Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of vibration for permanent magnet motors considering mechanical and magnetic coupling effects

Vibration of a rotor-bearing system driven by an electric motor is a coupled phenomena between mechanical characteristics and magnetic origins through the airgap. With the advent of new high energy magnetic material together with high precision motor applications, magnetic sources of vibration are becoming more serious. This paper investigates the transient whirl responses of a rotor system with purely mechanical origins and compares it with that of magnetically coupled origins. The results show that magnetically coupled sources significantly affect the vibration of the rotor-motor system.
Asymmetric Group Message Franking: Definitions & Constructions

As online group communication scenarios become more and more common these years, malicious or unpleasant messages are much easier to spread on the internet. Message franking is a crucial cryptographic mechanism designed for content moderation in online end-to-end messaging systems, allowing the receiver of a malicious message to report the message to the moderator. Unfortunately, the existing message franking schemes only consider 1-1 communication scenarios. In this paper, we systematically explore message franking in group communication scenarios. We introduce the notion of asymmetric group message franking (AGMF), and formalize its security requirements. Then, we provide a framework of constructing AGMF from a new primitive, called HPS-KEM. We also give a construction of HPS-KEM based on the DDH assumption. Plugging the concrete HPS-KEM scheme into our AGMF framework, we obtain a DDH-based AGMF scheme, which supports message franking in group communication scenarios.
Harnessing genetic variations improving seedling vigor for successful crop establishment in deep sown direct-seeded rice

Improving seedling vigour remains key objective for breeders working with direct-seeded rice (DSR). To understand the genetic control of seedling vigour in deep sown DSR, combined genome-wide association mapping, linkage mapping, fine mapping, RNA-sequencing to identify candidate genes and validation of putative candidate genes was performed. Significant phenotypic variations were observed among genotypes in both F3:4:5 and BC2F2:3 populations. The mesocotyl length showed significant positive correlation with %germination, root and shoot length. The 3.79 Mb region on chromosome 7 reported to be associated with mesocotyl elongation. RNA-seq data and RT-PCR results identified and validated seven potential candidate genes. The potential candidate gene, LOC_Os07g25150 codes for Myb-30-related transcription factor. The LOC_Os07g25150 codes for Myb 30-related transcription factor, LOC_Os07g17689 annotated as protein translation factor, LOC_Os07g17770 as tryptophanyl-tRNA synthetase, LOC_Os07g15440 as alanyl-tRNA synthetase family protein, LOC_Os07g24100 as retrotransposon protein and the remaining others as expressed proteins. The antagonistic relationship between Myb-30 and ethylene-mediated signalling (EIN-3) in regulating mesocotyl elongation have been observed. The functional characterization and knocking of LOC_Os07g25150 codes for Myb 30-related transcription factor may provide better understanding of the mechanism behind mesocotyl elongation. The four promising introgression lines with longer mesocotyl length, longer root length and semi-dwarf plant height have been identified.
Effect of caldesmon mutations in the development of zebrafish embryos

Cancer is a profound medical concern and better treatments are needed for cancer patients. Therefore, new cancer targets are constantly being studied. These targets need not only be relevant for cancer progression, but their modulation needs to be tolerated reasonably well by the host. Caldesmon is one of these proposed novel targets for cancer therapy. Therefore, we analysed effects of caldesmon mutations in normal development using genetically modified zebrafish embryos. We analysed mutations in both zebrafish caldesmon genes, cald1a and cald1b and analysed effects of either mutation alone or as in combination in double homozygous embryos using molecular, morphological and functional analyses. The effects of caldesmon mutations were mild and the gross development of zebrafish embryos was normal. The caldesmon mutant embryos had, however, alterations in response to light-stimulus in behavioral assays. Taken together, the effects of caldesmon mutations in the development of zebrafish embryos were reasonably well tolerated and did not indicate significant concerns for caldesmon being a potential target for cancer therapy.
simpleaf: A simple, flexible, and scalable framework for single-cell transcriptomics data processing using alevin-fry

Summary The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry’s single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf, a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools. Availability and implementation Simpleaf is written in Rust and released under a BSD 3-Clause license. It is freely available from its GitHub repository https://github.com/COMBINE-lab/simpleaf, and via bioconda. Documentation for simpleaf is available at https://simpleaf.readthedocs. io/en/latest/ and tutorials for simpleaf are being developed that can be accessed at https://combine-lab.github. io/alevin-fry-tutorials.
Competence for transcellular infection in the root cortex involves a post-replicative, cell-cycle exit decision in Medicago truncatula

During root nodule symbiosis (RNS), cell-division activity is re-initiated and sustained in the root cortex to create a hospitable cellular niche. Such temporary and spatially confined site is required to render host cells compatible with the intracellular progression of rhizobia. Although it has been suggested that early infection events might involve a pre-mitotic cell-cycle arrest, this process has not been dissected with cellular resolution. Here, we show that a dual-colour Medicago histone reporter robustly identifies cells with different mitotic or endoreduplication activities in the root cortex. By imaging deep root tissues, we found that a confined trajectory of cortical cells that are transcellularly passed by infection threads are in a stage of the cell-cycle that is distinct from directly adjacent cells. Distinctive features of infected cells include nuclear widening and large-scale chromatin rearrangements consistent with a cell-cycle exit prior to differentiation. Using a combination of fluorescent reporters demarcating cell-cycle phase progression, we confirmed that a reduced proliferation potential and modulating the G2/M transition, a process possibly controlled by the NF-YA1 transcription factor, mark the success of rhizobial delivery to nodule cells.
Structural and luminescent properties of europium doped TiO2 thick films synthesized by the ultrasonic spray pyrolysis technique

The structural and luminescent properties of trivalent europium-doped titanium dioxide films synthesized by the ultrasonic spray pyrolysis technique at several substrate temperatures are reported. These films are nanocrystalline and present a mixture of tetragonal (anatase and rutile) crystal structures of the titania as determined by x-ray diffraction. The rutile crystal structure became predominant as the substrate temperature during deposition was increased. Under UV and electron beam excitation, these coatings showed strong luminescence due to f–f transitions and the dominant transition was the hypersensitive 5D0 → 7F2 red emission of Eu3+. The photo- and cathodoluminescence characteristics of these films were studied as a function of growth parameters such as substrate temperature and europium concentration. Excitation with a wavelength of 396 nm resulted in photoluminescent emission peaks located at 557, 580, 592, 615, 652 and 703 nm, associated with the electronic transitions of the Eu3+ ion. The photoluminescence (PL) intensity as a whole is observed to decrease as the deposition temperature is increased. Also, with increasing doping concentration, a quenching of the PL is observed. The chemical composition and surface morphology characteristics of the films are also reported.
Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases

Gut microbiota has been implicated in the pathogenesis of multiple gastrointestinal (GI) and systemic metabolic and inflammatory disorders where disrupted gut microbiota composition and function (dysbiosis) has been found in multiple studies. Thus, human microbiome data has a potential to be a great source of information for the diagnosis and disease characteristics (phenotypes, disease course, therapeutic response) of diseases with dysbiotic microbiota community. However, multiple attempts to leverage gut microbiota taxonomic data for diagnostic and disease characterization have failed due to significant inter-individual variability of microbiota community and overlap of disrupted microbiota communities among multiple diseases. One potential approach is to look at the microbiota community pattern and response to microbiota modifiers like dietary fiber in different disease states. This approach is now feasible by availability of machine learning that is able to identify hidden patterns in the human microbiome and predict diseases. Accordingly, the aim of our study was to test the hypothesis that application of machine learning algorithms can distinguish stool microbiota pattern and microbiota response to fiber between diseases where overlapping dysbiotic microbiota have been previously reported. Here, we have applied machine learning algorithms to distinguish between Parkinson’s disease, Crohn’s disease (CD), ulcerative colitis (UC), human immune deficiency virus (HIV), and healthy control (HC) subjects in the presence and absence of fiber treatments. We have shown that machine learning algorithms can classify diseases with accuracy as high as 95%. Furthermore, machine learning methods applied to the microbiome data to predict UC vs CD led to prediction accuracy as high as 90%.
Batching Cipolla-Lehmer-Müller's square root algorithm with hashing to elliptic curves

The present article provides a novel hash function H to any elliptic curve of j -invariant ̸ = 0 , 1728 over a finite field F q of large characteristic. The unique bottleneck of H consists in extracting a square root in F q as well as for most hash functions. However, H is designed in such a way that the root can be found by (Cipolla–Lehmer–)M¨uller’s algorithm in constant time. Violation of this security condition is known to be the only obstacle to applying the given algorithm in the cryptographic context. When the field F q is highly 2-adic and q ≡ 1 (mod 3), the new batching technique is the state-of-the-art hashing solution except for some sporadic curves. Indeed, M¨uller’s algorithm costs ≈ 2 log 2 ( q ) multiplications in F q . In turn, original Tonelli–Shanks’s square root al-gorithm and all of its subsequent modifications have the algebraic complexity Θ (log( q ) + g ( ν )), where ν is the 2-adicity of F q and a function g ( ν ) ̸ = O ( ν ). As an example, it is shown that M¨uller’s algorithm actually needs several times fewer multiplications in the field F q (whose ν = 96) of the standardized curve NIST P-224.
Structural characterization of a novel luciferase-like-monooxygenase from Pseudomonas meliae – an in-silico approach

Background Luciferase is a well-known oxidative enzyme that produces bioluminescence. The Pseudomonas meliae is a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants. Methods The present study aims to model and characterize the luciferase-like monooxygenase protein in P. meliae for its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase from P. meliae infects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase from P. meliae and template from Geobacillus thermodenitrificans were analyzed to find the novelty of P. meliae. Results The results suggest that the absence of bioluminescence in P. meliae could be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids; P. meliae Tyr138 instead of His138 and Leu311 instead of His311. Therefore, the P. meliae will have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability. Conclusions This study will help further improve, activate, and repurpose the luciferase from P. meliae as a reporter for gene expression.
Metabolic multireactor: Practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

We present a system for carrying out small batch reactor oxygen consumption experiments on water and sediment samples for environmental questions. In general, it provides several advantages that can help researchers achieve impactful experiments at relatively low costs and high data quality. In particular, it allows for multiple reactors to be operated and their oxygen concentrations to be measured simultaneously, providing high throughput and high time-resolution data, which can be advantageous. Most existing literature on similar small batch-reactor metabolic studies is limited to either only a few samples, or only a few time points per sample, which can restrict the ability for researchers to learn from their experiments. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. As such we do not delve deeply into the specifics of the fluorescent dye sensing mechanism. Instead, we focus on practical considerations. We describe the construction and operation of the calibration and experimental systems, and answer many of the questions likely to come up when other researchers choose to build and operate a similar system themselves (questions we ourselves had when we first built the system). In this way, we hope to provide an approachable and easy to use research article that can help other researchers construct and operate a similar system that can be tailored to ask their own research questions, with a minimum of confusion and missteps along the way.
Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.
Abstraction Model of Probing and DFA Attacks on Block Ciphers

A thread of physical attacks that try to obtain secret information from cryptographic modules has been of academic and practical interest. One of the concerns is determining its e ffi ciency, e.g., the number of attack trials to recover the secret key. However, the accurate estimation of the attack e ffi ciency is generally expensive because of the complexity of the physical attack on a cryptographic algorithm. Based on this background, in this study, we propose a new abstraction model for evaluating the attack e ffi ciency of the probing and DFA attacks. The proposed model includes an abstracted attack target and attacker to determine the amount of leaked information obtained in a single attack trial. We can adapt the model flexibly to various attack scenarios and can get the attack e ffi ciency quickly and precisely. In the probing attack on AES, the di ff erence in the attack e ffi ciency is only approximately 0.3% between the model and experimental values, whereas that of a previous model is approximately 16%. We also apply the probing attack on DES, and the results show that DES has a high resistance to the probing attack. Moreover, the proposed model works accurately also for the DFA attack on AES.
High-intensity terahertz pulses at 1-kHz repetition rate

We report the generation of terahertz pulses with 0.4-/spl mu/J pulse energy at 1-kHz repetition rate using a large-aperture GaAs photoconductor with 3-cm gap aluminum electrodes, biased at voltages up to 45 kV. The terahertz output energy saturates at a laser fluence of 40 /spl mu/J/cm/sup 2/ at low-bias fields, while no clear saturation point was observed at high-bias fields. The output was found to be dependent on the repetition rate: at high fluences, pulse energy at 1 kHz is higher than that at 100 Hz by as much as 60%. A study of the behavior of the terahertz pulse energy and pulsewidth as a function of the pulsewidth of the laser excitation was conducted and compared with theoretical predictions. Propagation properties of the terahertz beam were also characterized, leading to a focal spot size as small as 800 /spl mu/m at the focus of a 2.5-in focal length parabolic mirror.
A continuous sampling plan using CUSUMs

A Continuous Sampling Plan, CSP-CUSUM, is proposed based on the use of Cumulative Sums (CUSUMs) for deciding when to switch between the phases of sampling inspection and 100% inspection. The Geometric CUSUM, also termed the Run-length CUSUM, is chosen for this purpose, and two separate CUSUMs are to be operated, one for each inspection phase. The conventional measures of performance for CSPs such as average outgoing quality, average fraction inspected, and average proportion passed under sampling inspection are evaluated for CSP-CUSUM, and comparisons with some standard CSPs are presented. An additional performance-measure, Average Cycle Length, is proposed. A table is provided to aid the choice of parameters for the operation of CSP-CUSUM. It is recommended that a Geometric CUSUM control chart be maintained in parallel with CSP-CUSUM to detect significant upward shifts in the incoming fraction defective.
Development of an intelligent and hybrid scheme for rapid INS alignment

Abstract Inertial navigation systems (INS) have been widely applied in many applications, such as precise positioning, navigation and guidance. Generally speaking, the initial attitude angles between the body and navigation frames are estimated during the INS alignment process using an optimal estimator such as the Kalman filter. Due to measurement errors, the Kalman filter takes about up to fifteen minutes to converge. In this article, a hybrid scheme integrating an Adaptive Neuro‐Fuzzy Inference System with the Kalman filter is proposed to achieve a faster and more accurate alignment process. The preliminary results indicate that a faster alignment with superior accuracy can be achieved through the use of the proposed scheme.
Ethical Dilemmas in Natural Theology and Valid Inference in Clinical Trials

This paper discusses some aspects of natural theology (theologia naturalis), following the ideas of Baruch Spinoza. Natural theology is presented as a powerful concept capable of bridging traditional theology, ethics, and natural science, enabling original and meaningful perspectives on contemporary problems, and fostering ways of ethical engagement in the modern world. General ethical problems in clinical trials, as well as particular studies on phosphoethanolamine and hydroxychloroquine for cancer and COVID-19 therapy, are used to ground the discussion into real-life applications
Symbolic Domain Music Generation System Based on LSTM Architecture

With the rapid advancement in artificial intelligence, the deep learning technique has changed its application domain from mere classification task to more of generative task. As music is an art of systematizing sounds, this sequential nature of music can be utilized by deep learning techniques like Long-Short Term Memory (LSTM) to generate music. This work emphasizes on LSTM based neural network architecture for generation of music and evaluating how we perceive the generated music. The model is trained on a Classical Piano dataset and implemented using Keras deep learning library. The Mean Opinion Score is used to evaluate the quality of the music generated by the proposed model. The result shows that the proposed model was able to create listenable compositions adhering to human aesthetics.
Battery Monitoring Based Charge Sharing Scheme for Electric Vehicles

The demand for Electric vehicles (EV) have risen over the last decade, as the researchers are motivated towards a solution for ever-deteriorating conventional fuels and maintaining emission rates. Charging time of battery and availability of charging stations pose the main hurdle in popularizing EV among public. Proper scheduling of EV charging can also be used to shape the load curves in the grid. In the absence of enough number of charging stations, other approaches like vehicle to vehicle $(\mathbf{V}2\mathbf{V})$ charging can be adopted. In a common place like an office complex, the SOC and charging requirements of available vehicle batteries are different. A modified bidirectional dc-dc converter which works on the proposed charging algorithm facilitates the charge transfer. This paper presents a scheduling strategy for charge sharing between vehicles using $\mathbf{V}2\mathbf{V}$ method.