Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully Time-Based PID Controller for a High Frequency Buck Converter

With the shrinkage of process nodes for integrated circuits, analog circuits, like analog feedback control, have become increasingly expensive and more challenging to design. Here time-based control is a compelling alternative to traditional analog feedback control circuits due to its better scaling with the process node. Several publications about time-based PID-type controllers exist. There the PI-part is implemented in the time domain, but the D-part remains in the analog domain thereby limiting the process scaling benefits. This paper proposes a new PID structure that fully integrates the PID in the time domain. The proposed structure has improved ripple attenuation and maps directly to a typical PID. Simulations shows that the performance of the theoretical small signal controller translates well to the large signal time-based response.
Effect of Condensed Tannins on Nitrogen Distribution and Metabolome after Aerobic Exposure of Sainfoin Silage

(1) Background: Previous studies have indicated that proteolysis is inhibited by the condensed tannins (CTs) that are present during sainfoin ensiling. Whether inhibiting this effect of CTs on proteolysis is functional during aerobic exposure is still unclear. (2) Methods: the present study investigated the effect of CTs on metabolite composition during the aerobic exposure of sainfoin silage via the use of polyethylene glycol (PEG), leading to the inactivation of CTs. (3) Results: The neutral detergent-insoluble protein (NDIP) and acid detergent-insoluble protein concentrations were both more concentrated in the control group than in the PEG-treated group. There were 587 and 651 different metabolites present in the control and PEG-treated groups after 3 and 7 days, respectively, of aerobic exposure of silage. Flavonoids (72 metabolites) were the most abundant among these different metabolites. The addition of PEG upregulated histidine, threonine, asparagine, tryptophan, and glutamine, but downregulated phenylalanine. The relative abundances of Lactococcus, Fructobacillus, Enterobacter, Cutibacterium, Citrobacter, and Rosenbergiella differed significantly between the control and PEG-treated groups (p < 0.05); all of these bacteria showed significant correlation with some of the 50 most abundant metabolites. (4) Conclusions: the results suggest that the antioxidant status of the silage increased and inhibited the activity of a variety of bacteria that coexist with CTs, and decreased the production of certain amino acids after the aerobic exposure of silage.
The Risk and Predictors of Malignancies in Ankylosing Spondylitis Patients in Israel—A Retrospective Electronic Data-Based Study

Background: Previous studies demonstrated unclear and vast variability in the association between Ankylosing Spondylitis (AS) and the risk of cancer. Objectives: To assess the risk of overall and site-specific malignancies for AS patients in Israel, while examining the role of comorbidities and immunomodulatory therapy. Methods: We conducted a retrospective electronic data-based study including all AS patients diagnosed between 2002 and 2018, with no history of cancer prior to enrollment, with 5:1 ratio matched-control by age, gender, and place of residence. The odds Ratios (OR) for site-specific malignancies, comparing AS patients and controls, were calculated using logistic regression. Risk factors for malignancies within the AS cohort were evaluated in the same manner. Results: This study comprised 5825 AS patients and 28,356 matched controls. There was a higher overall risk of cancer in AS patients compared to controls (OR = 1.4, 95% CI 1.24–1.6), specifically for solid malignancies (OR = 1.5, 95% CI 1.3–1.7), CNS (OR = 3.72, 95% CI 1.29–10.7), kidney (OR = 2.06, 95% CI 1.12–3.8), and malignancy of unknown primary (OR = 3.06, 95% CI 2.35–3.98). Regarding predictors for malignancy within AS patients, older age at diagnosis (OR = 1.31, 95%,CI 1.25–2.36), diabetes (OR = 1.52, 95% CI 1.18–1.97), IBD (OR = 2.61, 95% CI 1.75–3.89), and treatment with DMARDs (OR = 2.17, 95% CI 1.65–2.83) were associated with a higher risk of solid malignancies, while NSAIDs treatment alone had a protective effect for solid malignancies (OR = 0.78, 95% CI 0.61–0.99). No significant association was found between anti-TNF therapy and the risk of solid or hematologic malignancies within the AS group. Conclusion: AS is associated with an increased risk of overall and site-specific malignancies, with independently higher risk for older age, comorbidity of DM, IBD, and treatment with DMARDs.
Variational Mode Decomposition and a Light CNN-LSTM Model for Classification of Heart Sound Signals

Several cardiovascular diseases (CVDs) produce changes in heart sounds and murmurs. This paper presents the VMD-CNN-LSTM system developed for the diagnosis of CVDs by classification of heart sound signals. We use variational mode decomposition (VMD) to compensate for nonstationarity of the signal by decomposing acquired signals into a set of intrinsic mode functions. We propose to scale the mode functions and use them to calculate input features for training and evaluating a classifier. A light CNN-LSTM model used as the classifier integrates the convolutional neural network (CNN) and the long short-term memory network (LSTM) in one architecture. The features are extracted by applying a weighted logarithmic operation to the scaled mode functions. The developed system uses a few network layers and achieves efficient classification tasks. The classification performance of the system is evaluated using a database of five valvular heart conditions. The experimental results show that the system outperforms a recent method that used the same database, with an average accuracy of 98.65%.
Analysis of Block Level PnR Flow to Increase the Cell Density in Less Congested Regions

While going through each stage of a physical design, it is usual to encounter cell density in the placement stage. As cell density plays a vital role in timing management and congestion in a physical design process, a larger cell density results in congestion issues, while a smaller cell density affects the timing of the circuit. In this research, we present an analysis of the physical design process for a 40nm technology at increasing cell density in less congested regions using the ICC2 tool and placement blockage technique.
Classification of cashew based on the shape parameter

Cashew is one of the most popular tree nuts. It is very expensive and the prices of cashew depend on its quality.To ascertain the quality, grade standard have been designed by considering the length, width, color and the size (weight) of the cashew kernel as important characteristic.Cashew is a commercial commodity that plays a major role in earning foreign revenue among export commodities in India. The purpose of this study is to develop a computer vision based cashew nut grading system. Computer vision based cashew grading system is an alternative to the manual and mechanical method. Computer vision based method offers automated, high speed and cost effective solution for classification. The paper tried to build the classification model of cashew nuts and has analyzed the shape parameters of cashew nuts,Length (L), Width (W) and Thickness (T) of the cashew kernel plays vital role in deciding the grade of the cashew kernel.
Deep Learning-Based Alzheimer's Disease Classification using Transfer Learning and Data Augmentation

Alzheimer's disease is a pressing healthcare concern worldwide, highlighting the importance of early and accurate detection. Deep learning models, particularly those utilizing transfer learning, have shown promise for AD classification. This research study explores the effectiveness of transfer learning in training models based on deep learning for the classification of Alzheimer's condition. We trained several transfer models using DenseNet121, InceptionV3, Xception, and ResNet101. Based on performance evaluation, InceptionV3 was selected as the base model, outperforming in comparison to the accuracy of the other models. To enhance the accuracy of the InceptionV3 base model, we added capacity, tuned hyperparameters using Keras tuner, and utilized data augmentation techniques. The final model was trained on the Kaggle Alzheimer's Dataset, consisting of 4 classes of images, and achieving an AUC value of 87%. Our research demonstrates that transfer learning and other data augmentation approaches are useful in improving the accuracy of deep learning models for the categorization of Alzheimer's condition. This research has practical implications for improving public health outcomes, facilitating timely intervention and effective treatment. It contributes to the development of more accurate diagnostic tools for AD and can help address the challenges associated with this disease.
Self-Similarity of Traffic Within a 5G Standalone Network

Research has shown network traffic to be self-similar for various connection protocols, and 5G traffic has been modeled as self-similar based on the assumption that it adheres to these previously established traffic behaviors. However, to support the continued use of known self-similar traffic models in 5G technology, it is necessary to demonstrate self-similarity within 5G network traffic in a physical environment and investigate the factors that affect it. This research uses an AMARI Callbox Mini to develop a 5G Standalone (SA) network testbed to generate and analyze 5G network traffic. Using the Rescaled Range estimation method, the Hurst parameter of this traffic is measured to determine its degree of self-similarity. Our analysis demonstrates that traffic within a 5G standalone network is statistically self-similar, and the degree to which it displays this property increases with traffic load. Furthermore, this research shows that self-similarity varies dependent on the medium in which the traffic is collected and analyzed.
Virtual Investigation on the Response of Glare to Low Velocity Impact

Due to the increasing cost of experiments and the increased time required to complete the experimental process, it is more economical to conduct experiments in the form of virtual tests. Therefore, low velocity impact simulation was carried out on fiber metal laminates, FMLs with an impactor with various weights in our study. LS-DYNA software was selected to perform the simulation. Fiber metal laminate is composed of 2024-T3 Aluminum and Glass/Epoxy layers. The layers are modeled as isotropic, and anisotropic material. The stacking sequence of FML is (Al/0/90/Al). Two opposite sides of the square shaped FML are fixed and the other sides are left free. Steel strikers with masses of 0.15 kg, 0.30 kg and 0.45 kg were used in the analyses, respectively. The speed of the striker at first contact with the FML is 10 mm/ms. As a result of the tests, force-time, force-displacement, velocity-time and energy - time variations were also obtained. The results were presented as graphs. It has been found that the FML structure absorbs an average of 73.4% of the impact energy because of the low-velocity impact, thus preserving the integrity and functionality of the designed structure. Thanks to such numerical experiments carried out before production, the mechanical properties of the designed structure can be evaluated.
Attention-based BiLSTM Model for Rainfall Prediction

In order to further improve the accuracy of rainfall prediction, this paper proposes a Bi-LSTM prediction model incorporating an attention mechanism. The model splits the input sequence into a spatio-temporal sequence and a feature sequence, and introduces an attention mechanism before the Bi-LSTM network model to calculate the attention of the two sequences separately before fusion. The Bi-LSTM network can adaptively select the most important input features according to their importance, and the parameters of the attention layer are obtained by a competitive random search algorithm, which further enhances the robustness of the model. Finally, the prediction experiments are conducted on real data, and the results show that the Bi-LSTM model based on attention mechanism has better prediction accuracy compared with support vector regression (SVR) and LSTM models, which can provide technical support for precipitation prediction.
A framework for heart-lung interaction and its application to prone position in the acute respiratory distress syndrome

While both cardiac output (Qcirculatory) and right atrial pressure (PRA) are important measures in the intensive care unit (ICU), they are outputs of the system and not determinants. That is to say, in a model of the circulation wherein venous return and cardiac function find equilibrium at an ‘operating point’ (OP, defined by the PRA on the x-axis and Qcirculatory on the y-axis) both the PRA and Qcirculatory are, necessarily, dependent variables. A simplified geometrical approximation of Guyton’s model is put forth to illustrate that the independent variables of the system are: 1) the mean systemic filling pressure (PMSF), 2) the pressure within the pericardium (PPC), 3) cardiac function and 4) the resistance to venous return. Classifying independent and dependent variables is clinically-important for therapeutic control of the circulation. Recent investigations in patients with acute respiratory distress syndrome (ARDS) have illuminated how PMSF, cardiac function and the resistance to venous return change when placing a patient in prone. Moreover, the location of the OP at baseline and the intimate physiological link between the heart and the lungs also mediate how the PRA and Qcirculatory respond to prone position. Whereas turning a patient from supine to prone is the focus of this discussion, the principles described within the framework apply equally-well to other more common ICU interventions including, but not limited to, ventilator management, initiating vasoactive medications and providing intravenous fluids.
iSmartYog: A Real Time Yoga Pose Recognition and Correction Feedback Model Using Deep Learning for Smart Healthcare

This paper presents the iSmartYog system model, a deep learning-based approach for yoga pose recognition and correction feedback generation. The system utilizes a hybrid deep learning model consisting of a convolutional neural network (CNN) and a Gated Recurrent Unit (GRU) to recognize 24 different yoga asanas from videos. A new video dataset of yoga pose dataset with participation of 31 individuals to consider the yoga pose as an action. Body keypoints detection from the practitioner's pose was carried out using the Mediapipe library to extract features of the poses. The CNN layer was used to extract spatial features from the keypoints of each frame, and the GRU was used to give temporal predictions. The iSmartYog system offers a unique approach to hybridizing CNN-GRU for yoga pose recognition, providing new insights into the field of smart healthcare and human action recognition. By accurately recognizing yoga poses, the system generates feedback to aid individuals in correcting their poses, thereby reducing the risk of injury and enhancing the benefits of yoga.
ANTIMICROBIAL ACTIVITY OF CHITOSAN-VITAMIN E-NANOEMULSION

Chitosan is considered one of the most abundant polysaccharide in the world. Therefore, it has several applications in the food and pharmaceutical industries. However, the structure of chitosan may limit its solubility and bioavailability. The current study aimed to improve the physicochemical properties of chitosan by combining it with vitamin E nanoemulsion (CH-NE-vitE). The structural, thermal stability, and physical differences between chitosan and its new derivative were analyzed using the scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and differential scanning calorimetry (DSC). Furthermore, the antimicrobial activities of CH-NE-vitE against Staphylococcus aureus, Candida krusei and Enterobacter hormaechei were examined using disc and well diffusion methods in addition to the determination of the minimum inhibitory concentration (MIC). The morphology changes in the treated microbes were visualized using the SEM. The characterization of CH-NE-vitE exhibited noticeable changes in the chitosan physical properties and chemical structure including increased solubility, interaction rates, and stability. The new derivative has inhibited the growth of both S. aureus, and C. krusei while promoting the growth of E. hormaechei. The minimum inhibitory concentrations against the S. aureus, and C. krusei were 1.563 mg/mL and 3.125 mg/mL, respectively. The produced CH-NE-vitE can be used in drug delivery, dermal products, and food packaging.
Transport and capture of bacteria from urban stormwater runoff using bioretention

Title of Dissertation: TRANSPORT AND CAPTURE OF BACTERIA FROM URBAN STORMWATER RUNOFF USING BIORETENTION Lan Zhang, Doctor of Philosophy, 2008 Directed By: Professor Allen P. Davis Associate Professor Eric A. Seagren Department of Civil and Environmental Engineering Bioretention, a nature-based treatment practice, has significant potential for reducing the threat of microbial pollutants from urban stormwater runoff to receiving water bodies. The overall goal of this research was to evaluate the removal efficiency for bacteria from urban stormwater runoff in bioretention systems and the potential of an engineered media (iron oxide-coated sand (IOCS)) for enhancing bacterial removal. This investigation was accomplished through laboratory column studies coupled with field tests. Column studies on the transport and destruction of Escherichia coli O157:H7 strain B6914 (a surrogate of pathogenic E. coli) in conventional bioretention media (CBM) and IOCS demonstrated that the bacteria were well removed in CBM (a mean 70% efficiency), but IOCS significantly enhanced the capture of strain B6914 (a mean 99.4% efficiency) due to the greater positive charge and surface roughness. However, the decay of trapped strain B6914 cells was much faster in CBM compared to the IOCS. More than 99.98% of B6914 cells attached to CBM died off within one week, while approximately 48% of trapped cells still survived in the IOCS after one week. Predation by indigenous protozoa in the CBM appears to play a dominant role in the faster decline of the number of trapped B6914 cells in CBM. Additionally, long-term (18 months) column experiments indicated that during the periodic application of simulated rainfall, the removal efficiency for strain B6914 improved over time, achieving 97% or higher efficiency after six months. Consistent with the laboratory studies, two years of field studies showed that bioretention systems reduced the concentration of indicator bacteria in the outflow during most storm events and increased the probability of meeting specific water quality criteria. The concentration of indicator bacteria in the input flow generally increased with higher daily temperature. No clear trend for the bacterial removal efficiency with respect to temperature was found in laboratory and field studies. However, the bacterial decay coefficients in CBM increased exponentially with elevated temperature. Based on these results, it is concluded that CBM not only achieves good removal for bacteria, but also has the potential to render the process sustainable. TRANSPORT AND CAPTURE OF BACTERIA FROM URBAN STORMWATER RUNOFF USING BIORETENTION
FACTORS INFLUENCING SUSTAINABILITY OF COMMUNITY WATER PROJECTS IN KENYA: A CASE OF WATER PROJECTS IN MULALA DIVISION, MAKUENI COUNTY

The purpose of this study is to investigate the factors that influence sustainability of community water projects in semi arid areas in Kenya with a focus in Mulala division. The government of Kenya and non-governmental organizations have promoted interventions or projects aimed at improving water situations in the semi arid areas though with some low level of sustainability. This study has four objectives, namely, to examine the extent to which community participation influences sustainability of community water projects in semi arid areas; to determine the extent to which project financing influences sustainability of community water projects in semi arid areas; to assess the extent to which project management practices influences sustainability of community water projects in semi arid areas and to explore the extent to which community training influence sustainability of community water projects in semi arid areas. The study employed descriptive survey design with a sample of sixty respondents consisting of executive members of water management committees and two management staff of two non-governmental organizations. Data collected was edited, coded and analyzed using SPSS. The study was guided by two theories namely resource dependency theory and ecological modernization theory. The findings of the study indicated that community participation, project financing, project management practices and community training do influence sustainability of community water projects. It was also found out that the accountability and transparency among the committee members who manage the water resources is also a key factor which influences <sustainability. If there is a perceived lack of transparency and accountability, community members tend to withdraw their support for the water projects. The study recommends that community participation in the whole project cycle should be enhanced, there should be high level of transparency and accountability in the management of water projects, donors should have adequate budgets for any water projects designed for implementation and organizations should strongly support monitoring and evaluation of their water projects beside ensuring that community responsible for management and operation of water projects are well trained in operation and maintenance. o T y O f NA'*U)U' l ) N l V ^ blT M u 8 r a RY KlKû ‘ tvz ' OO1)0 '̂ o n Box • L p° K u a 'v u
Meteorological effects of heat and moisture releases from large power stations (precipitation modification)

The Bowen Electric Generating Plant (Plant Bowen) in Northwest Georgia has been chosen as a test site to investigate the atmospheric effects of cooling towers. This 3,200-MWe coal-fired power plant uses four natural-draft cooling towers and is the largest of U.S. power plants having cooling towers as the sole cooling method. Results are reported from both climatological and field studies. Extensive use has been made of the U.S. National Weather Service (NWS) data accumulated over several decades in Northwest Georgia. Apart from providing preliminary indications of precipitation modification effects, these data have aided in the general understanding of the climatology in the vicinity of Plant Bowen and have paved the way for the field studies currently underway. These field studies, employing a dense network of rain gauges and windsets, are expected to provide the statistical data base necessary to estimate the plant's effect (if any) on precipitation.
2-D Circularly Polarized Printed Metasurface Leaky-Wave Antennas on a Conformal Aperture

A design methodology for 2-D circularly polarized conformal printed leaky-wave antennas on a grounded dielectric substrate is presented. Via forward projection from a given planar aperture field associated with the desired target radiation pattern in a hypothetical plane, tangential fields on a curved aperture are derived. TM-polarized auxiliary surface waves are introduced and optimized to make the penetrable surface lossless and passive. To realize the resulting tensor surface impedance distribution, an array of printed Jerusalem crosses with tilted arms is adopted. An example antenna on a cylinder of a 13.108-wavelength radius is designed and numerically validated for uniform-aperture broadside radiation in left-handed circular polarization.