Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of The Health Impact of Cooking Fuel Choice based on Logit and probit model

Dozens of previous literatures consider the health impact of solid fuel emissions from different perspectives. They focus on different groups of people based on different databases. We, in contrast, discuss the health outcome including self-rated health status, incidence rates of related diseases, ADL, IADL, and overweight according to certain standards through multiple regression models including OLS, logit and order probit, we found a statistically significant health impact. Demographic variable controls such as education level, age, marital status, and region where respondents live, as well as behavioral indicator such as smoking and drinking are included to reduce the model error. We, specifically, found that households with solid (i.e. dirty) fuels are particularly susceptible to poor self-rated health status, lower life satisfaction, higher incidence rate of chronic-lung diseases, heart-problems, hypertension and lower cognitive function, higher chance to be overweight and increased rigors to deal with daily activities. Dyslipidemia was not found to be correlated with solid cooking fuels. Gender and age basis was discussed. Women are found to benefit more from the use of non-solid (i.e. clean) cooking fuel, possibly because of their major responsibility for cooking in traditional Chinese household. Overall, our research sheds light on the health impact of indoor air pollution induced by cooking fuel emissions, particularly for those elderly people and orients the direction of further research.
The potential of biosolid application for the phytostabilisation of metals

AbstractThe aim of this study was to determine the suitability of a biosolid originated from food industry for land remediation as well as the influence of soil heavy metal bioavailability on the Pb, Zn and Cd stabilisation/removal potential of five grass species to assess their potential use as phytoremediation agents. A column device assessment was conducted using contaminated soil from a site surrounding the zinc smelter. The application of sewage sludge from food industry decreased the bioavailability of metals (particularly of Pb and Cd). The shoot biomass of the investigated plant species increased in the following order: Festuca rubra L. < Dactylis glomerata L. < Lolium perenne L. < Lolium westerwoldicum L. < Festuca arundinacea Schreb. An analysis of the biomass confirmed that used amendment promoted plant growth and significantly increased plant yield. A significant decrease in the metals’ uptake in plants was achieved, reflecting a decrease in bioavailability and stabilisation of heavy metals in.
Production, additive printing and mechanical testing of PLA/PHB material with different concentrations of TAC emollient

In order to carry out mechanical testing of samples printed by using additive technology, it is necessary to specify the parameters of the production of filaments, the parameters of 3D printing and the parameters of mechanical testing. In this article, I will discuss the production of filaments, additive technology for printing samples from PLA/PHB material used for detailed mechanical tests and subsequently for evaluation of these mechanical tests. The real-world application of PLA/PHB products bring great benefits. The aim of this paper is to perform mechanical tests on extruded PLA/PHB samples with three different TAC solvent concentrations. Samples were printed using additive technology. The comparison of the results of the pressure and tensile testing carried out on the apparatus also contributed to the success of the research.
Mobilities of Various Ions in Non-Parent Gases; Compilation of the Mobility Data Measured with the Injected-Ion Drift Tube Mass Spectrometer in Tokyo Metropolitan University.

This paper is a compilation of the ionic mobility data measured with the injected-ion drift tube mass spectrometer in Tokyo Metropolitan University till the end of 1977. The reduced mobilities are presented as a function of the ratio of the electric field strength to the gas density, E/N. The ratio ranges from a few Td (10-17volt·cm2) to few hundreds Td. The mobilities compiled here are as follows; in He; Li+, Ne+, Ar+, Kr+, N+, O+, N2+, CO+, CO2+ and N20+, in Ne; CO2+, N20+, in Ar; Li+, Kr+, CO2+ and N20+, in N2; Li+, Kr+, CO2+ and N20+, in CO2; Li+, NO+ and 02+. These mobility data are not only useful for the dirft tube study of ion-neutral reactions.but also providing the important information about the interaction potentials between the ions and the gas molecules.
Implementation of BICM-ID Receiver for Coded Modulation in DTMB-A

Digital terrestrial television multimedia broadcasting-advanced (DTMB-A) has just been announced as one of the second generation digital terrestrial television broadcasting (DTTB) standard. In comparison with digital terrestrial television multimedia broadcasting (DTMB) standard, DTMB-A has innovations and upgrades in many aspects, including coding and modulation, which are essential modules in DTTB system. Low-density parity-check (LDPC) codes and amplitude phase shift keying constellations with Gray-mapping (Gray-APSK) are adopted. Bit-interleaved coded modulation with iterative demapping (BICM-ID) is also introduced, which is proved capable of obtaining performance gain comparing to conventional independent demapping. However, few quantitative results of the BICM-ID receiver under DTMB-A standard are presented. Comparison between DTMB-A and other DTTB standards is also deficient. In addition, introduction of iterative demapping (ID) makes hardware implementation more complex. Processing and scheduling strategies are different and more hardware resources are demanded, which indicates that more detailed studies are needed. This paper gives quantitative results of BICM-ID and BICM receiver under DTMB-A standard via both performance analysis and simulation, and makes comparison with other DTTB standards. Utilization of hardware resources is also evaluated and presented. The results show that in typical DTMB-A terrestrial broadcasting mode (256 Gray-APSK and 2/3-rate long LDPC codes), BICM-ID receiver brings 0.4 dB gain in the additive white Gaussian noise (AWGN) channel comparing to BICM receiver and its performance is better than those under the same conditions of other standards. Register transfer level (RTL) synthesis from hardware implementation is carried out and shows that limited extra hardware resources are needed to support ID.
5G New Radio LDPC Extensions for Multimedia Broadcast Multicast Service

The 3rd Generation Partnership Project (3GPP) introduced the Further evolved Multimedia Broadcast Multicast Service (FeMBMS) transmission mode as an extension of the Long-Term Evolution (LTE) standard in 3GPP Release 14. FeMBMS enables the broadcast of services on a dedicated carrier and is currently being tested in numerous field trials worldwide. The New Radio (NR) air interface, standardized in 3GPP Release 15, introduced Low-Density Parity Check (LDPC) codes as a new forward error correction scheme, but does not yet support broadcast transmissions. The 5G NR standard is designed for unicast transmissions and only supports short codeword lengths.In contrast, the digital terrestrial television broadcast standards DVB-T2 and ATSC 3.0 both utilize LDPC codes with long codeword lengths as these have proven to increase the error correction performance of transmission systems.Similarly to FeMBMS, broadcast services based on 5G NR may be introduced in a future 3GPP release and would benefit from longer LDPC codeword lengths.The introduction of new codeword lengths into the 5G NR standard requires careful consideration of the existing coding procedures. After detailing the 5G NR LDPC coding chain, this paper presents a set of minimal extensions to the 5G NR standard required in order to realize longer LDPC codes. These extensions are evaluated and the achievable performance is compared to that of the LDPC codes standardized in the ATSC 3.0 specification.
Efficient Data Uploads to Satellite Formations by Rateless Codes and Adaptive Tracking

The uplinks from ground stations to CubeSats typically support only low data rates. Additionally, the noise level in orbit is relatively high in the frequency bands used. Therefore, the upload of high amounts of data, e.g. software updates, can take a long time. Especially in case of satellite formations where updates need to be delivered to multiple satellites. We propose to make use of wireless broadcasts, rateless codes, and adaptive tracking algorithms to implement efficient data uploads to satellite formations. In this paper, we present appropriate algorithms and evaluate their performance for the CloudCT satellite formation scenario by discrete-event simulations based on detailed models of the system and in-orbit measurements.
Optical Fiber Delay Lines in Microwave Photonics: Sensitivity to Temperature and Means to Reduce it

One of the key functionalities in microwave photonics is to be able to define controllable time delays during the signal processing. Optical fibers are often used to achieve this functionality, especially when a long delay or a widely-tunable delay is needed. However, the stability of this delay in the presence of environmental changes (e.g., temperature) has not, to the best of our knowledge, been reviewed yet. Here, we firstly discuss the impact of temperature-induced variations on the signal propagation time in optical fibers and its implications in microwave photonics. We compare the impact of the thermal sensitivity of various delay lines for applications in which the signal is transported from point A to point B, as well as for applications in which the propagation time through a fiber or the fiber dispersion is used to create a fixed or tunable delay. In the second part of the article we show the impact of fiber thermal sensitivity on a narrow-band microwave photonics filter made of standard single mode fiber (SSMF) and a hollow core fiber (HCF), which has significantly lower thermal sensitivity of propagation time to temperature. The central frequency of the band-pass filter changes almost 16 times more in the filter made of SSMF as compared to that of HCF, dictating very tight (0.05 °C) temperature stabilization for SSMF-based filters. On the basis of our thermal sensitivity analysis we conclude that HCFs are very promising for environmentally stable microwave photonics applications.
Thermal Performance Enhancement With DRX in 5G Millimeter Wave Communication System

The evolution of the communication and computation systems enables the user equipment (UE) to handle tremendous transmission data. However, the high-speed data processing also makes UEs release heat and might burn the chips inside the devices. The thermal issue would be more critical in millimeter-wave communications. The massive antenna arrays and the radio frequency modules not only drain the UE battery but also heat the devices. 3GPP also identified the thermal issue and suppressed heat generation by temporarily reducing UE capability in Release 15. In this work, instead of reducing the UE capability, we propose to apply the beam-aware Discontinuous Reception (DRX) mechanism to manage the power consumption and temperature of UEs simultaneously. We are the first to analyze the temperature for UEs with DRX configured. A semi-Markov model is provided, and we employ it to estimate the sleep ratio, packet delay, and steady temperature. We use a simulation program to verify the proposed analytical model. When comparing the beam-aware DRX with the baseline 5G NR DRX operation, we find that the beam-aware scheme reduces the steady temperature from $38.2^\circ C$ to $26.7^\circ C$ . The results show that Beam-Aware DRX could solve the thermal issue without sacrificing much performance of packet delivery latency.
Evaluating the Potential of Augmented Reality Interfaces for Exploring Underwater Historical Sites

Underwater cultural heritage sites represent an attractive and exciting experience for diving tourists, even if often it is complicated for them to understand the significance and value of the remains that are usually strongly damaged and covered by the marine organisms. Thanks to the recent advancements in technologies that overcome these problems, augmented reality is nowadays possible even in such harsh conditions, opening new possibilities for enhancing the diver’s experience. However, no user study has formally evaluated the usefulness and usability of augmented reality in open sea underwater environments. This paper presents two novel solutions for underwater augmented reality: a compact marker-based system for small areas, and a complex acoustic system for large areas. Both of them were deployed at an underwater cultural heritage site and evaluated by ten divers in experiments analyzing their perception and remembrance, interests, and user experience. For comparison, the same study was also performed with non-divers assessing the marker-based system on land. Results show that both systems allow divers to encounter new and exciting moments and provide valuable insights for underwater augmented reality applications.
A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Feature selection is an NP-hard combinatorial problem, in which the number of possible feature subsets increases exponentially with the number of features. In the case of large dimensionality, the goal of feature selection is to determine the smallest possible features considering the most informative subset. In this paper, we proposed a hybrid feature selection optimization model for Cancer Classification called, ENSVM. Our model is based on using the Elastic Net (EN) method that regulates and selects variables for gene selection of genomic microarray data. We applied three different optimization techniques namely Social Ski-Driver (SSD), Randomized SearchCV (RS) and Elastic NetCV (ENCV) for determining Elastic Net with traditional Support Vector Machines for classification. To evaluate the model, we compared the results of applying ENSVM to seven genomic microarray data with the SSD-SVM model and SVM with (RBF) kernel without any feature selection method. The results of the comparison revealed the effect of ENSVM in selecting the optimal feature subset that maximized the classification performance. Accordingly, minimizing the number of features is significant when analyzing high dimensional data for performance nevertheless accuracy. Moreover, the ENSVM model is superior compared with the SSD-SVM model.
A Recommender Systems’ algorithm evaluation using the Lenskit library and MovieLens databases

A wide variety of algorithms can be found in the recommender system’s literature for computing predictions and obtaining recommendation lists. These algorithms’ performance can be evaluated by numerous metrics depending on which business goal is intended to be optimized. However, performance can vary drastically depending on the selected algorithm as well as the selected metric. In addition, algorithmic performance can be heavily influenced by the experimental setup such as: dataset, cross-validation strategy, recommender framework and the model hyper-parameters chosen. In this work we will compare nine different algorithms used for generating recommendations across seven different metrics. One vital aspect of our analysis is to provide a detailed and clear explanation of the methodology carried out as well as the experimental setup in order to ensure future reproducibility. Moreover, performance and computation times will be introduced for further comparison. Finally, optimal model hyper-parameter combinations will be presented for benchmarking purposes.
Highly Efficient Liquid-Quantum Dot/Melamine- Modified Urea-Formaldehyde Microcapsules for White Light-Emitting Diodes

A liquid matrix is beneficial for improving the luminescence performance of quantum dots (QDs), although it is difficult to be packaged with light-emitting diode (LED) chips. In this study, liquid-QDs with chlorobenzene solution and melamine-modified urea-formaldehyde (MUF) were used as the core and shell structures, respectively, successfully yielding liquid-QD/MUF microcapsules for white LED packaging. The concentration of the liquid-QDs in the microcapsules was optimized, demonstrating an increase of 15.9% in the luminous efficiency, as compared to the traditional solid-QDs at a specific QD energy proportion. The efficiency roll-off can be attributed to the increased reabsorption and back-scattering loss at higher liquid-QDs and microcapsules concentrations. It was also confirmed that the liquid-QD/MUF microcapsules have an excellent thermal stability below 200 °C, owing to the protection of the MUF shells. Finally, white LEDs compounding the red liquid-QD/MUF microcapsules and YAG phosphor particles were fabricated, achieving a high luminous efficiency of 149.2 lm/W at 20 mA, correlated color temperature of 3301 K, and color rendering index of 88.8. This study will open new research avenues for liquid-QD packaging in white LEDs.
Control Approaches for Enhanced sharing of Unbalanced Powers amongst Inverter based DGs in Islanded AC Microgrids

Inverter based distributed generators (DGs) in microgrids can be controlled in either of the centralized, distributed or de-centralized approaches. Droop control involves the control of these inverters so as to mimic the behaviour of synchronous generators as in the conventional power system. The strategy is advantageous as it is completely based on local measurements and does not require communication. Under unbalanced loading conditions, only the fundamental powers can be shared in proportion to the DG ratings, when the conventional droop control is employed. The work presented here proposes five modifications for the well known conductance vs. negative sequence power (G vs, Q-) based droop control for enhanced sharing the asymmetrical/imbalance currents among various DGs and can be integrated with the existing conventional or inverse droop controls to improve the power quality within the microgrid. The various control modifications are discussed and simulation results for the same have been presented to validate their performance.
Secrecy rate analysis for reconfigurable intelligent surface-assisted mimo communications with statistical CSI

In this paper, a reconfigurable intelligent surface (RIS)-assisted MIMO wireless secure communication system is considered, in which a base station (BS) equipped with multiple antennas exploits statistical channel state information to communicate with a legitimate multi-antenna user, in the presence of an eavesdropper, also equipped with multiple antennas. We firstly obtain an analytical expression of the ergodic secrecy rate based on the results of large-dimensional random matrix theory. Then, a jointly alternating optimization algorithm with the method of Taylor series expansion and the projected gradient ascent method is proposed to design the transmit covariance matrix at the BS, as well as the diagonal phase- shifting matrix to maximize the ergodic secrecy rate. Simulations are conducted to demonstrate the accuracy of the derived analytical expressions, as well as the superior performance of our proposed algorithm.
Hyper-parameter optimization with REINFORCE and Masked Attention Auto-regressive Density Estimators

Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS). In this paper, we demonstrate how its performance can be improved by using a simplified Transformer block to model the policy network. The simplified Transformer uses a 2-stream attention-based mechanism to model hyper-parameter dependencies while avoiding layer normalization and position encoding. We posit that this parsimonious design balances model complexity against expressiveness, making it suitable for discovering optimal architectures in high-dimensional search spaces with limited exploration budgets. We demonstrate how the algorithm’s performance can be further improved by a) using an actor-critic style algorithm instead of plain vanilla policy gradient and b) ensembling Transformer blocks with shared parameters, each block conditioned on a different auto-regressive factorization order. Our algorithm works well as both a NAS and generic hyper-parameter optimization (HPO) algorithm: it outperformed most algorithms on NAS-Bench-101 [1], a public data-set for benchmarking NAS algorithms. In particular, it outperformed RL based methods that use alternate architectures to model the policy network, underlining the value of using attention-based networks in this setting. As a generic HPO algorithm, it outperformed Random Search in discovering more accurate multi-layer perceptron model architectures across 2 regression tasks. We have adhered to guidelines listed in Lindauer and Hutter [2] while designing experiments and reporting results.
Stock Price Prediction Under Anomalous Circumstances

The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans", such as the COVID-19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout the history. Affected by the whole circumstance, the stock prices of individual corporations also plummeted by rates that were never predicted by any pre-developed forecasting models. It reveals that there was a lack of satisfactory models that could predict the changes of stocks prices when catastrophic, highly unlikely events occur. To fill the void of such models and to help prevent investors from heavy losses during uncertain times, this paper aims to capture the movement pattern of stock prices under anomalous circumstances. First, we detect outliers in sequential stock prices by fitting a standard ARIMA model and identifying the points where predictions deviate significantly from actual values. With the selected data points, we train ARIMA and LSTM models at the single-stock level, industry level, and the general market level, respectively. Since the public moods affect the stock market tremendously, a sentiment analysis is also incorporated into the models in the form of sentiment scores, which are converted from comments about specific stocks on Reddit. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98% which can be used to optimize existing prediction methodologies.
Development of A Wireless Power Transmission System for Agriculture Sensor Devices

The integration of wireless power technology in large sensor networks is highly sought for in many applications, including agriculture. This is due to the accessibility and lack of wiring complexity such technologies have to offer. In an agricultural setting, the working environment can be harsh on sensing equipment due to factors that include weather, constant deconstruction and re-installation with the changing plant cycles, and vehicle traffic. Since many agriculture plots reside in difficult to access locations, the use of self-sufficient energy capturing methods have become popular. These contemporary methods generally rely on the collection of solar, wind, or ambient radio waves to charge battery banks connected to the sensing device. These methods have major limitations as sunlight can be shadowed as crops mature, wind creates obstacles for equipment to navigate, and radio frequencies do not penetrate well through soil or plants. This ultimately reduces the quantity of sensors that can be instrumented throughout a field. To address such limitations, a new wireless power transfer method will be presented that utilizes a buried transmitter to generate conduction currents through the soil to power distant sensing devices scattered throughout a field. Impedance spectra of the soil is used to determine the optimal depth of the transmitter. The power capabilities of the system are demonstrated by operating, without a battery, a moisture sensor connected to a microcontroller at a 10 m distance from the transmitter.