Miracle-dz/UniFAR-FTUs huggingface dataset:
Political–Economic Determinants of External Import Protection under a Preferential Trade Agreement

Abstract The stalling of WTO multilateralism and the proliferation of preferential trade agreements in recent decades have drawn substantial attention to the impacts of preferential liberalization. A critical question is how they affect the trade barriers imposed against outsiders. I examine the relationship between preferential trade liberalization and protection against non-member countries by testing the predictions of a political–economy model based on the previous literature. Focusing on a specific model allows me to uncover the mechanisms via which preferential liberalization affects external import protection, whereas most of the existing literature has focused on establishing the sign of the effect only. Furthermore, I focus on not only tariffs, as most studies do, but also on the temporary trade barriers of antidumping and safeguards. I test the predictions for Latin America and obtain results that provide solid evidence supporting two mechanisms from the theory, which lead to lower protection against non-members of a preferential trade agreement. First, a lower preferential import protection level means that the increase in preferential imports from increasing the external tariff creates a smaller increase in tariff revenue. Second, as preferential import protection is cut, there is a decrease in the markup and sales of domestic firms, and thus raising the external import protection generates less profit. Moreover, this second effect is present when the political motivation of the government is sufficiently strong.
Research on Chinese Character Recognition Using Bag of Words

The traditional OCR obtains unsatisfactory results in the field of image recognition when images are processed in a complex background with low quality. This paper presents a novel application of the model of Bag of Words on Chinese character recognition, and extensively evaluated its effectiveness with 12 different fonts of Chinese character datasets under varying circumstances. Our experimental results demonstrate that this approach can achieve nearly 70% at its highest accuracy rate, which shows its performance far exceeds the traditional OCR’s
Lung Cancer Subtype Diagnosis by Fusing Image-Genomics Data and Hybrid Deep Networks

Accurate diagnosis of cancer subtypes is crucial for precise treatment, because different cancer subtypes are involved with different pathology and require different therapies. Although deep learning techniques have made great success in computer vision and other fields, they do not work well on Lung cancer subtype diagnosis, due to the distinction of slide images between different cancer subtypes is ambiguous. Furthermore, they often over-fit to high-dimensional genomics data with limited samples, and do not fuse the image and genomics data in a sensible way. In this paper, we propose a hybrid deep network based approach LungDIG for Lung cancer subtype Diagnosis by fusing Image-Genomics data. LungDIG first tiles the tissue slide image into small patches and extracts the patch-level features by fine-tuning an Inception-V3 model. Since the patches may contain some false positives in non-diagnostic regions, it further designs a patch-level feature combination strategy to integrate the extracted patch features and maintain the diversity between different cancer subtypes. At the same time, it extracts the genomics features from Copy Number Variation data by an attention based nonlinear extractor. Next, it fuses the image and genomics features by an attention based multilayer perceptron (MLP) to diagnose cancer subtype. Experiments on TCGA lung cancer data show that LungDIG can not only achieve higher accuracy for cancer subtype diagnosis than state-of-the-art methods, but also have a high authenticity and good interpretability.
Developing Leadership Skills among EMBA Students: Innovations in Design

Saying that doing business in a highly dynamic and volatile work environment poses unprecedented challenges to organizations and their leaders is stating the obvious. In order to effectively deal with various challenges, and succeed, organizations need leaders who could help them realize their objectives. Furthermore, it is required that people from each level in the organization are involved in leading. Management researchers and practitioners argue that it is vital for any organization to develop leadership skills among its members if it aspires to succeed and take an edge over others. Scholars, working in the area of leadership development, maintain that leadership skills can be developed among organizational members and MBA students through carefully designed programmes/interventions. The challenge often is that leadership training is extremely resource-intensive. Leadership development arguably needs an understanding of self, others, and the systems. It is assumed that it can be best done in small groups with attention to the individual, and it takes prolonged engagement. Thus, designing learning inputs for large groups is a challenge that is real and remains unaddressed by many practitioners and faculty in management schools.
Federal Highway Administration Vehicle Classification from Video Data and a Disaggregation Model

To assess highway system performance, states are required to submit compositional traffic count reports to the Federal Highway Administration (FHWA). A methodology was developed for obtaining compositional vehicle counts from low-cost portable video technology. Autoscope, an advanced video camera with machine vision processing, was used to capture, process, and store compositional counts based on vehicle length. A maximum of five length-based vehicle classes can be differentiated from video; therefore a methodology was developed that generates the FHWA 13-category vehicle classes. A disaggregation process, typically used in stochastic hydrology, is employed. Results from the disaggregation model are compared with actual FHWA classification data obtained from axle counters. It was observed that less than 1% of the vehicles were not included in the model output and 3.43% were misclassified. In addition, 95 and 99% confidence intervals calculated by the disaggregation model included 92.3 and 100% of the actual axle counter data.
Convection Initiation at a Coastal Rainfall Hotspot in South China: Synoptic Patterns and Orographic Effects

This study documents the detailed features and processes of convection initiation (CI) at a coastal rainfall hotspot in South China, with emphasis on the typical meteorological conditions and effects of small‐scale orography. The CI occurrences are identified using high‐quality radar mosaics during 7‐year warm seasons. They tend to maximize in low‐lying areas adjacent to coastal small‐scale mountains rather than on the windward slopes of major mountains as previously expected. The genesis of the coastal convection exhibits a remarkable diurnal peak in the early afternoon and mostly occurs on the days of low‐level southwesterlies with a jet‐nose structure in offshore region. These onshore flows enhance the low‐level moistening and convergence on coasts. The characteristic synoptic patterns become common after the onset of summer monsoon, suggesting the key role of monsoonal flows in triggering coastal convection. Driven by the atmospheric conditions of the dominant synoptic pattern, quasi‐idealized numerical simulations demonstrate that the coastal small‐scale mountains disturb the low‐level onshore flow and produce multiple enhanced convergent zones downstream of the mountains. The induced local variations in upward motion and moisture pooling explain the CI occurrences that are located close to these small‐scale mountains. When these mountains are artificially removed in the simulation, convection preferentially initiates on the windward slopes of major mountains as expected. The findings suggest that the small‐scale orography can strongly regulate the local patterns of CI hotspot through disturbing low‐level onshore flow. An explicit resolving of the small‐scale coastal orography is warranted to improve the modeling of local severe weather.
The use of neural networks for discrimination of partial discharges in transformer oil

The detection of partial discharges in laboratory samples of transformer oil has been performed using both electrical and ultrasonic techniques. The oil was subjected to a pin-plane electric field in a purpose built cell. The oil was contaminated using particles of a given size with only one size being used during any one experiment. Electrical signals produced by discharges were recorded using a current measuring resistor. Acoustic signals were taken by fixing a commercial acoustic emission probe to the exterior of the cell and recording acoustic signals given by the discharge. The electric and acoustic signals were analysed and yielded little to allow for discrimination of the particle type. Analysis of the signals in the frequency domain yielded similar negative results. A simple 3-layer back propagation neural network was used to try and discriminate between the particle types by using their frequency spectrum. The use of the frequency domain allows the network to be trained without having to accurately position the discharge signals in the data window. With acoustic signals, the training data was recognised to 100% accuracy, with test data then being recognised with over 95% accuracy. Electrical signals were less amenable to discrimination, with 95% of the training data being recognised correctly and over 90% of the test data. Reasons for the poorer recognised performance with electrical signals may rest with electromagnetic interference and stray capacitance.
Design and Characterization of 180 GHz Filters in Photoimageable Thick-Film Technology

The design and performance of 180 GHz substrate integrated waveguide filters is presented. It is shown that photoimageable thick film technology can achieve the precise dimensional control needed for design at G-band. TRL calibration standards are fabricated in the substrate integrated waveguide medium itself and excellent results are obtained for the 140–200 GHz frequency range. 0.1 and 0.01dB ripple filters are designed and fabricated, with 8 and 6.2% relative bandwidths. The measured results show excellent agreement with electromagnetic simulations.
Anti-Cancer Assessment of a Ramie (Boehmeria nivea L. Gaud.) Leaf Extract Using Mcf-7 Cell Line and a Yeast-Based Bioassay

Introduction: One strategy for molecular cancer therapy is to know the key mechanism of cytotoxic compounds that can kill cancer cells. Ramie (Boehmeria nivea L. Gaud.) leaves contain active compounds that have important effects on cancer chemoprevention. Objective: To obtain the active fraction of a Ramie leaf extract in inhibiting the proliferation of MCF-7 breast cancer cell lines and to determine the mechanism of apoptosis induction using MCF-7 and Saccharomyces cerevisiae strains 1140, 1353, and 1138. Method: Fractions were prepared using n-hexane, dichloromethane (CH2Cl2), ethyl acetate, and n-butanol as solvents. All fractions were tested qualitatively through phytochemical. The MTT-based cytotoxicity assay used MCF-7 (in vitro) to obtain the IC50 value, whereas the model system that targets the enzymatic (topoisomerase) used a yeast-based bioassay to obtain the IC12 value. Apoptotic induction of the active fraction in MCF-7 was performed using flow cytometry and qPCR (2-ΔΔCt method). Results: The phytochemical analysis indicated that the extract fraction consisted of alkaloids and steroids. The smallest IC50 value was obtained from the CH2Cl2 fraction as 3.79 g/mL, potentially act as an anticancer. A higher percentage of apoptosis than that of necrotizing cells and live cells was observed through flow cytometry. The CH2Cl2 fraction with an IC12 value < 8000 in strains 1140, 1353, and 1138 consistently showed the mechanism of apoptosis induction as topoisomerase I and II inhibitors. Also, another mechanism could be through the intrinsic pathway, indicated by the highest expression level in p53. Conclusions: The CH2Cl2 fraction of Ramie leaves can inhibits the proliferation of MCF-7 cells in the active and strong categories. The CH2Cl2 fraction induces apoptosis by increasing p53 gene expression and inhibiting topoisomerase I and II. Thus, it showed potential as an anticancer drug candidate.
Applications of Scanning Electron Microscopy Using Secondary and Backscattered Electron Signals in Neural Structure

Scanning electron microscopy (SEM) has contributed to elucidating the ultrastructure of bio-specimens in three dimensions. SEM imagery detects several kinds of signals, of which secondary electrons (SEs) and backscattered electrons (BSEs) are the main electrons used in biological and biomedical research. SE and BSE signals provide a three-dimensional (3D) surface topography and information on the composition of specimens, respectively. Among the various sample preparation techniques for SE-mode SEM, the osmium maceration method is the only approach for examining the subcellular structure that does not require any reconstruction processes. The 3D ultrastructure of organelles, such as the Golgi apparatus, mitochondria, and endoplasmic reticulum has been uncovered using high-resolution SEM of osmium-macerated tissues. Recent instrumental advances in scanning electron microscopes have broadened the applications of SEM for examining bio-specimens and enabled imaging of resin-embedded tissue blocks and sections using BSE-mode SEM under low-accelerating voltages; such techniques are fundamental to the 3D-SEM methods that are now known as focused ion-beam SEM, serial block-face SEM, and array tomography (i.e., serial section SEM). This technical breakthrough has allowed us to establish an innovative BSE imaging technique called section-face imaging to acquire ultrathin information from resin-embedded tissue sections. In contrast, serial section SEM is a modern 3D imaging technique for creating 3D surface rendering models of cells and organelles from tomographic BSE images of consecutive ultrathin sections embedded in resin. In this article, we introduce our related SEM techniques that use SE and BSE signals, such as the osmium maceration method, semithin section SEM (section-face imaging of resin-embedded semithin sections), section-face imaging for correlative light and SEM, and serial section SEM, to summarize their applications to neural structure and discuss the future possibilities and directions for these methods.
Retrofit Optimization for Resilience Enhancement of Bridges under Multihazard Scenario

AbstractBridge retrofitting is a common approach to enhance resilience of highway transportation systems. The study uses a multiobjective evolutionary algorithm to identify retrofit design configurations that are optimal with respect to resilience and retrofit cost. Application of this algorithm is demonstrated by retrofitting a bridge with column jackets and evaluating resilience of the retrofitted bridge under the multihazard effect of earthquake and flood-induced scour. Three different retrofit materials are used: steel, carbon fiber, and glass fiber composites. The inherent disparity in mechanical properties and associated costs of these different materials gives rise to a trade-off between cost and performance when it comes to retrofit operations. Results from the optimization, the Pareto near-optimal set, include solutions that are distinct from one another in terms of associated cost, contribution to resilience enhancement, and values of design parameters. This optimal set offers the best search re.
Lexical and Grammatical Errors in Developmentally Language Disordered and Typically Developed Children: The Impact of Age and Discourse Genre

Persistent lexical and grammatical errors in children’s speech are usually recognized as the main evidence of language delay or language disorder. These errors are usually treated as a sign of a deficit in language competence. On the other hand, some studies have revealed the same kinds of grammatical errors in children with developmental language disorder (DLD) and in typically developed (TD) children. Quite often, DLD children use grammatical markers properly, but sometimes they do this erroneously. It has been suggested that the main area of the limitations in DLD children is language performance but not language competence. From the perspective of the resource deficit model, the error rate in DLD children should be influenced by the cognitive demands of utterance and text production. We presume that different genres of discourse demand a different number of cognitive resources and, thus, should differently impact the error rate in children’s speech production. To test our hypothesis, we carried out an error analysis of two corpora of child discourse. The first corpus contained longitudinal data of discourse (personal narratives, fictional stories, chats, and discussions) collected from 12 children at four age points (4 years 3 months., 4 years 8 months., 5 years 3 months., and 5 years 9 months. years). Another corpus contained discourse texts (fictional stories and discussions) collected in the framework of a cross-sectional study from 6-year-old TD and DLD children; the DLD children had language expression but not comprehension difficulties. A comparative analysis between different discourse genres evidenced that the genre of discourse and age of assessment impacted the error distribution in the DLD and TD children. Such variables as the lexical and morphological error rates were impacted the most significantly. The results of the two studies confirmed our hypothesis regarding the probabilistic nature of lexical and grammatical errors in both DLD and TD children and the relationship between a cognitive loading of the genre and the error rate.
Integrating Visual Literacy Teaching Into The English For Specific Purposes Course

The purpose of the study is to design a model of integration of visual literacy (VL) teaching into the English for specific purposes (ESP) course at university. The research has focused on the systemic characteristics of the structural components of the integrative model: learning objectives, learning outcomes, content and methodology of a VL-oriented ESP course. Integrative learning objectives of the ESP course are formulated as development of VL and improvement of English communication skills in university students in profession-related contexts. Visual literacy is defined as a combination of cognitive abilities to understand communication of visual messages, express ideas visually and think critically about visual messages. For assessing VL (learning outcomes) in students, the criterion-based scheme has been designed. The ESP course content integrates three basic components: VL-related content, theme-based content and language content. Integrative content instruction has been designed on the basis of several visual education approaches: full-spectrum visual learning, active and performance-based learning, critical thinking and visual mapping approaches. The methodology of the ESP course includes visual-based instructional strategies: “deep viewing” method, “reading circles” strategy, sketchnoting method and visual thinking tools. These strategies engage students in both visual literacy and communication practices in profession-related contexts. Integration of VL teaching into the ESP course brings additional benefits: development of students’ creativity, critical thinking skills and open-mindedness.
Real-time polarization-diverse features extraction and automated target identification using neural networks

The authors have mapped the polarization-diverse features extraction problem onto the Lyapunov energy function of the Hopfield model neural network to obtain the real-time solution of the feature, i.e., the ellipticity, the tilt angle and the amplitude of the fit ellipse, derived from this parameterization. Simulated results are presented to shows its real-time capacity in the context of the polarization-diverse features extraction problem by using the Hopfield net as well as its strong target identification ability by applying the back-propagation neural network employing the resulting feature sets. The approaches considered here are expected to have a faster computational speed than those of the traditional approaches such as the LMS (least mean square) approximation method and classical pattern recognition. <<ETX>>
Distortion of a radio pulse reflected from an inhomogeneous lossy plasma

The effect of electron collision loss on the distortion of an electromagnetic pulse reflected from an inhomogeneous plasma or ionosphere is analyzed. The electron density of the ionosphere is assumed to increase linearly in the direction normal to the plane interface. The case of a plane wave carrying a pulsed FM signal is considered. The pulse is assumed to have a Gaussian envelope and an instantaneous frequency which varies linearly with time.
Novel Control of Dual-Grounded Soft-Switching Transformerless Single-Phase Inverter

A high-efficiency dual-grounded soft-switching transformerless single-phase inverter is proposed in this article. It is able to achieve the zero-voltage switching (ZVS) and inherent leakage current reduction. A control method based on digital frequency calculation is applied to the inverter to achieve the ZVS operation. Based on this control method, no additional circuits and components are required, and it has the advantages of simple adjustment, high reliability, and low cost. Also, a digital calculation method for the bias current of ZVS is proposed for implementing the real-time programmable control. Finally, the proposed method is tested on a digital experimental platform. The efficiency of the inverter with the proposed and conventional methods under different loads is tested and compared. The experimental results show that the efficiency can be improved from 96.39% to 98.4% at rating load.
Research of the Model and MPPT Algorithm of Solar Cells

According to the mathematic model of solar cells, builds its general simulation model based on the S-function designed under the Matlab/Simulink environment. Presents the incremental conductance algorithm based on the optimal gradient to simulate and verify the theories about the maximum power point tracking (MPPT) of photovoltaic power generation. Give the in-depth contrast analysis on the power, output voltage characteristics of photovoltaic power generation, which lays a solid foundation for engineering practice.
A high-Q broad-band active inductor and its application to a low-loss analog phase shifter

The proposed high-Q broad-band active inductor utilizes frequency-insensitive negative resistance to compensate constant internal losses caused by the drain-to-source conductance of the field-effect transistors (FETs), the dc bias circuit, and several other factors. The measured frequency range of the fabricated InAlAs/InGaAs/InP HEMT active inductor is 6 to 20 GHz for Q values greater than 100, and 7 to 15 GHz for Q values greater than 1000. A low-loss analog phase shifter is also fabricated at C-band. This is constructed with the active inductors, the varactor diodes and the low-loss multilayer broad-side coupler in a MIC structure. Since the constant negative resistance of the active inductors also compensates the line loss of the coupler and the varactor diodes' series resistance, the measured results show a good insertion loss performance with a large phase shift. A phase shift of more than 225/spl deg/ within a 0.8 dB insertion loss from 4.7 to 6.7 GHz, another of more than 180/spl deg/ within 1.3 dB insertion loss from 3.7 to 8.5 GHz, and one more of more than 90/spl deg/ within 1.4 dB insertion loss from 3.5 to 10.6 GHz were obtained.