Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation and Comparison of Different Transmitter Detection Techniques for Application in Cognitive Radio

Wireless communications and the utilization of the radio frequency spectrum have witnessed a tremendous boom during the past few decades. The current static frequency allocation schemes are unable to accommodate the requirements of an increasing number of higher data rate devices. Cognitive radio (CR) with effective primary user detection has become a candidate for more efficient spectrum utilization systems based on opportunistic spectrum sharing In this work, performance of energy, replica correlation, and cooperative detector have been evaluated and compared by various performance metrics. To evaluate the performance of the detection techniques MATLAB software has been used for simulation. From the simulated results the replica correlation detector is better than energy detector under both additive white Gaussian noise (AWGN) and Rayleigh fading channels. The cooperative detection helps to reduce the fading effect of single node detection. It has outperformed the performance of single user energy and replica correlation detector. Noise introduced considerable amount of degradation in the detection performance of energy and replica correlation detector and the cooperative detection helps to reduce the effect of noise uncertainty factor in the detection performance of CR.
VISIBLE LIGHT COMMUNICATIONS USING FOUR-COLOR LEDS

Visible light communications (VLC) technology has gained prominence in the development of high data rate transmission for the fifth generation (5G) networks. VLC technology can be used in conjunction with the existing Wi-Fi technology to improve access and data transmission. In optical wireless communications, LED transmitters are used in applications that desire mobility and the LED divergence enable larger coverage. VLC systems offer simultaneous data transmission and illumination. However, the LED’s limited modulation bandwidth and non-linear distortions pose difficulty in achieving high transmission data rates. Wavelength division multiplexing in VLC (WDM-VLC) can be used improve the data rates to meet gigabit data standards. This thesis work focuses on VLC experiments using WDM, discrete multitone modulation (DMT), and multi-input multi-output (MIMO) techniques. The VLC system is demonstrated over commercial four-color red-green-blue-amber (RGBA) LED transmitters in three cases of interest: (a) a single LED transmitter, (b) repetition encoding, and (c) spatial multiplexing. Our proposed VLC system used bit-power allocation strategies to maximize the data transmission under bit error rate (BER) threshold of 2×10, which is the typical hard-decision forward error correction (FEC) threshold.
Synthesis, Characterisation, and Density Functional Theory Study of Encapsulated Bioactive Components of Ginger Triati Dewi Kencana Wungu1,2*, Damar Rastri Adhika2,3, Meqorry Yusfi2,4, Atsarina Larasati Anindya2, Eduardus Bimo Aksono5, Raden Roro Fosa Sarassina6, Christofora Hanny Wijaya7 and Suprij

In this paper, we encapsulated ginger bioactive components in maltodextrin nanocapsules. Ginger nanocapsules were characterised using Transmission Electron Microscope (TEM) and Particle Size Analyser (PSA). The results show that the nanoparticles have a generally globular shape with particle size under 200 nm. In addition, the simulation of gingerol and dextran, as a representative for maltodextrin, was also investigated using Density Functional Theory (DFT) calculation. From the DFT calculation, gingerol exhibited a physisorption interaction with dextran by forming hydrogen bonds. Furthermore, the density of state analysis shows that the gingeroldextran system has a conductive-like behaviour that promotes the nanocapsules’ cell uptake.
Text Mining Economic Topic Sentiment for Time Series Modeling

Global businesses must react to daily changes in market conditions over multiple geographies and industries. Consuming reputable daily economic reports assists in understanding these changing conditions, but requires both a significant human time commitment and a subjective assessment of each topic area of interest. To combat these constraints, Dow's Advanced Analytics team has constructed a process to calculate sentence-level topic frequency and sentiment scoring from unstructured economic reports. Daily topic sentiment scores are aggregated to weekly and monthly intervals and used as exogenous variables to model external economic time series data. These models serve to both validate the relationship between our sentiment scoring process and also as near-term forecasts where daily or weekly variables are unavailable. This paper will first describe our process of using SAS® Text Miner to import and discover economic topics and sentiment from unstructured economic reports. The next section describes sentiment variable selection techniques that use SAS/STAT®, SAS/ETS®, and SAS® Enterprise Miner™ to generate similarity measures to economic indices. Our process then uses ARIMAX modeling in SAS® Forecast Studio to create economic index forecasts with topic sentiments. Finally, we show how the sentiment model components are used as a matrix of economic key performance indicators by topic and geography
Tracking Out-of-date Newspaper Articles

Local newspapers rely on their local correspondents to bring you the freshest news of your home town. Some of the articles written by these correspondents are not published immediately, but are put aside to be placed in a later edition. One of the challenges the editor in chief is confronted with, is to publish only up-to-date information about a local event. In this paper we present a system that tracks out-of-date newspaper articles to prevent their publishing. It firstly dates the event the article is talking about. The date detection grammar is written in a single but complex finite state automaton based on linguistic pattern matching. Secondly, it computes an absolute date for each relative date. A freshness score can then be deduced from the time difference between the extracted and the publication date. The system has been tested on several French local newspaper corpora. Baseline for the temporal extraction is our standard date extraction that was developed for general purposes.
ROAD DETECTION AND SEGMENTATION TECHNIQUE USING CNN

: Image segmentation is one of the important trends in image processing. It is a technique that divides or divides an image into multiple segments. There are various application segments for image segmentation, mainly image compression, medical applications, satellite images, object recognition, etc., because the efficiency of processing the whole image is not high. Image segmentation divides the image into sub-regions of interest and then these regions can be analyzed separately. To this end, there are many techniques that can divide an image into multiple parts based on certain features (such as color, texture, pixel intensity, etc.) and classify the technique based on the method used. In this case, the problem statement is that the image received from the satellite is segmented and then the roads, buildings and natural resources contained in the image must be detected. In this paper, we use intricate neural network-based algorithms to learn features from noisy labels to restore the layout of road images. The new thing with this algorithm is to generate training labels by using an algorithm trained in a general vehicle data set for vehicle image classification. In addition, we propose a new texture description based on the fusion of learned color schemes to achieve maximum uniformity in the road area. Finally, the acquired (offline) information and the current (online) information are combined to detect the road area in a single image.
CREATIVITY AND INNOVATION: THE CASE OF TWO MAJOR COMMUNICATIONS FIRMS AND AN AEROSPACE ENGINEERING ORGANIZATION

People, organizations and societies benefit when there is an appropriate fit between individuals and their environments, especially when it comes to innovation. This article elaborates on the findings of two case studies in the telecommunications industry dealing with different style preferences when innovating. The article also describes the findings of a research project within one of the biggest professional aerospace organizations and elaborates on the role of organizational climate and leadership influence on innovation. In addition, we tie both cases together and look for the impact of people and their environment on innovation. This article indicates the impact leadership has on organizational climate and what levering power climate has on innovation processes. Furthermore it underscores the necessity of not letting chance interfere with putting together a team, which needs to be done through a deliberate process. Finally, the article suggests that problem-solving styles make a difference for some of the dimensions of creative climate and therefore should be taken into account when leading or managing people.
Cache Behavior of Real-Time Physics Simulation

Real-time physics simulation is becoming increasingly important for creating immersive interactive entertainment applications. Physics simulation’s high computational demands along with the applications’ real-time constraint pose a unique computational load and challenge for microprocessor designers. Prior work has shown the tremendous performance required to satisfy physics simulation’s real-time constraint. Parallel threads executing on chip-multiprocessors (CMPs) has been shown to be a promising approach for reaching the desired frame-rate. However, detailed cache behavior for CMPs has not been presented. In this paper, we examine the cache behavior of real-time physics simulation for typical interactive entertainment (IE) scenarios. These scenarios include both constrained rigid body dynamics and cloth, and cover the main genres of games. Our study investigates single-thread and multi-thread executions utilizing private and shared caches. This detailed cache behavior study points toward dynamically adaptable L2 caches to exploit the varying cache demand between threads and computational phases. e-mail: tomyeh@cs.ucla. edu e-mail: pfal@cs.ucla. edu e-mail: reinman@cs.ucla. edu
PROSPECTS FOR DIATOMS IDENTIFICATION USING METAGENOMICS: A REVIEW

The marine environment is the largest ecosystem, richest in biodiversity and biological activity. Diatoms are almost omnipresent and common in the plankton and benthos in both freshwater and marine environments. They have a major influence on their environment whereas representing a significant part in primary production and carbon fixation in marine ecosystems. Microscopic examination and cell culture technique have been applied to detect, isolate and study the diatom species. On the other hand, molecular methods have contributed to overcome the drawbacks of the classical methods and also to accomplish the research objectives more efficiently. Metagenomics is an advanced molecular method utilizing direct collected microbial samples and has been used in various fields of microbial studies. It provides obvious details on the taxonomy, biodiversity, ecology and further information around the potential functions. Consequently, it can explore the biochemical components that have significant importance in various biotechnology, ecology, biomedicine and industry applications. This review focused on the effectiveness of the metagenomics method for exploring microbial communities and recruiting this to discover the diatom
Stress Among Accountancy Professors: Modulation from the Perspective of the Demand-Control-Support Theory

Objective: To identify and analyze the influence of demand, control, and support levels on self-reported stress among professors in Accountancy programs at Brazilian Higher Education Institutions (HEI). Method: A survey addressed the biopsychosocial condition of 614 professors, including stress perception measured by the Teacher Stress Inventory and demand, control, and emotional support measured by the Job Demand-Control-Support. Results: Support and Control significantly (p<0.01) contribute to decreasing professors’ perceived stress. The demands imposed by HEIs increase (p<0.01) stress levels though. Additionally, age, positive perception of mental health, and general satisfaction with the job and students are factors that negatively modulate professors’ stress levels (p<0.01). Being a woman, teaching in the morning shift, and being a professor in a public HEI, on the other hand, increase the respondents’ stress levels (p<0.01). Contributions: Considering that an individual’s ability to adapt is limited, this study enables unveiling the circumstances that can aggravate the effects caused by stress, considering increased physical and emotional demands, possibly leading to psychosomatic diseases.
Kobe University Repository : Kernel タイトル Tit le Comparing the effects of different individualized music intervent ions for elderly individuals with severe dement ia 著者

Background: Individuals with dementia often experience poor quality of life (QOL) due to behavioral and psychological symptoms of dementia (BPSD). Music therapy can reduce BPSD, but most studies have focused on patients with mild to moderate dementia. We hypothesized that music intervention would have beneficial effects compared with a no-music control condition, and that interactive music intervention would have stronger effects than passive music intervention. Methods: Thirty-nine individuals with severe Alzheimer’s disease were randomly and blindly assigned to two music intervention groups (passive or interactive) and a no-music Control group. Music intervention involved individualized music. Short-term effects were evaluated via emotional response and stress levels measured with the autonomic nerve index and the Faces Scale. Long-term effects were evaluated by BPSD changes using the Behavioral Pathology in Alzheimer’s Disease (BEHAVE-AD) Rating Scale . Results: Passive and interactive music interventions caused short-term parasympathetic dominance. Interactive intervention caused the greatest improvement in emotional state. Greater long-term reduction in BPSD was observed following interactive intervention, compared with passive music intervention and a no-music control condition. Conclusion: Music intervention can reduce stress in individuals with severe dementia, with interactive interventions exhibiting the strongest beneficial effects. Since interactive music intervention can restore residual cognitive and emotional function, this approach may be useful for aiding severe dementia patients’ relationships with others and improving QOL. The registration number of the trial and the name of the trial registry are UMIN000008801 and “Examination of Effective Nursing Intervention for Music Therapy for Severe Dementia Elderly Person” respectively.
Using deep learning algorithms for texture segmentation of ultra-high resolution satellite images

This paper presents the results of textural segmentation of satellite images with spatial resolution <1 m using U-Net convolutional neural networks. To conduct numerical experiments, a panchromatic image of the WorldView-2 test site on the territory of the Bronnitsky Forestry (Moscow region) used. The possibilities of automating the selection of neural network parameters based on genetic algorithms investigated. The proposed method makes it possible to effectively segment the main types of natural and man-made objects, as well as to distinguish structural classes of woodlands.
SRI RAMACHANDRA JOUNRAL OF MEDICINE

Background: Poor infant feeding practices and their consequences are one of the world’s major problems and a serious obstacle to social and economic development. Various studies have shown that infant feeding could be influenced by socio-economic status, maternal education, place of living and many other factors. Hence a prevalence study on exclusive breast feeding was conducted in rural Tamil Nadu. Aim & Objective: To assess the prevalence of exclusive breast feeding practices and the socio-demographic factors influencing them, among women in a rural area in Tamil Nadu. Material & Methods: A Cross-sectional study was conducted in Attyampatti Panchayat Union, Salem district, Tamil Nadu, from March 2011-June 2011. All the children in the age group of 6 months to 2 yrs, constituting 291 children, and their mother’s were included in the study, irrespective of any sample. The data was analysed using SPSS package. Results: Among the study population of 291 children, 52.6% were male children and 47.4% were females. Only 99 (34%) children were exclusively breast fed for 6 months. Majority of women 60.5% initiated breast feeding within half an hour after delivery. Factors like education of the mother, type of delivery, type of family, number of children, had direct influence on exclusive breast feeding. Most of the mothers (44.7%) inferred that main reason for giving bottle feed is because of inadequate breast milk secretion. Conclusion: The prevalence of exclusive breast feeding is low in this area. Hence to focus on the factors affecting them is highly warranted in this area.
Naive Bayes Classifier dapat digunakan untuk mengklasifikasi sebuah opini positif maupun negatif

Social media has now become an important part of everyday life not only for personal needs but can be used in business, as well as many things that can be done. Social media used such as Facebook, Twitter, Youtube, Instagram, Likenid, and Whatsapp. With the existence of social media, the amount of data available is in the form of images, comments in the form of text or emoticons, videos, and so on, so that people are free to have an opinion. With the analysis of the sentiment of opinion that is developing and many on social media, it can produce useful data and information. In sentiment analysis, data classification algorithms are needed including the Naive Bayes Classifier, Support Vector Machine, K-NN, RNN, C4.5, Lexicon Based, LDA Based Topic Modeling, and several other algorithms. This article examines some of the sentiment analysis literature on social media. Currently, the social media that is often used in analysis is Twitter and algorithm users that can increase the level of accuracy are the Naive Bayes Classifier algorithm and the Support Vector Machine. The results of calculating different data classification accuracy can be seen in the test data in the study. Intisari—Media sosial saat ini telah menjadi bagian penting dalam kehidupan sehari-hari tidak hanya untuk kebutuhan pribadi melainkan bisa di gunakan dalam bisnis, serta banyak hal yang bisa dilakukan. Media sosial yang digunakan seperti Facebook, Twitter, Youtube, Instagram, Likenid, dan Whatsapp. Dengan adanya media sosial tersebut banyaknya data yang ada berupa gambar, comment berupa text atau emoticon, video, dan lainnya, sehingga masyarakat bebas beropini. Dengan adanya analisis sentimen opini yang berkembang dan banyak di media sosial tersebut dapat menghasilkan data dan informasi yang bermanfaat. Dalam analisis sentimen diperlukannya algoritma klasifikasi data diantaranya Naive Bayes Classifier, Support Vector Machine, K-NN, RNN, C4.5, Lexicon Based, LDA Based Topic Modeling, dan beberapa algoritma lainnya. Artikel ini menelaah beberapa literature analisis sentimen pada media sosial. Saat ini media sosial yang sering digunakan dalam analisis adalah Twitter dan pengguna algoritma yang dapat meningkatkan tingkat akurasi adalah algoritma Naive Bayes Classifier dan Support Vector Machine. Hasil perhitungan akurasi klasifikasi data berbeda-beda terlihat pada data uji pada penelitian tersebut. Kata Kunci— Analisis Sentimen, Teks Mining, Naive Bayes Classifier
Interest rate liberalization and bank liquidity creation: evidence from China

This study aims to empirically analyze how interest rate liberalization affects bank liquidity creation, and investigate whether the relationship between them is linear. ,Based on panel data on 145 banks in China over the period 1997–2015, this paper regresses the econometric model by conducting feasible generalized least square estimation. ,The regression results show that, first, interest rate liberalization has a nonlinear impact on bank liquidity creation, and the relationship between them is inverted U-shaped. In other words, as interest rate liberalization progresses, bank liquidity creation increases first, and then decreases. Second, through the mediation effect tests, this study found that interest rate liberalization affects bank liquidity creation through bank risk-taking. That is, interest rate liberalization leads to changes in bank risk-taking, thus resulting in changes in bank liquidity creation. ,The effect of interest rate liberalization on bank liquidity creation is nonlinear, so promoting interest rate liberalization faces a trade-off because excessive bank liquidity creation may lead to asset price bubbles, while insufficient bank liquidity creation may inhibit economic growth. ,Interest rate liberalization has a significant impact on bank liquidity creation; therefore, bank liquidity creation should be added to the objective function of the regulator that determines interest rate liberalization reform in China. ,Interest rate liberalization has a direct impact on bank risk-taking, so the consequences of interest rate liberalization should be included in the framework of macro-prudential supervision. ,Interest rate liberalization is one of the most important financial reforms in China, yet its potential impact on firm-level bank liquidity is little explored. This paper attempts to fill the gap.
Merging Cluster Collaboration: Optical and Spectroscopic Survey of a Radio-selected Sample of 29 Merging Galaxy Clusters

Multi-band photometric and multi-object spectroscopic surveys of merging galaxy clusters allow for the characterization of the distributions of constituent DM and galaxy populations, constraints on the dynamics of the merging subclusters, and an understanding of galaxy evolution of member galaxies. We present deep photometric observations from Subaru/SuprimeCam and a catalog of 4431 spectroscopic galaxies from Keck/DEIMOS observations of 29 merging galaxy clusters ranging in redshift from z = 0.07 to 0.55. The ensemble is compiled based on the presence of radio relics, which highlight cluster-scale collisionless shocks in the intracluster medium. Together with the spectroscopic and photometric information, the velocities, timescales, and geometries of the respective merging events may be tightly constrained. In this preliminary analysis, the velocity distributions of 28 of the 29 clusters are shown to be well fit by single Gaussians. This indicates that radio-relic mergers largely occur transverse to the line of sight and/or near-apocenter. In this paper, we present our optical and spectroscopic surveys, preliminary results, and a discussion of the value of radio-relic mergers for developing accurate dynamical models of each system.
Monetary Policy, Fiscal Policy and Capital Structure

Instruments of monetary and fiscal policy are beyond the control of the management but they do influence the short-term as well as long-term decision making of the firm. Empirical studies with respect to their effect on financing decisions of the firm are somewhat under researched particularly in the context of developing countries. The aim of the study was to analyse the effect of these instruments on the financing decisions of the non-financial firms listed on PSX for the period 2008-2015. Fixed effect model was used to analyse the effect of instruments of monetary policy and fiscal policy on the financing decisions of firms. Based on sample of 338 firms, the findings of the study revealed that instruments of monetary policy and fiscal policy do influence the financing decisions of the firm. M2, tax revenue and government debt has a significant effect on the debt ratio of listed firms whereas real interest rate is insignificantly related. Moreover, the relationship between real interest rate, M2 and tax revenue and debt ratio is negative whereas in case of government debt it is positive.