Miracle-dz/UniFAR-FTUs huggingface dataset:
BCH codes in UFMC: A new contender candidate for 5G communication systems

Nowadays, fifth generation (5G) wireless network is considered one of the most important research topic in wireless industry and it will be substituting with fourth generation (4G) in several aspects. Although the robustness of orthogonal frequency division multiplexing (OFDM) system against channel delayed which is the reason behind using it in LTE/LTE Advanced however, it is suffering from high Peak to Average Power Ration (PAPR) and out of band side lobes. So, universal filtered multi-carrier (UFMC) technique is considered as new modulation scheme for emerging 5G wireless communication systems to overcome on the known OFDM demits. In contrast, to achieve the reliability in data transmission for digital communication systems, using error correcting codes are considered an essential over noisy channels. So, BCH codes have been used in this paper for UFMC system over AWGN. The results showed that using BCH codes in UFMC contributed in enhancing BER performance while could decreasing both of PAPR and OOBE values better than OFDM system. Where, PAPR for OFDM system was 9.9269 dB while it was 8.6229 dB and 8.6899 dB for Uncoded and BCH codes with UFMC system respectively. Furthermore, the OOBE level of UFMC system was around 40dB lower than OFDM system.
Feature Selection for Simple Color Histogram Filter based on Retinal Fundus Images for Diabetic Retinopathy Recognition

Applications of learning models for text-based datasets as well as image pixels-based datasets grow rapidly for prediction purposes. Pre-processing becomes challenging in carrying out image filtering and classifying. Retinal Fundus images plays important role in Diabetic Retinopathy (DR) diagnosis and treatment planning in various stages. Diabetic Retinopathy is diagnosed by observing the variation in retinal blood vessel, exudates, micro aneurysm, hemorrhages, and the new blood vessel growth inside the retina. The objective of this study is to enrich the diagnosis for the Diabetic Retinopathy from the retinal fundus images by applying machine learning algorithms. The proposed work implements normalization, parameter tuning, and optimal feature selection method to improve the classification accuracy offered by selected algorithms like decision tree algorithm and K-nearest neighborhood classifiers. The highest accuracy of 81.99%, Weighted Average of Receiver Operating Characteristics (ROC) 0.907 are obtained by k-Nearest Neighbor (KNN) classifier due to its best performance.
Electronic Structure of Molecular Solids: Bridge to the Electrical Conduction

Organic semiconductors, discovered in the mid-twentieth century [1–3], have larger bandgap and smaller bandwidth than their inorganic counterparts. The number of thermally excited carriers in the organic films is not enough to give sufficient current. We thus need injection of carriers into the organic film from electrodes to realize sufficient current in organic devices. This chapter describes fundamental aspects of electronic structure of organic semiconductors and the method to bridge the electronic structure and electrical property using ultraviolet photoemission spectroscopy (UPS). As it is well known, the electrical conductivity (s) is given by
A constrained maximum entropy method in polymer statistics

A modified version of the maximum entropy principle, called “constrained maximum entropy” method (MEC), is revisited to combine the information obtained in computer simulations of polymers with external information in the form of configurational averages. A random-temperature molecular dynamics trajectory is being proposed as a biased random walk in configurational space to be reweighted by using the given average information. This random walk, generating a “meta” configurational probability, has been found to contain relevant information on the system. The method is compared with other computational techniques, like the generalized-ensemble and configurational-biased Monte Carlo, for simple models in the field of polymers and biopolymers. The main features of polymer configurational distribution functions of interest in polymer physics are consistent among the different methods in a wide range of temperatures and especially at room conditions. The advantage of the MEC approach is in taking into account a.
Effects of Cymbopogon Citratus and Physical Exercises on Obese Women’s Anthropometric and Hemodynamic Parameters in Cameroon

The objective of this study is to assess the combined effects of exercise and consumption of Cymbopogon citratus on anthropometric and hemodynamic parameters in obese women. Forty (40) obese women were divided into four groups: group 1 (G1) was subjected to physical exercises and consumption of Cymbopogon citratus; group 2 (G2) had consumed only Cymbopogon citratus; group (G3) was practiced only physical exercises and group 4 (G4) have neither consumed Cymbopogon citratus and nor practiced physical exercises. The anthropometric and hemodynamic parameters were measured on three days: D0, D30 and D60. The results showed various modifications of the variables studied. In group G1, weight and Body Mass Index (BMI) decreased significantly (p < 0.001), as well as fat index and size circumference (p < 0.01). Fat mass, hip circumference, blood pressure and rest heart rate were significantly reduced (p < 0.05). For G2, weight and BMI decreased (p < 0.01). Loss of muscle mass was observed (p < 0.05). In G3, weight decreased (p < 0.05) while muscle mass increased (p < 0.05). On analysis, the most significant changes were observed in group G1. Association of physical exercise and Cymbopogon citratus consumption has more beneficial effects on anthropometric and hemodynamic parameters in obese people.
Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.
Antioxidants properties of Murraya koenigii: a comparative study of three different extraction methods

Parithy, M.T. , Mohd Zin, Z. , Hasmadi, M. , Rusli, N.D. , Smedley, K.L. and Zainol, M.K. Faculty of Fisheries and Food Sciences, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia Faculty of Agro Based Industry, Universiti Malaysia Kelantan, Jeli, Kelantan, Malaysia Institute of Food Security and Sustainable Agriculture, Universiti Malaysia Kelantan, Jeli, Kelantan, Malaysia Stratford School, Stratford-upon-Avon, Warwickshire, CV37 9DH, United Kingdom
DIETAS DE BAIXO TEOR DE CARBOIDRATO NO TRATAMENTO DO DIABETES MELLITUS TIPO 2

A low carbohydrate diet (low carb) is widely studied to manage type 2 diabetes, demonstrating better blood glucose control can be achieved with a lower carbohydrate intake. The aim of this study was to analyze the results of clinical trials that used low carbohydrate diets in comparison to the traditional diets recommended in type 2 diabetes treatment, as for the biochemical markers of improvement disease control, reduction or exclusion of medicines usage, reduction of the waist circumference and weight loss. The articles were selected by bibliographic research in the Pubmed database, fulfilling the following criteria: randomized clinical trials using the low carb diet, published between 1998 and 2018 in Portuguese, Spanish or English. It was considered as a low carbohydrate diet, those offer macronutrient percentages equal or less than 45% or less than 130g per day. The results of the 14 articles included in this review showed difference in weight loss and a significant difference in reductions in glycated hemoglobin, fasting glucose, triglycerides and reduction of drug use, in favor of low carb diets. This review demonstrates that a low carbohydrate diet is effective in controlling the markers of type 2 diabetes, and the patient may have a better quality of life through diet. Although, more long-term and increased sample studies are needed to support this diet as treatment of the disease.
POSSIBILITIES OF THE USE OF ELECTROLYZED WATER IN POULTRY BREEDING

Abstract the paper deals with verification of the effects of the electrolyzed NaCl-water solution where a new possibility for an alternative procedure to disinfect water is provided. The experiment was done in two chicken breeding halls where the solution was used to disinfect the stable premises and at the same time it was applied into the feed water. The effects of disinfection were verified directly in the stable premises at the interval of 20, 40, 60 minutes after application. Staphylococci and Streptococci and Enterococci were inactive always after 60 minutes of effect. There was significant decrease in the number of total number of microorganisms. An evaluation of the qualitative features of the produced chicken meat, focused mainly on the PSE meat disorder, was a part of the examination, too. After the research was finished, the chickens were slaughtered at the industrial slaughterhouse and samples of the meat were taken (gradually 157 pieces in total) from the individual halls and analysed. The determined share in water loss by dripping was the most significant of the monitored indicators, which could suggest a possible development of the PSE problem, however, the meat colour and the pH values did not confirmed this. The values of pH, colour and loss of water (dripping) ascertained, processed by the T-test did not confirm the hypothesis of the assumed possible differences in occurrence of critical values of these indicators in both groups observed.
Shaping positive and negative ties to improve team effectiveness: The roles of leader humility and team helping norms

Managing and harmonizing intragroup relationships are critical for effective team leadership. Though previous studies have confirmed how team leaders promote positive ties in work teams, it remains unclear whether or how they can benefit the team by helping to reduce negative ties. How can team leaders simultaneously encourage positive ties and prevent negative ties to elevate team effectiveness? Drawing on social ledger theory and leader humility, we propose that humble leaders can improve team performance and viability through first influencing intragroup positive and negative tie density and thereby promoting team-helping norms. In Study 1 (70 professional work teams), we find that leader humility is associated with elevated team-helping norms and team effectiveness through lower levels of hindrance density rather than promoting friendship density. In Study 2 (50 retail teams), we replicate Study 1 but additionally control for transformational leadership. The results suggest that the positive association between leader humility and friendship density becomes insignificant after transformational leadership is considered, whereas the association between leader humility and hindrance density remains significant. This research contributes to the literature by illuminating how leadership is associated with team social networks and highlights practical implications for managing professional teams and developing team leadership.
Multi-style image transfer system using conditional cycleGAN

ABSTRACT This paper aims to extend the capability of Cycle-Consistent Adversarial Network (CycleGAN) by equipping it with a conditional constraint and extend it into a multi-style image transfer system that can transfer images among more than two image domains. The conditional constraint is given in the form of the target style feature map instead of a one-hot vector, and has shown to provide better transfer results. The proposed system offers greater flexibility for users to choose the style for image transfer. Experimental results show that such an architecture is not only feasible but also yields good results. The proposed architecture can be extended to other transformation applications, such as facial expressions transfer, face aging, and synthesis of various features.
Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM.
“Maxed Out on Efficiency”: Logger Perceptions of Financial Challenges Facing Timber Operations

ABSTRACT Loggers play critical roles in forest management and resource production, yet supply chain pressures can challenge their economic sustainability. We examine logger perspectives on efficiency, financial challenges, and potential solutions from two contrasting perspectives: 1) business strategy and 2) treadmill of production, a sociological critique of resource production that links economic and ecological sustainability. To provide in-depth perspectives based on lived experiences, we conducted interviews with professional loggers and other stakeholders, focusing on firms with mechanized cut-to-length systems in Wisconsin and Michigan’s Upper Peninsula. Our qualitative analysis revealed the intersection of factors that help explain financial stress. Loggers work on contract or buy stumpage from landowners and then sell to mills. Perceived challenges included competition in stumpage markets, relationships to mills that disadvantaged small firms, debt from mechanization, and site access through variable environmental conditions. Loggers worked to increase efficiency and production volume, though some loggers expressed the limits of increasing efficiency and production for improving profitability. Proposed solutions to reduce financial stress included long-term mill contracts, access to loans, and improved financial literacy. Nonetheless, individual firm efficiency will not address structural challenges such as loggers’ role in the supply chain between landowners and mills.
Microparticle Deposition on Human Serum Albumin Layers: Unraveling Anomalous Adsorption Mechanism

Human serum albumin (HSA) layers are adsorbed on mica under controlled diffusion transport at pH 3.5 and various ionic strengths. The surface concentration of HSA is directly determined by AFM imaging of single molecules. It is shown that the adsorption kinetics derived in this way is quantitatively described using the random sequential (RSA) adsorption model. The electrokinetic characteristics of the HSA layers at various pHs comprising their zeta potential are acquired in situ while using the streaming potential method. It is shown that at pH 3.5 the zeta potential of mica becomes positive for HSA concentrations above 3000 μm−2. At larger pHs, HSA layers exhibit negative zeta potential for the entire range of coverage. Thorough characteristics of these monolayers at various pHs were performed applying the colloid deposition method involving negatively charged polystyrene microparticles. The kinetics of their deposition and their maximum coverage are determined as a function of the HSA layer surface concentration, pH, and ionic strength. An anomalous deposition of microparticles on substrates also exhibiting a negative zeta potential is observed, which contradicts the Derjaguin, Landau, Vervey, Overbeek (DLVO) theory. This effect is interpreted in terms of heterogeneous charge distribution that results from molecule concentration fluctuations. It is also shown that the maximum concentration of microparticles abruptly decreases with the electric double-layer thickness that is regulated by changing ionic strength, which indicates that their deposition is governed by electrostatic interactions. One can argue that the results obtained in this work can be exploited as useful reference data for the analysis of deposition phenomena of bioparticles on protein layers.
Microcytosis in Erythropoietic Protoporphyria

Partial deficiency of the last enzyme of the heme biosynthetic pathway, namely, ferrochelatase (FECH), is responsible for erythropoietic protoporphyria (EPP) in humans. This disorder is characterized by painful skin photosensitivity, due to excessive protoporphyrin IX (PPIX) production in erythrocytes. Although several papers report the presence of iron deficiency anemia in about 50% of EPP patients, there is still no a conclusive explanation of the why this occurs. In the present work, we explored hematological indices and iron status in 20 unrelated Italian EPP patients in order to propose a new hypothesis. Our data show that microcytosis is present in EPP patients also in the absence of anemia and iron deficiency with a link between PPIX accumulation and reduced MCV, probably indicating an indirect condition of heme deficiency. Patients studied had a downward shift of iron parameters due to increased hepcidin concentrations only in a state of repleted iron stores. Interestingly, hemoglobin synthesis was not limited by iron supply except in cases with further iron loss, in which concomitantly increased soluble transferrin (Tf) receptor (sTfR) levels were detected. The mechanisms involved in the iron uptake downregulation in EPP remain unclear, and the role of PPIX accumulation in microcytosis.
Learning network event sequences using long short‐term memory and second‐order statistic loss

Modeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space–time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is limited to event sequences following some well‐known distributions, which is not true for many real life event datasets. To overcome this limitation, in this work, we propose a composite recurrent neural network model for learning events occurring in the vertices of a network over time. Our proposed model combines two long short‐term memory units to capture base intensity and conditional intensity of an event sequence. We also introduce a second‐order statistic loss that penalizes higher divergence between the generated and the target sequence's distribution of hop count distance of consecutive events. Given a sequence of vertices of a network in which an event has occurred, the proposed model predicts the vertex where the next event would most likely occur. Experimental results on synthetic and real‐world datasets validate the superiority of our proposed model in comparison to various baseline methods.
Investigation on Charge Deterioration of Electrically Charged Filter Media using Electric Force Microscopy

KIM, JOOYOUN. Investigation on charge deterioration of electrically charged filter media using Electric Force Microscopy. (Under the direction of Dr. Roger L. Barker and Dr. Juan Hinestroza) Electret filter media is widely used in filtration application as the presence of embedded electrostatic charges allows for better particle capture efficiency with lower pressure drop. The effect of exposure to organic solvents on filtration performance of electret filter media was investigated using several different organic solvents and electret filter media. The exposure of electret filter media to liquid organic solvents caused a decrease in filtration performance without significant changes in resistance. It is hypothesized that the lowered performance of the electret filter media is due to the changes in electric field caused by the liquid organic solvents or to morphological changes. Scanning Electron Microscopy (SEM) was used to examine any observable morphological changes in the fibers after exposure to liquid organic solvents. There were no observable changes in morphology of fibers, suggesting the performance deterioration by chemical exposure was not caused by morphological changes in fibers. Electric Force Microscopy (EFM) was used to analyze the electric field of electret filter media prior to and after exposure to organic liquid solvents. Changes in the electric field were detected and quantified. Electric field gradient imaging (EFGI) by phase and frequency detection was shown to be a feasible method for the analysis of charge deterioration in electret filtration media. Two mathematical models based on electrostatics fundamentals were used to explain the behavior of EFM experimental data. Model I accounted for both a coulombic force and induced polarization phenomena. Model II incorporates a capacitance term. Both models exhibited good quantitative agreement with the experimental data hence the performance deterioration of the electret filter media after chemical immersion was proven to be directly related to changes in the electric field of the sample. INVESTIGATION ON CHARGE DETERIORATION OF ELECTRICALLY CHARGED FILTER MEDIA USING ELECTRIC FORCE MICROSCOPY