Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations between parent-teacher and teacher-child relationships and children’s socioemotional functioning

ABSTRACT This study examined whether mother-teacher, father-teacher, teacher-parent, and teacher-child relationships were linked to the socioemotional functioning of children (N = 42) aged 18–40 months. Results indicated that when mothers perceived their child’s teacher more positively, fathers perceived the teacher more positively. In turn, teachers also perceived the parents more positively, which was related to their relationship with the child. Though teachers’ perceptions of the parent were not directly related to their perceptions of children’s socioemotional functioning, the link was mediated through the teacher-child relationship. However, mothers’ perceptions of the teacher were not related to the teacher’s relationship with the child nor the teacher’s perception of the child’s socioemotional functioning. Our findings stress the importance of building positive relationships between teachers and children’s parents, and that these relationships contribute to teachers’ relationships with children and children’s socioemotional functioning.
Silicon (Si) use efficiency in sandy soil amended by Si-loaded hydrogel

ABSTRACT Silicon (Si) plays an essential role in the mineral nutrition of plants. This creates a need to improve tools can provide plant-available Si (PAS) and increase its use efficiency (UE), especially under sandy soil conditions. This study was conducted to estimate Si use efficiency (Si-UE) in sandy soil treated by a hydrogel (water-absorbing polymer) loaded by Si. A field experiment was carried out using a hydrogel (H, 0.7 g kg−1 soil) that was mixed with quartz silica (S, 98.4% SiO2) to give HS homogenous mixtures with three mixing ratios 1:0.5 (HS1), 1:1 (HS2) and 1:1.5 (HS3). The HS mixture was sprayed by potassium humate solution (K-humate, 0.5 mL g−1 hydrogel) to obtain HS1K, HS2K, and HS3K treatments. All treatments were mixed with soil subsurface before wheat (Triticum aestivumL.) cultivation. Results showed that wheat yield and seed yield (t ha−1) increased significantly by applied treatments compared to the control C in the range 9.52–23.80% and 2.59–30.60%, respectively. The HS3 treatment significantly increased the total nitrogen N (mg kg−1) in seeds by 34.99%, phosphorus P by 17.46%, and potassium K by 4.84%. Available Si in soil (mg kg−1) increased in the order HS1 < HS2 < HS3, and with K-humate in the order HS1K < HS2K < HS3K. Total Si (g kg−1) in wheat seeds increased significantly in the order HS1 < HS2 < HS3, and HS1K < HS2K < HS3K with K-humate. Silicon use efficiency may decrease as the rate of applied Si increases.
Fast SVM training using data reconstruction for classification of very large datasets

This paper proposes a fast support vector machine (SVM) training method for the classification of very large datasets using data reconstruction. The idea is to scale down the training data by removing the samples that have low probability to become support vectors (SVs) in the feature space. For this purpose, it applies a series of gradually refined rough SVM classifiers with a quasi‐linear kernel to build rough separation boundaries and remove those samples that are far away from the boundary. In order to make the proposed algorithm efficient for both low‐dimensional and high‐dimensional datasets, efforts are made on three aspects. The first one is to compose a quasi‐linear kernel using the information of data manifold and potential separation boundary such that the samples mapped to feature space keep a sparse distribution, especially in the direction perpendicular to the separation boundary. The second one is to avoid computing Euclidean distances between samples, which may lose its effect on very high dimensional datasets when mapping the samples to feature space and selecting the samples for training data reconstruction. The third one is to design a sophisticated iterative algorithm to gradually refine the rough SVM classifier so as to remove non‐SVs efficiently. The proposed fast SVM training method is applied to different real‐world large datasets and compared with different methods, and simulation results confirm the effectiveness of the proposed method, especially for very high dimensional datasets. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Constraints on the Rheology of the Lithosphere From Flexure of the Pacific Plate at the Hawaiian Islands

The rheology of oceanic lithosphere is important to our understanding of mantle dynamics and to the emergence and manifestations of plate tectonics. Data from experimental rock mechanics suggest rheology is dominated by three different deformation mechanisms including frictional sliding, low‐temperature plasticity, and high‐temperature creep, from shallow depths at relatively cold temperatures to large depths at relatively high temperatures. However, low‐temperature plasticity is poorly understood. This study further constrains low‐temperature plasticity by comparing observations of flexure at the Hawaiian Islands to predictions from 3‐D viscoelastic loading models with a realistic lithospheric rheology of frictional sliding, low‐temperature plasticity, and high‐temperature creep. We find that previously untested flow laws significantly underpredict the amplitude and overpredict the wavelength of flexure at Hawaii. These flow laws can, however, reproduce observations if they are weakened by a modest reduction (25–40%) in the plastic activation energy. Lithospheric rheology is strongly temperature dependent, and so we explore uncertainties in the thermal structure with different conductive cooling models and convection simulations of plume‐lithosphere interactions. Convection simulations show that thermal erosion from a plume only perturbs the lithospheric temperature significantly at large depths so that when it is added to the thermal structure, it produces a small increase in deflection. In addition, defining the temperature profile by the cooling plate model produces only modest weakening relative to the cooling half‐space model. Therefore, variation of the thermal structure does not appear to be a viable means of bringing laboratory‐derived flow laws for low‐temperature plasticity into agreement with geophysical field observations and modeling.
Ventriculoperitoneal shunt infection in Haji Adam Malik Hospital, Medan

Installation of ventriculoperitoneal shunts (VP) represented a substantial progress in the neurosurgical management of hydrocephalus in children. However, infection is the most commonpostoperative complication of aventriculoperitoneal shunt. It is important because it is related to substantial morbidity and mortality, and exerts a negative impact on the quality of life of patients. We retrospectively analyzed all 20 cases of shunt infection from 2013 to 2016. The types of infections found were exposed shunts15 cases (75%), and 5 cases of ventriculitis (25%). Length of infection time which calculated from the beginning of surgery was 350.20 days or 11 months. The most common pathogen types are S. epidermidis followed by P. aeruginosa, E. coli, and A. baumanii. There were many risk factors for shunt infection, but the interesting fact was the level of pre-operative albumin. There was a significant difference between low albumin levels (<3.0) and normal albumin (≥3.0) levels against the risk of exposure shunt, p = 0.015. It means there is asignificant difference between low pre-operative albumin and normal level for the risk of theexposed shunt.
PLYOMETRICS: Power training for judo: Plyometric training with medicine balls

S ince its inception as an Olympic event in 1964, judo has developed immensely, both as an art and sport. Like most sports today, judo training has become more sophisticated in recent years and many good technical books have been published as a result (1, 8). The bulk of literature, however, has focused primarily on judo techniques in contrast to the training of judoka for competition. Although judo is highly technical in nature, it is commonly accepted that when all considerations are equal or nearly equal, victory will fall to the stronger opponent.
Management und Diagnostik von Staphylococcus-aureus-Blutstrominfektionen

ZUSAMMENFASSUNG Blutstrominfektionen durch Staphylococcus aureus sind schwere Infektionserkrankungen, die mit dem Risiko von sekundären Absiedlungen, Rekurrenzen und hohen Sterblichkeitsraten assoziiert sind. Bei adäquater Diagnostik und Therapie kann das Outcome wesentlich verbessert werden. Alle Patienten mit Staphylococcus-aureus-Bakteriämie sollten infektiologisch betreut werden, um die Einhaltung der Therapiestandards zu gewährleisten. Medikamente der Wahl sind Cefazolin und Flucloxacillin bei MSSA und Daptomycin (cave: nicht bei pulmonalem Fokus) und Vancomycin bei MRSA-Infektionen. Die Therapie sollte für mindestens 14 Tage intravenös erfolgen. Inwiefern eine Oralisierung bei Patienten mit unkomplizierten Bakteriämien erfolgen kann, ist derzeit Gegenstand von Studien. Erste Daten lassen drauf schließen.
An Online Learning and Optimization Approach for Competitor-Aware Management of Shared Mobility Systems

An important trend in mobility is the consumption of mobility as-a-service heralding in the age of free-floating vehicle sharing (FFVS) systems. In many markets such fleets compete. We investigate how real-time competitor information can create value for operators in this context. We focus on the vehicle supply decision which is a large operational concern. We show empirically that local market shares directly depend on the share of available vehicles in a location, which underlines the value potential of competitor awareness. We leverage this insight by proposing a novel decision support system for optimal management of FFVS systems under competition. We proceed in two phases, (1) a predictive phase and (2) a prescriptive phase. In phase (1), we compile a spatio-temporal dataset based on Car2Go and DriveNow transactions in Berlin, which we supplement with temporal, geographical and weather data. We partition the city into hexagonal tiles and observe vehicle supply per tile at the start of each period. We train machine learning models to predict vehicle inflows and vehicle outflows during the next period to derive total supply and demand. We find that inflows and outflows can be predicted with high accuracy using similar models. We test different temporal and spatial resolutions and find that spatial resolution incurs larger performance penalties. In phase (2), we formulate a myopic mixed integer non-linear programming model with a margin-maximizing objective function. The model trades off additional market share gains against the cost of re-locating vehicles, which enables operators to assign vehicles optimally across the service network. Our numerical studies on the case of Car2Go and DriveNow demonstrate that this competitor-aware model is capable of profitably improving market share by up to 1.4% or 3.4% for human-based and autonomous re-location respectively in a prefect foresight scenario and by up to 0.8% and 1.8% respectively when using predicted values.
Use of SfM-MVS approach to nadir and oblique images generated throught aerial cameras to build 2.5D map and 3D models in urban areas

Abstract The use of Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) approaches to build 3D models of structures belonging to the Cultural Heritage environment is becoming more widespread. Due to the big dimensions of the digital aerial images generated by airborne sensors and, consequently, computation problems, these images were rarely used for the construction of 3D models through SfM-MVS approach. In addition, the values of overlap used in traditional aerial surveys would lead to a geometry of acquisition that is quite weak. For these reasons, the use of aerial images generated by airborne sensors in the SfM-MVS approach was limited. However, taking into account the possibility to acquire aerial nadir and oblique images according to multi-view, the increasing of High Performance Computation and the use of Direct Georeferencing, the quality of the city model obtained with the SfM/MVS approach was evaluated on a dataset of aerial images involving the Old Town of Bordeux.
Analysis, Design, and Implementation of a ZVT High Step-Up DC–DC Converter With Continuous Input Current

In this article, high step-up dc–dc converter with zero-voltage-transition (ZVT) characteristics and continuous input current is presented. The proposed converter applies coupled inductor and super-lift technique to provide tenfold and more voltage gain and an auxiliary circuit with minimum extra components to provide ZVT characteristics. The presented converter provides high voltage gain and low voltage stress on the active switch, with a proper duty cycle and just unity turn ratio of coupled inductor. It also utilizes the leakage inductor of the coupled inductor as the resonance inductor of the ZVT auxiliary circuit. So, the size and cost of the presented converter is reduced. In this converter, the reverse recovery of diodes is alleviated. A low on resistance switch is used to reduce the conduction losses due to its low voltage stress. Furthermore, the diodes are placed at low current side of the converter and capacitors are mostly chosen from low ESR polyester capacitor family. Due to the mentioned reason, the efficiency is improved. A 200 W prototype with 30 V input voltage and 380 V output voltage is implemented, which confirms the theoretical analysis.
Finishing Cattle in All-Natural and Conventional Production Systems

Beef cattle producers in the North America have a variety of production and marketing options and must choose the best production system for their situation. This review describes considerations involved in choosing between feeding cattle conventionally versus feeding them in programs that prohibit the use of certain technologies. Data from peer-reviewed journals, extension publications, nutritional consultants, governmental organizations, and feed companies were used to construct this review. Most cattle in North America are fed in conventional production systems. Conventional beef production systems typically use steroidal implants, ionophores, and beta-adrenergic agonists to improve animal productivity; as well as feed grade and injectable antimicrobials to control, treat or prevent disease and improve animal health. These technologies have been shown to lower the cost of production, allowing for beef to be competitive in the global protein market. Some consumers have expressed a preference for beef produced without these technologies. These “All-natural” (AN) cattle may bring a premium price in the market. The economic impact of differing productions systems can be described in relation to 1) cost of production, 2) operating costs of the feedlot, 3) price paid for feeder calves, and 4) price received for fed cattle. Conventional production provides the most favorable outcome for factors 1, 2, and 3, while AN production provides the most favorable outcome for item 4. There are also industry wide and societal aspects related to differing beef production systems related to health and safety of beef, land use, and cost of production allowing for a greater share of the global protein market. Technologies used in conventional production are critical tools to North American beef production. Differences in efficiencies between each type of non-conventional production systems must be re-captured in added premiums when cattle are marketed and sold. Premiums for AN cattle are enticing, but the true differences in the cost of production between the AN and conventional cattle must be evaluated in order for a producer to make the correct decision for their operation.
Detection of Common Respiratory Viruses in Patients with Acute Respiratory Infections Using Multiplex Real-Time RT-PCR

Background: Acute respiratory infection (ARI) is caused by human metapneumovirus (HMPV), respiratory syncytial virus type A and B (RSV-A, RSV-B), human parainfluenza viruses 1, 2, and 3 (HPIV-1, HPIV-2, and HPIV-3), influenza viruses A and B (IfV-A, IfV-B), and human coronaviruses (OC43/HKU1, NL63, 229E) worldwide. Objectives: This study was conducted to assess the causative agents of viral ARI among hospitalized adults by real-time PCR. Methods: Clinical nasopharyngeal swabs of 112 patients including 55 (49.1%) males and 57 (50.89%) females with ARI were analyzed using multiplex real-time RT-PCR. Results: Out of 112 specimens, 61 (54.46%) were positive including 10 (8.9%) for influenza H3N2, one (0.89%) for influenza B, 28 (25%) for RSV-A, 18 (16.07%) for HMPV-A, two (1.78%) for HPIV-1, and three (2.67%) for HPIV-3. Two (1.78%) specimens were positive for two agents, RSV-A/HMPV-A and RSV-A/HPIV-3. The distribution of viral infections was 30 among males (26.78%) and 31 (27.67%) among females (P = 0.862). High frequency of RSV-A infection (25%) and the low frequency of influenza B virus infection (0.89%) were detected among patients. The remaining 51 (45.53%) samples were negative for RSV-B, HMPV-B, IfV-A, HPIV2-4A-4B, and HCoVs (OC43/HKU1, NL63, 229E). Conclusions: The role of other viruses such as human adenoviruses rhinovirus/enterovirus (RV/EV), human bocavirus (HBoV), and human parechovirus (HpeV) was not investigated. Multiplex PCR can be used as a rapid test for the diagnosis of viruses causing acute respiratory infection, which results in decreased length of hospitalization.
College Readiness: Are California Charter School Students Better Prepared?

Using US data from the Los Angeles Unified School District (LAUSD) I analyse student outcomes from Advanced Placement tests and Smarter Balanced Summative Assessments to determine whether charter school students display higher levels of college readiness. Using panel data from 2011–12 through 2014–15 for the Advanced Placement tests, I find a gain of between 10.8 and 21.0 percentage points for charter students on average over students attending traditional public schools. For the Smarter Balanced Assessment using school years 2014–15 and 2015–16, I again find that charter students outperform traditional public students on average with gains in all categories for both the English and the mathematics portions of the exam. The results of this study seem to offer some support for the view that offering choice in schooling tends to improve performance.
GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network

Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.
Non-IID representation learning on complex categorical data

Learning complex categorical data requires proper vector or metric representations of the intricate characteristics of that data. Existing methods for categorical data representation usually assume data is independent and identically distributed (IID). However, real-world data is often hierarchically associated with diverse couplings and heterogeneities (i.e., non-IIDness, e.g., various couplings such as value cooccurrences and attribute correlation and dependency, as well as heterogeneities such as heterogeneous distributions or complementary and inconsistent relations). Existing methods either capture only some of these couplings and heterogeneities or simply assume IID data in building their representations. This thesis aims to deeply understand and effectively represent non-IIDness in categorical data. Specifically, it focuses on (1) modeling heterogeneous couplings within and between attributes in categorical data; (2) disentangling attribute couplings with a mixture of heterogeneous distributions; (3) hierarchically learning heterogeneous couplings; (4) integrating complementary and inconsistent heterogeneous couplings; and (5) adaptively identifying and learning dynamic couplings and heterogeneities. Accordingly, this thesis proposes (1) a non-IID similarity metrics learning framework to model complex interactions within and between attributes in non-IID categorical data; (2) a decoupled non-IID learning framework to capture and embed heterogeneous distributions in non-IID categorical data with bounded information loss; (3) a heterogeneous metric learning method with hierarchical couplings to learn and integrate the heterogeneous dependencies and distributions in non-IID categorical data into a representation of a similarity metric; (4) an unsupervised heterogeneous coupling learning approach to integrate the complementary and inconsistent heterogeneous couplings in non-IID categorical data; and (5) an unsupervised hierarchical and heterogeneous coupling learning method to learn hierarchical and heterogeneous couplings on dynamic non-IID categorical data. Theoretical analyses support the effectiveness of the proposed methods and bound the information loss in their generated high-quality representations. Extensive experiments demonstrate that the proposed non-IID representation methods for complex categorical data perform significantly better than state-of-the-art methods in terms of multiple downstream learning tasks and representation-quality evaluation metrics.
Endosomal Confinement of Gold Nanospheres, Nanorods, and Nanoraspberries Governs Their Photothermal Identity and Is Beneficial for Cancer Cell Therapy

Gold nanoparticles can act as photothermal agents to generate local tumor heating and subsequent depletion upon laser exposure. Herein, photothermal heating of four gold nanoparticles and the resulting induced cancer cell death are systematically assessed, within extra‐ or intracellular localizations. Two state‐of‐the‐art gold nanorods are compared with small nanospheres (single‐core) and nanoraspberries (multicore). Heat generation is measured in water dispersion and in cancer cells, using lasers at wavelengths of 680, 808, and 1064 nm, covering the entire range used in photothermal therapy, defined as near infrared first (NIR‐I) and second (NIR‐II) windows, with NIR‐II offering more tissue penetration. When dispersed in water, gold nanospheres provide no significant heating, gold nanorods are efficient in NIR‐I, and only gold nanoraspberries are still heating in NIR‐II. However, in cells, due to endosomal confinement, all nanoparticles present an absorption red‐shift translating visible and NIR‐I absorbing nanoparticles into effective NIR‐I and NIR‐II nanoheaters, respectively. The gold nanorods then become competitive with the multicore nanoparticles (nanoraspberries) in NIR‐II. Similarly, once in cells, gold nanospheres can be envisaged for NIR‐I heating. Remarkably, nanoraspberries are efficient nanoheaters, whatever the laser applied, and the extra‐ versus intra‐cellular localization demonstrates treatment versatility.