Miracle-dz/UniFAR-FTUs huggingface dataset:
A simplified parameterization of isoprene-epoxydiol-derived secondary organic aerosol (IEPOX-SOA) for global chemistry and climate models: a case study with GEOS-Chem v11-02-rc

Abstract. Secondary organic aerosol derived from isoprene epoxydiols (IEPOX-SOA) is thought to contribute the dominant fraction of total isoprene SOA, but the current volatility-based lumped SOA parameterizations are not appropriate to represent the reactive uptake of IEPOX onto acidified aerosols. A full explicit modeling of this chemistry is however computationally expensive owing to the many species and reactions tracked, which makes it difficult to include it in chemistry–climate models for long-term studies. Here we present three simplified parameterizations (version 1.0) for IEPOX-SOA simulation, based on an approximate analytical/fitting solution of the IEPOX-SOA yield and formation timescale. The yield and timescale can then be directly calculated using the global model fields of oxidants, NO, aerosol pH and other key properties, and dry deposition rates. The advantage of the proposed parameterizations is that they do not require the simulation of the intermediates while retaining the key physicochemical dependencies. We have implemented the new parameterizations into the GEOS-Chem v11-02-rc chemical transport model, which has two empirical treatments for isoprene SOA (the volatility-basis-set, VBS, approach and a fixed 3 % yield parameterization), and compared all of them to the case with detailed fully explicit chemistry. The best parameterization (PAR3) captures the global tropospheric burden of IEPOX-SOA and its spatiotemporal distribution (R2=0.94) vs. those simulated by the full chemistry, while being more computationally efficient (∼5 times faster), and accurately captures the response to changes in NOx and SO2 emissions. On the other hand, the constant 3 % yield that is now the default in GEOS-Chem deviates strongly (R2=0.66), as does the VBS (R2=0.47, 49 % underestimation), with neither parameterization capturing the response to emission changes. With the advent of new mass spectrometry instrumentation, many detailed SOA mechanisms are being developed, which will challenge global and especially climate models with their computational cost. The methods developed in this study can be applied to other SOA pathways, which can allow including accurate SOA simulations in climate and global modeling studies in the future.
On the Design of the Integer and Fractional PID Controllers Using Particle Swarm Optimization

In the recent years, using the fractional calculus has been increased in the applications of control engineering.This paper presents an Integer and Fractional PID controllers designed using particle swarm optimization technique for two different industrial applications, the first one is the heat solid. The simulation results show that the Integer PID controller can behave as well as the fractional PID controller if the parameters are well tuned.
A Survey on Constellation Based Attribute Selection Method for High Dimensional Data

Attribute Selection is an important topic in Data Mining, because it is the effective way for reducing dimensionality, removing irrelevant data, removing redundant data, & increasing accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces compatible results as the original entire set of attribute. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense or another to each other than to those in other groups (Clusters). There are various approaches & techniques for attribute subset selection namely Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some disadvantages like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To get the upper hand on some of these issues in attribute selection this paper proposes a technique that aims to design an effective clustering based attribute selection method for high dimensional data. Initially, attributes are divided into clusters by using graph-based clustering method like minimum spanning tree (MST). In the second step, the most representative attribute that is strongly related to target classes is selected from each cluster to form a subset of attributes. The purpose is to increase the level of accuracy, reduce dimensionality; shorter training time and improves generalization by reducing over fitting.
ICN based Architecture for IoT - Requirements and Challenges

The Internet of Things (IoT) promises to connect billions of objects to Internet. After deploying many stand-alone IoT systems in different domains, the current trend is to develop a common, "thin waist" of protocols forming a unified, defragmented IoT platform. Such a platform will make objects accessible to applications across organizations and domains. Towards this goal, quite a few proposals have been made to build a unified host centric IoT platform as an overlay on top of today's Internet. Such overlay solutions, however, are inadequate to address the important challenges posed by a heterogeneous, global scale deployment of IoT, especially in terms of mobility, scalability, and communication reliability, due to the inherent inefficiencies of the current Internet. To address this problem, we propose to build a common set of protocols and services, which form an IoT platform, based on the Information Centric Network (ICN) architecture, which we call ICN-IoT. ICN-IoT leverages the salient features of ICN, and thus provides seamless mobility support, scalability, and efficient content and service delivery. This draft describes representative IoT requirements and ICN challenges to realize a unified ICN-IoT framework. Towards this, we first identify a list of important requirements which a unified IoT architecture should have to support tens of billions of objects. Though we see most of the IoT requirements can be met by ICN, we discuss specific challenges ICN has to address to satisfy them. Then we discuss important and popular IoT scenarios including the "smart" home, campus, grid, transportation infrastructure, healthcare, Education, and Entertainment.
High-Purity Digitally Intensive Frequency Synthesis Exploiting Millimeter-Wave Harmonics

This thesis focuses on improving the phase noise and power efficiency of millimeter-wave (mm-wave) frequency synthesizers in nanometer CMOS. The mm-wave frequency spectrum is widely adopted in various upcoming volume commercial wireless applications. These new applications provide more interconnection between the physical and digital worlds. It entails a demand for high speed data communications and accurate object sensing, which are enabled by the large bandwidth available at mm-wave frequencies. These systems also require good signal-to-noise ratio (SNR) on mm-wave transceivers. It sets stringent phase noise specifications on the mm-wave frequency synthesizers. On the other hand, the power budget on the mm-wave frequency synthesizers are limited for long battery lifetime and/or thermal reliability. The low phase noise should be achieved at high power efficiency. Advanced nanometer CMOS technologies are preferred for the integration of mm-wave frequency synthesizers. The scaled transistor size favors the cointegration with baseband circuits and large-scale SoCs. The upgrowing speed of the MOSFETs also extends the upper limits on the operating frequency of the CMOS circuits. On the other hand, the performance of mm-wave frequency synthesizers suffers from various constraints and imperfections in nanometer CMOS technologies. For example, the mm-wave oscillators is inferior in phase noise due to the low quality-factor LC tank and exacerbated flicker noise upconversion. Mm-wave frequency dividers/multipliers are power hungry and limit the power efficiency of the frequency synthesizers. There is a clear gap in performance between mm-wave and RF frequency synthesizers.
Modelling northern peatland area and carbon dynamics since the Holocene with the ORCHIDEE-PEAT land surface model (SVN r5488)

Abstract. The importance of northern peatlands in the global carbon cycle has been recognized, especially for long-term changes. Yet, the complex interactions between climate and peatland hydrology, carbon storage, and area dynamics make it challenging to represent these systems in land surface models. This study describes how peatlands are included as an independent sub-grid hydrological soil unit (HSU) in the ORCHIDEE-MICT land surface model. The peatland soil column in this tile is characterized by multilayered vertical water and carbon transport and peat-specific hydrological properties. The cost-efficient version of TOPMODEL and the scheme of peatland initiation and development from the DYPTOP model are implemented and adjusted to simulate spatial and temporal dynamics of peatland. The model is tested across a range of northern peatland sites and for gridded simulations over the Northern Hemisphere (>30∘ N). Simulated northern peatland area (3.9 million km2), peat carbon stock (463 Pg C), and peat depth are generally consistent with observed estimates of peatland area (3.4–4.0 million km2), peat carbon (270–540 Pg C), and data compilations of peat core depths. Our results show that both net primary production (NPP) and heterotrophic respiration (HR) of northern peatlands increased over the past century in response to CO2 and climate change. NPP increased more rapidly than HR, and thus net ecosystem production (NEP) exhibited a positive trend, contributing a cumulative carbon storage of 11.13 Pg C since 1901, most of it being realized after the 1950s.
Topology and Formation of Production Input Interlinkages: Evidence from Japanese microdata

Recent studies emphasize the role of the network structure of production input interlinkages for a wide range of economic outcomes including shock propagation and the emergence of aggregate fluctuations. In most of these studies the input-output architecture is fixed and exogenously given and relatively little work has considered its evolution and the underlying mechanism of network formation. Here we provide evidence on the evolution of production input interlinkages on the most granular level of economic activity, building on a diverse set of more than 80,000 companies sampled across nearly all industries of the Japanese economy. We review the main empirical properties of the Japanese production network level and find that these features are remarkably stable over time. To estimate the mechanism of supplier selection inducing this stability, we employ a stochastic actor-oriented model that sidesteps the econometric problem of mutual dependencies inherent to networked environments. Building on this approach, we find that topological features of the network such as the geodesic distance between the firms and their current number of relationships are a main driver of network dynamics in subsequent periods, and are quantitatively more important than selection based on productivity.
Reliability analysis using Bayesian networks

This paper considers the problem of reliability analysis of that consists groups of components organized in parallel and/or in serial given the reliability of the basic component. The mathematical tool used to tackle this problem is that of Bayesian networks (BN) that derive from convergence of statistics and Artificial Intelligence (AI). It consists of the representation of probabilistic causal relation between variables of a system.
Problem-based Projects in Computer Programming: Students’ Cooperation, Responsibilities and Dependencies

This research reports on second-year students who designed and developed an integrated C# programming and database project as one of the course outcomes in a problem-based environment. Project teamwork involved three key actions, namely cooperation, taking responsibility and dealing with dependencies. For these actions, several competencies were required. Based on the literature, a framework was proposed involving five categories of competencies as well as associated mechanisms to manage team cooperation, responsibilities and dependencies in joint programming projects. The purpose of the research was to test the proposed framework of competencies using the data at hand. An interpretivist approach directed this research and a qualitative methodology was followed. Ninety-nine second-year BSc Computer Science and Information Technology students participated in the research at a South African university. Students worked mainly in teams of two members and selected the partners themselves. Data collection involved weekly project sheets, which indicated a team’s responsibilities and cooperation as well as their planning, design, development and reflection on project activities. The data were manually analysed. Several quotes provided supporting evidence of the proposed framework and it was validated by referring to instances of some mechanisms that team members used to manage their problem-based projects.
Employee Ridesharing: Reinforcement Learning and Choice Modeling

Many governments and companies offer incentive programs (e.g., allowance) to encourage employees to share rides for their daily work commute because ridesharing helps reduce travel costs, road congestion, and greenhouse gas emissions. For the program success, it is critical to understand employees’ motivation to use this service and effectively incentivize new users’ adoption and retain existing users by providing the best ridesharing experience. In this study, our goal is to learn user incentives and preferences as well as risks in employee ridesharing. We use a choice model to estimate the personalized utility of different choices among possible transportation modes and learn from longitudinal behaviors using reinforcement learning. The integrative model is derived theoretically and evaluated empirically using a real-world employee ridesharing data. We find that ridesharing is risky and company employees, especially drivers, adopt the service mainly because of social relationship with colleagues rather than the financial incentive.
A SLAM Algorithm Based on Multi-Constraint Learning Model

Unsupervised Learning based SLAM algorithm has lately drawn significant attention for its potential in label-free leaning ability and robustness to camera parameters and environmental variations. In order to achieve better robustness and accuracy, a multi-constraint learning model is proposed. In contrast to traditional geometry-based methods, multi-constraint unsupervised learning models optimize the photometric consistency over image sequences by warping one view into another; make the Network learning more geometrically information. A lot of experiments on the KITTI data set show that our model is superior to previous unsupervised methods and has comparable results with the supervised method.
Progress in Experimental and Theoretical Evaluation Methods for Textile Permeability

A great amount of attention has been given to the evaluation of the permeability tensor and several methods have been implemented for this purpose: experimental methods, as well as numerical and analytical methods. Numerical simulation tools are being seriously developed to cover the evaluation of permeability. However, the results are still far from matching reality. On the other hand, many problems still intervene in the experimental measurement of permeability, since it depends on several parameters including personal performance, preparation of specimens, equipment accuracy, and measurement techniques. Errors encountered in these parameters may explain why inconsistent measurements are obtained which result in unreliable experimental evaluation of permeability. However, good progress was done in the second international Benchmark, wherein a method to measure the in-plane permeability was agreed on by 12 institutes and universities. Critical researchers’ work was done in the field of analytical methods, and thus different empirical and analytical models have emerged, but most of those models need to be improved. Some of which are based on Cozeny-Karman equation. Others depend on numerical simulation or experiment to predict the macroscopic permeability. Also, the modeling of permeability of unidirectional fiber beds have taken the greater load of concern, whereas that of fiber bundle permeability prediction remain limited. This paper presents a review on available methods for evaluating unidirectional fiber bundles and engineering fabric permeability. The progress of each method is shown in order to clear things up.
Automatic ECG Analysis using Principal Component Analysis and Wavelet Transformation

The main objective of this book is to analyse and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorders. Detecting predisposition to Torsade de Points (TDP) by analysing the beat-to-beat variability in T wave morphology is the main core of this work. The second main topic is detecting small changes in QRS complex and predicting future QRS complexes of patients. Moreover, the last main topic is clustering similar ECG components in different groups.
Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

Students’ feedback is crucial for academic institutions in order to evaluate faculty performance. Handling the qualitative opinions of students efficiently while automatic report generation is a challenging task. Indeed, most organizations deal with quantitative feedback effectively, whereas qualitative feedback is either processed manually or ignored altogether. This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model. The first layer predicts the aspects described within the feedback and later specifies the orientation (positive, negative, and neutral) of those predicted aspects. The model was tested on a manually tagged data set constructed from the last five years students’ comments from Sukkur IBA University as well as on a standard SemEval-2014 data set. Unlike many other LSTM models proposed for other domains, the proposed model is quite simple in terms of architecture which results in less complexity. The system attains a good accuracy using the domain embedding layer in both tasks: aspect extraction (91%) and sentiment polarity detection (93%). To the best of our knowledge, this study is a first attempt that uses deep learning approach for performing aspect based sentiment analysis on students’ feedback for evaluating faculty teaching performance.
Literary Exploration Machine A Web-Based Application for Textual Scholars

This paper presents a design of a web-based application for textual scholars. The goal of this project is to create a complex and stable research environment allowing scholars to upload the texts they analyse and either explore them with a suite of dedicated tools or transform them into a different format (e.g. text, table, list, spreadsheet). The latter functionality is especially important for research focusing on Polish texts (due to the rich morphology and weakly constrained word order of Polish) because it allows for their further processing with tools built for English. This project utilises the existing CLARIN-PL applications and supplements them with new functionalities.
Computational Protein Interface Design and Prediction with Experimental Constraints

STEVEN LEWIS: Computational Protein Interface Design and Prediction with Experimental Constraints (Under the direction of Brian Kuhlman) Computational modeling is a powerful companion to direct experimental testing, because it allows researchers to answer questions that are too expensive to test in the lab or insurmountable with existing techniques. The development of algorithms and a computational framework in which to use them form an upfront cost of modeling. The Rosetta software suite is one such framework. Our recent rewrite of Rosetta using modular, reusable, object-oriented code allows Rosetta developers to rapidly and easily create modeling protocols to address new biological questions, reducing the investment needed to use computer modeling and enabling easier biologist-modeler collaborations. One such Rosetta protocol, AnchoredDesign, performs single-sided flexiblebackbone protein-protein interface design. It borrows information from known partners of some protein target to help redesign arbitrary scaffolds into protein affinity reagents against that target. Benchmarking results indicate that the protocol performs well, and we used the protocol in combination with library display selections to generate fibronectin monobody binders to the Keap1 β-propeller domain with dissociation constants in the single nanomolar range. We also modeled the interaction between monobody 1F11 and cSrc-SH3. 1F11 has been functionalized with a solvent-sensitive fluorophore to allow it to report when and where in a cell Src-family kinases are active, and we used AnchoredDesign models to support structural hypotheses explaining this sensing ability.
Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

: Relation extraction is an important task in NLP community. However, some models often fail in capturing Long-distance dependence on semantics, and the interaction between semantics of two entities is ignored. In this paper, we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM (SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information, and capture Long-distance dependence on semantics. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrated that the proposed method is effective against relation classification, which can obtain state-of-the-art classification accuracy just with minimal feature engineering.
Parallel Discovery of Trajectory Companions from Heterogeneous Streaming Data

Trajectory streams consist of large volumes of timestamped spatial data that are constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies needs to process each record when it arrives and correlates across multiple records near real-time. Thus techniques for handling high-speed trajectory streams should scale on distributed cluster computing. The main issues encapsulate three aspects, namely a data model to represent the continuous trajectory data, the parallelism of a discovery algorithm, and end-to-end performance improvement. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectories sampled on different time intervals; 2) definition on distance measurements of trajectories; and 3) a parallel discovery algorithm. We develop this method in a stream processing workflow. From parallelization point of view, we investigate system performance, scalability, stability. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.
Review Sentiment Orientation Analysis based on Deep Learning

The increase of network users has led to a large number of commentary languages on various network platforms. Traditional manual processing is time-consuming and labor-intensive. We need a mechanized way to process these commentary corpora and quickly uncover the emotional tendencies. A method of sentimental orientation analysis of comment text based on deep learning is proposed. First, we used GloVe model to train the word vector. Then, Give the different weight on word vector by using TF-IDF. Finally, the processed word vectors would be classificated by TextCNN. Experiments were carried out on the six categories of commodity review data crawled by Jingdong. This method can effectively identify the emotional tendency of the review text, which is more accurate than the traditional deep learning method.