Miracle-dz/UniFAR-FTUs huggingface dataset:
BIG DATA MIGHT JUST CURE CANCER - THE RESEARCH AND THE REALITY

This paper demonstrates how big data might be instrumental in curing cancer. Cancer is a disease that continues to cause many deaths all over the world due to its difficulty in detecting, diagnosis, and treatment. Figures from the World Health Organization show that the number of cases diagnosed in the U. S in 2018 was 1,735,350 while the number of cancer deaths was 609,640 [1]. Looking at these statistics demands more action in terms of research on various aspects of the disease especially early detection and finding its cure. To find the cure for cancer, this paper proposes the application of big data in predicting the cancer sequences and identifying its targeted therapy and cure. The application of big data transforms the way treatment decisions is made through an understanding of cancer’s inner working [1]. The proposed big data analytical technique to treat cancer applies the information retrieved from multiple cancer types which will be vital in identifying the potential cure for cancer. The findings from this research show that the use of high-powered computer systems to collect and process large amounts of biological data has the potential to significantly impact progress in finding a cure for cancer. The main big data technique for discovering novel drugs could be molecular profiling. It's a method that scientists use to examine specific patients' cancerous cells to distinguish genetic variants and biomarkers that are unique to them. Biological markers are molecules that can be used to confirm the existence of a disorder or illness, and they are also being used to monitor how well an individual respond to treatments [1,2]. The amount of druggable tumor-specific genetic anomalies has increased significantly over the last decade, with biological markers matching interventions providing a major protective effect in many cancer types. As a result, molecular biology has been critical not just for tumor detection and pathogenesis, but also for therapeutic decision-making in clinical practice. The science of precision oncology has been transformed by the advent of next-generation sequencing technology and the growing number of large-scale cancer molecular profiling projects in many medical and research institutions across the world.
Oxidative Impact of Dietary Triclabendazole in Galleria mellonella

Recent studies have shown that anthelmintic and antifungal agents are recommended as alternative agents in chemical control of insect pests. In this study, first instar larvae of the greater wax moth, Galleria mellonella, were reared on artificial diets containing 0.001, 0.01, and 0.1 g/100 g of triclabendazole, an anthelmintic benzimidazole. The eff ects of these triclabendazole concentrations on the lipid peroxidation product, malondialdehyde, (MDA) and glutathione-S-transferase (GST) activity were investigated in all G. mellonella developmental stages, as well as in the midgut tissue of the insect. Compared to controls, the highest triclabendazole concentration (0.1%) increased MDA content from 0.182±0.03 to 0.415±0.04 nmol/mg protein in the larvae, from 0.190±0.04 to 0.626±0.06 nmol/mg protein in the pupae, and from 0.354±0.06 to 0.451± 0.04 nmol/mg protein in the adults. MDA content was determined to be 0.141±0.02 to 0.835±0.13 nmol/mg protein in the midgut. The highest concentration of triclabendazole was found to significantly increase GST activity in the larvae, pupae, adults, and in the midgut tissue of G. mellonella in comparison to controls. The three dietary concentrations of triclabendazole resulted in a high degree of oxidative stress and an increase in the activity of the detoxification enzyme GST following an increase in MDA levels in the midgut and in all developmental stages of G. mellonella.
Mobile-based Student Council Voting System Case of Federal Technical Institute (FTI)

:- This mobile-based student council voting system is mainly designed for the Federal Technical Institute students’ council. This android application voting system is an easy, fast, and efficient voting system. And it can be performed simply with a little knowledge of the android phone. Mobile application voting system helps its users (students) to vote for their representatives. So Generally, this all features of the system are making the students interested to use mobile voting system. This project emphasizes the manual system that is described above. After studying this manual system by using different types of methodologies we have developed this project that simplifies the coordinating and advising system by automating some of the activities that are done manually. Generally, this system has a major mechanism to submit vote by the mobile application, enable students and candidates to get fair elections and other users. This project will show the design of the project and the information of the tools and techniques that were used to document the project and implementation tools of this system such as XML, PHP, and android java. And we try to put sample UI and sample algorithm. Our system will have a centralized database and each user will have a user account that enables them to do some tasks such that votes check their status is vote or not, the candidate also checks the current point during the election, and Authorized Administrators will manage the system on their android mobile phone with the network. They will have the privilege to log in to the system and they can Register new and view existing voters, candidates, and EC records. And this project also talks about the advantages of the mobile-based student council voting system that motivate different institutes and organizations to develop mobile-based student council voting system applications. This project also expresses the key issues that need to be considered during the development of this project
Evaluation of the Relationship Between Microalbuminuria and Early Neonatal Sepsis, Respiratory Distress Syndrome in Preterm Infants

Objective: Early neonatal sepsis and respiratory distress syndrome lead to neonatal mortality in preterm infants during the first days of life. The objectives of this study were to identify whether spot urine albumin/creatinine ratio is an early diagnostic indicator of early neonatal sepsis and respiratory distress syndrome in preterm infants. Material and Methods: This was a prospective longitudinal analysis of 126 preterm infants born at less than 34 weeks of gestation. In this study, we evaluated serum concentrations of albumin, creatinine, C-reactive protein (CRP) and spot urine albumin/creatinine ratio on the first, third and seventh day of life. We also investigated the association of these parameters with early neonatal sepsis and respiratory distress syndrome in preterm infants during the first days of life. Results: There was a statistically significant difference between spot urine albumin/creatinine ratio and early neonatal sepsis group in the first and seventh day of life (p=0.01). Urinary albumin/creatinine ratios were not statistically significant in the respiratory distress syndrome group. Serum albumin concentrations and spot urine albumin/creatinine ratio were not correlated during the first days of life. There was also no relationship between gestational age, birth weight and spot urine albumin/creatinine ratio during the first days of life. Conclusion: Our results suggest that spot urine albumin/creatinine ratio during the early postnatal period can identify early neonatal sepsis in preterm infants.
Plant Leaf Recognition and Disease Detection Using GoogLeNet

: Manually detecting disease of a plant can be a wearisome and difficult task. Furthermore, the level of accuracy is something that unintentionally may get compromised. To overcome this issue, an automated leaf disease detection system can be consolidated, via the means of image processing. In this paper, we propose a system using GoogLeNet that scans the leaf, identifies its type, and further recognizes the disease based on image recognition. GoogLeNet is advisable as it can run even on low computational devices. Leaf disease detection using GoogLeNet achieves a good level of accuracy. Furthermore, there can be some error factors which can be corrected using a discussion forum. It will also store information regarding the disease and the discussion taking place. In this forum, forward-looking farmers could take an initiative to spread agricultural information by being an active part of the community, and also help others to do so. Thus, the use of this leaf disease detection system will reduce both the time and effort required.
Sliding mode control design of wind power generation system based on permanent magnet synchronous generator

This paper aims to implement a new contribution for sliding mode control (SMC) of permanent magnet synchronous generator (PMSG) for wind systems conversion with track the maximum power point tracking (MPPT) power. The SMC is a very popular approach due to its robustness in dealing with the non-linear electrical power systems. In this work, the application of the SMC control is by using the non lineare model of the PMSG. The objective of this work is to control stator active and stator reactive power, and the voltage-frequency for a better injection into the network. The results obtained show better robustness.
Global Prediction of Soil Saturated Hydraulic Conductivity Using Random Forest in a Covariate‐Based GeoTransfer Function (CoGTF) Framework

Saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in land surface models. For large scale applications, Ksat is often estimated from pedotransfer functions (PTFs) based on easy‐to‐measure soil properties like soil texture and bulk density. The reliance of PTFs on data from uniform arable lands and the omission of soil structure limits the applicability of texture‐based predictions of Ksat in vegetated lands. To include effects of terrain, climate, and vegetation in the derivation of a new global Ksat map at 1 km resolution, we harness technological advances in machine learning and availability of remotely sensed surrogate information. For model training and testing, a global compilation of 6,814 geo‐referenced Ksat measurements from the literature was used. The accuracy assessment based on spatial cross‐validation shows a concordance correlation coefficient (CCC) of 0.16 and a root mean square error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC = 0.79 and RMSE = 0.72 for non‐spatial cross‐validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on easy‐to‐measure soil properties. The validation of the model indicates that Ksat could be modeled without bias using Covariate‐based GeoTransfer Functions (CoGTFs) that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.
Elastase Activity from Staphylococcus epidermidis

The gene sepA from Staphylococcus epidermidis TU3298-P, encoding the extracellular neutral metalloprotease SepPl, was cloned into pTl81mcs. DNA sequencing revealed an open reading frame of 1,521 nucleotides encoding a 507-amino-acid protein with an Mr of 55,819. The sepA-containing DNA fragment did not hybridize with Staphylococcus hyicus or Staphylococcus carnosus DNA. Expression of sepA in the protease-negative S. camnosus(pT181mcsPl) resulted in overproduction of a 33-kDa protease found in the culture medium. The first 15 N-terminal amino acids of the partially purified protease completely matched the deduced DNA sequence starting at GCA (Ala-208). This finding indicated that SepP1 is synthesized as a preproenzyme with a 28-amino-acid signal peptide, a 179-amino-acid hydrophilic pro region, and a 300-amino-acid extracellular mature form with a calculated Mr of 32,739. In activity staining, the mature protease prepared from S. carnosus(pT181mcsPl) corresponded to the extracellular S. epidermidis T03298-P protease. The partially purified protease had a pH optimum between 5 and 7, and its activity could be inhibited by zincand metal-specific inhibitors such as EDTA and 1,10-phenanthroline, indicating that it is a neutral metalloprotease. The protease had a low substrate specificity. Glucagon was cleaved preferentially between aromatic (Phe) and hydrophobic (Val) amino acids. The protease hydrolyzed casein and elastin. The amino acid sequence of the mature form of SepP1 revealed pronounced similarities with the thermolabile and thermostable neutral proteases of various bacilli (44 to 55% identity) and a central part of the mature form of the Pseudomonas aeruginosa elastase (31% identity). From homology comparison with the Baciflus thermoproteolyticus thermolysin, we predict that mature SepP1 binds one zinc ion at a conserved zinc-binding site.
Implementation of an incremental deep learning model for survival prediction of cardiovascular patients

Cardiovascular diseases remain the leading cause of death, taking an estimated 17.9 million lives each year and representing 31% of all global deaths. The patient records including blood reports, cardiac echo reports, and physician’s notes can be used to perform feature analysis and to accurately classify heart disease patients. In this paper, an incremental deep learning model was developed and trained with stochastic gradient descent using feedforward neural networks. The chi-square test and the dropout regularization have been incorporated into the model to improve the generalization capabilities and the performance of the heart disease patients' classification model. The impact of the learning rate and the depth of neural networks on the performance were explored. The hyperbolic tangent, the rectifier linear unit, the Maxout, and the exponential rectifier linear unit were used as activation functions for the hidden and the output layer neurons. To avoid over-optimistic results, the performance of the proposed model was evaluated using balanced accuracy and the overall predictive value in addition to the accuracy, sensitivity, and specificity. The obtained results are promising, and the proposed model can be applied to a larger dataset and used by physicians to accurately classify heart disease patients.
A comprehensive review on neurokinin-1 receptor antagonism in breast cancer

Breast cancer is one of the most prevalent cancer types globally, and has caused more deaths in women. The undecapeptide substance P is participated in various physiological processes like emesis, pain, inflammation, antitumor activity, and also regulates the function of gastro intestinal tract and central nervous system by binding to NK1R. substance P (SP)/NK1R regulates the proliferation, migration, and metastasis of breast cancer cells and also controls the proliferation of endothelial cells for angiogenesis. It exerts an antiapoptotic influence as well. In breast cancer, NK1R is over expressed. It has been demonstrated that the NK1R antagonist inhibits the breast cancer cell proliferation in a concentration dependent manner, induces the cell death by apoptosis and inhibit angiogenesis. The antagonists of the NK1 receptor are safe and well tolerated. In this paper, we reviewed the extensive studies that have been done on the SP/NK1R system in breast cancer. In fact, only currently available are NK1R antagonist Aprepitant and its analogues. The research must, therefore, focus on drugs with elevated anti-tumor activity with lesser side effects. Modulating the function of the NK1 receptor as a therapeutic strategy is, therefore, the most attractive area of research for the design and creation of new molecules with important clinical values for the future.
Application of Collaborative Representation in Audio Signal Recognition

Feature extraction and classifier design are the main problems of acoustic signal recognition algorithms. In this paper, we extract the mel-frequency cepstral coefficients of the acoustic features of vehicles in complex scenes. Collaborative representation is introduced for the design of a classification scheme (Collaborative Representative Classification, CRC), which synthetically considers the relationship among samples. Experiments show that the proposed algorithm produces good performance in vehicle recognition for the case of a complex data set. Compared with other classification algorithms, the method improves the precision of recognition.
Esterification of chitosan with L-alanine and a study on their effect in removing the heavy metals and total organic carbon (TOC) from wastewater

Abstract In this work, chitosan was modified by the esterification with L-alanine in the presence of H2SO4 as a catalyst to increase the number of amino groups with the aim of increasing the adsorption efficiency. Chitosan (CS) and chitosan-O-alanine (CS-Aln) were characterized and investigated by elemental analysis, Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). The results obtained from elemental analysis and IR indicated the presence of sulfuric acid after neutralization as a crosslinker between CS-Aln chains. Also CS-Aln is more amorphous than CS due to the ionic bonds of crosslinker. The removal of three heavy metals (Mn2+, Pb2+ and Al3+) and total organic carbon (TOC) from wastewater by CS and CS-Aln in the batch mode has been studied at different adsorbent dosages, temperatures and contact times. The maximum metal ions removal efficiency using CS achieved 99.6%, 99.1% and 98.9%, respectively, while by using CS-Aln 95.3%, 99.3% and 98.9% were achieved. However, the maximum adsorption capacity of TOC by CS achieved 50 mg/g and 89 mg/g by CS-Aln. The total maximum adsorption capacity of CS-Aln is higher than CS.
On the quasicompactness of the moduli stack of logarithmic G-connections over a curve

Fix a smooth projective curve over a ﬁeld of characteristic zero and a ﬁnite set of punctures. Let G be a connected linear algebraic group. We prove that the moduli of G -bundles with logarithmic connections having ﬁxed residue classes at the punctures is an algebraic stack of ﬁnite type.
Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)

The population structure of Crematogaster subdentata Mayr, 1877 in the primary (native) (Uzbekistan) and secondary ranges (Crimea, Rostov-on-Don) is analyzed. The data obtained indicate an uncommon behavior for invasive ants – the formation of supercolonies in the primary range (the size of the foraging area is about 600 m2) in the urban territory. Nesting in houses and in trunks of old trees occurs both in the zone of invasion and in the primary range. The data of the distribution of the second invasive species in the same regions – Lasius neglectus Van Loon et al., 1990 are provided. Comparison of the population structure (ratio of the mono- and polycalic colonies, presence of the supercolonies and their sizes), parameters of the colonies (average number of the nests and forage trees per colony) showed the ad- vantage of Crematogaster subdentata over Lasius neglectus, which is gradually crowded out by the first species in the places of contact.
Comparation of the Montreal Cognitive Assessment Score Indonesian Version (MoCA-INA) and the Personal And Social Performance Scale (PSP) Score in Schizophrenic male with Dominant Negative Symptoms and Non-Dominant Negative Symptoms at Mental Hospital Prof. DR. M. Ildrem Medan

Objective: Schizophrenia is a serious neuropsychiatric disorder involving major disturbances in the patient's thinking, perception, emotions and behavior, associated with morbidity, disability, and a large potential for death, thus carrying a large socioeconomic burden on patients, caregivers and health care providers . Predictors of disability include cognitive deficits and negative symptoms, both of which account for the disability in schizophrenia that is difficult to treat. Aims: To determine the comparison of the Montreal Cognitive Assessment Score Indonesian Version (MOCA-INA) and Personal and Social Performance Scale (PSP) scores in Schizophrenic male with Dominant Negative Symptoms and NonDominant Negative Symptoms at Mental Hospital Prof. DR. M. Ildrem Medan. Methods: This is an analytical study with a cross-sectional study approach, conducted at the Mental Hospital Prof. DR. M. Ildrem Medan, from January to June 2019 with 80 subjects include (40 subjects with dominant negative symptoms and 40 subjects with non-dominant negative symptoms) who met the inclusion and exclusion criteria. Statistical test analysis used the Mann-Whitney U test. Results: The results showed that the median MoCA-INA score for schizophrenic male with dominant negative symptoms was 20.00 with a minimum score of 16.00, the maximum score was 24.00 and the median MoCA-INA score for schizophrenic male with non-dominant negative symptoms was 21.50 with a minimum score of 16.00 and a maximum score of 26.00. The median PSP score of schizophrenic male with dominant negative symptoms was 55.00 with a minimum score of 45.00, the maximum score was 70.00 and the median PSP score for schizophrenic male with non-dominant negative symptoms was 56.50 with the minimum score is 45.00 and the maximum score is 71.00. Conclusion: There was no difference in the MoCA-INA score between Schizophrenic male with Dominant Negative Symptoms and non-Dominant Negative Symptoms with p value = 0.114. There was no difference in the PSP score between Schizophrenic male with Dominant Negative Symptoms and nonDominant Negative Symptoms with with p value = 0.174.
Tsunami propagation kernel and its applications

Abstract. Tsunamis rarely occur in a specific area, and their occurrence is highly uncertain. Generated from their sources in deep water, they occasionally undergo tremendous amplification over decreasing water depth to devastate low-lying coastal areas. Despite the advancement of computational power and simulation algorithms, there is a need for novel and rigorous approaches to efficiently predict coastal amplification of tsunamis during different disaster management phases, such as tsunami risk assessment and real-time forecast. This study presents convolution kernels that can instantly predict onshore waveforms of water surface elevation and flow velocity from observed/simulated wavedata apart from the shore. Kernel convolution involves isolating an incident-wave component from the offshore wavedata and transforming it into the onshore waveform. Moreover, unlike previous derived ones, the present kernels are based on shallow-water equations with a damping term and can account for tsunami attenuation on its path to the shore with a damping parameter. Kernel convolution can be implemented at a low computational cost compared to conventional numerical models that discretise the spatial domain. The prediction capability of the kernel method was demonstrated through application to real-world tsunami cases.