Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of chemical and physical conditions on the production of bacteriocin by Aeromonas hydrophila

Aeromonas hydrophila have been isolated as a cause of a cute gastroenteritis in 23 (5.6%) of 410 patients. Other bacterial enteropathogens have been isolated from 387 patients with diarrhea, were 19 different strains. A. hydrophila occurred more commonly in children with acute diarrhea, the results showed that 18(78.26%) isolates of A. hydrophila found in children under 10 years old ,distributed to 10(43.47%) in male and 8(34.78%) in female ,and in adults with diarrhea 5 (21.73%). In the other hand, we noticed frequency of isolation was higher in male 14(60.86%) when compared with 9(39.14%) in female. Six strains of A. hydrophila have been observed to have bacteriocin activity against 12 of 23 different A. hydrophila ,as well as Staphylococcus aureas, Pseudomonas aeruginosa, Escherichia coli, Enterobacter cloacea and Shigella dysenteria. The results showed Bacteriocin-like substances (BLS11) had isoinhibitory activity on 10 same A. hydrophila species and heteroinhibitory activity effects on all pathogenic bacterial strains used, while BLS5 showed isoinhibitory activity on 2 same A. hydrophila species and heteroinhibitory activity by effecting on gram negative only, and BLS3& BLS12 showed activity on E. coli isolates only, and none of BLS1& BLS10(isoinhibitory activity on 1 A.hydrophila respectively) had effect on all pathogenic bacteria. Among the standard laboratory media used Brain Heart Infusion broth (BHI) showed the maximum production and poor yields resulted from growth in Peptone Glyserol (PG) and Nutrient broth. We selected BLS11 to their wide range effect on same species and enteric pathogenic strains, to study the Influence of chemical and physical conditions on the production of BLS by A.hydrophila. The BLS11 preparations from A.hydrophila11 strains of A. hydrophila were tolerant to all three treatments of surfactant. In the other hand, effect of organic acid on BLS production BLS11 has been studied and showed no remarkable difference in zone of inhibition when used acetone as affecter element, while both of isopropanol and ethanol have narrow inhibition zone range when compared with control strain. These results indicated that most A. hydrophila might be harboring plasmid mediated bacteriocin like substance, and there are no relation between BLS production and number of plasmid bands present in bacteria.
Hypnotic Modulation of Autonomic Nervous System (ANS) Activity

Hypnosis, a time-honored therapeutic approach, has gained widespread recognition for its effectiveness in addressing a range of psychological and somatic disorders. While its primary effects are mediated by central top–down mechanisms, hypnosis also exerts peripheral influence by modulating the autonomic nervous system (ANS). Psychophysiological measures, such as heart rate (HR) and its variability (HRV), electrodermal activity (EDA), respiratory rate (RR), and the analgesia nociceptive index (ANI), serve as reliable indicators of ANS activity. Studies have consistently demonstrated hypnosis’ ability to significantly impact ANS functions, lowering sympathetic activity and enhancing parasympathetic tone. This effect is particularly pronounced during relaxation procedures and is influenced by mediating factors like hypnotizability and task conditions. Despite methodological limitations, this review highlights the potential of enhanced ANS modulation through hypnosis, including its connections to the central nervous system (CNS), to optimize therapeutic outcomes in patients with psychosomatic disorders associated with ANS dysfunction.
Inhibitory Efficacy of Main Components of Scutellaria baicalensis on the Interaction between Spike Protein of SARS-CoV-2 and Human Angiotensin-Converting Enzyme II

Blocking the interaction between the SARS-CoV-2 spike protein and the human angiotensin-converting enzyme II (hACE2) protein serves as a therapeutic strategy for treating COVID-19. Traditional Chinese medicine (TCM) treatments containing bioactive products could alleviate the symptoms of severe COVID-19. However, the emergence of SARS-CoV-2 variants has complicated the process of developing broad-spectrum drugs. As such, the aim of this study was to explore the efficacy of TCM treatments against SARS-CoV-2 variants through targeting the interaction of the viral spike protein with the hACE2 receptor. Antiviral activity was systematically evaluated using a pseudovirus system. Scutellaria baicalensis (S. baicalensis) was found to be effective against SARS-CoV-2 infection, as it mediated the interaction between the viral spike protein and the hACE2 protein. Moreover, the active molecules of S. baicalensis were identified and analyzed. Baicalein and baicalin, a flavone and a flavone glycoside found in S. baicalensis, respectively, exhibited strong inhibitory activities targeting the viral spike protein and the hACE2 protein, respectively. Under optimized conditions, virus infection was inhibited by 98% via baicalein-treated pseudovirus and baicalin-treated hACE2. In summary, we identified the potential SARS-CoV-2 inhibitors from S. baicalensis that mediate the interaction between the Omicron spike protein and the hACE2 receptor. Future studies on the therapeutic application of baicalein and baicalin against SARS-CoV-2 variants are needed.
SVC-Based Frequency Compensation with Empirical System Modeling and Bode Loop Shaping

Increased inverter-based Renewable Energy Source (RES) penetration has led to frequency regulation challenges in some power systems due to decreased inertia. FACTS devices such as SVCs have been shown to be effective in damping small-signal frequency oscillations in systems with high renewable energy penetrations. This paper proposes a novel control scheme for a SVC that utilizes a synchrophasor as the primary frequency sensor to regulate system voltage and frequency simultaneously. Empirical system modeling is used to identify oscillatory system modes to ease deployment complexity, and basic control principles are used to design a simple and effective control law that provides good damping at the system modes. The simulated system, using the novel SVC-based frequency regulator, reduces the frequency oscillation at the dominant mode by a factor of nearly $40 dB$ compared to the system without it.
Kinematics Model for Guiding a 3D Printing Nozzle Using a Continuum-soft Arm

This article presents a solution based on a soft-continuum arm for depositing material in predictive maintenance of complex structures susceptible to deformation-induced micro-cracks. The soft-continuum robots are chosen for their agility in navigating confined spaces and accessing hard-to-reach areas, making them ideal for precise material injection into cracks. The soft-continuum arm is attached to an Unmanned Aerial Vehicle (UAV) equipped with a camera, enabling the localisation of cracks on wall surfaces and material deposition for on-site repairs, offering a swift and cost-effective intervention without the need for scaffolding. To ensure precise material deposition, maintaining the soft-continuum arm's bending speed and trajectory tracking is essential. This article primarily focuses on modelling the soft arm, assuming a stationary drone and minimal force during material deposition. The modelling approach draws inspiration from concepts used in parallel rigid manipulators and incorporates effort-based deformation into a kinematic model. To evaluate the proposed approach's efficiency, a series of experiments using a flexible section of the CBHA is conducted, complemented by a study of deformation as a function of pressure using Finite Element Method (FEM) analysis through Ansys software. A series of experiments using a section flexible of the CBHA is conducted to evaluate the efficiency of the proposed approach.
Efficient Detection of Missing and Misplaced Components in Electronic Boards through Edge Impulse’s Automated Inspection

The detection of missing and misplaced components on electronic boards is a critical and important task in the quality assurance process of electronic manufacturing. Traditional manual inspection methods are prone to human errors and are time consuming. In this work, we proposed an automated approach using Edge Impulse’s transfer learning technique. We trained a neural network model MobileNetV2 using transfer learning and evaluated the model’s performance on a test set. The results show that our trained model have achieved an accuracy of 94.2% validation set of trained data set and 96.55% misplaced components. We trained our model with twelve possible cases of errors and demonstrate the practical applicability of our approach by deploying the model on a Raspberry Pi 4 using Edge Impulse’s deployment tools. Furthermore, the use of Edge Impulse’s transfer learning and deployment tools makes our approach cost-effective, scalable, and easy to implement in real-world manufacturing environments.
Comparing the Performance of Numba and CUDA for Historical Analysis of Synchrophasor Data

Modern Wide Area Monitoring systems (WAMS) incorporating Phasor Measurement Unit (PMU) technology are producing big datasets. Historical analysis of PMU data is beneficial in development of online WAMS applications, quantifying baseline normal performance, and discovering anomalous events. Energy and time-efficient computational techniques are beneficial for historical analysis of PMU data. Application workflows that include historical analysis typically combine higher-level (but slow) languages like Python with faster (but older) languages like C. This paper compares the performance of Numba Python and C for historical analysis of PMU data, on both the CPU and GPU. We augment a known PMU anomaly detection scheme with linear state estimation, implement it separately in Numba and C, test the approaches on two real-world datasets, and measure their performance on the CPU and GPU of the NVIDIA Jetson Xavier single board computer, varying the available power modes. Results demonstrate that while Numba is significantly faster than traditional Python, simplifies application development, and holds promise for PMU applications, there is a noticeable performance gap between Numba and C on the GPU.
Grains of Trust: Nurturing Repurchase Intentions With Expertise and Likeability in Rice Sales

This study examines the influence of salesperson likeability and expertise on repurchase intention within the framework of small and medium-sized enterprises (SMEs). In the highly competitive SME market, likeability—which goes beyond basic customer service to encompass qualities like relatability and empathy—has become a critical differentiation. Repurchase intention, which denotes consumer happiness and loyalty, is essential to the profitability and expansion of SMEs. The purpose of the study is to ascertain how salesperson expertise and likeability relate to repurchase intention. Using an online survey questionnaire, a sample of Padang, West Sumatera, clients of Beras Charissa is used in this quantitative study. The findings show that likeability and expertise both significantly influence the intention to repurchase. The study emphasizes how crucial likeability-based emotional ties and personal ties are in shaping consumer attitudes and actions. Furthermore, a salesperson’s expertise gives clients confidence, which improves trust and the customer experience. Although skill is still important, the article makes the case that improving a salesperson’s likeability through training that emphasizes customer engagement can boost client loyalty and promote repeat purchase.
Optimization Analysis of Business Circle Based on Machine Learning

This paper begins with a discussion of data preprocessing in order to improve the data quality, standardize the feature ratio, and comprehend the data pattern and trend. The preprocessing technology utilized in this paper is feature aggregation, which aggregates the transaction data based on the transaction time and date to determine the transaction type (weekday or weekend) and calculates the transaction frequency and average amount for each store as the final feature of cluster analysis. Then, for the processed data, this paper compares the advantages and disadvantages of three different clustering algorithms, including K-means clustering, spectral clustering, and DEC deep clustering, in terms of their clustering effects. The conclusion of the paper is that deep clustering is superior to traditional clustering algorithms in terms of clustering precision, but it requires more computing resources and offers practical suggestions for existing shop clustering.
A Tool for Database Masking and Anonymization of PostgreSQL

Managing and securing sensitive data in a production database can be a complex and challenging task, especially when creating a staging database for testing and development purposes. This is why we have designed a tool that can automate the process of creating a staging database from a backup of the production database while anonymizing critical data based on user input. Our tool is specifically designed for PostgreSQL databases and can be easily integrated into the database management workflow. It allows users to select which tables and fields in the database should be anonymized, with options for different anonymization techniques based on the data type. This ensures that sensitive data is properly protected and complies with data privacy regulations. To ensure the success of the anonymization process, the tool utilizes machine learning algorithms that analyze past user input to predict which fields are likely to be selected for anonymization. This helps reduce the risk of errors and ensures that all critical data is properly anonymized with high accuracy. In the future, we plan to enhance the tool with more advanced anonymization techniques and incorporate additional machine-learning algorithms to improve the accuracy of the prediction models. This will help further streamline the database management process and ensure that sensitive data is always properly secured.
Odontochitina dilatata sp. nov. from the Campanian (Upper Cretaceous) of Poland: the importance of wall structure in the taxonomy of selected ceratiacean dinoflagellate cysts

ABSTRACT Rich and well-preserved assemblages of organic-walled dinoflagellate cysts have been recovered from the upper Campanian of the Middle Vistula River section, central Poland. Among the ceratiacean dinocysts, the majority of the recovered specimens are attributed to the newly described Odontochitina dilatata sp. nov. Additional material comes from the Roztocze Hills (SE Poland). The most distinctive features of Odontochitina dilatata sp. nov. are: (i) widely divergent antapical and lateral horns, not connected by pericoel; and (ii) thick central body wall with a three-layered structure, consisting of endo-, meso-, and periphragm. The mesophragm, which is distinctly less homogeneous (spongy) than the other two layers, gives the central body wall a reticulate appearance. The three-layered wall structure is rarely recognised in dinoflagellate cysts, and this is its first record from the ceratiacean dinocysts. The genus Odontochitina is emended to emphasise its more complex wall structure and the importance of other features, such as the angle of divergence between the antapical and lateral horns and compression of the central body. Odontochitina dilatata sp. nov. shares its more complex wall structure with O. streelii and possibly also with Xenascus wetzelii, but differs in this aspect from O. costata, O. diducta, O. operculata and O. porifera. The distinction between the genera Odontochitina and Xenascus needs further discussion, since the only feature distinguishing them is a better development of the processes in the latter genus.
Endogenous trans-translation structure visualizes the decoding of the first tmRNA alanine codon

Ribosomes stall on truncated or otherwise damaged mRNAs. Bacteria rely on ribosome rescue mechanisms to replenish the pool of ribosomes available for translation. Trans-translation, the main ribosome-rescue pathway, uses a circular hybrid transfer-messenger RNA (tmRNA) to restart translation and label the resulting peptide for degradation. Previous studies have visualized how tmRNA and its helper protein SmpB interact with the stalled ribosome to establish a new open reading frame. As tmRNA presents the first alanine codon via a non-canonical mRNA path in the ribosome, the incoming alanyl-tRNA must rearrange the tmRNA molecule to read the codon. Here, we describe cryo-EM analyses of an endogenous Escherichia coli ribosome-tmRNA complex with tRNAAla accommodated in the A site. The flexible adenosine-rich tmRNA linker, which connects the mRNA-like domain with the codon, is stabilized by the minor groove of the canonically positioned anticodon stem of tRNAAla. This ribosome complex can also accommodate a tRNA near the E (exit) site, bringing insights into the translocation and dissociation of the tRNA that decoded the defective mRNA prior to tmRNA binding. Together, these structures uncover a key step of ribosome rescue, in which the ribosome starts translating the tmRNA reading frame.
Pangenome Analysis of Helicobacter pylori Isolates from Selected Areas of Africa Indicated Diverse Antibiotic Resistance and Virulence Genes

The challenge facing Helicobacter pylori (H. pylori) infection management in some parts of Africa is the evolution of drug-resistant species, the lack of gold standard in diagnostic methods, and the ineffectiveness of current vaccines against the bacteria. It is being established that even though clinical consequences linked to the bacteria vary geographically, there is rather a generic approach to treatment. This situation has remained problematic in the successful fight against the bacteria in parts of Africa. As a result, this study compared the genomes of selected H. pylori isolates from selected areas of Africa and evaluated their virulence and antibiotic drug resistance, those that are highly pathogenic and are associated with specific clinical outcomes and those that are less virulent and rarely associated with clinical outcomes. 146 genomes of H. pylori isolated from selected locations of Africa were sampled, and bioinformatic tools such as Abricate, CARD RGI, MLST, Prokka, Roary, Phandango, Google Sheets, and iTOLS were used to compare the isolates and their antibiotic resistance or susceptibility. Over 20 k virulence and AMR genes were observed. About 95% of the isolates were genetically diverse, 90% of the isolates harbored shell genes, and 50% harbored cloud and core genes. Some isolates did not retain the cagA and vacA genes. Clarithromycin, metronidazole, amoxicillin, and tinidazole were resistant to most AMR genes (vacA, cagA, oip, and bab). Conclusion. This study found both virulence and AMR genes in all H. pylori strains in all the selected geographies around Africa with differing quantities. MLST, Pangenome, and ORF analyses showed disparities among the isolates. This in general could imply diversities in terms of genetics, evolution, and protein production. Therefore, generic administration of antibiotics such as clarithromycin, amoxicillin, and erythromycin as treatment methods in the African subregion could be contributing to the spread of the bacterium's antibiotic resistance.
Music Instrument Recognition using Deep Learning

Music plays a significant role in our daily lives for a vast majority of people worldwide. Various parameters can be used to classify music, including genre, performer, or composer. Automatic musical instrument recognition is crucial for complex tasks such as melody extraction, music information retrieval, and identifying dominant instruments from audio. Recognizing instruments automatically is particularly essential for organizing multiple tracks in an audio clip since manual recognition can be a tedious process. This is where automatic musical instrument recognition comes in handy. It involves detecting and categorizing active instruments, which is the focus of this research. To enhance musical instrument classification, we suggest employing a deep Convolutional Neural Network algorithm designed to efficiently identify musical instruments. Our algorithm includes convolutional layers, and we utilize the Mel spectrogram and Mel Frequency Cepstral Coefficient (MFCC) along with the Convolutional Neural Network to identify instruments present in the Medley Solos DB dataset. This dataset contains over 21,931 audio clips of eight instruments. We perform training based on the model described above, and our testing results show that our suggested method considerably recognizes instruments and achieves better accuracy than the existing approach.
Interaction of eight steroid derivatives with VEGFR-1 using a theoretical model

Some vascular endothelial growth factor receptor-1 (VEGFR-1) inhibitors drugs have been used to cancer cells; however, their interaction with VEGFR-1 is very confusing. The objective of this research was to evaluate the possible interaction of eight steroid derivatives with VEGFR-1 surface using 3hgn protein, cabozantinib, pazopanib, regorafenib, and sorafenib as theoretical tools in DockingServer program. The results showed some differences in the interaction of the steroid derivatives (1-8) with the 3hng protein surface such as i) differences in the number of amino acids; ii) different position of some amino acids compared to cabozantinib, pazopanib, regorafenib, and sorafenib. Besides, the inhibition constant (Ki) for steroid derivatives 1, 3, 6 and 8 was lower compared to cabozantinib and sorafenib drugs. In addition, other data display that Ki for steroid analogs 1, 3, 4, 6, 7 and 8 was lower compared with pazopanib and regorafenib. In conclusion, all these data suggest that steroid derivatives 1, 3, 4, 6, 7 and 8 could act as VEGFR-1 inhibitors and this phenomenon could be translated as good compounds to treat cancer cells.
Automatic Summarization for Arabic Scientific Articles Using Combined Approaches

Summarizing Arabic text from articles is a crucial step in Natural Language Processing (NLP) and remains an ongoing challenge. The presence of graphical elements such as graphs and figures further complicates the development of an effective and precise automatic text summarization algorithm. This paper presents a simple and robust summarization algorithm for Arabic scientific articles. Our approach consists of two stages: preprocessing and text summarization. It successfully extracts article summaries, even when the pages have a complex structure. Our method combines projection profiles, a feature-based OCR system, a hybrid approach that utilizes BART sequence-to-sequence model, cosine similarity, graph construction, the PageRank algorithm, and sentence weight score assignment. The presented method demonstrates efficient performance, mainly when dealing with non-uniform articles where the page structure varies across different pages. We conducted experiments on a collected dataset to evaluate our approach. The results of these experiments indicate that our proposed method is highly efficient compared to existing methods.