Miracle-dz/UniFAR-FTUs huggingface dataset:
KiuFunza: Pre-Analysis Plan for 2nd Year Results∗

This documents presents the pre-analysis plan for the second year’s data of the KiuFunza project which is a randomized evaluation of the impact of input and incentive based approaches to improving early grade learning outcomes, as well as the interaction of the two approaches. The project is being implemented and overseen by Twaweza a non-profit initiative in Tanzania, and the evaluation is being led by Innovations for Poverty Action (IPA), with the lead researchers being Prof. Isaac Mbiti and Prof. Karthik Muralidharan. This document is being prepared and registered after the first year of data has been collected and analyzed (to generate hypotheses) but before the second year of data have been analyzed. The aim of this pre-analysis plan is to discipline the types of heterogeneity that we will analyze and to specify the specific questions in the survey instruments that will be used to define variables of interest. The KiuFunza project features 3 treatment arms and a control group and is implemented across a representative sample of 350 schools across 10 districts in Tanzania. The treatment arms are:
Using Monte Carlo Tree Search for EDA – A Case-study with Designing Cross-layer Reliability for Heterogeneous Embedded Systems

Continued transistor scaling and increasing power density have led to considerable increase in fault-rates in silicon nanotechnology-based real-time systems. Cross-layer fault tolerance techniques present a more cost-efficient methodology for adapting to such increased fault rates by distributing fault-tolerance to different layers. To this end, we propose a methodology for integrating the design space exploration (DSE) for taskmapping on heterogeneous hardware-platforms with designing cross-layer reliability. Specifically, we model the DSE for task-mapping with cross-layer reliability as a tree search problem and use Monte Carlo Tree Search for task-mapping and scheduling applications with specific reliability requirements. The proposed methodology results in considerable improvements over a standalone approach to task-mapping and implementing cross-layer reliability.
A new decade for social changes

In the political and social life of the last centuries, almost every social aspect has been debated in a context of political influences and interests, of the opposition of different groups of more or less political nature. The family has always been the most favorable environment for the birth and perfection of the human being. The procreation, care, upbringing and preparation for life of a new creature have been and are a fundamental concern of any family. Children represent the "golden fund of a people" and maintain the natural human potential, give natural and spiritual strength to a people. One of the aspects that received special attention was the right of women to have a say in their own reproduction, namely the right of women to choose whether or not to keep a pregnancy. Immoral in terms of the Church, outlawed by the legislation of some states, the right to abortion has had a sinuous evolution on the social scene of many states. This issue has many political, moral and social connotations, being politically regulated differently by different states.
Damage Diagnosis of Cable of Large Span Cable-Stayed Bridge Based on the Support Vector Machine

The dynamic characteristic of cable damage is calculated of Tianxingzhou long-span cable-stayed bridge. Firstly, a normalized natural frequency is defined and the affection of cable damage on normalized natural frequency is analyzed. Through comparative studies came to the following conclusion: the damage effect on the natural frequencies of different levels of cable mode are essential differences, thus establishing a description of cable damage model based on support vector machine, the different damage cases are used to train and recognize. The numerical example shows that the method can be effectively used to identify the damage of cable of long-span cable-stayed bridge.
Joint Deep Clustering: Classification and Review

Clustering is a fundamental problem in machine learning. To address this, a large number of algorithms have been developed. Some of these algorithms, such as K-means, handle the original data directly, while others, such as spectral clustering, apply linear transformation to the data. Still others, such as kernel-based algorithms, use nonlinear transformation. Since the performance of the clustering depends strongly on the quality of the data representation, representation learning approaches have been extensively researched. With the recent advances in deep learning, deep neural networks are being increasingly utilized to learn clustering-friendly representation. We provide here a review of existing algorithms that are being used to jointly optimize deep neural networks and clustering methods.
Transatlantic Dual Masters Degree Program in Transportation and Logistics Systems

In August 2010, the University of Texas at El Paso, the Czech Technical University, and the University of Zilina jointly launched the Transatlantic Dual Master's Degree Program in Transportation and Logistics Systems. Under this program, a graduate student spends 1 year of study at the University of Texas at El Paso and a second year at Czech Technical University or University of Zilina, or vice versa. On successful completion of the 2-year program, a student earns two master's degrees. Two years of effort led to a successful proposal submitted to the funding agencies in the United States and the European Union, followed by 1 year of administrative preparation before the actual student mobility began in August 2011. The first cohort of eight students successfully completed the program in June 2012. This paper reports the sequence of events that led to the proposal submission and award, major issues that surfaced throughout the course of the project, and challenges that were overcome during the administrative preparation phase. The experiences of students and professors who have participated in this program are also documented. Key factors leading to the successful implementation in the initial years are discussed.
A Novel Color-Image Enhancement Approach for Night-Vision Images

Night-vision devices are becoming more and more important now, but images captured by these devices often have unpleasing visual effects. According to the characteristics of night-vision images, in this paper we propose an image enhancement approach maintaining color fidelity. Firstly, the image is divided into RGB channel and decomposed into reflectance component and illuminance component. Secondly, the non-uniform illuminance component of the image is filtered again to eliminate the uneven illumination. Finally, we combine the reflectance component and the corrected illuminance component and adjust the brightness of the combined image. The experiment result shows that the proposed enhancement approach has high-performance.
Bridgeless electrolytic capacitor-less valley-fill AC/DC converter for offline twin-bus light-emitting diode lighting application

To match the key features of light-emitting diode (LED) lighting source and further save power, LED lighting driver also requires long life, while maintaining high efficiency, high power factor, pulse-width modulation dimming and low cost. However, a typical LED lighting driver has the following drawbacks: (i) utilise bulky electrolytic capacitor as storage capacitor with short lifetime; (ii) employ a low-frequency diode bridge as the rectifier cell; and (iii) engage multiple stages cascade structure for multiple LED strings. To overcome the aforementioned shortages, this study proposed a bridgeless electrolytic capacitor-less AC/DC converter for offline LED lighting application. In the proposed converter, the conventional diode rectified bridge is replaced by Totem-pole bridgeless configuration for reducing the number of semiconductors in the line-current path. Meanwhile, the valley-fill circuit is introduced to further reduce the capacitor size. As comparison to its counterpart, the proposed circuit requires only one quarter of the capacitor energy when considering the energy amount (CV 2 ) as the capacitor sizing criterion. Furthermore, the isolation type of the studied circuit is compatible with Twin-Bus configuration for achieving higher overall system efficiency. Finally, the experimental results, taken from a laboratory prototype rated at 50 W, are presented to verify the effectiveness of the proposed converter.
Clonal selection algorithm application to simple microwave matching network

Artificial immune systems are classified as computational systems inspired by theoretical immunology and are mechanisms that can solve complex problems. Clonal selection algorithm is a selection algorithm, which is derived as a special selection algorithm to this artificial intelligence concept. In this work, Clonal selection algorithm is applied to design a simple microwave matching network for a load with arbirtary impedance. The results obtained were tested in a microwave simulator and indicated that clonal selection algorithm can be very effectively applied for microwave design problems. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:991–993, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25935
Noise reduction method for the multisensor system measuring DC current

Multi-Sensor system has been proposed to measure electric current in a non-contact way. According to the Ampere's Law, the value of the current flowing in a conductor can be obtained through processing the outputs of the magnetic sensors around the conductor. As the discrete form of the Ampere's Law is applied, measurement noises are introduced when there exists the interference magnetic field induced by nearby current flowing conductors. In this paper, the measurement noises of the multi-sensor system measuring DC current are examined to reveal the impact of the interference magnetic field and the number of the magnetic sensors on the measurement accuracy. A noise reduction method based on Kalman filtering is presented. Computer simulation and experiment results show that the method greatly improves the accuracy without seriously increasing the computation load in comparison with other approaches.
The application of filterability as a parameter to evaluate the biological sludge quality in an MBR treating refinery effluent

AbstractMembrane bioreactors (MBR) have been extensively employed at industrial effluent treatment plants; however the membrane fouling has restrained its more extensive application. Filterability is an important parameter to evaluate sludge properties and the potentiality of membrane fouling in MBR, nevertheless the lack of standardization of its assessment method has made it difficult to understand its actual role on MBR performance and compare its results. In this context, this work aims to compare three filterability assessment methods described in the literature (TTF, FT, and SFI) regarding its capability to sense sludge quality variation and reproducibility, and evaluate the application of this parameter as a tool to monitor and control fouling in MBR treating petroleum refinery effluents. This study showed that, among the methods evaluated, time to filter was the most effective to assess the filterability, both in terms of its capability to sense sludge quality variation and reproducibility. The re.
Separating the impacts of heat stress events from rising mean temperatures on winter wheat yield of China

Warming due to climate change has profound impacts on regional crop yields, and this includes impacts from rising mean growing season temperature and heat stress events. Adapting to these two impacts could be substantially different, and the overall contribution of these two factors on the effects of climate warming and crop yield is not known. This study used the improved WheatGrow model, which can reproduce the effects of temperature change and heat stress, along with detailed information from 19 location-specific cultivars and local agronomic management practices at 129 research stations across the main wheat-producing region of China, to quantify the regional impacts of temperature increase and heat stress separately on wheat in China. Historical climate, plus two future low-warming scenarios (1.5 °C/2.0 °C warming above pre-industrial) and one future high-warming scenario (RCP8.5), were applied using the crop model, without considering elevated CO2 effects. The results showed that heat stress and its yield impact were more severe in the cooler northern sub-regions than the warmer southern sub-regions with historical and future warming scenarios. Heat stress was estimated to reduce wheat yield in most of northern sub-regions by 2.0%–4.0% (up to 29% in extreme years) under the historical climate. Climate warming is projected to increase heat stress events in frequency and extent, especially in northern sub-regions. Surprisingly, higher warming did not result in more yield-impacting heat stress compared to low-warming, due to advanced phenology with mean warming and finally avoiding heat stress events during grain filling in summer. Most negative impacts of climate warming are attributed to increasing mean growing-season temperature, while changes in heat stress are projected to reduce wheat yields by an additional 1.0%–1.5% in northern sub-regions. Adapting to climate change in China must consider the different regional and temperature impacts to be effective.
A software risk management capability model for medical device software

The Medical Device industry is currently one of the fastest growing industries in the world and a guarantee of the integrity of medical device software has become increasingly important. Failure of the software can have potentially catastrophic effects, leading to injury of patients or even death. Consequently there is a tremendous onus on medical device manufacturers to demonstrate that sufficient attention is devoted to the area of software risk management throughout the software lifecycle. Failure to do so can lead to a lack of approval from the various regulatory bodies with a consequent surrender of the right to market the device in a particular country. Several different standards, guidance papers and industry guides exist which make it difficult to guarantee conformance in all cases. This diverse set of requirements can make software risk management difficult and this thesis examines the possibility of a unified approach whilst investigating the relevance of the Capability Maturity Model Integration (CMMI) SPI model to the regulatory requirements. It is demonstrated that existing SPI models are not comprehensive enough to satisfy medical device safety requirements and an alternative is proposed. The research presented in this thesis develops a software Risk Management Capability Model (RMCM) for the medical device sector, which meets medical device regulatory requirements and the CMMI Software Process Improvement (SPI) risk management practices. The RMCM has been evaluated within a medical device company producing medical device software. During the creation of the model a mapping has been performed between medical device regulations and the CMMI guidelines for software risk management. The research identifies the potential strengths and weaknesses of the CMMI risk management process area in the specific context of medical device software. The research also identifies weaknesses of current medical device software regulations, through the analysis of the CMMI SPI model.
Robotic 3D dual-drive force/velocity control for tactile sensing and object recognition

This work describes the development and testing of a 3-D dual-drive surface tracking controller that enables a robot to track along any specified path on the surface of an object. The dual-drive controller computes the normal and tangent vectors relative to movement along the path. The result is controlled movement in 3-D on the surface of an object. It is assumed that the path is generated by an external Recognizer in such a way that the data points collected by tactile sensing along the path will maximize the probability of correctly identifying the object. This tactile data collection method is referred to as `object-dependent' sensing because the location of the sensing paths is driven by comparisons made by the Recognizer to a model data base. The application for such a data collection system is object recognition tasks in environmental exploration and manipulation.
Orthogonal cascade realization of real multiport digital filters

A generally applicable theory for multiport orthogonal digital filter synthesis is discussed, using a direct algebraic method. Standard (complex) degree 1 and irreducible (real) degree 2 chain scattering matrices are derived and the computability problem which normally occurs in such filters is discussed and solved for the general case. It is shown that any finite causal and lossless real multivariate system can be realized as a computationally compatible cascade of real degree 1 and degree 2 factors consisting of an orthogonal constant interconnecting part and a lossless dynamic portion. In order to increase the practical interest of the paper, realizations of these sections are explicitly given. The known scalar wave digital filter ‘adaptors’ emerge as a special case of the theory presented in this paper. The general results are of interest in multiport digital signal processing, in multivariate interpolation theory and in multichannel prediction and modelling theory and techniques.
A Forest 3-D Lidar SLAM System for Rubber-Tapping Robot Based on Trunk Center Atlas

Currently, there are many simultaneous localization and mapping (SLAM) algorithms for 3-D light detection and ranging (lidar) relying on features such as planes and edges. However, if a robot needs to work in a rubber forest, these simple features can be unstable due to the complexity of the environment. To address this problem, in this article, we propose the first SLAM system to make a deep adaptive adjustment for the particularity of the rubber-tapping robot and the rubber forest environment. In our article, tree trunks are used to construct a set of sparse maps called trunk atlas, which contain environmental information for localization and trunk position for rubber tapping. To ensure the quality of feature extraction, density field correction and multicriteria trunk detection are proposed. Due to the sparsity and stability of the trunk atlas, our SLAM system can ensure real-time performance. Experiments show that our lidar-only SLAM system can achieve nearly the same performance as state-of-the-art inertial-measurement-unit-aided algorithms in terms of runtime and localization accuracy. In addition, our trunk atlas also takes up less memory for storage than other algorithms. More specifically, it takes less than 50 kB to store a map of an area of approximately 5000 m$^2$.
A Single Symbol Maximum-Likehood Decoder for Four Transimit Antenna QOSTBC

This letter proposes a very low-complexity maximum likelihood (ML) detection algorithm for the quasi-orthogonal space-time block code (QOSTBC) with four transmits antennas. The low-complexity single-complex-symbol detection is based on QR decomposition and the expansion of equalization via a diagonal matrix transformation. The proposed strategy enables the QOSTBC to achieve ML performance with significant reduction in computational load for any high-level modulation scheme.
An Outperforming Artificial Intelligence Model to Identify Referable Blepharoptosis for General Practitioners

The aim of this study is to develop an AI model that accurately identifies referable blepharoptosis automatically and to compare the AI model’s performance to a group of non-ophthalmic physicians. In total, 1000 retrospective single-eye images from tertiary oculoplastic clinics were labeled by three oculoplastic surgeons as having either ptosis, including true and pseudoptosis, or a healthy eyelid. A convolutional neural network (CNN) was trained for binary classification. The same dataset was used in testing three non-ophthalmic physicians. The CNN model achieved a sensitivity of 92% and a specificity of 88%, compared with the non-ophthalmic physician group, which achieved a mean sensitivity of 72% and a mean specificity of 82.67%. The AI model showed better performance than the non-ophthalmic physician group in identifying referable blepharoptosis, including true and pseudoptosis, correctly. Therefore, artificial intelligence-aided tools have the potential to assist in the diagnosis and referral of blepharoptosis for general practitioners.
Quality models in an SME context

This paper adopts a critical perspective in seeking to inquire how TQM‐related models, developed initially in large organisations, are applied in the SME context. Using a grounded inductive approach, a study is made of 20 SME cases, where a quality model, the balanced scorecard, has been applied. A grounded model is derived which shows the critical factors in regard to the principles of TQM, when quality models are applied in an SME context. The findings indicate TQM, when a quality model is applied in an SME context. The findings indicate that SMEs can be compromised by the mechanistic and formalised nature of such models, which the SMEs consider as adding bureaucracy, while needing to remain flexible and innovative in a very dynamic environment over which they have little control.
A compact UWB CPW bandpass filter with short‐ended H‐shaped resonator and controllable notched band

An ultra‐wideband (UWB) bandpass filter (BPF) using the hybrid microstrip and coplanar waveguide (CPW) structure is proposed in this article.The BPF composes of CPW feed lines and H‐shaped microstrip resonator. By a CPW/microstrip back‐to‐back coupling structure, the UWB BPF was realized with the passband covering the entire UWB band (3.1–10.6 GHz). A narrow notched band achieved by etching slots on the lower impedance part of the H‐shaped resonator to form an open circuit stub. At last, three U‐slot DGSs are used to achieve a good upper‐stopband rejection. The measured results are in good agreement with the simulated results. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1577–1581, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27663
Tunable InP/air MOEMS filter with record breaking selectivity

We describe the design and fabrication of novel Fabry-Perot tunable filters based on a deformable structure composed of surface micromachined indium phosphide (InP) / Air Distributed Bragg Reflectors (DBRs). As compared to conventional designs, superior spectral selectivity has been achieved by displacing the resonant cavity into the high index material (InP) rather than in air. This configuration is expected to reduce significantly the lateral losses of the cavity. The filters feature also a novel doping structure for bi-directional electrostatic actuation. We present simulations and experimental results that demonstrate the effectiveness of this high index cavity concept for improving the selectivity of small dimension tunable MOEMS filters. The devices are fabricated using a multiple InP-air-gap MOEMS technology based on the sacrificial etching of an InP / InGaAs stack A spectral linewidth better than 0.15 nm over a tuning range of 40 nm is experimentally demonstrated. Design improvements for doubling the tuning range are also proposed.