Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of Patients with Meningeal Carcinomatosis Combined with Myelopathy

Objective To explore the disease manifestations and radiological characteristics of patients with meningeal carcinomatosis (MC) combined with myelopathy. Patients and Methods The detailed medical information of patients who suffered from MC with myelopathy in record system were collected and reviewed. Results In these patients, five cases were male and two cases were female. The age was from fifteen to fifty-seven years. In the course of disease, tumor cells were discovered in cerebrospinal fluid of three patients and in biopsy samples of four patients. Cerebrospinal fluid (CSF) test results showed white blood cell counts increased in seven patients, protein increased in six patients and glucose reduced in five patients. In addition, MRI revealed that the white matter abnormalities showed in all cases and pia mater was enhanced in four patients, meningeal enhanced was observed in three patients. All patients were given appropriate therapy during hospitalization. Follow-up result showed that all patients passed away two to five months after diagnosis. Conclusion MC causes spinal membrane, spinal nerve root to be involved besides, also can produce the matter of myelopathy. Early detection of intramedullary lesion is conducive to strengthening the awareness of the diagnosis of MC.
Failure simulation of shear‐critical RC columns with non‐ductile detailing under lateral load

Reinforced concrete columns with non‐ductile detailing typically exhibit a softening behavior characterized by severe degradation when subjected to cyclic lateral loads. Whether the response is brittle or ductile, shear failure occurs with an inclined through crack along which sliding occurs coupled with loss of horizontal and vertical load‐bearing capacity of the member. The rapid loss of resistance after the peak strength is reached is because of one or more of the following local failure mechanisms: brittle failure of poorly confined concrete; buckling of longitudinal reinforcing bars because of lack of adequate transverse reinforcement or following opening of stirrups after spalling of cover concrete; bond failure. In this study, a modeling strategy to build a detailed 3D finite element model capable of capturing all of the above‐mentioned local failure mechanisms is presented. In particular, a steel–concrete interface model for representing the interaction within the member between concrete core, cover and longitudinal and transverse reinforcement is proposed. Comparison with results of an experimental test of a shear‐sensitive column demonstrates the effectiveness of the simulation up to failure of the element. Copyright © 2016 John Wiley & Sons, Ltd.
Inulin-Modified Liposomes as a Novel Delivery System for Cinnamaldehyde

Cinnamaldehyde as an antioxidant was encapsulated in inulin-modified nanoliposomes in order to improve its physical and antioxidant stability. The microstructure, particle size and volume distribution of cinnamaldehyde liposomes were characterized by atomic force microscopy (AFM) and dynamic light scattering (DLS). The particle size and polydispersion index (PDI) values of the inulin modified liposomes were 72.52 ± 0.71 nm and 0.223 ± 0.031, respectively. The results showed that the liposomes after surface modification with inulin remained spherical. Raman and Fourier transform infrared (FTIR) spectra analysis showed that hydrogen bonds were formed between the inulin and the liposome membrane. Inulin binding also restricted the freedom of movement of lipid molecules and enhanced the order of the hydrophobic core of the membrane and the polar headgroup region in lipid molecules. Therefore, the addition of different concentrations of inulin influenced the permeability of the liposome bilayer membrane. However, when inulin was excessive, the capacity of the bilayer membrane to load the cinnamaldehyde was reduced, and the stability of the system was reduced. Additionally, the encapsulation efficiency (EE) and retention rate (RR) of cinnamaldehyde from inulin-modified liposomes during storage were determined. The EE value of the inulin modified liposomes was 70.71 ± 0.53%. The liposomes with 1.5% inulin concentration had the highest retention rate (RR) and the smallest particle size during storage at 4 °C. The addition of inulin also enhanced the thermal stability of the liposomes. Based on the results, the surface modification improved the oxidation stability of liposomes, especially the DPPH scavenging ability. In conclusion, these results might help to develop inulin as a potential candidate for the effective modification of the surface of liposomes and provide data and conclusions for it.
An EEG-study on L2 categorization of emotional prosody in German

Previous behavioral studies on the processing of emotional prosody in L2 learners showed similarities and differences between L1- and L2-processing and suggested that emotional perception has both universal and culture-specific aspects. However, little is known about the processing of emotional prosody in L2 learners' brains. Therefore, the present study used event-related potentials to compare the processing of emotional prosodies between German native speakers and Chinese L2 learners of German. Participants performed a prosody recognition task with semantically neutral German words recorded with emotional "neutral" , "like" , and "disgust" prosodies. The accuracy ratings of categorizing emotional prosodies of L2 learners were above chance but significantly better for the L1 speakers. Both groups yielded an early and a late positivity for processing "like" in comparison to "disgust" , reflecting the emotional prosodic predictive processing. However, an early left anterior negativity (ELAN) and a late anterior negativity observed in the L2 learners suggest that they are more sensitive to acoustic differences of the presented stimuli. Overall, our findings support the assumption that the processing of emotional prosody is in principle universal across languages, but that in addition to the general mechanisms involved in the processing of emotional speech language-specific aspects also modify emotional processing.
Hippo: Sharing Computations in Hyper-Parameter Optimization

Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits. However, a hyper-parameter optimization job, referred to as a study, involves numerous trials of training a model using different training knobs, and therefore is very computation-heavy, typically taking hours and days to finish. We observe that trials issued from hyper-parameter optimization algorithms often share common hyper-parameter sequence prefixes. Based on this observation, we propose Hippo, a hyper-parameter optimization system that reuses computation across trials to reduce the overall amount of computation significantly. Instead of treating each trial independently as in existing hyper-parameter optimization systems, Hippo breaks down the hyper-parameter sequences into stages and merges common stages to form a tree of stages (a stage tree). Hippo maintains an internal data structure, search plan, to manage the current status and history of a study, and employs a critical path based scheduler to minimize the overall study completion time. Hippo applies to not only single studies but multi-study scenarios as well. Evaluations show that Hippo's stage-based execution strategy outperforms trial-based methods for several models and hyper-parameter optimization algorithms, reducing end-to-end training time by up to 2.76X (3.53x) and GPU-hours by up to 4.81X (6.77x), for single (multiple) studies.
Hydrogel based multianalyte microsensor arrays for metabolic monitoring

This thesis describes the design and assembly process optimization of smart hydrogel based micropressure sensor arrays used for metabolic monitoring. Surface, electrical and pressure characterizations were performed on the sensor arrays. An assembly process was developed to integrate the sensor arrays with the hydrogels. Micropressure sensor arrays with both pH sensitive hydrogels and glucose sensitive hydrogels were assembled and tested in vitro in phosphate buffered saline. Sensor arrays were also tested in vitro for stability analysis. Response times of 10 minutes for swelling cycle and 8 minutes for shrinking cycle were obtained for pH sensitive hydrogel based sensor arrays and 11 minutes for swelling cycle and 5 minutes for shrinking cycle were obtained for glucose sensitive hydrogel based sensor arrays. Such low response times were obtained using hydrogel based pressure sensors to detect glucose for the first time to our knowledge. Sensitivities of pH sensitive hydrogel based sensors were calculated to be 220 mV/V-M, 149 mV/V-M and 62 mV/V-M for pressure sensor diaphragm sizes of 1.5 X 1.5 mm, 1.25 X 1.25 mm and 1 X 1 mm, respectively. Sensitivities for glucose sensitive hydrogel based sensors were determined as 203 mV/V-M, 213 mV/V-M and 74 mV/V-M for diaphragm sizes of 1.5 X 1.5 mm, 1.25 X 1.25 mm and 1 X 1 mm, respectively. Stability experiments performed on the sensor arrays with pH hydrogels have been found to last for over 400 hours.
Syncing Up: A Process Model of Emergent Interdependence in Dynamic Teams

Increasingly, organizational teams form quickly and change shape during their short lifespans, meaning they break from traditional definitions of “real” teams and experience instability in team membership and boundaries. While scholars have examined conditions that support effective teamwork in more-stable teams, we know little about how these dynamic teams can come to look like real teams that work interdependently rather than independently. My observations of and interviews with medical inpatient teams in a U.S. children’s hospital revealed a small subset of teams that succeeded at working interdependently within a core group (internally) and with a shifting set of peripheral contributors (externally). Brief periods of synchronous internal and external teamwork distinguished these emergently interdependent teams. To achieve these synchronous periods, core team members distributed their focus on internal team members and on peripheral members such as nurses, specialists, patients, and patients’ family members. Furthermore, core teams intertwined synchronous periods with cycles of external and internal coordination as team boundaries expanded and contracted. Such interdependence was associated with more-efficient work: faster morning rounds and, for patients, shorter hospital stays. Additionally, initial meetings among core team members set the stage for more-interdependent work. My findings contribute to dynamic teams research by illuminating the process of how teams can work interdependently as team boundaries expand and contract, to external activities research by suggesting that synchronous periods hold together previously documented cycles of separate internal and external activities, and to team launches research by extending work with more-stable teams to dynamic teams.
Measurement and simulation analysis of CO2 leakage at the Crystal Geyser site and reconciliation of the mechanisms for the amount of flux from the geyser and adjacent faults

Global warming enhanced by anthropogenic CO 2 is an ever-increasing concern to scientists, policy makers, and the public at large. One plausible method of mitigating growing emissions is to inject CO 2 into subsurface aquifers, which have large storage potential, thus helping diminish the accumulation of CO 2 in the atmosphere. The earth stores and releases large amounts of CO 2 naturally. Studying known natural sources and leaks of CO 2 can help to better understand deep-saline CO 2 storage. This study focuses on the effects of barometric pressure on surface CO 2 leakage through a fault from subsurface CO 2 storage. We measured the natural release of CO 2 at the Little Grand Wash Fault near the Crystal Geyser, a cold-water geyser located near Green River, Utah. We observed that barometric pressure affects CO 2 flux in two different manners, an immediate direct and inverse effect and a longer, 21.5-hour delayed indirect relationship. Two one-dimensional simulation models were developed using the fluid flow equation for vertical flow. Both models simulated gaseous phase CO 2 flow from a fully saturated reservoir to the surface. The first model was run to identify a range of permeabilities that resulted in the mean observed surface flux. The second model was run to understand the indirect and time-lagged influence of barometric pressure on the CO 2 fault. The observed barometric pressure and
Integral correction of initial and model errors in system of multigrid NLS‐4DVar data assimilation for numerical weather prediction (SNAP)

Model error has a marked influence on forecast error, and integral correction of the initial and model errors in a novel four‐dimensional variational data assimilation (4DVar) method (i4DVar) has recently been developed. Unlike traditional 4DVar, in which the analysis increment focuses only on the initial time point, i4DVar introduces an averaged integral correction term to correct the error evolution at multiple times with the same time interval in the assimilation window; that is, the assimilation window is divided into several sub‐windows. In this study, we incorporated i4DVar into the System of Multigrid Nonlinear Least‐squares‐4DVar Data Assimilation for Numerical Weather Prediction (NWP) (SNAP), hereafter referred to as SN‐i4DVar, and explored its impacts on NWP. The effectiveness and performance of SN‐i4DVar were evaluated using two extreme rainstorm cases assimilating conventional and satellite observations, respectively. The results indicated that integral correction of the initial and model errors reduced analysis error and forecast error growth rates. SN‐i4DVar performed better in precipitation forecasting than the original SNAP and exhibited generally better forecasting capability in terms of the horizontal wind speed (u and v), temperature, and humidity variables, indicating that SN‐i4DVar simultaneously countered initial model errors to improve NWP capability. Meanwhile, the 4DEnVar method, which considers the optimal analysis in the ensemble space, benefits from the improved ensemble space generated according to the formulas of i4DVar, and the observation information can be more effectively absorbed and a more accurate analysis can be obtained by SN‐i4DVar.
Exploiting trust, influence, and physical proximity for improving routing performance in vehicular ad hoc networks: The SCALE protocol

Vehicular ad hoc networks (VANETs) are receiving increased attention, and several routing protocols have been proposed already by the research community. In such a framework, social aspects and human mobility are important performance enablers, which however are mostly overlooked. In this work, we propose the socially aware CLWPR (SCALE) protocol, significantly enhancing cross‐layer weighted position‐based routing (CLWPR), a routing protocol for urban VANET environments, combining social properties such as trust, influence, and users' individual mobility patterns to its core design in order to support an efficient content dissemination scheme. The design principles of SCALE that rely on both online and offline social metrics quantifying relationships on social networking platforms and opportunistic contacts of nodes due to physical proximity, respectively, are presented, while nodes with close online and offline social relationship are favored as next forwarder nodes. Subsequently we provide the performance evaluation of SCALE against other routing protocols in distributed vehicular networks, employing representative urban scenarios with synthetic and real traffic. It is shown that the proposed approach presents improved performance in terms of Packet Delivery Ratio (PDR) and throughput when compared with other similar protocols by an average of 37% in scenarios with synthetic dataset and 58% with real dataset for PDR and by an average of 45% in scenarios with synthetic dataset and 61% with real dataset for throughput.
Preparing Teachers to Implement Technology: The CCMS Experience

The Classroom Connectivity for Mathematics and Science (CCMS) program was a randomized control trial to examine the efficacy of Classroom Connectivity Technology (CCT) in Algebra I. CCT is a type of technology that allows the teacher to wirelessly communicate with his or her students’ handheld calculators. Students in the classes that implemented CCT outperformed their comparison counterparts with effect sizes ranging from 0.19 to 0.37 (Irving et al., 2014; Pape et al., 2013). In this chapter, the professional development program that supported participating teachers to implement the technology is described. Categories of professional development activities including pedagogical instruction, modeling pedagogy, technological pedagogical instruction, technology instruction, student role-play, practice, small-group instruction, and participant presentation are presented in relation to their potential for supporting teacher participants’ knowledge growth. Recommendations for the training of teachers to implement technology will be explicated.
Spectral variational mode extraction and its application in fault detection of rolling bearing

The core of fault diagnosis of rolling bearing is to extract the narrowband sub-components containing fault feature information from the bearing fault signal. Variational mode extraction (VME), a novel single sub-component separation algorithm originated from variational mode decomposition (VMD), provides a promising solution to bearing fault detection. However, its performance is closely related to the hyperparameter selection, including the center frequency ω d and the penalty factor α. This paper proposes a non-recursive and adaptive signal decomposition algorithm termed spectral variational mode extraction (SVME). SVME can be seen as a spectral decomposition technique whose framework is composed of the adaptive spectral boundary division and boundary constrained VME. In the adaptive spectral boundary division, an adaptive iterative spectral envelope method referring to the continuous envelope correlation (CCE) index is developed to integrate with the parameterless scale-space division to adaptively locate the frequency band boundary. The presented adaptive spectral boundary division approach can effectively inhibit the spectral boundary over-division. In the boundary constrained VME, the dominant frequency of each frequency band determined by the optimal spectral division is distinguished as the preset center frequency. Meanwhile, the optimal penalty factor is determined based on the envelope spectral kurtosis (ESK) index and the boundary-constraint principle. The SVME method is utilized in the simulation and experimental case studies to investigate its capability. Furthermore, its superiority is highlighted through the comparison with the variational mode decomposition (VMD) and Autogram methods.
Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes

Metasurfaces, a special class of metamaterials, have recently become a rapidly growing field, particularly for thin polarization converters. They can be fabricated using a simple fabrication process due to their smaller planar profile, both in the microwave and optical regimes. In this paper, the recent progress in MSs for linear polarization (LP) to circular polarization (CP) conversion in transmission mode is reviewed. Starting from history, modeling and the theory of MSs, uncontrollable single and multiple bands and LP-to-CP conversions, are discussed and analyzed. Moreover, detailed reconfigurable MS-based LP-to-CP converters are presented. Further, key findings on the state-of-the-arts are discussed and tabulated to give readers a quick overview. Finally, a conclusion is drawn by providing opinions on future developments in this growing research field.
Increasing Resource Parents’ Access to Training and Data: An Overview of Two Child Welfare Initiatives

Objectives: Resource parents are critical to young people’s well-being, resilient functioning, and placement stability. However, child welfare often experiences challenges in retaining resource parents, which may be partially due to the limited availability of in-service resources. We describe two in-service training initiatives for resource parents in Ontario (Canada) that can support their important caregiving work. We also present preliminary mixed-methods findings on training reactions and learning.Methods: The first study sampled 91 resource parents who completed the Resource Parent Curriculum (RPC) and collected module evaluations as well as post-program satisfaction data. The second study collected post-training evaluations from 26 resource parents who completed training using the Assessment and Action Record (AAR) to better understand youth in-care.Results: Resource parents responded positively to the RPC content and delivery; they appreciated the online format (due to COVID-19 restrictions). Parents noted it was helpful to learn how trauma shapes young people’s expectations and how thoughts, feelings, and behaviours are interconnected. For the second study, parents’ overall training rating was positive. They noted it was helpful to learn how different perspectives could be integrated through AAR findings and highlighted the importance of collaboration with child welfare workers. The training initiatives were well-received and attested to resource parents’ motivation to keep improving their parenting practices. Findings indicated gains in knowledge around trauma-informed parenting and indicated the value of data to support young people’s well-being.Implications: Retention will likely improve when resource parents feel supported and capable of handling young people’s complex needs. To improve outcomes for both youth in-care and resource parents, it seems important to make training and support available on a regular and ongoing basis and in a collaborative way with child welfare partners.
Multistate Outbreak of Infection with SARS-CoV-2 Omicron Variant after Event in Chicago, Illinois, USA, 2021

Bars and restaurants are high-risk settings for SARS-CoV-2 transmission. A multistate outbreak after a bar gathering in Chicago, Illinois, USA, highlights Omicron variant transmissibility, the value of local genomic surveillance and interstate coordination, vaccination value, and the potential for rapid transmission of a novel variant across multiple states after 1 event.
Explaining Intelligent Agent’s Future Motion on basis of Vocabulary Learning with Human Goal Inference

Intelligent agents (IAs) that use machine learning for decision-making often lack the explainability about what they are going to do, which makes human-IA collaboration challenging. However, previous methods of explaining IA behavior require IA developers to predefine vocabulary that expresses motion, which is problematic as IA decision-making becomes complex. This paper proposes Manifestor, a method for explaining an IA’s future motion with autonomous vocabulary learning. With Manifestor, an IA can learn vocabulary from a person’s instructions about how the IA should act. A notable contribution of this paper is that we formalized the communication gap between a person and IA in the vocabulary-learning phase, that is, the IA’s goal may be different from what the person wants the IA to achieve, and the IA needs to infer the latter to judge whether a motion matches that person’s instruction. We evaluated Manifestor by investigating whether people can accurately predict an IA’s future motion with explanations generated with Manifestor. We compared Manifestor’s vocabulary with that from optimal acquired in a situation in which the communication-gap problem did not exist and that from ablation, which was learned with a false assumption that an IA and person shared a goal. The experimental results revealed that vocabulary learned with Manifestor improved people’s prediction accuracy as much as with optimal, while ablation failed, suggesting that Manifestor can enable an IA to properly learn vocabulary from people’s instructions even if a communication gap exists.