of the effective voltage vectors. Experimental results are provided to verify the effectiveness of the proposed MPTC method.
Adapting to Climate Change: The Role of Science and Data in Responding to Opportunities and Challenges in the Water-Soil-Waste Nexus

The Water-Soil-Waste (W-S-W) Nexus provides a framework for the main factors in food production. This framework, which facilitates the assessment of how policies and activities inuence agricultural productivity, is important for all levels of the agricultural industry. It facilitates the assessment of issues that deal with water, soil and waste in a balanced way with full consideration of their impact on the long-term sustainability of the agricultural system and its productivity. This paper explores the linkages between water, soil, and waste in the context of climate and climate change in order to clarify needs for scientic information and observational data. Climate affects water availability, soil integrity and the production and movement of waste material. As the climate changes, its inuences on the W-S-W Nexus will also change and new strategies for coping with climate change will therefore be necessary. This paper describes the types of information needed to select and support these new strategies and reviews the current availability of this information. The paper recommends a more comprehensive study of the effects of climate change on the W-S-W Nexus and an evaluation of the current observational systems and datasets to assess changes in W-S-W variables. The paper also encourages responsible agencies to ensure that their data systems are modernized, their data collection programmes for soil and waste data are enhanced, and their data products and knowledge are made freely available to the world’s science community.
Professional Learning in Student Affairs Graduate Preparation

Abstract:This study explores how students’ professional learning is distributed across and mediated by multiple learning environments during student affairs graduate preparation. Utilizing activity system analysis and qualitative case study, the study reports on the experiences of four second-year Master’s students at a Midwest preparation program. Findings demonstrate how fieldwork experiences shape what graduate students deem as essential or useful knowledge for their practice and, consequently, the value they place on coursework experiences. Participants often rejected formal knowledge when it did not offer clear and immediate solutions to their daily problems. This study raises implications for individuals working with graduate students regarding their professional development, especially faculty members and fieldwork supervisors.
A Bayesian network-based approach for identifying risk factors and predicting ischemic stroke in infective endocarditis patients

Objective This study aimed to seek the risk factors and develop a predictive model for ischemic stroke (IS) in patients with infective endocarditis (IE) utilizing a Bayesian network (BN) approach. Methods Data were obtained from the electronic medical records of all adult patients at three hospitals between 1 January 2018, and 31 December 2022. Two predictive models, logistic regression and BN, were used. Patients were randomly assigned to the training and test sets in a 7:3 ratio. We established a BN model with the training dataset and validated it with the testing dataset. The Bayesian network model was built by using the Tabu search algorithm. The areas under the receiver operating characteristic curve (AUCs), calibration curve, and decision curve were used to evaluate the prediction performance between the BN and logistic models. Results A total of 542 patients [mean (SD) age, 49.6 (15.3) years; 137 (25.3%) female] were enrolled, including 151 (27.9%) with IS and 391 (72.1%) without IS. Hyperlipidemia, hypertension, age, vegetation size (>10 mm), S. aureus infection, and early prosthetic valve IE were closely correlated with IS. The BN models outperformed the logistic regression in training and testing sets, with accuracies of 76.06% and 74.1%, AUC of 0.744 and 0.703, sensitivities of 25.93% and 20.93%, and specificities of 96.27% and 90.24%, respectively. Conclusion The BN model is more efficient than the logistic regression model. Therefore, BN models may be suitable for the early diagnosis and prevention of IS in IE patients.
Spatio-Temporal Feature Alignment Transfer Learning for Cross-Turbine Blade Icing Detection of Wind Turbines

Supervisory control and data acquisition (SCADA) data-based wind turbine blade icing detection has been widely studied due to its low cost and easy access. However, SCADA data often present severe class imbalance and thus challenge accurate icing detection. Moreover, since data distribution discrepancy exists in both spatio-temporal features of SCADA data from different wind turbines, the well-trained model has poor classification performance on new turbines. Building new models for different turbines is high-cost and time-consuming. Thus, model cross-turbine generalizability needs improvement. To solve these problems, a cross-turbine icing detection model is proposed based on the spatio-temporal alignment transfer learning method. Specifically, building an attention-based network to extract temporal and spatial features. Then, we apply maximum mean discrepancy (MMD) algorithms on shallow and deep networks to align spatio-temporal features of source and target domains. Besides, a self-adaptive weight (SAW) loss function is employed to address the class imbalance. Finally, we develop a loss weight assignment method based on analyzing the generated loss value variations with the number of training iterations for performance enhancement. The proposed method is evaluated on real SCADA datasets. Experiment results show our proposed transfer learning method significantly improves the model cross-turbine generalizability and classification performance.
UAV swarm neural network: infrastructure of relevant telecommunication systems

In the article it is compared the architecture options of artificial neural networks, the elements of which are placed on unmanned aerial vehicles, in particular light and ultralight, and the physical infrastructure of the corresponding telecommunication systems. Architectures of single-layer and multi-layer forward-directed, recurrent and fully connected networks are considered. The given comparison will make it possible to use simple adequate physical structures of telecommunications systems and appropriate information technologies in the creation of onboard neural networks and neurocomputing systems. It will also allow determining the physical parameters of network architectures adapted to operating conditions in an adverse environment. As an example, a variant of the telecommunications system architecture for placing the simplest swarm neurocomputer for pattern recognition is given.
Assessing risk factors associated with breakthrough early post-traumatic seizures in patients receiving phenytoin prophylaxis

Objective Post-traumatic seizure (PTS) is a well-known complication of traumatic brain injury (TBI). The objective of this study was to identify risk factors associated with breakthrough early PTS in TBI patients receiving phenytoin prophylaxis. Methods This was a single-centered retrospective study including adult patients admitted to the intensive care unit (ICU), had a TBI, and started on phenytoin for seizure prophylaxis within 24 h of admission. The primary outcome was the incidence and factors associated with early PTS, defined as a confirmed seizure on a continuous electroencephalogram within 7 days of TBI. Secondary outcomes included the association between early post-traumatic seizures and ICU length of stay, hospital length of stay, and in-hospital mortality. Results A total of 105 patients were included in the final analysis. Patients with early PTS were older (65 vs. 48 years old, p = 0.01), had a higher Marshall score (5 vs. 2, p = 0.01), were more likely to have a Marshall score > 2 (73 vs. 37%, p = 0.01), and had more neurosurgeries for hematoma evacuation (57 vs. 19%, p = 0.01). In patients with early PTS, 57% had a level at the time of seizure, and of those, 87.5% had a therapeutic level (>10 mcg/mL). Patients with early PTS had a longer ICU length of stay (14.7 vs. 5.9 days, p = 0.04) and a greater proportion of hospital mortality (21 vs. 2%, p = 0.02). Conclusion Patients with higher age, Marshall score, and neurosurgical procedures for hematoma evacuation had higher incidences of breakthrough early PTS despite the use of phenytoin prophylaxis. The majority of patients with early PTS had therapeutic phenytoin levels at the time of seizure when a level was available; however, approximately half (43%) did not have a level.
Impact of negative energy balance and postpartum diseases during the transition period on oocyte quality and embryonic development in dairy cows

Transition period is a critical time for dairy cows because a large proportion of clinical and subclinical diseases are observed in the first month after parturition. Occurrence of negative energy balance is associated with depressed immunity and these conditions can affect oocyte quality and further embryonic development. The aim of this study was to assess the effects of negative energy balance-associated disorders on in vitro embryo production (IVP) in dairy cattle. We hypothesized that subclinical metabolic and/or inflammatory disorders have a negative effect on oocyte developmental competence and morphokinetic parameters of the resulting embryos. The study was conducted on 30 lactating Holstein-Friesian cows which were assigned into four groups: healthy (HEAL, n = 6), metabolic disease (META, n = 8), inflammatory disease (INFL, n = 8), or combined metabolic and inflammatory disease (COMB, n = 8). Ovum pick-up (OPU) was performed twice weekly on all cows over a period of four weeks (n = 8 OPU sessions/cow) starting on the fifth week postpartum, and the collected oocytes were subjected to routine IVP. Donor’s health status did not affect the number of oocytes/OPU or the recovery rate (p > 0.05). The number of quality 1 oocytes collected from INFL and COMB cows was lower compared to HEAL cows (p < 0.05). Also, the percentage of quality 1 embryos was reduced in META and COMB compared to HEAL cows (p < 0.05). Cleavage, blastocyst and hatching rates were similar among groups (p > 0.05). Presence of disease did not affect the time required by zygotes to reach specific developmental stages, as recorded by means of time-lapse monitoring. Nevertheless, there was a higher probability of direct cleavage after IVF in oocytes of COMB cows compared to those of HEAL cows (p < 0.05). In conclusion, oocytes and embryos derived from dairy cows diagnosed with subclinical metabolic and/or inflammatory diseases during the transition period showed reduced quality but similar developmental potential and morphokinetics when compared to healthy cows. These results shed light on the consequences of subclinical disease on embryonic development in dairy cows which might be important for embryo transfer programs.
On the connectivity in the central nervous system and the age-induced changes of its network organization

Intercellular communication plays a crucial role as the structural substrate for the brain functions. It occurs according to two main processes, namely wiring transmission, i.e. the transmission of signals through physical contacts between cells, and volume transmission, i.e. the chemical signal diffusion along the interstitial fluid pathways. Intercellular communication represents the main rationale for the emerging field of connectomics, defined as the comprehensive study of all aspects of central nervous system connectivity, aimed at creating a comprehensive map (connectome) of the cellular networks in the brain to better understand brain functions. A consensus exists that the brain connectome structure follows a hierarchical or nested architecture, and macro-, meso- and microscales have been defined. Available data on network organization at these different miniaturization levels will be here briefly reviewed. The connectome, however, is also a dynamical entity, undergoing changes during lifetime. Thus, a specific focus will be maintained on the changes the network organization undergoes during normal aging.
A Coherence - Based Post-Filtering for Minimum Variance Distortionless Response Beamformer

At present, the performance of traditional digital signal processing for mobile telecommunications, teleconference and processing speech often degrades due to many reasons, such as the non-stationary surrounding noise, the presence of third-party speakers, or the lack of installed equipment’s limitations. Therefore, the implementation of microphone array technology deal the above problem by exploiting the spatial beampattern or a priori information about the distributed of microphone array, the direction of arrival of interest signal, the characteristics of background noise to achieve the advantage of noise reduction while saving the target talker without speech distortion. In this article, the authors proposed an useful Post-Filtering to suppress the remaining noise at the output of MVDR beamformer’s output to enhance the overall evaluation of MVDR beamformer, increase the speech quality in the term of the signal-to-noise ratio (SNR). The illustrated experiment shows that the proposed post-filtering has the capability of eliminating annoying noise while saving the original speech component.