Miracle-dz/UniFAR-FTUs huggingface dataset:
SOH Estimation of Li-Ion Battery Using Discrete Wavelet Transform and Long Short-Term Memory Neural Network

The state of health (SOH) of a lithium-ion battery (LIB) should be accurately estimated to ensure its safe operation under various driving conditions for electric vehicles. To estimate the SOH of LIBs, it is necessary to develop a technique that can properly process data with nonlinear characteristics related to the voltage and temperature of various electrochemical reactions in the batteries. To this end, we adopted wavelet transform methods to facilitate feature extraction for the preprocessing of nonlinear characteristic data from LIBs. Convolutional neural network (CNN) and long short-term memory (LSTM) techniques were used for the lithium-ion SOH estimation using the wavelet transform method. SOH estimation models using either the conventional data preprocessing technology normalization or wavelet transform method were compared. The SOH estimation accuracy of the model in which the wavelet transform was applied to the LSTM learning technology was 98.92%, and it was confirmed that the performance was improved compared with that of the normalization technology. In summary, this paper proposes a method that can improve the SOH estimation accuracy of LIBs compared to conventional methods by using data with nonlinear characteristics related to intrinsic changes in the voltage and temperature of LIBs.
Predicting the academic progression in student’s standpoint using machine learning

Graduate students are unaware of their final qualification for a course. Even though there were many models available, few works with feature selection and prediction with no control over the number of features to be used. As a result of the lack of an improved performance forecasting system, students are only qualified on the second or third attempt. A warning system in place could help the students reduce their arrear count. All students undertaking higher education should obtain the qualification at their desired level of education without delay to transit to their careers on time. Therefore, there should be a predictive system for students to warn during the course work period and guide them to qualify in a first attempt itself. Although so many factors were present that affected the qualifying score, here proposed a feature selection technique that selects a minimal number of well-playing features. Also proposed a model Supervised Learning Approach to unfold Student’s Academic Future Progression through Supervised Learning Approach for Student’s Academic Future Progression (SLASAFP) algorithm that recommends the best fitting machine learning algorithm based on the features dynamically. It has proven with comparable predictive accuracy.
Are the incomes of agricultural households lagging behind? Evidence from Czechia

Abstract The aim of this article is to compare the income of agricultural households with the income of other economically active households in Czechia and to determine the extent to which a difference can be explained by the difference in household characteristics. The research was carried out on the basis of the Czech SILC survey (2005–2016). To determine the level of poverty of agricultural households, we used the Foster–Greer–Thorbecke poverty measures. To decompose the income gap, we used Blinder-Oaxaca decomposition in the modification for longitudinal data. The incomes of agricultural workers are lower compared to other sectors, and this difference was also reflected in the equalised household incomes. Households differ in the poverty rate, but do not differ in the depth of poverty. The income gap can be explained by the difference in household characteristics of only 20%, with education and the type of occupation having the most significant influence.
Identifying indicators of functional recovery after ankle sprain in physically active people

Background: Ankle sprain is common and can lead to chronic ankle instability (CAI), characterised by decreased functional performance. However, there is still a limitation regarding the optimal approach used to assess functional recovery. This thesis aimed to explore the functional performance of those subjects and to investigate their progress plus to identify recovery predicator. Methods: 60 subjects (Acute Sprain (AS)=20, CAI=20, Healthy=20) were recruited. Parameters investigated: decision-making (DMT) and task time (TT) under unanticipated and planned conditions, Time to Peak muscle activity (TTP), jump height, muscle strength, and dorsiflexion ROM. These were also used to investigate the progress of the AS=10 over a six-month period and to identify recovery predicator. Results: The healthy subjects demonstrated faster DMT only during unanticipated conditions comparing with AS (p=0.003) and CAI (p=0.001). DMT was also longer in CAI compared with AS by 51.8ms (p=0.03). A significant difference was found in jump height between injured and non-injured ankles for the AS and CAI subjects and better dfROM in the healthy subjects. Measuring TTP revealed that the TTP occurred in a different order in each group. In the longitudinal study the AS subjects performed better DMT by 74.2ms and jump higher by 1.68cm when compared with the 1st visit. Additionally, Jump height found predictor of functional outcome score at 1-month using FADI-ADL% and also muscle strength (Eversion Con-PT, Eversion Con-PT/BW and Eversion Ecc-PT/BW) can predict the functional outcome at 12-month using FADI-Sport %. Conclusion: This thesis suggest that using unanticipated conditions in functional tests could provide important performance outcomes such as DMT in assessing the ability to return to previous sporting activity. A combination between jump height and fast contact time is also useful. Overall the result provides a foundation for further investigations. Larger and more detailed studies are also needed to investigate muscle activity in response to an unanticipated condition.
Controls on buffering and coastal acidification in a temperate estuary

Estuaries may be uniquely susceptible to the combined acidification pressures of atmospherically driven ocean acidification (OA), biologically driven CO2 inputs from the estuary itself, and terrestrially derived freshwater inputs. This study utilized continuous measurements of total alkalinity (TA) and the partial pressure of carbon dioxide (pCO2) from the mouth of Great Bay, a temperate northeastern U.S. estuary, to examine the potential influences of endmember mixing and biogeochemical transformation upon estuary buffering capacity (β–H). Observations were collected hourly over 28 months representing all seasons between May 2016 and December 2019. Results indicated that endmember mixing explained most of the observed variability in TA and dissolved inorganic carbon (DIC), concentrations of which varied strongly with season. For much of the year, mixing dictated the relative proportions of salinity‐normalized TA and DIC as well, but a fall season shift in these proportions indicated that aerobic respiration was observed, which would decrease β–H by decreasing TA and increasing DIC. However, fall was also the season of weakest statistical correspondence between salinity and both TA and DIC, as well as the overall highest salinity, TA and β–H. Potential biogeochemically driven β–H decreases were overshadowed by increased buffering capacity supplied by coastal ocean water. A simple modeling exercise showed that mixing processes controlled most monthly changes in TA and DIC, obscuring impacts from air–sea exchange or metabolic processes. Advective mixing contributions may be as important as biogeochemically driven changes to observe when evaluating local estuarine and coastal OA.
A Physiologically Based Pharmacokinetic Framework for Quantifying Antibody Distribution Gradients from Tumors to Tumor-Draining Lymph Nodes

Immune checkpoint blockades prescribed in the neoadjuvant setting are now under active investigation for many types of tumors, and many have shown early success. The primary tumor (PT) and tumor-draining lymph node (TDLN) immune factors, along with adequate therapeutic antibody distributions to the PT and TDLN, are critical for optimal immune activation and anti-tumor efficacy in neoadjuvant immunotherapy. However, it remains largely unknown how much of the antibody can be distributed into the PT-TDLN axis at different clinical scenarios. The goal of the current work is to build a physiologically based pharmacokinetic (PBPK) model framework capable of characterizing antibody distribution gradients in the PT-TDLN axis across various clinical and pathophysiological scenarios. The model was calibrated using clinical data from immuno-PET antibody-imaging studies quantifying antibody pharmacokinetics (PK) in the blood, PTs, and TDLNs. The effects of metastatic lesion location, tumor-induced compression, and inflammation, as well as surgery, on antibody concentration gradients in the PT-TDLN axis were characterized. The PBPK model serves as a valuable tool to predict antibody exposures in various types of tumors, metastases, and the associated lymph node, supporting effective immunotherapy.
Measuring Glance Legibility of Wearable Heads-Up Display Interfaces Using an Adaptive Staircase Procedure

Heads-up displays (HUDs) are growing in popularity and utility, providing novel ways to interact with environments and other individuals. HUD interfaces must allow users to quickly view information without distracting them from their primary task. We test the use of an adaptive staircase as a method to investigate the glance legibility of two Google Glass heads-up display interfaces. Glance legibility refers to an interface’s legibility when viewed in short amounts of time (also known as glance-like conditions). We measure glance legibility by the minimum presentation time required to read an interface and respond correctly to a yes-no question. The applications of this research can help inform the design and evaluation of future heads-up display interfaces under glance-like conditions.
Human-Computer User Interface Design for Semiliterate and Illiterate Users

Information and Communication Technology (ICT) has revolutionized the lives of the people. The technology is embedded in daily life of literate or semiliterate/illiterate users. However, the user interface (UI) requirements for semiliterate/illiterate users are different from that of an educated person. The researchers of Human Computer Interaction for Development (HCI4D) face challenges to improve the usability of a UI for the semiliterate users. Therefore, a Systematic Literature Review (SLR) is conducted to provide a set of design factors and guidelines for UI development of semiliterate users. The study is based on extensive research gathered from literature to understand the user-centered design (UCD) approach, enhancing user experience (UX) for semiliterate users. This study analyses fifty two research articles that are published during 2010-2020. The findings shed light on the systematization of UI design guidelines for semiliterate/illiterate users. These guidelines can help in taking advantage of ICT during the COVID-19 pandemic. The analysis shows that seventeen main design factors are indispensable for designing UI of semiliterate users. The most suggested design factors include localization and graphics, which should be incorporated in UI for the target population. Moreover, the lag in the design factors as personalization and consistency open a road for future research.
Multifunctional fiber-reinforced composites for passive sensing and energy harvesting with enhanced mechanical performance

Fiber-reinforced polymer composites (FRPCs) are valid alternatives to metals, especially in the aerospace industry, for their superior strength-to-weight ratio. FRPCs are generally manufactured by stacking layers of fibers and infusing them with epoxy-based adhesives to result in laminates. Such layer-by-layer structure often ensues a poor interlaminar property of the composites. Additionally, FRPCs can sustain complex damage modes because of their inherent anisotropic characteristics. For example, delamination can occur due to low-velocity impact at a subsurface level, which can result in premature catastrophic failures if undetected. Thus, structural monitoring is crucial to identify such damage. Point-based sensors (e.g., strain gauges, accelerometers, among many) can be embedded in the FRPCs for structural monitoring. However, their electrical power demand often embroils their usage. Therefore, the main objective of this research is to design and implement multifunctional composites that can simultaneously perform passive sensing and energy harvesting while exhibiting better mechanical performance. Previous research efforts have demonstrated that a continuous feedthrough deposition of functional materials on fiber surfaces simultaneously enhanced the mechanical and sensing properties of the FRPCs. This study explores a similar approach to encode passive sensing and energy harvesting properties in the FRPCs by integrating ferroelectric microparticles on the fiber surfaces. Upon ensuring their superior interlaminar shear strength, sensing and energy harvesting properties were characterized through experimental studies. The outcome is a multifunctional composite fabricated by coating the fibers with functional microparticles through a high throughput, scalable, and low-cost approach that enables passive sensing, energy harvesting, and improved mechanical performance.
Carbon nanotubes doped concrete-based sensor for strain measurements

Construction, design, and maintenance of civil structures involves huge money and manpower. To extract the maximum potential use of such structures they need continuous monitoring, for checking any deformity in early stages. Many studies incorporating conventional sensors like electric strain gauge (ESG), optical fibers wire, piezo ceramic materials i.e., Lead zirconate titanate (PZT) have been proven successful in detecting damage of structures but accompanied with many challenges including environmental and operational challenges, compatibility, and durability issues. This paper covers the development of a concrete-based sensors in size 50×50×50 mm suitable for embedding in concrete structures by doping four different dosages of multi-walled carbon nanotubes (MWCNTs) in concrete to develop concrete-based sensors ensure compatibility with surrounding concrete. They were evaluated under different loading conditions. Sensors possess enhanced sensitivity, high durability, and compatible life as the life of structure. They were found successful in detecting static strain measurements obtained in form of resistance changes. A frequency response function was also obtained using LCR meter in the range of frequency 100-500 kHz.
The Impact of the Main Negative Socio-Economic Factors on Female Fertility

The negative relationship between fertility and income is well known to economists and demographers. Developed countries have experienced a remarkable decline in their fertility rate as they have become richer. Lifestyle choices can affect a woman’s ability to conceive. Tobacco use and heavy drinking is associated with an increased risk of ovulation disorders, and being overweight or significantly underweight can inhibit normal ovulation. Our research is focused on evaluating the main risk factors that influence female fertility. We assembled a country-specific dataset on birth rate and socio-economic factors for 171 countries, using data integrated from publicly available data sources. The regression model shows that the negative factor with the greatest impact on female fertility is represented by the level of income per capita. The negative effects of smoking, alcohol consumption, and body weight on female fertility are also demonstrated, but with a lower impact compared to the average income per capita.
Electronic Textile-Based Sensors and Systems for Long-Term Health Monitoring

MERRITT, CAREY REID. Electronic Textile-Based Sensors and Systems for Long-Term Health Monitoring. (Under the direction of Edward Grant). Personalized long-term health monitoring has the potential to improve medicine’s capabilities for diagnosing and correctly treating diseases at an early stage. Recently, progress has been made towards producing clothing that is suitable for such long-term monitoring. This work first reviews the current electronic textile based sensors that are used to measure two vital healthcare parameters, ECG and respiration. The techniques used for designing and fabricating these sensors are discussed and summarized. Furthermore, recommendations are proposed in regards to the development of an unobtrusive, wireless health monitoring garment. The second part of this research involved designing and fabricating two versions of fabric based active electrodes to provide a solution for long-term ECG monitoring clothing. The first version of active electrode involved attaching surface mountable components directly to a textile screen printed circuit using polymer thick film techniques. The second version involved attaching a significantly smaller active electrode interposer board to a simplified electronic textile circuit. Results from ECG tests on the active electrodes indicate that the performance of these new devices is comparable to commercial Ag/AgCl electrodes. The interposer based active electrodes were even found capable of surviving a five cycle washing test. This research also explores the potential for using capacitive sensing to serve as an inexpensive method for long-term respiration sensing. Two capacitive sensors were designed and fabricated for detecting chest or abdominal circumference changes. These sensors gave good linearity, sensitivity, and resolution. Respiration measurements obtained with these new sensors that were implemented into a prototype belt show that they are capable of measuring respiration rate and possibly lung function parameters. Finally this research presented a new modular wireless sensor node (MWSN) system for health monitoring clothing applications. The applications for this research involved integrating the MWSN into a custom designed ECG belt, a capacitive sensor respiration belt and an activity patch. Results obtained from these applications demonstrate that the MWSN is capable of interfacing with a diverse selection of health monitoring sensors while maintaining signal fidelity. Electronic Textile-Based Sensors and Systems for Long-Term Health Monitoring by Carey Reid Merritt A dissertation submitted to the Graduate Faculty of North Carolina State University In partial fulfillment of the Requirements for the degree of Doctor of Philosophy Electrical Engineering Biomedical Engineering Raleigh, North Carolina
Low-frequency noise in nFinFETs of different dimensions processed in strained and non-strained SOI wafers

The results of low-frequency noise investigation in fully-depleted (FD) nFinFETs of Weff = 0.02 to 9.87 μm, Leff = 0.06 to 9.9 μm, processed on standard (SOI) and strained (sSOI) wafers are presented. It is shown that the McWhorter noise is typical at zero back gate voltage for the devices studied and the density of the corresponding noisy traps in the SiO2 portion of the gate oxide is, as a rule, much higher than that in the HfO2 portion. The results on the McWhorter noise are used for studying the behavior of the electron mobility μ and the free electron density NS in the channel at V ≥ 0.4 V where V is the gate overdrive voltage. It is also shown that the Linear Kink Effect (LKE) Lorentzians appear in the low-frequency noise spectra at an accumulation back gate voltage and that the parameters of those Lorentzians are different for the sSOI and SOI nFinFETs. This is the first observation of the LKE noise under a back-gate accumulation bias for sufficiently wide nMuGFET.
Optimization of Dual-Mass Flywheel Parameters Based on a Multicondition Simulation Test

A dual-mass flywheel is an important part of an automobile transmission system whose function is to ensure the smooth transmission of engine power to a gearbox. Vehicle vibration and noise are major indicators of vehicle comfort, while dual-mass flywheel technology can preferably solve the comfort problem. Based on the created torsional vibration model, this paper describes the testing of the torsional characteristics under various conditions with an LMS AMESim simulation. The results show that the noise in the cab is fundamentally below 60 dB. The computational formula for the angular stiffness of an arc coil spring is derived, and then the spring angular stiffness is optimized with arithmetic averaging, which reveals an adjusted angular stiffness of 12.8 Nm/°. Additionally, the requirement for the angular acceleration of the WOT input shaft (i.e., being less than 500 rad/s2) is satisfied for various gears. The test results show that the double-mass flywheel can attain a vibration isolation efficiency of around 85%, which more effectively reduces the transmission vibration and noise and improves the vehicle comfort.
Designing data governance in Brazil: an institutional analysis

Abstract Data governance is a decision-making process focused on authority building to specify decision rights and accountability that encourage desired behaviors regarding data use, security, integrity, and availability. The emergence of big data technologies to design public policy and deliver public services requires governments to design data policy and governance. This paper analyzes the dynamics of data governance design in the Brazilian Federal Government, based on institutional analysis and policy development. The paper reports a series of interviews with Brazilian Federal Government policymakers to frame the data policy design dynamics. The paper concludes that the policy design dynamics to data governance are path dependent and shape actions situations that reinforces previous institutions. In the case of the Brazilian Federal Government, the institutional framework is ambiguous, creating situations of conflict and ineffectiveness in the design of the data policy.
Ambient Learning Model For Research Project Supervision Support: An Application Of Mobile Phone-centric Ambient Intelligence Technologies In ‘mobile-rich’ Universities.

vi GLOSSARY AND LIST OF DEFINITION OF TERMS . viii ABBREVIATIONS. ix LIST OF FIGURES . xvi CHAPTER ONE : INTRODUCTION . 1 1.1 Background . 1 1.2 Problem Statement. 4 1.3 Proposed Solution . 6 1.4 Main Objective . 7 1.5 Specific objectives . 7 1.6 Research Questions . 7 1.7 Significance of the study . 9 1.8 Scope of the Study . 10 1.9. Achievements of the Study . 10 CHAPTER TWO : LITERATURE REVIEW . 14 2.
Interactions between Nanoparticles and Intestine

The use of nanoparticles (NPs) has surely grown in recent years due to their versatility, with a spectrum of applications that range from nanomedicine to the food industry. Recent research focuses on the development of NPs for the oral administration route rather than the intravenous one, placing the interactions between NPs and the intestine at the centre of the attention. This allows the NPs functionalization to exploit the different characteristics of the digestive tract, such as the different pH, the intestinal mucus layer, or the intestinal absorption capacity. On the other hand, these same characteristics can represent a problem for their complexity, also considering the potential interactions with the food matrix or the microbiota. This review intends to give a comprehensive look into three main branches of NPs delivery through the oral route: the functionalization of NPs drug carriers for systemic targets, with the case of insulin carriers as an example; NPs for the delivery of drugs locally active in the intestine, for the treatment of inflammatory bowel diseases and colon cancer; finally, the potential concerns and side effects of the accidental and uncontrolled exposure to NPs employed as food additives, with focus on E171 (titanium dioxide) and E174 (silver NPs).