Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of the Logical Framework Approach in a Small-scale Aid Project - A Case Study on Infrastructure Improvements at Mkula Hospital in Tanzania

The purpose of this master thesis is to evaluate the Logical Framework Approach (LFA). The LFA was applied in a case study at Mkula Hospital in Tanzania in order to investigate if the LFA can help create sustainable implementations in a smallscale foreign aid project. The small-scale foreign aid project was performed at the Mkula Hospital during seven weeks. The aim of the project was to investigate the infrastructure at the hospital, find the most crucial problem and implement a measure to solve it. During the execution of this aid project a collaboration has been between the Hospital Management Team, several local actors, two NGOs: Engineers Without Borders and Architects Without Borders as well as the initiators to the project the Healthy Hospital Project Group. The LFA method was used in the planning phase of the project. The method consist of several steps that were carried out through meetings, workshops and inspections at the hospital area, together with the hospital management team. Using the LFA method in this aid project entailed that the planning phase was structured and that important parts were included. However, the method was time demanding and would benefit from having more participation from the stakeholders than was possible in this project. The main conclusion of this study is that the LFA is a suitable method for implementing sustainable solutions in a small-scale foreign aid project. However, the main challenge with the method is that it is very time demanding. Therefore, it is important to take the time aspect into consideration when planning the time frame of the project two months to perform the LFA was too short to be able to conduct it in the most correct way.
Effects of hydrogen sulfide in hyperhomocysteinemia-mediated skeletal muscle myopathy.

vi LIST OF FIGURES.x LIST OF TABLES.xii CHAPTER I: OVERVIEW.1 1. Homocysteine and Hyperhomocysteinemia.5 2. Skeletal muscle structure and functions.8 3. Hyperhomocysteinemia-mediated skeletal muscle pathophysiology.10 3.1. Hyperhomocysteinemia-mediated oxidative/ER-stress responses.12 3.2. Hyperhomocysteinemia-mediated cell death pathways.16 3.3. Hyperhomocysteinemia-mediated dysregulation of angiogenesis.18 3.4. Hyperhomocysteinemia-mediated skeletal muscle atrophy.20 4. Effects of hydrogen sulfide on hyperhomocysteinemia-mediated skeletal muscle myopathy.22 CHAPTER II: HYPOTHESIS AND SPECIFIC AIMS.29 CHAPTER III: HYDROGEN SULFIDE ALLEVIATES HHCY-MEDIATED OXIDATIVE AND ER-STRESS RESPONSES IN SKELETAL MUSCLE Introduction.31 Materials and Methods.33
Data fusion based on-line product quality evaluation of ternary cathode material cyber-physical systems

In the manufacturing process of ternary cathode material, batching, mixing, loading, and sintering procedures play decisive roles in product quality, and data generated by these procedures are collected by the ternary cathode material cyber-physical system (TCM-CPS). However, the data of the process are heterogeneous and have different properties, and sampling of key quality variables is difficult. On this basis, on-line evaluation of product quality is hard. In this study, a data fusion based on-line product quality evaluation method of TCM-CPS is proposed. Data features of each procedure are firstly extracted and fused by combining feature indices and kernel principal component analysis. Then, in order to establish the model between the extracted data features and the key quality variables such as particle size and surface-free lithium content, a semi-supervised double-weighted probabilistic principal component regression is proposed. After that, a distance cost index is brought to cluster and grade the two quality variables, which are predicted by the semi-supervised model. Moreover, the on-line evaluation of product performance is achieved by setting a rule table in accordance with production experience. Finally, an industrial application verifies the proposed method, of which the effective and accuracy can be acquired from results.
Slope One collaborative filtering recommendation algorithm based on neighbor

Collaborative filtering is the commonly used technology in recommendation systems. Sparsity in the data set is the main limitation in collaborative filtering algorithm. In this paper,a new recommendation algorithm is proposed. First,the similarity between items is calculated. Then the k-nearest items are chosen to calculate the deviation between the target item and its k-nearest items. Finally,the similarity between items is employed as the weight to calculate predictive value of the target item. In the proposed algorithm,only the k-nearest items are chosen to calculate predict value,which not only ensures the accuracy of prediction but also reduces the calculated amount. Experiments on the MovieLens dataset show that the proposed algorithm gives better recommendations. It also outperforms other collaborative filtering algorithms on prediction accuracy.
A new embedded zero tree wavelet ECG signal coding algorithm

In this paper, a new embedded zero tree wavelet ECG signal coding algorithm is presented, in which the bit allocation is changed, the zero tree set is improved and the partition-sorting strategy is revised. The experimental results show that our proposed coding method performs better than that of SPIHT I the aspects of PRD, encoding time and decoding time.
Predictability of School Administrator’s Job Satisfaction through Hybrid Segmentation-based Prediction Model

Today, various researchers have amplified the use of data mining techniques in almost all sectors of society. In this study, the effectiveness of data mining techniques, particularly the use of K-Means segmentation in aid of increasing prediction accuracy when used to job satisfaction dataset is observed. Researches on job satisfaction have long been acclaimed as the prime index of worker’s wellbeing as well as a core motivational construct that plays key implications for organizational and administrative policy. Due to the advent of technology, well-established mining and extraction of knowledge from databases are optimized. In this paper, the use of predictive models such as C4.5, DT, Naïve Bayes, OneR, KNN, and JRip algorithms was observed. However, prior to prediction, the variables from the dataset obtained from the 157 school administrator-respondents from the Department of Education, Division of Surigao del Norte, Philippines was segmented using the K-Means algorithm. This study strengthens the notion that a reduced number of variables in the dataset increases the accuracy of a prediction model. Simulation results showed that with the integration of K-Means segmentation, an accuracy of 83.54% using Decision Table algorithm is depicted from the previously identified 82.16% using the DT algorithm alone.
Fluoride Leachate Profile of Silver Diamine Fluoride

Recently, there has been a renewed interest in the use of silver diamine fluoride (SDF) in the management of caries especially in the high-risk groups. Clinical trials have shown SDF to be an easy to use, effective and safe topical agent with no significant adverse effects [1-4]. As a result, SDF is positioned as an efficient and equitable caries control agent that can be used to help meet the WHO Millenium oral health goals and fulfil the US Institute of Medicine’s criteria for 21st-Century medical care [1-4]. Nevertheless, the resulting black staining following topical application of SDF, as well as the fluoride concentration, potential toxicity and chemical stability of silver fluoride compounds have all been commonly reported as barriers for SDF use [1,5-7]. In an attempt to overcome the unsightly black stain, use of potassium iodide (KI) immediately following SDF application has been advocated [8]. However, to date, there is limited literature investigating the fluoride release from SDF, and to our knowledge, there are no reports investigating the effect of KI application on fluoride release [9]. Therefore, this study sought to compare the fluoride leachate profiles of 38% SDF, 38% SDF with Potassium Iodide (SDF+KI), 5% sodium fluoride varnish (NaF) and 0.32% fluoridated toothpaste (FTP). Furthermore, as fluoride release has been shown to vary depending on pH, a secondary objective of this study was to evaluate the fluoride leachate profiles of the aforementioned agents in neutral, acidic and basic artificial saliva to better reflect the oral environment.
Nominal and Real Effective Exchange Rates for Europe, 1870-2016: Some methodological issues

This paper presents and discusses a new database on nominal and real effective exchange rates for an extensive range of European countries spanning 1870-2016. Indeed, with the exception of a few countries, such long run historical series have not been previously constructed. To gage the validity of these series, comparisons with the BIS and IMF indices are conducted. In addition to stretching further back in time, it is shown that the new indices are more consistent and transparent in construction, even over the recent period. Limitations of the new series, relating to both some underlying data and the index problem are considered. Supplementary to the effective exchange rate indices, is a collection of cost of living or CPI indices 1870-1990, which are based on a critical survey in this paper of those indices which are widely used and abused.
Deep lifelong reinforcement learning for resilient control and coordination

The objective of this effort was to develop deep lifelong learning methods that can successfully handle sequential decision making in complex dynamic environments, focusing on multi-agent intelligence, surveillance, and reconnaissance (ISR) scenarios. We developed a novel architecture for deep convolutional neural networks that supports lifelong learning via deconvolutional factorization (DF-CNN), explored a combination of policy distillation via Distral and Sobolev training, and developed a hybrid controller for applying deep learning to ISR agents. Our approaches were evaluated on standard benchmark deep learning datasets, the DOOM environment, and on ISR scenarios in the ATE3 simulation environment.
Automated design of Bayesian signal processing algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This chapter explores a specific probabilistic *Joint first authors. 2 16 An FG Approach to Automated Design of Bayesian Signal Processing Algorithms programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed “ForneyLab” as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools “Edward” and “Stan”. In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.
Trustworthy translation for the requirements state machine language without events

Although formal requirements specifications can provide a complete and consistent description of a safety-critical software system; designing and developing production quality code from high-level specifications can still be a time-consuming and error-prone process. Automated translation, or code generation, of the specification to production code can alleviate many of the problems associated with design and implementation. However, current approaches have been unsuitable for safety-critical environments because they employ complex and/or ad-hoc methods for translation. This dissertation discusses the issues involved in automatic code generation for high-assurance systems. It first defines a set of requirements that code generators for this domain must satisfy, then describes a translation from the Requirements State Machine Language without Events (RSML−e ) to a simplified imperative language (SIMPL) that satisfies these requirements. Both the evaluation semantics and transformations of the source and target languages are specified using extended natural semantics rules. The central work of the thesis is in two parts: first, a complete formalization of the RSML−e language, and second, a proof that the translation preserves the behavior of the original specification. The primary novelty is in using higher-order abstract syntax and extended natural semantics for the transformations. This choice significantly simplifies the proof effort and utilizes a different reasoning style than first-order natural semantics proofs.
Joint User Pairing, Mode Selection, and Power Control for D2D-Capable Cellular Networks Enhanced by Nonorthogonal Multiple Access

Nonorthogonal multiple access (NOMA) and device-to-device (D2D) are two promising technologies that have great potential in improving user connectivity. In this paper, we incorporate NOMA into the D2D-capable cellular networks and propose a new NOMA-aided D2D access scheme. In the proposed scheme, the D2D users (DUEs) can operate in four spectrum-sharing modes, which are the extension of the traditional underlay mode. To fully exploit the advantages of the NOMA-and-D2D integrated framework, we formulate a connectivity-maximization problem by jointly considering user pairing, mode selection, and power control under the constraints of the decoding thresholds of cellular users and DUEs. Based on the graph theory, we devise an efficient algorithm with polynomial complexity to solve the formulated problem optimally. We first analytically obtain the optimal transmission power and spectrum-sharing mode for every possible user pair through a graphical method. Based on the power control and mode selection policies, we transform the user pairing problem into a min-cost max-flow problem which can be tackled by the Ford–Fulkerson algorithm. Finally, simulation results indicate that the NOMA-aided D2D access scheme outperforms the traditional underlay mode, and the proposed algorithm yields a large performance gain in comparison with other schemes in terms of user connectivity and power consumption.
Optimizing Finite Volume Method Solvers on Nvidia GPUs

As scientific applications are increasingly ported to GPUs to benefit from both the powerful computing capacity and high throughput, accelerating explicit solvers for GPU-based finite volume methods is gaining more and more attention. In this paper, based on the detailed analysis of the FVM algorithm, we present a set of novel optimization methods, including the explicit data cache mechanism, optimal global memory loading strategy, as well as the inner-thread rescheduling method, which derives a suitable mapping from the solver algorithm to the underlying GPU hardware architecture, so as to remarkably improve the solving performance of structured mesh based FVM. We demonstrate the impact of our tuning techniques on two widely-used atmospheric dynamic kernels (3-D Euler and 2-D SWE) on five kinds of mainstream GPU platforms, and make a detailed analysis of the different tuning methodologies so as to demonstrate how to select the proper tuning strategy to different applications on various GPU platforms. Specifically, 93.9x speedup is achieved for the 3D Euler solver on Nvidia V100 over one 12-core Intel E5-2697 (v2) CPU, which is a 77 percent improvement compared with the original speedup without adopting the tuning techniques presented in this work.
Key Management Protocol for WSN Based on ECC

【Abstract】Aiming at the limitation of Wireless Sensor Network(WSN) in power, computation and storage, this paper presents a key management protocol based on Elliptic Curve Cryptosystem(ECC) for WSN. It gives the accomplished courses which consists of building communication key, adding new node, renewing and recycling key. The performance of security, resilience and efficiency are analyzed, experimental results indicate that the scalability and resilience of the protocol is much better. 【Key words】Wireless Sensor Network(WSN); key management; Elliptic Curve Cryptosystem(ECC); key recycling
System Design and Service Scenario for the Second Screen Service

, the proliferation of various mobile devices, such as smart phones and tablet PC, brought changes in the existing TV viewing behavior. People use smart devices as secondary device while watching TV. Researches on a wide range of services linked with second-screen devices around the smart TV in the home network have been actively conducted. While there exist several Web-related technologies for connections between devices, specialized techniques for a second screen service are quite insufficient. There are still some problems related to the display of contents from multiple devices and the efficient transfer of these contents. Considering the characteristics of broadcasting systems In this study, we focus on a second screen service that permits a dynamic transfer of contents by connecting a television (TV) with mobile devices. Here, we propose a second screen service model to enable the personalization of TV contents by combining the existing broadcasting and Web-related techniques.
Synergistic High-flux Oil–Saltwater Separation and Membrane Desalination with Carbon Quantum Dots Functionalized Membrane

The treatment of offshore oil spills requires effective oil–water separation, where oil would be recovered and the collected seawater would be used to generate fresh water for an emergency water supply. Herein, a hydrophobic membrane was fabricated simply by a controlled cross-linking reaction between C18-CQDs (octadecylamine-functionalized carbon quantum dots) and commercial textiles using tolylene-2,4-diisocyanate. The modified membrane not only achieved efficient oil–saltwater separation (up to 99%) but also exhibited unique permselectivity: highly permeable to water vapor yet impermeable to liquid water, which is feasible for water desalination. Our research offers an “all-in-one” prototype for oil–water separation and saltwater desalination using a single type membrane.
Large-scale Data Analysis and Deep Learning Using Distributed Cyberinfrastructures and High Performance Computing

ix CHAPTER
Barley and Oat Meal Supplemented Chapaties and its Impact on Serum Biochemical Profile in Normal Individuals

The present study was taken to prepare barley and oat meal supplemented flours with specialreference to chapati making quality. For this purpose, nine treatments of supplemented flours were preparedby gradually replacing whole wheat. Chapaties were prepared from all compositions along with control(100% whole wheat flour) and analysed for dietary fibre content and sensory attributes like colour, taste,aroma, texture, breakability, folding ability, chewability and overall acceptability at stated intervals. Efficacystudy was carried out on healthy individuals to explore the hypocholesterolemic and hypoglycemic worthof chapaties prepared from supplemented flours. Results of dietary fibre analysis showed that there wassignificant increase in level of soluble 0.96 to 3.13%, insoluble 1.92 to 5.57% and total dietary fibre content2.88 to 8.13% with increase in the supplementation level of barley and oat meal. The highest soluble (3.13),insoluble (5.57) and total dietary fibre (8.13) content were found in wheat flour supplemented with 7.5%oat meal and 7.5% barley flour while their concentration was not changed during storage. Sensory attributesshowed that chapaties prepared from wheat flour supplemented with 7.5% oat meal and 7.5% barley flourwere liked the most due to better overall acceptability. On the basis of nutritional and sensory characteristics,three best chapaties along with control were served to normal humans. The results revealed that consumption of chapaties supplemented with 15% oat meal greatly reduced serum cholesterol (7.7%), low density lipoprotein (6.8%), triglycerides (28.5%), blood glucose (5.5%) and weight (4.7%) while increased highdensity lipoprotein (2.0%), serum protein (12.5%) and albumin protein (15.7%) as compared to other twochapaties. It is concluded that the consumption of barley and oat meal supplemented chapaties tackle thehyperglycemia and hypercholesterolemia in healthy humans as well as in obese persons because it deliverthree times more dietary fibre as compared to chapaties prepared from wheat flour only.
Direct and Energy-Transfer-Mediated Charge-Transfer State Formation and Recombination in Triangulene-Spacer-Perylenediimide Multichromophores: Lessons for Photovoltaic Applications

We study the dynamics of primary photoexcitations in three symmetric donor–spacer–acceptor–spacer–donor multichromophores with increasing oligophenylene spacer length, following selective donor or acceptor excitation. Energy levels of the donor and acceptor moieties are tailored to facilitate splitting of the excited state into a lower-lying charge-transfer (CT) state, mimicking the functionality of a donor–acceptor interface for charge generation, thus resulting in long-lived charge separation. Ultrafast electronic energy transfer (ET) from the donor followed by fast hole (back)transfer from the acceptor populates the molecules’ CT states. However, the CT efficiency is found to be close to unity, independent of the donor or acceptor photoexcitation. The ratio of CT and recombination rates, which reflects the population of CT states, increases with the oligophenylene spacer length for both direct hole transfer and hole transfer following ET, boosting the population of CT states under continuous excitation.
A Improved Adaptive Cuckoo Search Algorithm Based the Population Feature and Iteration Information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Levy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.