Miracle-dz/UniFAR-FTUs huggingface dataset:
An adaptive network prediction and judgment method for unstable communication on moving medical vehicles

To evaluate the wireless network condition on moving medial vehicles and make reasonable judgment among multiple network channels, an automatic network judgment method is presented for unstable communication environment based on adaptive network speed analysis and prediction method. Historical network observation data are collected; time series analysis method and ARMA model are employed to build prediction model using historical data statistics with periodical update mechanism. Predicted network speed value is then compared with real speed value and a criterion to identify the condition of network is summarized from comparison result analysis. Experiments are conducted in both normal network and interfered network situations. Empirical studies results show that the adaptive prediction method could be used for wireless network speed prediction on moving vehicles. Under different network environments, the fit value of each group of data varies and this value could be used for network problem identification.
Comparation of predictive value of CAT and change in CAT in the short term for future exacerbation of chronic obstructive pulmonary disease

Abstract Purpose Our study aimed to compare the predictive value of the COPD Assessment Test (CAT) score at baseline and short-term change in CAT for future exacerbations in chronic obstructive pulmonary disease (COPD) patients. Methods This was a multicentre prospective study. Patients with COPD were recruited into the study and followed up for one year. CAT score and exacerbation in the previous year were collected at baseline. Change in CAT was defined as CAT score changing between baseline and the 6-month follow-up. Exacerbation was recorded during the one-year follow-up from 0th to 12th month. Result A total of 536 patients were enrolled for final analysis. The mean baseline CAT score was 14.5 ± 6.6 and the median (IQR) change in CAT was −2 (8). On Cox regression analysis, baseline CAT score, change in CAT and history of exacerbation were independent risk factors for exacerbation in the one-year follow-up. Compared with the r value of correlation between baseline CAT score and frequency of exacerbations during the one-year follow-up (r = 0.286, p < .001), that correlation between the change in CAT and frequency of exacerbations during follow-up was higher (r = 0.421, p < .001). The receiver operating characteristic (ROC) curves showed that change in CAT had a better predictive capacity for future exacerbation than baseline CAT (0.789 versus 0.609, p = .001). The ROC showed that change in CAT also had a better predictive capacity for future exacerbation than exacerbation in the previous year (0.789 versus 0.689, p = .011). Conclusion The correlation between baseline CAT score and future exacerbation was weak, however, the correlation between change in CAT and future exacerbation was moderate. Change in CAT in the short term had a better predictive value for future exacerbations of COPD than baseline CAT and exacerbation in the previous year.
Factors affecting turnover intentions among Millennial public relations professionals: The Latin American case

As the Millennial generation becomes the largest generation of the global workforce, it is vital that organizations understand Millennials’ work values, motivational factors, and expectations to adjust existing employee retention techniques that may not suit this generation. The purpose of this study is to identify and analyze the major factors that affect employee retention among Latin American Millennials working in the public relations industry. Additionally, we examine generational differences in work attitudes across three generations, with an emphasis on the nature of the similarities and differences of Millennials when compared to prior generations. Findings show a highly significant positive relationship among job satisfaction, trust in the organization, job engagement, excellent leader performance, and supportive organizational culture and Latin American Millennial turnover intentions. Compared to Boomers and Gen-Xers, Millennials reported lower levels of overall job satisfaction, work engagement, and organizational trust.
Use characteristics and demographics of rural transit riders: a case study in Tennessee

ABSTRACT Intercity bus (ICB), deviated fixed route transit (DFRT) and demand responsive transit (DRT) are three major modes of rural public transportation. This paper focuses on the characteristics and motivations of DFRT and DRT riders, compared to non-riders, in Tennessee. A rural DFRT rider survey, a rural DRT rider survey and a rural (non-rider) resident survey were performed. It is found that DFRT and DRT riders have similar demographics to ICB riders. The most common trip purpose for DFRT and DRT passengers is medical care, which is different from ICB trips. Ninety percent of the riders have difficulty finding alternative transportation modes, suggesting they are captive riders, not choice riders. Regression results indicate that people choosing transit modes tend to have lower personal and household income, own fewer cars, to not be homeowners, and be of non-white race. Rural residents who receive more education are more likely to be open-minded to use rural transit.
Optimizing multimedia content delivery over next-generation optical networks

i Declaration I declare that this thesis has not been submitted as an exercise for a degree at this or any other university and is entirely my own work. I agree to deposit this thesis in the University's open access institutional repository or allow the Library to do so on my behalf, subject to Irish Copyright Legislation and Trinity College Library conditions of use and acknowledgement. iii Summary This thesis analyzes the performance of a Peer-to-Peer (P2P) multimedia content delivery system for a network architecture based on next-generation Passive Optical Networks (PONs). A PON is an optical access technology that is able to deliver high bandwidth capacities at a fraction of the cost of traditional point-to-point fiber solutions; this is achieved by sharing the same feeder fiber among several customers through the use of optical splitters. Established standards such as GPON and EPON have a reach from the Central Office of about 20 Km and a fan-out of around 32-64 users. Next-generation PONs aim to increase this reach, in order to enable the consolidation of central offices; to increase the split size, in order to help reducing the cost per customer; and to increase the bandwidth capacity to multiple 10 Gbps channels. One of the reasons why operators are investing on Fiber-to-the-X (FTTX) solutions is to increase the capacity of the access section in order to remove what traditionally was the bandwidth bottleneck of the network. Over the last decade multimedia streaming services have become increasingly popular, with companies like Netflix, Amazon Prime and many more reaching millions of customers and billions of revenues. With both the number of active subscribers and the quality of the streamed videos steadily on the rise, network infrastructures are being put under an increasing strain to support these data-intensive services. Several reports have shown that the vast majority of the data currently traversing the Internet on a daily basis is related in one form or another to multimedia content retrieval. Increasing the capacity in the access is certainly going to remove one of the obstacles to high-definition streaming of multimedia content. However, as users start to take advantage of the increased bandwidth allowance granted to them by fiber, the aggregation section of the network (i.e., the core) will start to suffer; and as we do not have any better technology than fiber, the only way we have to improve core capacity is to stack …
OPTIMALISASI PEMANFAATAN ENERGI LISTRIK TENAGA SURYA SKALA RUMAH TANGGA

Indonesia as a country has various types of energy resources a sufficient number of abundance. Location of Indonesia is in the Khatulistiwa region, those regions Indonesia would always be sunlit for 10 until 12 hour a day. Indonesia as a tropic country has development potential and energy utilization as one of many solar energy conversion systems, this solar energy conversion systems can be applied to resolve the depletion fuel reserves existing conventional. Data Directorate General of Electricity and Energy Development in 1997. Electricity installed capacity solar power in Indonesia reached 0,88 MW of the available potential 1.2 x 109 MW. Types of Monocristalline solar cell is the most efficient panels, generate electric power per unit area of the highest. Have efficiences up to 15%. The downside of this type of panel is will not work either in the light of the sun is less (shady), efficiency will drop dramatically in cloudy weather. Photovoltaic cell always covered by a cover which is derived from a glass. Like other glass items, then the optical output from photovoltaic cell also heavily influenced by the orientation to the sun due to variations in the angle of reflection glass.
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Nutrient Availability for Lactuca sativa Cultivated in an Amended Peatland: An Ionic Exchange Study

Few conservation strategies have been applied to cultivated peatland. This field study over one growth cycle of Lactuca sativa examined the effect of plant-based, high-C/N-ratio amendments in a real farming situation on peatland. Plant Root Simulator (PRS®) probes were used directly in the field to assess the impacts of incorporating Miscanthus x giganteus straw and Salix miyabeana chips on nutrient availability for lettuce. The results showed that lettuce yield decreased by 35% in the miscanthus straw treatment and by 14% in the willow chip treatment. In addition, the nitrogen flux rate was severely reduced during crop growth (75% reduction) and the plant N uptake index was much lower in the amended treatments than in the control. The phosphorus supply rate was also significantly lower (24% reduction) in the willow treatment. The influence of sampling zone was significant as well, with most macro-nutrients being depleted in the root zone and most micronutrients being mobilized. Additional work is needed to optimize the proposed conservation strategy and investigate the effects of consecutive years of soil amendment on different vegetable crops and in different types of cultivated peatlands to confirm and generalize the findings of this study. Future field studies should also explore the long-term carbon dynamics under plant-based, high-C/N-ratio amendments to determine if they can offset annual C losses.
Recognition of Eudaemonic and Hedonic Qualities from Song Lyrics

This work addresses the research question of how to use machine learning methods in order to develop a computational model which is able to predict the hedonic and eudaemonic qualities of songs from song lyrics. We conducted a survey on 1991 users (1904F, 87M) with an average age of 28 years (SD = 9 years), where we gathered demographics, big five personality test, eudaemonic and hedonic song tendencies, overall music sophistication and data for song classification. After that we gathered song lyrics by web scraping. Song lyrics were normalized, tokenized, lemmatized and stemmed, and a TF-IDF scored was assigned to each word in a song. The collected data was transformed into meaningful data and fed to machine learning models. We used classification and regression machine learning models. The classification models that were used are kNN, logistic regression, random forest, bagging, SVC and ridge, while the regressor models that were used are random forest and XGBoost. We created two models, one hedonic and one eudaemonic. The best models were achieved with bagging classifiers for both machine learning models, while the random forest regressor gave the best results out of the regressors. Our preliminary results indicate that there exists a connection between hedonia and eudaemonia and song lyrics, and that we can classify songs into highly hedonic or highly eudaemonic, with the created models. This study also showed that there exists a difference between eudaemonic and hedonic tendencies between genders, as well as that there exists a strong connection between emotions and eudaemonia.
SHORT COMMUNICATION Intracellular Catabolism of an Antibody Drug Conjugate with a Non-Cleavable Linker

Antibody drug conjugates are emerging as a powerful class of anti-tumor agents with efficacy across a range of cancers; therefore, understanding the disposition of this class of therapeutic is crucial. Reported here, is a method to enrich a specific organelle (lysosome) to understand the catabolism of an anti-CD70 Ab-MCC-DM1, an antibody drug conjugate with a non-cleavable linker. With such techniques a higher degree of concentration-activity relationship can be established for in vitro cell lines which can aid in understanding the resultant catabolite concentrations necessary to exert activity.
Explanatory Artificial Intelligence, Results and Prospects

Describes the DARPA Explanatory Artificial Intelligence (XAI) program, which seeks to create artificial intelligence systems whose learning models and solutions can be understood and properly validated by end users. DARPA considers XAI as artificial intelligence systems AI that can explain their decision to a human user, characterize their strengths and weaknesses, and how they will behave in the future. To achieve this goal, methods have been developed for constructing explainable models of intelligent systems that are effective explanatory interfaces and psychological models of users for effective explanation. The XAI development teams are described that solve these three problems by creating and developing explainable machine learning (ML) technologies, developing principles, strategies, and methods of human-computer interaction for obtaining effective explanations and applying psychological explanatory theories to assess the quality of XAI systems.
Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

Simple Summary We reviewed the literature on the publicly available datasets used to automatically recognise emotion and affect using artificial intelligence (AI) techniques. We were particularly interested in databases with cardiovascular (CV) data. Additionally, we assessed the quality of the included papers. We searched the sources until 31 August 2020. Each step of identification was carried out independently by two reviewers to maintain the credibility of our review. In case of disagreement, we discussed them. Each action was first planned and described in a protocol that we posted on the Open Science Framework (OSF) platform. We selected 18 works focused on providing datasets of CV signals for automated affect and emotion recognition. In total, data for 812 participants aged 17 to 47 were analysed. The most frequently recorded signal was electrocardiography. The authors most often used video stimulation. Noticeably, we did not find much necessary information in many of the works, resulting in mainly low quality among included papers. Researchers in this field should focus more on how they carry out experiments. Abstract Our review aimed to assess the current state and quality of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI), and emphasising cardiovascular (CV) signals. The quality of such datasets is essential to create replicable systems for future work to grow. We investigated nine sources up to 31 August 2020, using a developed search strategy, including studies considering the use of AI in AAER based on CV signals. Two independent reviewers performed the screening of identified records, full-text assessment, data extraction, and credibility. All discrepancies were resolved by discussion. We descriptively synthesised the results and assessed their credibility. The protocol was registered on the Open Science Framework (OSF) platform. Eighteen records out of 195 were selected from 4649 records, focusing on datasets containing CV signals for AAER. Included papers analysed and shared data of 812 participants aged 17 to 47. Electrocardiography was the most explored signal (83.33% of datasets). Authors utilised video stimulation most frequently (52.38% of experiments). Despite these results, much information was not reported by researchers. The quality of the analysed papers was mainly low. Researchers in the field should concentrate more on methodology.
Android Application for Food Allergy Detection using Machine Learning Technique

: Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an earsly age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. Here, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life. This application will be helpful to predict composition of food using Machine Leaning Technique and will be able to predict the item in the composition that causes Allergy for the concerned person.
Reconstruction of low voltage networks: From GIS data To power flow models

(paper accepted) The creation of realistic models of low voltage (LV) distribution feeders is a fundamental step to understand the potential impacts from low carbon technologies (LCTs). Unfortunately, Distribution Network Operators (DNOs) do not, in general produce these models mainly because of the historic passive nature of LV circuits. Available information at LV level is in most cases limited to Geographic Information Systems (GIS) typically produced for asset management purposes. Although this GIS data is useful, is likely to have some issues due to the large amount of information handled. One of the main problems sometimes found is the network connectivity, i.e., feeder segments not adequately connected, which can lead to a model unsuitable for power flow analysis. This work proposes a systematic, practical and implementable methodology to achieve the full reconnection of LV feeders and, as a result, the production of suitable computer-based models. This proposed methodology can help DNOs around the world facing similar challenges. Indeed, it has already been successfully applied to create more than five hundred real residential, underground UK LV feeders.
Intelligent Agent Based Traffic Signal Control on Isolated Intersections

– The purpose of this paper is to develop an adaptive signal control strategy on isolated urban intersections. An innovative approach to defining the set of states dependent on the actual and primarily observed parameters has been introduced. Тhe Q– learning algorithm has been applied. The developed self-learning adaptive signal strategy has been tested on a reаl intersection. The intelligent agent results have been compared to those in cases of fixed-time and actuated control. Regarding the average total delay, the total number of stops and the total throughput, the best results have been obtained for unknown traffic demand and over-capacity.
Biomarkers of Breast Cancer

Introduction: Breast cancer treatment has experienced several changes in the past decades due to the discovery of specific prognostic and predictive biomarkers that enable the application of more individualized therapies to different molecular subgroups. This study determined biomarkers of breast cancer. Material and methods: This was a hospital based cross sectional observational study conducted for one year from 1st June 2019 to 31st May 2020 in the Department of Pathology, Indira Gandhi Medical College, Shimla. Results: 31 (60.8%) cases were ER positive and 20 (39.2%) were ER negative as per Allred Scoring. In our study, 28 (54.9%) cases were PR positive and 23 (45.1%) were PR negative as per Allred Scoring. Conclusion: Assessment of ER and PR is important for prognosis.
The Solar Orbiter SPICE instrument An extreme UV imaging spectrometer

Aims. The Spectral Imaging of the Coronal Environment (SPICE) instrument is a high-resolution imaging spectrometer operating at extreme ultraviolet (EUV) wavelengths. In this paper, we present the concept, design, and pre-launch performance of this facility instrument on the ESA / NASA Solar Orbiter mission. Methods. The goal of this paper is to give prospective users a better understanding of the possible types of observations, the data acquisition, and the sources that contribute to the instrument’s signal. Results. The paper discusses the science objectives, with a focus on the SPICE-speciﬁc aspects, before presenting the instrument’s design, including optical, mechanical, thermal, and electronics aspects. This is followed by a characterisation and calibration of the instrument’s performance. The paper concludes with descriptions of the operations concept and data processing. Conclusions. The performance measurements of the various instrument parameters meet the requirements derived from the mission’s science objectives. The SPICE instrument is ready to perform measurements that will provide vital contributions to the scientiﬁc success of the Solar Orbiter mission.