Miracle-dz/UniFAR-FTUs huggingface dataset:
2 Specifying and Implementing Systems via DPC

Distributed systems are hard to get right, model, test, debug, and teach. Their textbook definitions, typically given in a form of replicated state machines, are concise, yet prone to introducing programming errors if näıvely translated into runnable implementations. In this work, we present Distributed Protocol Combinators (DPC), a declarative programming framework that aims to bridge the gap between specifications and runnable implementations of distributed systems, and facilitate their modeling, testing, and execution. DPC builds on the ideas from the state-of-the art logics for compositional systems verification. The contribution of DPC is a novel family of program-level primitives, which facilitates construction of larger distributed systems from smaller components, streamlining the usage of the most common asynchronous message-passing communication patterns, and providing machinery for testing and user-friendly dynamic verification of systems. This paper describes the main ideas behind the design of the framework and presents its implementation in Haskell. We introduce DPC through a series of characteristic examples and showcase it on on a number of distributed protocols from the literature.
Objectives and Constraints for Wind Turbine Optimization

Efficient extraction of wind energy is a complex, multidisciplinary process. This paper examines common objectives used in wind turbine optimization problems. The focus is not on the specific optimized designs, but rather on understanding when certain objectives and constraints are necessary, and what their limitations are. Maximizing annual energy production, or even using sequential aero/structural optimization, is shown to be significantly suboptimal compared to using integrated aero/structural metrics. Minimizing the ratio of turbine mass to annual energy production can be effective for fixed rotor diameter designs, as long as the tower mass is estimated carefully. For variable-diameter designs, the predicted optimal diameter may be misleading. This is because the mass of the tower is a large fraction of the total turbine mass, but the cost of the tower is a much smaller fraction of overall turbine costs. Minimizing the cost of energy is a much better metric, though high fidelity in the cost modeling is as important as high fidelity in the physics modeling. Furthermore, deterministic cost of energy minimization can be inadequate, given the stochastic nature of the wind and various uncertainties associated with physical processes and model choices. Optimization in the presence of uncertainty is necessary to create robust turbine designs.
Volatility transmission between Dow Jones stock index and emerging Islamic stock index : case of subprime financial crises / Amir Saadaouia and Younes Boujelbene

In the course of the recent global crisis, the stock shocks are distributed and transmitted from their homes in the developed stock market to emerging stock markets. By supporting the development of emerging stock markets, this study aims to see the transmission of volatility between the Dow Jones stock index and the Dow Jones emerging Islamic stock indiex. In this study we have divided the period into three, periods, before, during and after this crisis to demonstrate the resilience of the Islamic market index in response to the global financial crisis. Another aim of this study is to provide a new guide line for investors in emerging stock market before making investment decisions. The data are daily, going from 02/01/2005 until 31/12/2012. To measure the transmission we used bivariate BEKK-GARCH and DCC-GARCH model. The result shows that there is a transmission mainly during the crisis period which means that the crisis affects all the financial assets whether Islamic or not. The same result also shows the preference to invest in both Islamic and classical stock indexes since they are less risky. Keywords: Volatility transmission, DJ Index, Islamic DJ Emerging Index, Subprime crisis.
Colour transfer and shape registration using functional data representations

In this thesis we propose new colour transfer and shape registration methods based on the robust L2 distance. For colour transfer, we present an approach inspired by techniques recently proposed in shape registration. We model the colour distribution of a palette and target image using Gaussian Mixture Models and register them using the L2 distance. We estimate a parametric transfer function φ which can be easily stored in memory for later use and allows for the interpolation of several colour transfer functions which can create interesting special effects. We also show that pixel correspondences can be easily incorporated into our method to enhance the colour transfer result. We show that our method compares well both qualitatively and quantitatively to other colour transfer approaches and that our recolouring step is computationally the fastest. We also propose a new shape registration technique which extends previous registration methods that model shapes as probability density functions and estimate the registration parameters by minimising a divergence between them. Our proposed technique models the point positions and directional information of a shape, and we investigate mixture models with Dirac, Gaussian and von Mises-Fisher kernels. We validate our framework experimentally on shapes differing by both a rotation and non-rigid deformation and show that in general using both point and normal vector information allows for better registration of shapes. Finally, we present a short exploration of the results generated when two optimal transport techniques are applied to the 3D shape registration problem.
Predicting Methane Storage in Open-Metal-Site Metal–Organic Frameworks

The development of high-capacity methane adsorbents would accelerate the adoption of natural gas as a vehicular fuel, thereby lowering CO2 emissions from the combustion of gasoline. In this regard metal–organic frameworks (MOFs) have emerged as promising methane storage materials due to their high capacities and tunable properties. Within this class, HKUST-1 ([Cu3(BTC)2]n, BTC = 1,3,5-benzenetricarboxylate) is an important benchmark, as it exhibits methane densities that are among the highest reported. Furthermore, uptake in HKUST-1 can potentially be tuned by altering the methane–MOF interaction through metal substitution on coordinatively unsaturated sites (CUS). Predicting the impact of metal substitution remains a challenge, however, because general interatomic potentials commonly used in calculating uptake do not properly describe interactions involving CUS. Here, a new interatomic potential that explicitly accounts for these interactions is derived from quantum-mechanical calculations. The potential.
Survey on Categorization and Detection of Adaptive Novel Class of Feature Evolving Data Streams

: Classification in the data stream is the challenging fact for the data mining community. In this paper, we tackle four major challenges which are infinite length, concept drift, concept evolution, and feature evolution. As we know that the data streams are huge in amount, so practically it is not possible to store the data and used it for the training purpose. The results of changes in the underlying concepts are occurred because of concept drift, which is the general observable fact in the data streams. The result of new classes surfacing in the data streams occurs because of concept evaluation. The feature evaluation generally occurs in many streams like text streams, in text streams new features emerge as stream advancement. Many existing methods of the data stream classification tackle only first two challenges and ignore last two challenges. Here in this paper we proposed an ensemble classification skeleton, in which each classifier is prepared with a novel class detector to tackle the concept drift and concept evolution. We also proposed the feature set homogenization methods for feature evaluation. We improve the component of novel class detection by making it more adaptive to the evolving stream, and enable it to notice more than one novel class at a time. As comparing with the existing methods of the novel class detector method the efficiency of the proposed method is more than the existing one.
The role of glucagon-like peptide-1 on food intake, glucokinase expression and fatty acid metabolism

The increase in blood levels of GLP-1 with dietary resistant starch is thought to be associated with the activities of hypothalamic NPY/AgRP neurons and the gene expression of glucokinase in the ARC of the hypothalamus. Exendin-4 has been shown to be associated with fatty acid synthesis and oxidation. In this project, it was proposed that 1) food intake will be decreased in GLP-1R KO mice, the glucokinase (GK) mRNA expression in the liver and in the hypothalamus, the hypothalamic NPY mRNA expression will be up-regulated by exendin-4 treatment; and 2) exendin-4 will decrease fatty acid synthesis and increase fatty acid oxidation. In order to investigate these hypotheses, animal experiments were performed. Exendin-4 treated WT and GLP-1R KO mice were injected with exendin-4 at 100μg/Kg body weight or vehicle for 7 days. The exendin-4 treated mice and the vehicle mice were subdivided into fed and fasted groups. There were 8 groups of mice (n=8). Food intake after refeeding at 1h, 2h and 4h was measured. At end of the study, the blood glucose level, the GK, CPT1, AOX1, SCD1, FAS, SREBP1c and PPARγ mRNA expressions in liver, and the GK and NPY mRNA expressions in hypothalamus were measured. In this study, the food intake of GLP-1R KO mice at 1h, 2h and 4h was significantly less than in WT mice, and the FAS expression in liver was significantly higher in GLP-1R KO mice than WT mice. Exendin-4 treatment decreased blood glucose, and increased the GK gene expression in liver, but not in the hypothalamus. The food intake and the NPY gene expression were not altered by exendin-4. Only the AOX mRNA expression was increased by exendin-4. CPT1, SCD1, FAS, SREBP1c, and PPARγ mRNA were not changed. GLP-1 might have beneficial effects on improving the impairment of nutrient sensing system induced by aging. The up-regulated FAS gene expression might be one possible mechanism for higher body fat in
Card Terminal ( with SAM ) Read basic info Success Request Random Number Verify Random Number ( R ) Calculate MACEntrance Data ( with MAC ) Success

Automated Fare Collection (AFC) systems have been globally deployed for decades, particularly in the public transportation network where the transit fee is calculated based on the length of the trip (a.k.a., distance-based pricing AFC systems). Although most messages of AFC systems are insecurely transferred in plaintext, system operators did not pay much attention to this vulnerability, since the AFC network is basically isolated from the public network (e.g., the Internet)—there is no way of exploiting such a vulnerability from the outside of the AFC network. Nevertheless, in recent years, the advent of Near Field Communication (NFC)-equipped smartphones has opened up a channel to invade into the AFC network from the mobile Internet, i.e., by Host-based Card Emulation (HCE) over NFC-equipped smartphones. In this paper, we identify a novel paradigm of attacks, called LessPay, against modern distance-based pricing AFC systems, enabling users to pay much less than what they are supposed to be charged. The identified attack has two important properties: 1) it is invisible to AFC system operators because the attack never causes any inconsistency in the back-end database of the operators; and 2) it can be scalable to affect a large number of users (e.g., 10,000) by only requiring a moderate-sized AFC card pool (e.g., containing 150 cards). To evaluate the efficacy of the attack, we developed an HCE app to launch the LessPay attack; and the real-world experiments demonstrate not only the feasibility of the LessPay attack (with 97.6% success rate) but also its low cost in terms of bandwidth and computation. Finally, we propose, implement and evaluate four types of countermeasures, and present security analysis and comparison of these countermeasures on defending against the LessPay attack.
Surface Rupturing and Buried Dynamic-Rupture Models Calibrated with Statistical Observations of Past Earthquakes

In the context of the slip-weakening friction model and simplified asper- ity models for stress state, we calibrate dynamic rupture models for buried and surface-rupturing earthquakes constrained with statistical observations of past earth- quakes. These observations are the kinematic source models derived from source in- versions of ground-motion and empirical source models of seismic moment and rupture area. The calibrated parameters are the stress-drop distribution on the fault and average stress drop. We develop a set of dynamic rupture models that consist of asperities and surrounding background areas. The distribution of dynamic stress drop outside the asperity is characterized by a fraction of the stress drop on the as- perity. From this set of models, we identify dynamic fault models with defined stress- drop characteristics that satisfy the observations. The selected dynamic fault models show that surface-rupturing earthquakes are characterized by a large area of negative stress-drop surrounding the asperities, while buried earthquakes present positive or zero stress drop. In addition, the calibrated fault models that match the observations show that the average stress drop is independent of earthquake size for buried earth- quakes, but scale dependent for surface-rupturing earthquakes. This suggests that, in the context of our parameterization, buried earthquakes follow self-similarity scaling, and surface-rupturing earthquakes break this self-similarity. We apply the calibrated dynamic models to simulate near-source ground motion consistent with observations that suggest that buried earthquakes generate stronger ground motion than surface- rupturing earthquakes at high frequency. We propose possible mechanisms that satisfy this observation, as follows: buried rupture has a hypocenter location below the as- perity; this can produce strong directivity of the slip velocity function toward the free surface. That effect, in addition to a reduced fault area and low fracture energy during rupture, may be significant in enhancing high-frequency ground motion. On the other hand, surface-rupturing earthquakes have a shallow hypocenter, large fracture energy on the asperities, and enhanced energy absorption due to large areas of negative stress drop in the background area. These characteristics of large earthquakes inhibit severe directivity effects on the slip velocity function directly toward the free surface, redu- cing the high-frequency ground motion.
Rainfall and Landslide Correlation Analysis and Prediction of Future Rainfall Base on Climate Change

The aim of this study is to analyze the quantitative relationship between the volume of rainfall and landslide occurrence in South Korea. To predict future rainfall, a future climate scenario was developed by downscaling the regional climate model (RCM) from the global climate model (GCM) based on the Intergovernmental Panel on Climate Change (IPCC) A1B scenario. In this study, for a quantitative analysis of correlation between rainfall and landslides occurrence, data on rainfall and landslides in Korea in the 2000s was analyzed using the correlation between the occurrence of landslides and rainfall volume (daily and accumulated) and the maximum hourly intensity of rainfall. Daily rainfalls exceeding 164.5 mm is categorized as high risk for landslide. A rainfall that continued for 3 days was found to affect the occurrence of landslide in Korea in the 2000s more than any other number of days during which rainfall lasted. The research area for the future climate change scenarios (A1B) covers the entire area of South Korea. Annual average rainfall had increased by 271.23 mm during 1971–2100. The develop‐ ment of downscaling method using GIS and verification with observed data could reduce the uncertainty of future climate change projection.
Obstacle Avoidance Task for a Wheeled Mobile Robot – A Matlab-Simulink-Based Didactic Application

In recent years, the development of wheeled mobile robots (WMR) has allowed for significant progress in various fields of science due to the wide scope of applications in which such robots can be employed [1]. These fields include medical assistance, space and marine exploration, leisure, entertainment, technology research and development, metal-mechanics and chemical industries, military research, cleaning in diverse surroundings, agriculture, inspection, security, and transportation, among others. The discussion regarding mobile robotics has been undergoing a slowly-evolving transition and continues even now to be a topic of great interest at the international level.
Fluctuation-induced forces on an atom near a photonic topological material

We theoretically study the Casimir-Polder force on an atom in an arbitrary initial state in a rather general electromagnetic environment wherein the materials may have a nonreciprocal bianisotropic dispersive response. It is shown that under the Markov approximation the force has resonant and nonresonant contributions. We obtain explicit expressions for the optical force both in terms of the system Green function and of the electromagnetic modes. We apply the theory to the particular case wherein a two-level system interacts with a topological gyrotropic material, showing that the nonreciprocity enables exotic light-matter interactions and the opportunity to sculpt and tune the Casimir-Polder forces on the nanoscale. With a quasistatic approximation, we obtain a simple analytical expression for the optical force and unveil the crucial role of surface plasmons in fluctuation-induced forces. Finally, we derive the Green function for a gyrotropic material half-space in terms of a Sommerfeld integral.
Pediatric rheumatology Growth patterns in juvenile rheumatoid arthritis

Methods The study population comprised 67 children with JRA who had been followed for 5 years or longer and whose follow-up period did not extend beyond 18 years of age. Height and weight z scores were calculated with reference to age-related standards for each of the annual follow-up intervals and correlated with JRA subtype, the presence of rheumatoid factor (RF), the erythrocyte sedimentation rate (ESR), alkaline phosphatase level (ALP) and medication history.
Driving behaviour-based event data recorder

A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.
Graph-based Discovery of Architecture Change Patterns from Logs

Modern software continuously evolves as a consequence of frequently varying requirements and changes in operational environments. Architecture-centric software evolution (ACSE) enables change in a system’s structure and behavior while maintaining a global view of software to address evolution tradeoffs. Lehman’s law of continuing change demands for long-living and continuously evolving architectures to prolong productive life and economic value of existing software. To tackle problems of recurring change, solutions for architectural maintenance and evolution must rely on reuse knowledge and expertise that promote evolution-off-shelf. State-of-the-art in ACSE suggests, although change patterns and evolution styles facilitate application of reuse knowledge to guide architecture evolution. However, there is a clear lack of research for a continuous acquisition of reuse knowledge in terms of patterns, frameworks and processes that can be discovered, shared and reused to guide ACSE. In this paper, we propose to exploit architecture change logs (ACLs) to perform post-mortem analysis of evolution histories and to discover reusable operationalisation and usage-determined architecture change patterns. The primary contribution lies with proposed algorithms applied on change logs to automate and customise pattern discovery process. An algorithm-based experimental investigation of change logs resulted in discovery of 7 architecture change patterns facilitating reuse in ACSE. We evaluate algorithmic efficiency in terms of discovering exact as well as inexact pattern instances when only central pattern feature suffice for its discovery. We provide a template-based specification to document patterns and demonstrate pattern-driven evolution of a peer-to-peer system towards a client server architecture. We highlight pattern discovery as a continuous process by acquiring data from different logs for mining new patterns over-time.
Real-time Image Processing for Camera-based Driver Assistance Applications

Future driver assistance systems will rely on a variety of sensors, including camera-based systems, to achieve an optimal perception of a vehicle’s surroundings. With increasing resolutions and the inevitable real-time requirements of these safety-relevant systems, multi-camera setups demand processing performances and data rates that cannot be satisfied by current standard PC components. To meet these requirements, this paper presents a flexible FPGA-based design approach for a central onboard image preprocessing unit, which offloads computation intensive tasks to a dedicated FPGA accelerator platform with support for real-time execution.
Dyslipidemia in diabetes: A cross sectional study

Background: Diabetes is commonly associated with abnormalities in plasma lipids and lipoprotein levels commonly referred to as “dyslipidemia. About 50% of all diabetic patients have dyslipidemia. Lipid abnormalities are more common in type 2 diabetes than in type 1 diabetes. Thus present study was conducted with primary objective of finding alternations of lipid profile in diabetes mellitus patients. Methods: The study was carried out at a tertiary government medical hospital in Jamnagar city during period of 1 year among 160 patients. Results: Type-2 DM subjects were dyslipidemic, which was characterized by elevated S. Triglyceride, S. Cholesterol, S. LDL, S. VLDL, with low HDL; such dyslipidemia was not significant in Type-1 Subjects. The complications mainly cardiovascular of Diabetes Mellitus have Major role in much of diabetes related morbidity & mortality. Conclusion: The important impact of dyslipidemia on complications requires undivided attention throughout the course of disease.