Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Motor Imagery EEG Signals Using a CNN Architecture and a Meta-heuristic Optimization Algorithm for Selecting Training Parameters

A Brain Computer Interface (BCI), is a system created for performing communication and control of computational devices by the analysis of brain signals. Particularly, Motor Imagery (MI) based BCI systems allow a real-time interaction by using the electrical signals that are generated by the brain when the user imagining certain movements or actions. Acquisition of these signals can be done by invasive and non-invasive devices, among which, electroencephalography is a non-invasive technique that is widely used. It is based on the superficial placement of the electrodes on the scalp, which avoid affecting the health and well-being of potential users, also is a portable technology and lower-cost than other alternatives. From the different stages required to develop a BCI, the signal characterization and the classification tasks continue to be the main research challenges, since the performance of the whole system depends on them. This thesis proposes the use of convolutional neural networks (CNN) for the classification of electroencephalographic signals, in order to identify the action imagined by a person. The proposed network architecture is trained with representations of the spectral power density (PSD) of the signals; and the hyperparameters of the network are defined by a metaheuristic optimization algorithm, which obtains the best accuracy in the signal classification in training stage. The proposed approach was evaluated using two public and well-known databases, i.e. BCI Competition IV 2a and BCI Competition IIIa. According to the results, this approach provides a reliable strategy to differentiate movement imagination, outperforming state of the art that used the same datasets. These results demonstrate that this is a valuable and promising strategy for the design of brain computer interfaces.
Execution Details and Assessment Results of a Summer Bridge Program for Engineering Freshmen

This paper reports the execution details and the summary assessment of a Summer Bridge Program (SBP) that is a part of an ongoing National Science Foundation (NSF) Scholarships in Science, Technology, Engineering, and Math (S-STEM) project in the College of Engineering at the University of Illinois at Chicago. The project supports 18 Scholars (academically-talented, lowincome engineering students). The primary goals of the SBP are facilitating Scholars’ transition to their first-year and improving their academic success. To achieve these goals, the SBP is implemented as a two-week on-campus intensive experience that occurs in the summer before the student’s first year. The first round of the SBP was completed in Summer 2018 and the current paper offers the details, lessons learned, and a brief evaluation of the SBP. Based on the assessment data, it is concluded that the SBP was successful in achieving its stated goals. The evaluation results and the lessons learned from the SBP execution can be used to build a sustainable Summer Bridge Program for all first-year engineering students in the future.
PCR Based Strategies to Identify and Differentiate Species Specific Meat

Food authenticity and adulteration are the major issues where food industry and society is concerned. Due to various religious, economic and sanitary reasons specific and well founded techniques to distinguish the species of meat is required now a days as contamination of meat product with meat of cheaper or unwanted species is a common practice worldwide. Since last few years, researchers are trying to implement molecular genetic approaches for identification of meat species due to its advancements and sensitivity in addition to cost effectiveness and rapid. In the given review we will focus on PCR-based techniques for identification and differentiation of species specific meat.
Polycystic Ovary Syndrome: An Overview

Polycystic ovary syndrome (PCOS) is the most common endocrinopathy that affects women. PCOS is also a leading cause of infertility. Women with PCOS may present with obesity, amenorrhea, oligomenorrhea, infertility, or androgenic features. Those with PCOS are also at increased risk for both diabetes and diabetic complications and cardiovascular disease, with a risk of a myocardial infarction 7 times the normal. We know that if patients with PCOS are screened for these diseases, many long-term complications can be prevented.
Accountability Of Budget Performance Of Poverty Alleviation In Regency Of Sidoarjo

Accountability of budget performance on poverty alleviation, which become object of the research consisted of 4 (four) issues. Firstly, process accountability which was implemented in planning process and budget decision on poverty alleviation. Secondly, program accountability which was implemented in management of program implementation on poverty alleviation. Thirdly, result accountability which was implemented in outcome outreach of poverty alleviation budget. Fourthly, budgeting accountability which was implemented in outreach of value for money (consisted of economic, efficiency, and effectivity) of budget of poverty alleviation.
Road Extraction from Satellite Image Via Auxiliary Road Location Prediction

Road extraction from satellite images is usually corrupted with several disconnected segments so that it does not satisfy the real application. The segmentation-based methods fail to correct separated roads due to the incompleteness information. Therefore, this paper introduces auxiliary Road Location Prediction(RLP), a task leveraging global context information to help road segmentation infer each road segment. The auxiliary task has two branches: horizontal location prediction and vertical location prediction which can predict locations of all the roads. By combining road segmentation and RLP, road extraction performance is effectively improved. As a result, the additional training signals help the primary road segmentation task to aggregate surrounding scene information to reason about its connectivity. The experiments on two public datasets have demonstrated the effectiveness of the proposed method.
Interference in memory consolidation of non-native speech sounds

For several decades, researchers have been investigating the challenges of and constraints on learning the speech sound inventory of a second language in adulthood. Commonalities that have emerged from these findings include the immense individual variability reported in non-native speech sound learning studies and the rare attainment of native-like proficiency in perception or production of second language speech sounds. While numerous studies have shed light on various aspects of this challenging process, many questions about the extent and nature of these difficulties remain. A nascent line of research suggests that some of the difficulty in non-native speech sound learning could be attributed to various sources of interference that disrupt the memory consolidation process, thus interfering with the retention of learned phonetic information. It is well documented in the broader learning literature that interference from competing stimuli or subsequently learned skills can disrupt memory consolidation processes. However, this phenomenon has received little attention in the speech literature, and the potential sources of interference in the speech domain have yet to be identified. In this review, I discuss how integrating theories of memory consolidation with non-native speech sound learning models can more accurately capture patterns of learning observed in the non-native speech sound learning literature, specifically patterns showing failures of memory consolidation due to interference.
Effect of Cocoa and Thyme Powder Alone or in Combination on Humoral Immunity and Serum Biochemical Metabolites of Broiler Chicks

This experiment was conducted to evaluate the effect of cocoa and thyme powder separately or in combination on serum biochemical parameters and humoral immune responses in broiler chicks. Two hundred and forty day-old broiler chicks (Ross 308) were randomly assigned to four treatments with four replicates of 15 chicks based on a completely randomized design. The dietary treatments included the basal diet as control, supplemented groups receiving 3 g/kg cocoa powder, 5 g/kg thyme powder and 2+3 g/kg a combination of cocoa and thyme added to the basal diet. Blood samples were taken at 28 d to quantify antibody titer production against Newcastle and Influenza viruses and SRBC. Serum biochemical metabolites and leucocyte types were determined at 42 d of age. Serum total protein and globulin concentration increased significantly (p<0.05) in birds fed diets supplemented with a combination of additives. Diet supplementation resulted in a marked reduction of serum total and HDL cholesterol compared to control group (p<0.05). Dietary treatments did not induce any significant impacts on antibody titer production, heterophil, lymphocyte, monocyte and eosinophil counts, albumin to globulin and heterophil to lymphocyte ratios. In conclusion application of thyme and cocoa powder, particularly their combination in broiler diets has the potential to improve serum biochemical values but failed to have favourable impact on humoral immune responses of the birds.
ERC Leitlinien Nach

The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation phase guidelines for adults, which are based on the 2020 International Liaison Committee on Resuscitation consensus on cardiopulmonary resuscitation. The topics covered include post-cardiac arrest syndrome, the differential diagnosis of the causes of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.
Application of validated LC-MS/MS method development to quantify pexidartinib in biological media: In-vivo bioavailability studies in healthy rabbits

A specific liquid chromatographic with tandem mass spectrometric technique was established and validated for the analysis of pexidartinib in plasma samples. These samples were presented by selecting liquid–liquid extraction process with ethyl acetate. The procedure was established and subjected for validation with respect to the parameters’ specificity, sensitivity, carryover, recovery, precision, matrix effect, accuracy, and stability. Pexidartinib and Ledipasvir were eluted within 6.5 minutes on ZorbaxSB-C18 column (250 × 4.6 mm × 5 μm) with formic acid (0.1% v/v) and acetonitrile in the proportion of 20:80 in the movable phase with 0.9 ml/minutes infusion flow rate. This analytical method was also applied successfully to assess the bioavailability studies in six healthy rabbits. From in-vivo bioavailability studies, pexidartinib showed Tmax of 3.09 ± 0.14, and mean Cmax and AUC0→last for test formulation were 211.63 ± 11.045 and 4,448.8 ± 257.892, respectively.
Cost-effectiveness of breast, lung, colon, prostate and cervical cancer outcomes in Brazil: a worldwide comparison

Introduction Cancer is the second leading cause of death in the world, and it is expected to be the main cause by the year 2030. Current trends of higher incidence and the introduction of new treatments lead to the challenge of treating more people with increasing costs per capita. In Brazil, current and future challenges are even more significant because of the limited resources destined for healthcare. Methods We propose a methodology to compare cost-effectiveness performance with a regression of cancer lethality against the resources available for different nations, using the gross domestic product and the mortality-to-incidence ratio. Our objective is to evaluate and compare outcomes observed in Brazil. Results According to our methodology, Brazil is performing well in breast and prostate cancer (observed lethality 9% and 15% lower than expected, respectively). It performs close to expected in colon (0.8% higher) and cervix (2% higher). However, lung cancer had a higher lethality than expected (6.5% higher). We also found that breast, prostate and cervical cancers are the primary sites more related to income. Lung cancer had the weakest relationship with resources. Conclusion Brazil has different cost-effectiveness results in the management of cancer depending on the primary site. Also, national income has a significant and heterogeneous effect on the lethality of different tumour types. This economic analysis is important for low- to middle-income countries seeking to evaluate cancer outcomes in limited-resource settings.
Design and Implementation of SAT Solver in Script and System Language with Analysis of Language Features

In this project, we first design a SAT solver by leveraging two language-free optimization occurrence list and heuristic branching and several language-specific optimizations, based on naive DPLL algorithm. We implement the SAT solver in two types of language, i.e. script language Python and system language C++. In experiment, our optimized SAT solver outperforms the naive DPLL solver by 1000 time faster. We also test their performance in 3 datasets generated by ourselves, including random generate CNF, N queens and Sudoku. In summary, the experiment shows that C++ is much faster than Python and the gap increases with scale and complexity of problem to solve. The language features are analyzed based on experiments results and we found Python is inefficient in management usage but takes up less memory due to automatic garbage collection. It also owns lower IO ratio due to its slowness. We also found even though well-defined data structure in Python can decrease complexity significantly and make programming easier, it causes some drawback such as no pointer and passing via reference introduce more computation in duplication and indexing, making C++ faster than Python. In the last, we make an interesting application on encryption using our own SAT solver.
HEDCM: Human Emotions Detection and Classification Model from Speech using CNN

Emotion Detection and Classification using Speech is still in an emerging stage in the research community. In this paper, a speech-based classification model is proposed, categorizing speech into six basic emotion categories: anger, surprise, sadness, fear, happiness, and neutral. There are different techniques which are used in speaker discrimination and sentiment analysis task. Every method has its pros and cons. In the proposed model, the Mel Frequency Cepstrum Coefficient (MFCC) feature calculates the various attributes of the speech signal and Convolutional Neural Network (CNN) model to classify different types of emotions. The numerical evaluation has been performed with the RAVDESS dataset, which consists of recorded audio files by 12 Actors & 12 Actresses. For the emotion classification, prediction accuracy of 70.22% was obtained in our proposed model, along with a model accuracy of 96%. The accuracy is improved compared to other similar implementations as deep learning works better than old ML classification methods.
Effect of Public Financial Management Practices on Performance of Kisumu County Government

: The main purpose of the study was to establish effects of public financial management practices on performance of Kisumu County Government. The specific objectives of the study were to establish the effect of auditing, Information technology, regulatory practices and public involvement in decision making on performance of Kisumu County Government. The study was guided by System Theory. The researchers employed a survey research design. The target population for the study was composed of ICT officers, Accountants, Finance officers, Auditors, County Secretary and Ward Administrators from Kisumu County Government. And a total of 70 respondents were targeted from Kisumu county. Data collected from the field was imported into a computer programmed statistical package for social sciences (SPSS) for analysis. Both descriptive and inferential statistics was used to analyze the data. The findings were presented in form of tables. The study found that all constructs of the study had a significant effect on performance of Kisumu County Government. The most influential construct was auditing practices, Information Technology, Public involvement and lastly regulatory practices in that order. The study concluded that assurance provided by auditing, Identification of risks and control of weaknesses, reports and opinion provided by auditor general enhances county government performance. Public hearings and meetings collect views regarding budgeting process thereby enhancing performance. The study recommends that the county governments should consider adopting and implementing sound financial management practices as envisaged in relevant legislative framework and particularly Public Financial Management Act and auditing to be done on a regular basis.
Motion Deblurring Analysis for Underwater Image Restoration

Exploration of Underwater Images will be a challenging task due to its degradations by haze, blur, colour cast and noise. In order to extract the latent information from the blurred portion, its parameters are estimated and various algorithms were analysed. Initially, length and angle of motion blurred image are estimated. Then, filters and restoration algorithms were implemented for controlling of ringing artifacts, removal of noise and latent information restoration. Some of the model based classical methods were implemented and analysed for underwater motion blurred image restoration.
Potential edible coating of pectin obtained from banana peel for fruit preservation

Edible coating is one of the strategies to preserve food because it provides protection by covering the food with its coating. Pectin is one of the sources of edible coating which can be obtained from biomass. This research aims to (1) investigate the potential banana peel as the source edible coating, (2) characterize the characteristics of pectin obtained, and (3) evaluate the edible coating effect on tomato in different temperatures storage. This research used the experimental method in the laboratory. The making of edible coating solution used banana peel pectin was varied with concentration of 2% and 10% added the following additives, namely: CMC, glycerol, potassium sorbate, and stearate fatty acids. The edible coating solution was applied to tomatoes with variations of temperature storage. The results showed that: (1) banana peel has potential as source edible coating, (2) the characterizations of pectin are in accordance with standard of international pectin quality, and (3) in the room and cold temperature storage, the coating tomato is 10% better than that of 2% and without treatment seen from the shrinkage value of weight.
A comparative study of energy and task efficient load balancing algorithms in cloud computing

The future growth of the Internet of Services has fundamentally changed the emergence of cloud computing. Cloud data centres serve multiple tenant demands for cloud applications that discharge vast amounts of electricity, leading to high operating costs and environmental diffusion of carbon dioxide (CO2). To fix this is the need for preservation to enable potential use by building a new structure and measuring the effect in a cloud data centre. Consequently, the use of pruned electricity reduces the cost of processing power. In order to meet energy-efficient data centres in the cloud, adjusting to optimal load balancing processing a good way for energy savings. To minimise the large energy use of cloud data centres herds, this focuses on increasing efficacy by breaking the workload evenly. In this paper, we plan to provide a comprehensive comparative analysis in cloud computing of current load balancing algorithms.
STUDY ON THE ADSORBING AND REFINING ALKALOID FROM CYNANCHUMKOMAROVIIAL. ILJINSKI WITH MACRO-POROUS RESIN

Cynanchum Komarovii Al.Iljinski is used as the raw material to be studied for the condition and method of adsorbing and refining alkaloid.In several types of Macro-porous resin tested,the results show that the adsorbing ability of AB-8 type is the best and acidity can influence the adsorbing ability.At pH=2 AB-8 type Macro-porous resin has the greatest adsorbing ability.The concentration of alcohol can influence the desorption.When the concentration of alcohol is 70%,the desorption is the most effective.
An intelligent system for transforming natural language queries into SQL and its execution

In today’s world information storing and retrieval plays an important role. Database systems play a key role in the new commercial system for information storage. For accessing the data from database, a person who is not having the knowledge of SQL may find themselves handicapped while dealing with the database. This presents a significant limitation in a developing country such as India, because even today, a very large majority of the population does not have the technical knowledge of how to deal with database systems. In such a case, Natural language processing (NLP) assumes a significant job. NLP is getting perhaps the most dynamic strategies utilized in Human-PC Interaction. It is a part of AI which is utilized for Information Retrieval, Machine interpretation, and Language Analysis. This paper, proposes a system which allows the user to query the database in a compatible mode language, through a convenient Graphical User Interface which results in the data required by the user.
A nine-point sampling point scheme for twenty node brick element

A new sampling point scheme with nine evaluation points were introduced in this research study for twenty noded brick elements. The new sampling points were located inside the brick element at the corners and center point of the 20 node brick element. This integration scheme can be assumed to be an imitation of Gaussian integration scheme. Standard benchmark problems were chosen from the different research works and compared with our proposed scheme. Finally, the proposed integration scheme achieves good results for twenty node brick element on different performance parameters of finite element analysis.
Review on toxic metal ions removal by using activated carbon prepared from natural biomaterials

Heavy metals has wide range of applications including industrial, domestic, medical, agricultural and technological field. Due to industrialization and urbanization variety of heavy metal ions are released in the environment. Toxicity of heavy metal ions depends on the route of exposure, media of exposure and its dose. Some of the metals, due to its high degree of toxicity are incorporated in CERCLA (Comprehensive Environmental Response, Compensation and Liability Act) priority list of hazardous substances. According to CERCLA, arsenic is the top most toxic metal followed by lead and mercury. These metals are proven to be carcinogens for human beings even at lower concentration. Contamination of water resources by toxic metal ions is a common problem since past years. There exists different methods for removal of toxic metal ions from waste water but adsorption is the most widely used and effective method. There are several adsorbents used for adsorption of toxic metal ions but, adsorbents prepared from low-cost and ecofriendly raw materials like coconut shells, rice husk, sugarcane bagasse, banana peels, potato peels, corncob, wheat straw, neem barks, almond shells and others are capturing researcher’s attention due to its easy availability and effective removal efficiency. This review highlights low cost adsorbents used for elimination of heavy metal ions from waste water and its other usage possibilities based on the literature study. This paper also highlights emerging potential bio-adsorbents which can provide maximum removal efficiency.