Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Relationship between Fractional Exhaled Nitric Oxide (FeNO) with Indoor PM10, PM2.5 and NO2 in Suburban and Urban Schools

Numerous epidemiological studies have evaluated the association of fractional exhaled nitric oxide (FeNO) and indoor air pollutants, but limited information available of the risks between schools located in suburban and urban areas. We therefore investigated the association of FeNO levels with indoor particulate matter (PM10 and PM2.5), and nitrogen dioxide (NO2) exposure in suburban and urban school areas. A comparative cross-sectional study was undertaken among secondary school students in eight schools located in the suburban and urban areas in the district of Hulu Langat, Selangor, Malaysia. A total of 470 school children (aged 14 years old) were randomly selected, their FeNO levels were measured, and allergic skin prick tests were conducted. The PM2.5, PM10, NO2, and carbon dioxide (CO2), temperature, and relative humidity were measured inside the classrooms. We found that the median of FeNO in the school children from urban areas (22.0 ppb, IQR = 32.0) were slightly higher as compared to the suburban group (19.5 ppb, IQR = 24.0). After adjustment of potential confounders, the two-level hierarchical multiple logistic regression models showed that the concentrations of PM2.5 were significantly associated with elevated of FeNO (>20 ppb) in school children from suburban (OR = 1.42, 95% CI = 1.17–1.72) and urban (OR = 1.30, 95% CI = 1.10–1.91) areas. Despite the concentrations of NO2 being below the local and international recommendation guidelines, NO2 was found to be significantly associated with the elevated FeNO levels among school children from suburban areas (OR = 1.11, 95% CI = 1.06–1.17). The findings of this study support the evidence of indoor pollutants in the school micro-environment associated with FeNO levels among school children from suburban and urban areas.
Electronic Cigarette Topography: A Quantitative Validation Study of the Wireless Personal Use Monitor

i Acknowledgements . ii 1 Chapter 1: Introduction . 1 1.1 Electronic Cigarette Functionality . 1 1.2 Types of Electronic Cigarettes . 2 1.3 History and Growing Use of Electronic Cigarettes . 4 1.4 FDA Regulation . 5 1.5 Current Methods for Topography Measurement . 6 2 Chapter 2: RIT Equipment Background . 8 2.1 Programmable Emissions System (PES) . 8 2.1.1 Alicat Flow Meter – The Primary Instrument . 10 2.1.2 PES Hardware Set-Up . 12 2.1.3 PES Software Set-Up . 14 2.1.4 Running the PES to Characterize the wPUM . 15 2.2 Information about the wPUM . 16 2.2.1 How the wPUM Hardware Works . 16 2.2.2 How the wPUM Software Works . 21 3 Chapter 3: Literature Review . 31 3.1 Emissions Studies . 31 3.1.1 No Reported Reference Usage Data . 31 3.1.2 Puffing Machine Studies Driven by Standard Testing Protocols . 42 3.1.3 Puffing Machine Studies Driven by Referenced Topography Data . 45 3.1.4 Measured Testing Protocols . 51
Prediction of local scour depth downstream of sluice gates using data-driven models

Abstract Numerous investigations have already been conducted to characterize the effects of influencing parameters on scouring process in order to derive an accurate predictive equation of local scour depth downstream of a sluice gate. However, due to the complexity of the scour phenomena, available empirical equations, originally derived on the basis of regressive methods, do not always offer accurate scour depth prediction. Previous studies have clarified that artificial intelligence techniques may be alternatively considered to solve a complex phenomenon like local scouring. In present study, Gene-Expression Programming, Model Tree (MT), and Evolutionary Polynomial Regression approaches, which are among the newest artificial intelligence approaches, were evaluated for prediction of local scour depth downstream of sluice gates with an apron. The input variables affecting the scour depth are sediment size and its gradation, apron length, sluice gate opening, and the flow conditions downstream of the sluice gate. Six non-dimensional variables were selected to determine a functional relationship between the input and output parameters. Results of training and testing stages indicated that MT approach yielded the most precise predictions in comparison with the other proposed models and conventional equations.
Design and High-Order Precision Numerical Implementation of Fractional-Order PI Controller for PMSM Speed System Based on FPGA

In this paper, the design of a fractional-order proportional integral (FOPI) controller and integer-order (IOPI) controller are compared for the permanent magnet synchronous motor (PMSM) speed regulation system. A high-precision implementation method of a fractional-order proportional integral (FOPI) controller is proposed in this work. Three commonly used numerical implementation methods of fractional operators are investigated and compared for comprehensively evaluating the numerical implementation performance in this work. Furthermore, for the impulse response invariant implementation method, the effects of different discretization orders on the control performance of the system are compared. The high-order fractional-order controller can be implemented accurately in a control system with the field-programmable gate array (FPGA) with the capability of parallel calculation. The simulation and experimental results show that the high-precision numerical implementation method of the designed high-order FOPI controller has better performance than the ordinary precision fractional operation implementation method and traditional order integer order PI controller.
Towards Extending Forward Kinematic Models on Hyper-Redundant Manipulator to Cooperative Bionic Arms

Forward Kinematics is a stepping stone towards finding an inverse solution and subsequently a dynamic model of a robot. Hence a study and comparison of various Forward Kinematic Models (FKMs) is necessary for robot design. This paper deals with comparison of three FKMs on the same hyper-redundant Compact Bionic Handling Assistant (CBHA) manipulator under same conditions. The aim of this study is to project on modeling cooperative bionic manipulators. Two of these methods are quantitative methods, Arc Geometry HTM (Homogeneous Transformation Matrix) Method and Dual Quaternion Method, while the other one is Hybrid Method which uses both quantitative as well as qualitative approach. The methods are compared theoretically and experimental results are discussed to add further insight to the comparison. HTM is the widely used and accepted technique, is taken as reference and trajectory deviation in other techniques are compared with respect to HTM. Which method allows obtaining an accurate kinematic behavior of the CBHA, controlled in the real-time.
Opinion: Insights into updating Ambient Air Quality Directive 2008/50/EC

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality
Non-Destructive Inspection of Twisted Wire in Resin Cover Using Terahertz Wave

SUMMARY Weproposedanewmethodforevaluatingthedeterioration ofmessengerwiresbyusingterahertzwaves. Weuseterahertztime-domain spectroscopy to measure several twisted wire samples with diﬀerent levels of deterioration. We ﬁnd that each twisted wire sample had a diﬀerent distribution of reﬂection intensity which was due to the wires’ twist structure. We show that it is possible to assess the degradation from the straight lines present in the reﬂection intensity distribution image. Furthermore, it was conﬁrmed that our method can be applied to wire covered with resin.
A novel design of the sample holder for broadband complex permittivity measurements

The paper provides a novel design of the sample holder for the complex permittivity determination of nonmagnetic linear dielectric materials by measuring reflection and transmission coefficients. The sample holder is made of the common N connector female-female adapter. The design described is very simple and compact. Especially the price of our design is much cheaper than price for commonly used airline coaxial waveguide from verification kit with comparative measurement accuracy. Several plastics in the frequency range from 0.4 GHz up to 18 GHz were measured and reached results are presented and discussed.
Effects of sucrose and lactose as partial replacement to corn in lactating dairy cow diets: a review

Abstract Carbohydrates are one of the three macronutrients that provides energy in diets and are classified by their structures. Starch is a nonstructural carbohydrate and polysaccharide made of glucose monomers used for storage in plant cells. When starch makes up greater than 30% of the DM in diets there can be adverse effects on NDF digestibility due to decreases in ruminal pH. Sugars are water soluble carbohydrates that consist of monosaccharide and disaccharide units. Sugars ferment faster than starch because microorganisms in the rumen can ferment carbohydrates at different rates depending on their structure; however, this has not been shown to have negative effects on the ruminal pH. Sources of sugars such as molasses (sucrose) or whey (lactose) can be included in the diet as a partial replacement for starch in dairy cow diets. The purpose of replacing starch with sugars in a diet would be to add differing sources of carbohydrates in the diet to allow for continual fermentation of carbohydrates by the microorganisms in the rumen. It has been seen in studies and previous literature that the partial replacement of starch with sugars has the potential to maintain the ruminal environment and milk yield and composition in dairy cows without reducing NDF digestibility. The objective of this review is to evaluate the effects of partially replacing starch with sugars in dairy diets and its implication on ruminal fermentation, nutrient utilization, milk production, and feeding replacement strategy.
Mitigation Measures for Windfarm Effects on Radar Systems

Windfarms can have a signi ﬁ cant impact on radar systems, especially air surveillance radar. This is because it is usually designed to show only moving targets and cancel out stationary objects. However, the rotation of wind turbine blades can be detected by radar as a false ﬂ ight or target. Clutter or interference caused by windfarms can reduce radar sensitivity in critical regions, making real targets disappear. This could in turn a ﬀ ect the deployment of windfarms and lead to the cancellation of future renewable energy projects and plans. This paper presents the di ﬀ erent strategies and techniques used to mitigate the windfarm impact on radar systems. Precisely, we aim throughout this article to classify the main currently adopted approaches, view the recent trends and research directions in this ﬁ eld, and try to trace the advantages and drawbacks along with the challenges of deploying such schemes.
System for rugged surface detection based on MEMS inertial sensor signals analysis

The paper reports an application of the accelerometer for the sensing of rugged surface detection. MEMS accelerometers were investigated for steering control of the autonomous floor-cleaning robot to perform cleaning. Accelerometer signals were used to support signals from other ultrasound and vision sensors to detect the type of the floor and distinguish between rough and smooth floor. The main aim of the developed control system for an autonomous robot was to improve the detection of the floor type and investigate the use of the accelerometer for solving this problem. The test results have shown that the proposed system can be equipped with accelerometer sensor and reduce the error detection for floor type during cleaning.
A Bioeconomic Projection of Climate‐Induced Wildfire Risk in the Forest Sector

Under the influence of climate change, wildfire regimes are expected to intensify and expand to new areas, increasing threats to natural and socioeconomic assets. We explore the environmental and economic implications for the forest sector of climate‐induced changes in wildfire regimes. To retain genericity while considering local determinants, we focus on the regional level and take Mediterranean France as an example. Coupling a bioeconomic forest sector model and a model of wildfire activity, we perform spatially explicit simulations under various levels of radiative forcing. By using a probabilistic framework, we also assess the propagation of several sources of uncertainty to the forest sector, considering both climate‐induced uncertainty and the intrinsic stochasticity of the fire process. By the end of the century, summer burned areas increase by up to 55%, causing moderate losses of merchantable timber and forest carbon stocks, with cascading impacts for industrial activities and climate mitigation in the forest sector. Implications for industries remain limited, but we observe price increases, especially for softwoods, as well as spatially differentiated changes in producer welfare. Inter‐annual fluctuations explain most of uncertainty in wildfire activity, but their impacts on the forest sector are quickly dampened. Over time, owing to the cumulative nature of wildfire impacts on forest resources, uncertainty related to climate warming, climate models’ response and stochasticity intrinsic to the wildfire phenomenon strongly increase in relative importance. Results reassert the need to consider multiple futures in prospective assessments, including uncertainty inherent to natural processes, often omitted in large‐scale economic assessments.
Collaborative Graph Learning for Session-based Recommendation

Session-based recommendation (SBR), which mainly relies on a user’s limited interactions with items to generate recommendations, is a widely investigated task. Existing methods often apply RNNs or GNNs to model user’s sequential behavior or transition relationship between items to capture her current preference. For training such models, the supervision signals are merely generated from the sequential interactions inside a session, neglecting the correlations of different sessions, which we argue can provide additional supervisions for learning the item representations. Moreover, previous methods mainly adopt the cross-entropy loss for training, where the user’s ground truth preference distribution towards items is regarded as a one-hot vector of the target item, easily making the network over-confident and leading to a serious overfitting problem. Thus, in this article, we propose a Collaborative Graph Learning (CGL) approach for session-based recommendation. CGL first applies the Gated Graph Neural Networks (GGNNs) to learn item embeddings and then is trained by considering both the main supervision as well as the self-supervision signals simultaneously. The main supervisions are produced by the sequential order while the self-supervisions are derived from the global graph constructed by all sessions. In addition, to prevent overfitting, we propose a Target-aware Label Confusion (TLC) learning method in the main supervised component. Extensive experiments are conducted on three publicly available datasets, i.e., Retailrocket, Diginetica, and Gowalla. The experimental results show that CGL can outperform the state-of-the-art baselines in terms of Recall and MRR.
Multi-Language Sentiment Analysis for Hotel Reviews

Touristes and traveler use avariety of information sources (e.g. travelportals, blogs, or social networking sites like twitter) to help them decide for a hotel room. These sources all contain highly subjective text that expresses the opinions of many. We took a preliminary view on user generated hotel reviews from two travel portals in English and Thai. We developed a taxonomy of features and specifically investigated how accurately they can be predicted with three classification methods. The results indicate that support vector machines perform best for this specific domain.
High-Agility, Miniaturized Attitude Control Sensors and Actuators in an All-in-one Module

A three-axis attitude control function module was assembled into a highly integrated cubic package (103 cm3 dimension: 1U-sized). The key feature of the cubic module is its high-agility functionality, which is realized by a novel, compact electromagnetic braking mechanism combined with a ferromagnetic wheel. In this study, the proposed concept is compared with existing 1-axis wheel assemblies and the conventional CMG scheme in terms of momentum storage per mass/volume, maneuvering speed, and compactness. A prototype of the module was manufactured to demonstrate this innovative concept.
Passivity-Based Control of Single-Phase Cascaded H-Bridge Grid-Connected Photovoltaic Inverter

This article presents a nonlinear passivity-based control for a grid-connected cascaded H-bridge (CHB) photovoltaic (PV) system. The proposed control system is based on a port-controlled Hamiltonian model extracted from the synchronous reference frame system. Grid current dynamics is included in this model. Compared to existing solutions, the proposed control method has a robust performance and enhances the stability of the CHB-based PV system in the case of disturbances such as unequal irradiance over distinct PV arrays or a sudden sag or swell in the grid voltage. This controller also provides the independent control of each dc-link voltage. Therefore, the individual maximum power point tracking algorithms can be realized in each PV array, and the harvested energy can be maximized. Finally, simulation results and experimental investigations are conducted to verify the effectiveness of the proposed strategy.
Application of Support Vector Machine for Prediction of Slope Stability Coefficient Considering the Influence of Rainfall and Water Level

Slope stability estimation is a complex engineering problem involving many factors. A hybrid model based on the combination of finite element software GEO-STUDIO and support vector machine (SVM) is proposed to address the problem. The study took a high slope of Jingjiang reach of Yangtze River as the research object. Several important parameters, including values of geometric and geotechnical properties of slope as well as rainfall and water level data were used to establish the finite element model for the high slope. Besides, the validity of the model was estimate using the measured data of pore water pressure. The slope stability coefficients were calculated in GEO-STUDIO environment. And the data were used as the input samples to train and test SVM model. Results show that the agreement achieved in pore water pressure between measurement and analysis using the finite element model can be considered very reasonable. And the slope stability coefficient results by SVM coincided well with that of finite element analysis. It suggests that the proposed model has the potential to be a useful tool for the prediction of slope stability coefficient considering the influence of rainfall and water level.
Optimization of Genetic Algorithm through Use of Back Propagation Neural Network in Forecasting Smooth Wall Blasting Parameters

With the continuous development in drilling and blasting technology, smooth wall blasting (SWB) has been widely applied in tunnel construction to ensure the smoothness of tunnel profile, diminish overbreak and underbreak, and preserve the tunnel’s interior design shape. However, the complexity of the actual engineering environment and the deficiency of current optimization theories have posed certain challenges to the optimization of SWB parameters under arbitrary geological conditions, on the premise that certain control targets are satisfied. Against the above issue, a genetic algorithm (GA) and back propagation (BP) neural network-based computational model for SWB design parameter optimization is proposed. This computational model can comprehensively reflect the relation among geological conditions, design parameters, and results by training and testing the 285 collected sets of test data samples at different conditions. Moreover, it automatically searches optimal blasting design parameters through the control of SWB targets to acquire the optimal design parameters based on specific geological conditions of surrounding rocks and under the specified control targets. When the optimization algorithm is compared with other current optimization algorithms, it is shown that this algorithm has certain computational superiority over the existing models. When the optimized results are applied in practical engineering, it is shown that in overall consideration of the geological conditions, control targets, and other influencing factors, the proposed GA_BP-based model for SWB parameter optimization has high feasibility and reliability, and that its usage can be generalized to analogous tunneling works.
Extraterrestrial Resource Prospecting Using Particle Swarm Optimization and Conflict Based Search

Extraterrestrial Resource Prospecting Using Particle Swarm Optimization and Conflict Based Search by Michael Johnson University of New Hampshire, December, 2016 Extraterrestrial prospecting missions will be an important first step in preparation of the harvesting and extraction of natural resources in space. Additionally, knowledge from a prospecting mission will allow the subsequent harvesting or mining mission to efficiently extract resources. Due to the risk and cost of manned space-flight, these prospecting missions will be robotic in nature, and will enable investors to weigh risk and return of a harvesting mission. The research presented here explores a hypothetical automated prospecting mission on the Martian surface using landscapes simulated from the Mars Global Surveyor satellite. In this mission, a swarm of autonomous exploration rovers have been sent to the Martian surface to cooperatively search for some natural resource. This study examines optimization and path planning algorithms that would enable such an autonomous mission to accomplish its goals. Particle Swarm Optimization is the search algorithm that is used to identify high concentrations of the desired resource. However, no maps exist of subterranean resource distributions on Mars. Therefore in this study (without a loss of generality), the search algorithm identifies minimum elevations in the search area. Conflict Based Search is the multiple agent path planning algorithm used for planning the paths of each search rover. This algorithm is responsible for planning optimal paths from one search location to the next, while avoiding obstacles and other rovers in the area. This study demonstrates the plausibility of using these algorithms for such a prospecting mission, and sets a performance benchmark for the algorithms used. This research also presents a novel derivation of the state-space form of the Particle Swarm equations. This state-space representation allows for common analysis techniques from Control Theory and Signal Processing like
Data mining no contexto de customer relationship management em uma franquia coca cola company

Companies are increasingly concerned about understanding customer behavior in order to improve the relationship and loyalty of customers towards the company. Customer Relationship Management CRM therefore integrates an information acquisition process called Data Mining DM to acquire knowledge about the profile and behavior involved. The sequence of DM is presented together with several statistical techniques to orient the implementation of DM in the context of CRM.