Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of coagulation on the appearance time method for new particle growth rate evaluation and their corrections

Abstract. The growth rate of atmospheric new particles is a key parameter that determines their survival probability to become cloud condensation nuclei and hence their impact on the climate. There have been several methods to estimate the new particle growth rate. However, due to the impact of coagulation and measurement uncertainties, it is still challenging to estimate the initial growth rate of sub-3 nm particles, especially in polluted environments with high background aerosol concentrations. In this study, we explore the feasibility of the appearance time method to estimate the growth rate of sub-3 nm particles. The principle of the appearance time method and the impacts of coagulation on the retrieved growth rate are clarified. New formulae in both discrete and continuous spaces are proposed to correct the impacts of coagulation. Aerosol dynamic models are used to test the new formulae. New particle formation in urban Beijing is used to illustrate the importance to consider the impacts of coagulation on sub-3 nm particle growth rate and its calculation. We show that the conventional appearance time method needs to be corrected when the impacts of coagulation sink, coagulation source, and particle coagulation growth are non-negligible compared to the condensation growth. Under the simulation conditions with a constant vapor concentration, the corrected growth rate agrees with the theoretical growth rates. The variation of vapor concentration is found to impact growth rate obtained with the appearance time method. Under the simulation conditions with a varying vapor concentration, the average bias of the corrected 1.5–3 nm particle growth rate range from 6–44 %. During the test new particle formation event in urban Beijing, the corrected condensation growth rate of sub-3 nm particles was in accordance with the growth rate contributed by sulfuric acid condensation, whereas the conventional appearance time method overestimated the condensation growth rate of 1.5 nm particles by 80 %.
IDENTIFYING INTERGENERATIONAL PATTERNS OF CORRELATED METHYLATION SITES

DNA methylation can be transmitted through generations. This paper proposes a clustering method to identify the intergenerational patterns from parents to their offspring. Motivated by the potential of correlation between DNA methylation sites, we use the multivariate generalized beta distribution to model the block-wise correlation structure among the sites. A stochastic EM algorithm is implemented to estimate the parameters and BIC is applied to determine the optimal number of clusters. Simulations demonstrate the feasibility of the proposed method. We further applied the approach to cluster DNA methylation data generated from a cohort study on asthma and allergic conditions.
ARID1A Upregulation Predicts Poor Survival in Patients with Liver Cancer

Objective: ARID1A has been identified as a possible biomarker for certain cancers. There is, however, some debate regarding its function in liver cancer. Methods: Associations between clinical variables and ARID1A were evaluated. Cox and Kaplan – Meier analysis were used to examine clinicalopathological factors linked to overall survival of patients with liver cancer. Gene Set Enrichment Analysis (GSEA) was conducted using the dataset of the Cancer Genome Atlas. Results: High expression of ARID1A was correlated with the gender and tumor topography (T) diagnosis of liver cancer. Patients with elevated ARID1A expression had poorer prognosis than those with low ARID1A expression. The study also showed that ARID1A was an independent risk factor for overall survival. GSEA established pathways involved in ERBB signaling, cancer, insulin signaling, mTOR signaling, MAPK signaling, VEGF signaling, Ubiquitin signaling, and Wnt signaling as differentially enriched in ARID1A-high expression liver cancer. Conclusion: ARID1A has been shown to be expressed at high rates of liver cancer and to represent a possible independent molecular marker for diagnosis and prognosis of liver cancer.
Decode-and-Forward Short-Packet Relaying in the Internet of Things: Timely Status Updates

In this paper, a decode-and-forward (DF) short-packet relaying model is developed to achieve timely status updates for intelligent monitoring within the Internet of Things (IoT), where the status updates generated at an IoT device are delivered to a remote server with the aid of a relay in both half-duplex (HD) and full-duplex (FD) modes. To characterise the data freshness of status updates, we exploit the age of information (AoI) as a metric, which is defined as the time elapsed since the generation of the latest successfully decoded status update. The average AoI is formulated and minimised for both HD-DF and FD-DF relaying IoT networks in finite blocklength regime. For the HD-DF relaying, we introduce a perfect approximation of the average AoI to solve the problem of average AoI minimisation with the optimal blocklengths in two phases. For the FD-DF relaying, we propose an iterative algorithm to solve the problem of average AoI minimisation by optimising the relay’s transmit power and the blocklength. Illustrative numerical results not only substantiate the validity of our proposed algorithms, but also provide useful references for the IoT monitoring network design, specifically for the transmit power thresholds at the IoT device and the relay.
Decimeter Level Indoor Localization Using WiFi Channel State Information

Indoor localization using WiFi signal parameters is challenging, with encouraging decimeter localization results available with enough line-of-sight coverage and hardware infrastructure. This paper proposes a new 2-dimensional multiple packets based matrix pencil (2D M-MP) method to estimate the Angle of Arrival (AoA) and Time of Flight (ToF) based on WiFi channel state information (CSI). Compared with the conventional parameter estimation algorithms, this method has two advantages. First, 2D M-MP method uses the discrete Fourier transform (DFT) to convert the complex computation into real computation to reduce the computational complexity significantly without losing accuracy. Second, it accumulates multiple CSI packets to improve the parameter estimation accuracy effectively, especially at low values of signal-to-noise-ratio (SNR) environment. To verify the practicability of our proposed 2D M-MP method, we set up a localization system in an actual scenario using commodity WiFi cards which demonstrates that the performance of 2D M-MP method is better than conventional parameter estimation algorithms and can achieve a localization accuracy of 42 cm in indoor hall deployment.
Feedback Control of the Locomotion of a Tailed Quadruped Robot

The traditional locomotion paradigm of quadruped robots is to use dexterous (multi degrees of freedom) legs and dynamically optimized footholds to balance the body and achieve stable locomotion. With the introduction of a robotic tail, a new locomotion paradigm becomes possible as the balancing is achieved by the tail and the legs are only responsible for propulsion. Since the burden on the leg is reduced, leg complexity can be also reduced. This paper explores this new paradigm by tackling the dynamic locomotion control problem of a reduced complexity quadruped (RCQ) with a pendulum tail. For this specific control task, a new control strategy is proposed in a manner that the legs are planned to execute the open-loop gait motion in advance, while the tail is controlled in a closed-loop to prepare the quadruped body in the desired orientation. With these two parts working cooperatively, the quadruped achieves dynamic locomotion. Partial feedback linearization (PFL) controller is used for the closed-loop tail control. Pronking, bounding, and maneuvering are tested to evaluate the controller’s performance. The results validate the proposed controller and demonstrate the feasibility and potential of the new locomotion paradigm.
KNIFE, KAshima Nobeyama InterFErometer

: By connecting two antennas, Kashima 34 m and Nobeyama 45 m, an east-west baseline of 200 km is formed. At that time, because Nobeyama 45 m had the world’s number one sensi-tivity in the 43 GHz band, and also Kashima 34 m was the world’s
MEANINGS FOR ANGLE THROUGH GEOMETRICAL CONSTRUCTIONS IN 3D SPACE

The construction of meanings for the notion of angle in 3d geometrical space is studied during the implementation of specially designed geometrical tasks in the classroom. 13-year-old pupils were engaged in exploring the mathematical nature of angles while controlling and measuring the behaviours of geometrical objects in 3d simulations. Pupils worked in pairs using a specially designed computational environment that combines symbolic notation (by means of programming) and dynamic manipulation of graphically represented geometrical objects. The findings reveal a progressive coordination of different facets of angle in 3d space facilitated by the use of the available tools
DERIVATIZATION OF SMALL ACIDS TO ENHANCE SENSITIVITY IN GAS CHROMATOGRAPHY MASS SPECTROMETRY AND GAS CHROMATOGRAPHY VACUUM ULTRAVIOLET SPECTROSCOPY

ii ACKNOWLEDGEMENTS . v CHAPTER ONE: Introduction.1 CHAPTER TWO: Detection of Acetic Acid and Haloacetic Acids in Water.14 2.1 Experimental for Acetic Acid . 14 2.1. 1 Reagents and Chemicals . 14 2.1. 2 Sample Preparation and Derivatization.15 2.1. 3 Instrumentation.16 2.1. 4 Results and Discussion.19 2.2 Experimental for Haloacetic Acids and Real Sample.26 2.2.1 Reagents and Chemicals.26 2.2.2 Preparation and Derivatization of Tap Water Sample . 27 2.2. 3 Instrumentation.29 2.2. 4 Results and Discussion.32 CHAPTER THREE: Conclusion and Future Perspectives.39 REFERENCES.40
THE WATERS® PROTEIN EXPRESSION SYSTEM FOR QUALITATIVE AND QUANTITATIVE PROTEOMICS

The Waters® Protein Expression System sets the standard for multiplexed qualitative and quantitative proteomics. It combines the highly reproducible nanoACQUITY Ultra Performance LCTM (UPLCTM) System, with the specificity of exact mass measurement from the Waters Micromass® Q-Tof PremierTM Mass Spectrometer. Integrated with proprietary Protein Expression System Informatics Tools, this complete solution provides enhanced qualitative protein identification simultaneously with relative quantification across sample sets, without the need for stable isotope labelling. This allows both the identification and characterization of potential biomarkers.
Travel Behavior and Responses to Change in Transit Fare: A Comparative Study of Urban Rail Passengers between Bangkok and Manila

Bangkok, Thailand and Manila, Philippines each have three urban rail systems. Focusing on two urban rail systems in Bangkok and Manila, Bangkok Mass Transit System (BTS) and Metro Rail Transit (MRT3), the transit fare for the BTS is high and wide-ranging compared to the subsidized transit fare in the MRT3. Using the concept of price elasticity the authors demonstrated the sensitivity of urban rail passengers when a change in transit fare is implemented. Fare elasticity values obtained were -0.46 and -0.06 for the BTS and the MRT3, respectively, implying that the responsiveness of BTS users was much higher compared to the MRT3 when a price change is implemented. A modified Price Sensitivity Measurement (PSM), Kishi's Logit PSM (KLP), was used to determine reasonable price range for transit fare in both cities. Results indicated that the MRT3 transit fare could be increased, while the BTS system already had a reasonable fare setting.
Nowcasting in Microsimulation Models

In this paper, we survey the use of nowcasting methods in Microsimulation models. These nowcasting methods differ in a number of respects to the more established methods of forecasting. The main distinction is that while forecasting extrapolates from current data to estimate the future, the methods of nowcasting extrapolate from data of the recent past to reflect the present situation. In this paper, we undertake a survey of a number of modelling teams globally, selected for their experience and breadth of use with the methodologies of nowcasting and to ascertain the modelling choices made. Different methodologies are used to adjust the different components, with indexation or price uprating applied for the adjustments to growth in wages or prices, the updating of tax-benefit policy to adjust for policy change and either static or dynamic ageing to account for changes to the population and labour market structure. Our survey reports some of the choices made. We find that these model teams are increasingly utilising variants of these methods for short-term projections, which is relatively novel relative to the published literature.
Speech emotion recognition based on SVM and KNN classifications fusion

Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons.
Real-time FPGA implementation of concatenated AES and IDEA cryptography system

The data encryption is one of the most critical issues in the communication system design. Nowadays, many encryption algorithms are being updated to keep pace with the remarkable progress in the communication field. The advanced encryption standard (AES) is a common algorithm that has proved its efficacy. The main drawback of AES is that it uses too simple algebraic structures, since every block is always encrypted in the same way that makes the hacking process possible if the hacker captures the key and the uses S-Box in the input stage. This especially applies to the unwired communication systems where chances of hacking exceed those found in the wired systems. The paper proposes a security enhancement method that is based on utilizing concatenated AES and international data encryption algorithm (IDEA) algorithms. Upon applying the proposed algorithm, the hacking process becomes a great challenge. The paper incorporates the real-time FPGA implementation of the proposed algorithm in the encryption and the decryption stages. Besides, the paper presents a clear analysis of the system’s performance.
O fenômeno global da desertificação nos livros didáticos de geografia no Brasil

A desertificacao e um problema das regioes secas do globo exigindo acoes de prevencao e combate, as quais passam necessariamente pela educacao escolar. Diante do exposto, a pesquisa analisa a abordagem do fenomeno da desertificacao nos 14 livros didaticos (LD) de Geografia para o Ensino Medio aprovados pelo Programa Nacional do Livro Didatico (2018), a partir da avaliacao de criterios como definicao conceitual, causas, efeitos, escalas temporais e espaciais e politicas publicas e/ou medidas mitigadoras da desertificacao. Entre os resultados, podemos destacar que 71% dos LD apresentam pelo menos um dos aspectos analisados com destaque para cinco livros (36%) com abordagem satisfatoria: ALMEIDA e RIGOLIN (2017), SOUZA et al. (2016), MOREIRA (2016), MARTINS et al. (2016) e LUCCI et al. (2016). Enquanto isso, 29% dos LD nao abordam a tematica dificultando a sua adocao nas escolas do Semiarido Brasileiro e, consequentemente o combate nas areas afetadas e invisibilizando nacionalmente o debate cientifico e politico. Quanto aos aspectos do fenomeno, os LD abordam principalmente as causas, os efeitos e as espacialidades de ocorrencia, justamente as dimensoes do campo empirico do fenomeno em detrimento dos aspectos conceituais e da dimensao politica de luta contra a desertificacao.
Design and monitoring body temperature and heart rate in humans based on WSN using star topology

Electronic health (E-health) uses information and communication technology including electronics, telecommunications, computers, and informatics to process various types of medical information, to carry out clinical services (diagnosis or therapy). Health is the most important asset in human life, therefore maintaining health is a top priority and serious attention needed. Heart rate and body temperature are vital signs that the hospital routinely checks for clinical signs and are useful for strengthening the diagnosis of a disease. In this research monitoring heart rate and body temperature with the wireless sensor network (WSN) method that uses NodeMCU 1.0 as a controller module and wireless as communication between nodes, the wireless network used in this research Wi-Fi network. As a data taker, a DS18b20 temperature sensor and a heart rate sensor (pulse sensor) are needed, which will be displayed by the ThingSpeak web and smartphones. From the test results, the success rate of the system in detecting heart rates is 97.17%. Whereas in detecting body temperature the success rate of the system is 99.28%. For data transmission, the system can send data smoothly at a maximum distance of 15 meters with a barrier.
Bond Rating Change Announcement and the Effect on Stock and Bond Return

This research uses event study method in order to examine the difference in abnormal returns for stocks (average abnormal return) and bonds (spread yield). The sample used is listed companies in Indonesian Stock Exchange for the period 2007-2011 which issue corporate bonds and have bond rating changes issued by PT Pefindo. The analyses of this research were performed using one sample t test, paired t test, and multiple regression method. The results showed that: 1) There is no significant difference on average abnormal stock returns and abnormal bond returns before the announcement, during the announcement, and after the announcement of bond rating changes, 2) Cumulative return of stock increases following bond rating upgrades and decreases following bond rating downgrades although both are insignificant. In contrast, the cumulative return for bonds decreases significantly following bond rating upgrades and increases insignificantly following bond rating downgrade, and 3) The magnitude of bond rating changes gives no significant positive effect on average abnormal stock returns
MAIZE LEAF DISEASES DETECTION AND YIELD PREDICTION USING DEEP NEURAL NETWORKS

--- Agriculture is one subject which has a excessive effect on existence and financial fame of human beings. Improper control ends in loss in agricultural products. Farmers lack the expertise of disorder and for this reason they produce much less production. Kisan name facilities are to be had however do now no longer provide provider 24*7 and on occasion conversation too fail. Farmers are not able to give an explanation for disorder well on name want to evaluation the photograph of affected vicinity of disorder. Identification of the plant sicknesses is the important thing to stopping the losses withinside the yield and amount of the rural product. The research of the maze leaf sicknesses suggest the research of visually observable styles visible at the maze leaf. Due to the development and improvement in era wherein gadgets are clever sufficient to understand and locate plant sicknesses. Recognizing infection can spark off quicker remedy which will reduce the terrible affects on harvest. This paper consequently cognizance upon maze leaf disorder detection the usage of photograph processing approach.This paintings makes use of an open dataset of 5000 snap shots of bad and solid plants, wherein convolution machine and semi supervised strategies are used to symbolize crop species and locate the illness fame of four wonderful classes.
MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data

Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to datadriven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256× 256) that cover eastern China with a domain of 12.8×12.8. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.
SU-chef: The hands-free kitchen helper Natural Language Processing

For this project, I created a solution to a problem that I, and I imagine many other people, have had while cooking: your hands are wet or dirty, and you just need to know the next step of a recipe. These days, most everyone accesses recipes on their phone or computer, and it seems like the screen always times out right when you’re ready to look for the next ingredient. Instead of wiping your hands off to enter a password or swipe a fingerprint, what if you could just ask your device, for example, “how many cups of sugar do I add? ” or “I just mixed the dry ingredients, what’s next? ” and it gave a response! Even if you are an effortlessly tidy chef, we’ve all struggled with scrolling past endless life stories on a blog page just to get to the recipe. My project helps to eliminate all annoying practicalities of using online recipes; the user can simply upload a webpage, and begin asking their electronic sous-chef for everything else.