Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-Rex

Abstract. We evaluate a regional-scale simulation with the WRF-Chem model for the VAMOS (Variability of the American Monsoon Systems) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx), which sampled the Southeast Pacific's persistent stratocumulus deck. Evaluation of VOCALS-REx ship-based and three aircraft observations focuses on analyzing how aerosol loading affects marine boundary layer (MBL) dynamics and cloud microphysics. We compare local time series and campaign-averaged longitudinal gradients, and highlight differences in model simulations with (W) and without (NW) wet deposition processes. The higher aerosol loadings in the NW case produce considerable changes in MBL dynamics and cloud microphysics, in accordance with the established conceptual model of aerosol indirect effects. These include increase in cloud albedo, increase in MBL and cloud heights, drizzle suppression, increase in liquid water content, and increase in cloud lifetime. Moreover, better statistical representation of aerosol mass and number concentration improves model fidelity in reproducing observed spatial and temporal variability in cloud properties, including top and base height, droplet concentration, water content, rain rate, optical depth (COD) and liquid water path (LWP). Together, these help to quantify confidence in WRF-Chem's modeled aerosol-cloud interactions, especially in the activation parameterization, while identifying structural and parametric uncertainties including: irreversibility in rain wet removal; overestimation of marine DMS and sea salt emissions, and accelerated aqueous sulfate conversion. Our findings suggest that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing, and may do so with the reliability required for policy analysis.
Identity Authentication and Capability Based Access Control (IACAC) for the Internet of Things

In the last few years the Internet of Things (IoT) has seen widespread application and can be found in each field. Authentication and access control are important and critical functionalities in the context of IoT to enable secure communication between devices. Mobility, dynamic network topology and weak physical security of low power devices in IoT networks are possible sources for security vulnerabilities. It is promising to make authentication and access control attack resistant and lightweight in a resource constrained and distributed IoT environment. This paper presents the Identity Authentication and Capability based Access Control (IACAC) model with protocol evaluation and performance analysis. To protect IoT from manin-themiddle, replay and denial of service (Dos) attacks, the concept of capability for access control is introduced. The novelty of this model is that it presents an integrated approach of authentication and access control for IoT devices. The results of other related study have also been analyzed to validate and support our findings. Finally, the proposed protocol is evaluated by using security protocol verification tool and verification results shows that IACAC is secure against aforementioned attacks. This paper discusses performance analysis of the protocol in terms of computational time compared to other existing solutions. This paper also addresses challenges in IoT and security attacks are modelled with the use cases to give an actual view of IoT networks.
Analysis and application of image segmentation and edge detection operator

Edges are the main feature of image, also an essential part of the computer visual and pattern recognition, so edge detection is a crucial step in the process of image processing. This article analyzes the algorithm of image segmentation and edge detection, compared the advantages and disadvantages of various operators by MATLAB. Through the experimental comparison, The overall effect of Canny operator is relatively well, but less detailed. So there is not an universal edge operator. The most important is how to choose a suitable threshold, this will be a decisive role.
Artificial Neural Networks ( ANN ) and Kalman Filter Algorithms to Predict Output Temperatures on a Heat Exchanger

Artificial neural networks (ANN) were applied for the prediction of the output temperature in a heat exchanger with triangular arrangement with air-water as work fluids, currently operating at a hydroelectric power plant. Four similar heat exchanger time data series were analyzed. A feed-forward ANN configuration was used to predict the output temperatures. The ANN was trained, tested and validated using the experimental time series. In order to test the robustness of the ANN scheme as a predictor, only data from three exchangers was used to train the ANN, while data from a fourth heat exchanger was used for validation. The ANN was also coupled to a Kalman Filter in order to improve the predictions. The scheme showed to be successful and can be used in realtime to handle slowly varying behavior due to fouling implicit in the operation of the exchangers.
An Indoor Positioning Method for Smartphones Using Landmarks and PDR

Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.
Consistent Positive Co-Variation between Fluctuating Asymmetry and Sexual Trait Size: A Challenge to the Developmental Instability-Sexual Selection Hypothesis

The developmental instability (DI)-sexual selection hypothesis proposes that large size and symmetry in secondary sexual traits are favored by sexual selection because they reveal genetic quality. A critical prediction of this hypothesis is that there should exist negative correlations between trait fluctuating asymmetry (FA) and size of condition dependent sexual traits; condition dependent traits should reveal an organism’s overall health and vigor, and be influenced by a multitude of genetic loci. Here, we tested for the predicted negative FA-size correlations in the male sex comb of Drosophila bipectinata. Among field-caught males from five widely separated geographic localities, FA-size correlations were consistently positive, despite evidence that sex comb size is condition dependent. After controlling for trait size, FA was significantly negatively correlated with body size within several populations, indicating that developmental instability in the comb may reveal individual genetic quality. We suggest the possibility that condition dependent traits in some cases tap into independent units of the genome (a restricted set of genes), rather than signaling overall genetic properties of the organism. There were pronounced among-population differences in both comb FA and size, and these traits were positively correlated across populations, recapitulating the within-population patterns. We conclude that the results are inconsistent with the DI-sexual selection hypothesis, and discuss potential reasons for positive FA-size co-variation in sexual traits.
Affect sensing and contextual affect modeling from improvisational interaction

We report work on adding an improvisational AI actor to an existing virtual improvisational environment, a text-based software system for dramatic improvisation in simple virtual scenarios, for use primarily in learning contexts. The improvisational AI actor has an affect-detection component which is aimed at detecting affective aspects (concerning emotions, moods, value judgements, etc) of human-controlled characters' textual "speeches". The AI actor will also make an appropriate response based on this affective understanding, which intends to stimulate the improvisation. The work also accompanies basic research into how affect is conveyed linguistically. A distinctive feature of the project is a focus on the metaphorical ways in which affect is conveyed. Moreover, we have also introduced affect detection using context profiles. Finally, we have reported user testing conducted for the improvisational AI actor and evaluation results of the affect detection component. Our work contributes to the journal themes on affective user interfaces, affect sensing and improvisational or dramatic natural language interaction.
Packing paths and Steiner trees: routing of electronic circuits

One of the challenging problems in the design of electronic circuits is the so-called routing problem. Roughly speaking, the task is to connect so-called terminal sets via wires on a predeened area. In addition, certain design rules are to be taken into account and an objective function such as the wiring length must be minimized. The routing problem in general is too complex to be solved in one step. Depending on the user's choice of decomposing the chip design problem into a hierarchy of stages, on the underlying technology, and on the given design rules, various subproblems arise. We discuss several variants of practically relevant routing problems and give a short overview on the underlying technologies and design rules. Many of the routing problems that come up this way can be formulated as the problem of packing so-called Steiner trees in certain graphs. We consider the Steiner tree packing problem from a polyhedral point of view and present three possibilities to deene an appropriate polyhedron. Weighing their pros and cons we decide for one of these polytopes and sketch some of our investigations.
Evaluation of Multi-part Models for Mean-Shift Tracking

Mean-shift tracking is a data-driven technique for tracking objects through a video sequence. We propose an innovation to mean-shift tracking that combines the background exclusion constraint with multi-part appearance models. The former constraint prevents the tracker from moving to regions where no foreground objects are present, while the multi-part nature of the models enforces a spatial structure on the tracked object. We also use a simple formula to determine the scale of the object in each video frame, and note the importance of setting an appropriate convergence condition. An evaluation of our proposed tracker and several existing trackers is performed using a ground truth dataset. We demonstrate that our innovation yields more accurate tracking than existing mean-shift techniques.
Th1/Th17-Related Cytokines and Chemokines and Their Implications in the Pathogenesis of Pemphigus Vulgaris

Pemphigus vulgaris (PV) is an autoimmune disease characterized by the presence of IgG autoantibodies against desmoglein-3. Despite the variety of findings, the chemokine and cytokine profiles that characterize the immune response in the disease are still poorly explored. Thus, 20 PV patients and 20 controls were grouped according to gender, ethnicity, place of residence, and clinical parameters of the disease. Then, the levels of chemokines and of Th1/Th2/Th17/Treg/Th9/Th22-related cytokines were assessed in the serum. PV patients had higher levels of inflammatory Th1/Th17 cytokines (IFN-γ, IL-17, and IL-23), as well as higher levels of CXCL8 and reduced levels of Th1/Th2-related chemokines (IP-10 and CCL11). However, no differences in the levels of IL-2, IL-6, TNF-α, IL-1β, IL-4, IL-9, IL-12, TGF-β, IL-33, MCP-1, RANTES, and MIP-1α were found between PV patients and their control counterparts. Furthermore, PV patients with skin lesions had higher serum levels of IL-6 and CXCL8 when compared to PV patients without lesions. Taken together, our findings describe the role of cytokines and chemokines associated with Th1/Th17 immune response in PV patients. Finally, these data are important for better understanding of the immune aspects that control disease outcome, and they may also provide important information about why patients develop autoantibodies against desmogleins.
Low-complexity pre and post-FFT channel estimation approach for UW MB-OFDM based UWB systems

This paper proposes low-complexity channel estimator for Unique Word (UW) MultiBand Orthogonal Frequency Division Multiplexing (MB-OFDM) based Ultra WideBand (UWB) communication systems. The proposed channel estimator is performed in two stages. The pre-FFT stage employs the Least Square (LS) estimation and smoothing technique to estimate the Channel Frequency Response (CFR). The post-FFT stage employs this CFR estimate to extract the UW influence and then refines the CFR estimate using a decision feedback technique. Via simulation results, we show that the proposed estimator provides higher performance gain over the conventional LS channel estimator and performs closely to the Linear Minimum Mean-Squared Error (LMMSE) one. Additionally, the computational complexity of our proposal is comparable with the LS approach and significantly lower than that of LMMSE one.
PAPR and Fiber Nonlinearity Mitigation ofDFT-Precoded OFDM System

High Peak-to-Average Power Ratio (PAPR) is one of the major drawback of Orthogonal Frequency Division Multiplexing (OFDM) based systems that can cause intermodulation among the subcarriers due to the nonlinearities of the fiber. The high PAPR increases the complexity of the Analog to Digital (A/D) and Digital to Analog (D/A) converters. In this paper, I present an analysis of Discrete Fourier Transform (DFT) precoded OFDM system for different FFT sizes (where FFT = 128, 256, 512). I compare the simulation results of DFT-precoded OFDM system with conventional OFDM system. Simulation results show that the PAPR of DFT-precoded OFDM system is lower than conventional OFDM system
Geometric Thinking of Malaysian Elementary School Students

Geometry is one of the basic skills to be mastered in Malaysian mathematics education. However, there is not much information on geometry attainment of the elementary school students based on van Hiele’s levels. The purpose of this study was to determine the van Hiele’s levels of geometric thinking among the elementary school learners. Three different instructional strategies based on modules were tested on three groups of students ;(i) van Hiele’s phases of learning (VH-PL) module that guides students through the van Hiele’s learning process, (ii) van Hiele’s theory which is integrated with the Google SketchUp software (VHGSU) module and (iii) use of conventional instruction (NVH-CI) module which refers to teaching strategy without the use of van Hiele’s theory of learning or any software in the teaching. This true experimental study involved 96 Year Five students of a Malaysian public school. Data were collected using Wu’s Geometry Test (WGT) and sample responses identified by Fuys, Geddes, and Tischler (1988). Results showed that at the onset of the experiment, the students were operating at the lower levels of van Hiele’s levels of geometric thinking. After the intervention, results of the WGT showed that most of the students in all three groups achieved a higher level of van Hiele’s geometric thinking.
Representing and Reasoning About the Rules of General Games With Imperfect Information

A general game player is a system that can play previously unknown games just by being given their rules. For this purpose, the Game Description Language (GDL) has been developed as a high-level knowledge representation formalism to communicate game rules to players. In this paper, we address a fundamental limitation of state-of-the-art methods and systems for General Game Playing, namely, their being confined to deterministic games with complete information about the game state. We develop a simple yet expressive extension of standard GDL that allows for formalising the rules of arbitrary finite, n-player games with randomness and incomplete state knowledge. In the second part of the paper, we address the intricate reasoning challenge for general game-playing systems that comes with the new description language. We develop a full embedding of extended GDL into the Situation Calculus augmented by Scherl and Levesque's knowledge fluent. We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players.
Expressed sequence tags (ESTs) from desiccated Tortula ruralis identify a large number of novel plant genes.

The desiccation-tolerant moss Tortula ruralis [Hedw. ] Gaerten. , Meyer & Scherb. has both a constitutive protection system and an active rehydration induced recovery mechanism apparently unique to bryophytes. Immediately following rehydration, desiccated T.ruralis gametophytes produce a set of polypeptides whose synthesis is unique to the rehydrated state. We report the construction of a cDNA expression library from the polysomal mRNA of desiccated gametophytes and the single-pass sequencing of randomly selected clones. 152 expressed sequence tags (ESTs) were generated representing more than 60,000 bp of non-redundant DNA sequence. 44 ESTs (29%) demonstrated significant homology to previously identified nucleotide and/or polypeptide sequences, such as ribosomal proteins, desiccation-related peptides, early light-inducible proteins and a V-type ATPase. Analysis of a subset of these homologous ESTs reveals that codon preference in T.ruralis is similar to that of vascular plants, particularly the Magnoliopsida. 108 ESTs (71%) demonstrated no significant homology to deposited sequences and represent a large number of novel plant genes. Analysis of these ESTs will define the range of genes involved in cellular repair and recovery and may provide greater insight to the complex phenotype of vegetative desiccation-tolerance.
Edinburgh Research Explorer Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics

We propose a framework to model human comprehension of discourse connectives. Following the Bayesian pragmatic paradigm, we advocate that discourse connectives are interpreted based on a simulation of the production process by the speaker, who, in turn, considers the ease of interpretation for the listener when choosing connectives. Evaluation against the sense annotation of the Penn Discourse Treebank conﬁrms the superiority of the model over literal comprehension. A further experiment demonstrates that the proposed model also improves automatic discourse parsing.
Proposal of a Distributed Cooperative M2M System for Flood Disaster Prevention

Effective applications of M2M (Machine-to-Machine) systems for disaster prevention, such as an early warning system for flood and landslide, are highly expected. In particular, in applications for flood disaster prevention, an early warning of the flash flood caused by local torrential rain is required as well as a flood impact analysis based on water level and rainfall monitoring in the whole basin. However, in a conventional server-centric M2M system, an increase of network load and a delay of feedback control has become one of the most critical issues. Therefore, it is difficult to apply the conventional server-centric M2M system to applications of flood disaster prevention, in which both a real-time flood alert and a flood impact analysis are required. In this paper, we propose a distributed cooperative M2M system to solve such problems, and make an attempt to apply the proposed system to applications for flood disaster prevention. This proposed system provides an event-driven data collection mechanism for efficient data collection in low-bandwidth networks on the Never Die Network to achieve disaster tolerance of the access network. In addition, a rule-based autonomous control mechanism for a real-time flood alert in the M2M gateway is offered.
Monetary and Fiscal Policies: Ordinary Recessions and Financial Crises

This paper uses two different samples to study the effects of monetary and fiscal policies on the profiles of recessions and recoveries. Several results emerge from the econometric analysis presented. First, monetary policy during ordinary recessions and banking crises is a powerful tool with lasting effects that extend to recovery growth rates. However, the effect of monetary policy during financial crises is strongly diminished in the case of forbearance – banks left to function despite being technically insolvent. Second, the effectiveness of fiscal policy is reversed – it is a powerful tool during banking crises, but it does not seem to significantly affect recovery growth rates during ordinary recessions. Finally, the policy response during past financial crisis does not seem to be particularly expansionary – on the contrary, fiscal policy is markedly procylcical, while monetary policy is neutral. This is proposed as an alternative explanation to the one usually given for the sluggishness of financial crises.
Fiber-Optic Fabry-Pérot Interferometers for Axial Force Sensing on the Tip of a Needle

A range of complex percutaneous procedures, such as biopsy or regional anesthesia, rely heavily on accurate needle insertion. Small variations in the mechanical properties of the pierced tissue can however cause deviations from the projected needle path and can thus result in inaccurate placement of the needle. Navigation of a rigid needle towards the target tissue is traditionally based on the surgeons capacity to interpret small variations in the needle insertion force. A more accurate measurement of these small force variations enables improvement in needle targeting, can potentially aid in enhancing force feedback in robotic needle placement and can provide valuable information on tissue-tool interaction. In this study we investigated several concepts for the design of a force sensor based on a fiber-optic Fabry-Pérot interferometer to measure needle-tissue interaction forces on the tip of a 18 G needle, where special attention was given to concepts for a sensor with (1), an intrinsic low cross-sensitivity to temperature and (2), elementary design and fabrication. Three concepts, using either a quartz capillary, an Invar capillary or a thin polyimide film as the force sensitive element were prototyped and subjected to both static and dynamic testing. The force transducer based on a quartz capillary presented the lowest cross-sensitivity to temperature (12 mN/∘C) and good accuracy (maximum measurement error of 65 mN/10 N) in a measurement of static forces. However, limited strength of the sensor is expected to prevent usage of the quartz capillary in small diameter needles. The concepts for a sensor based on an Invar capillary or a thin polyimide film proved a higher cross-sensitivity to temperature (50 mN/∘C and 220 mN/∘C, respectively) and higher maximum measurement error (350 mN/10 N, 800 mN/10 N), comparable to those of FBG-based sensors reported in literature, but are likely to be more suitable for integration in very small biopsy needles.