Binary Formation Research Articles

Are all models wrong? Falsifying binary formation models in gravitational-wave astronomy using exceptional events

Abstract As the catalogue of gravitational-wave transients grows, several entries appear ‘exceptional’ within the population. Tipping the scales with a total mass of ∼150M⊙, GW190521 likely contained black holes in the pair-instability mass gap. The event GW190814, meanwhile, is unusual for its extreme mass ratio and the mass of its secondary component. A growing model-building industry has emerged to provide explanations for such exceptional events, and Bayesian model selection is frequently used to determine the most informative model. However, Bayesian methods can only take us so far. They provide no answer to the question: does our model provide an adequate explanation for exceptional events in the data? If none of the models we are testing provide an adequate explanation, then it is not enough to simply rank our existing models—we need new ones. In this paper, we introduce a method to answer this question with a frequentist p-value. We apply the method to different models that have been suggested to explain the unusually massive event GW190521: hierarchical mergers in active galactic nuclei and globular clusters. We show that some (but not all) of these models provide adequate explanations for exceptionally massive events like GW190521.

Driving Forces in the Formation of Biocondensates of Highly Charged Proteins: A Thermodynamic Analysis of the Binary Complex Formation

A thermodynamic analysis of the binary complex formation of the highly positively charged linker histone H1 and the highly negatively charged chaperone prothymosin α (ProTα) is detailed. ProTα and H1 have large opposite net charges (−44 and +53, respectively) and form complexes at physiological salt concentrations with high affinities. The data obtained for the binary complex formation are analyzed by a thermodynamic model that is based on counterion condensation modulated by hydration effects. The analysis demonstrates that the release of the counterions mainly bound to ProTα is the main driving force, and effects related to water release play no role within the limits of error. A strongly negative Δcp (=−0.87 kJ/(K mol)) is found, which is due to the loss of conformational degrees of freedom.

Functional modulation of RAGE activation by multimeric S100B using single-domain antibodies.

S100B is a multifunctional protein primarily found in the brain, where it plays crucial roles in cell proliferation, differentiation, and survival. It has intra- and extracellular functions and, depending on S100B levels, can exhibit both neurotrophic and neurotoxic activities, both mediated by the receptor for advanced glycation end products (RAGE). Here, we report the discovery and characterization of nanobodies (Nbs) targeting dimeric and tetrameric S100B, which are the two most abundant oligomeric functional forms of the protein, aiming to modulate S100B-mediated RAGE activation. Two Nbs were selected for detailed structural and functional studies, and found to bind tetrameric S100B with high affinity, as determined by biolayer interferometry analysis and SEC-stable binary complex formation. Structural and docking analyses revealed preferential contact sites of Nbs with S100B regions implicated in interactions with RAGE, namely residues at the interfacial cleft on dimeric S100B and the at hydrophobic cleft formed by the association of two homodimeric units in the tetramer. In accordance, assays in SH-SY5Y cells showed that Nbs modulate the RAGE-mediated neurotrophic activity of S100B by hindering its functional interactions with the receptor. Biolayer interferometry competition assays between tetrameric S100B and the RAGE-VC1 domain, confirmed that Nbs selectively block S100B-mediated RAGE engagement, in agreement with cell activation experiments. These findings highlight Nbs as powerful tools for elucidating molecular and cellular mechanisms through the modulation of S100B and RAGE functions, inspiring potential therapeutic applications.

Dual Active Galactic Nuclei: Precursors of Binary Supermassive Black Hole Formation and Mergers

The presence of dual active galactic nuclei (AGNs) on scales of a few tens of kiloparsecs can be used to study merger-induced accretion on supermassive black holes (SMBHs) and offer insights about SMBH mergers, using dual AGNs as merger precursors. This study uses the Romulus25 cosmological simulation to investigate the properties and evolution of dual AGNs. We first analyze the properties of AGNs (L bol > 1043 erg s−1) and their neighboring SMBHs (any SMBHs closer than 30 pkpc to an AGN) at z ≤ 2. This is our underlying population. We then applied the luminosity threshold of L bol > 1043 erg s−1 to the neighboring SMBHs thereby identifying dual and multiple AGNs. Our findings indicate an increase in the number of both single and dual AGNs from lower to higher redshifts. We also find that the number of dual AGNs with separations of 0.5–4 kpc is twice the number of duals with separations of 4–30 kpc. All dual AGNs in our sample resulted from major mergers. Compared to single AGNs, duals have a lower black hole-to-halo mass ratio. We found that the properties of dual AGN host halos, including halo mass, stellar mass, star formation rate, and gas mass, are generally consistent with those of single AGN halos, albeit tending toward the higher end of their respective property ranges. Our analysis uncovered a diverse array of evolutionary patterns among dual AGNs, including rapidly evolving systems, slower ones, and instances where SMBH mergers are ineffective.

Temporal and geogrpahic trends in female representation within European cardiovascular imaging among 13,436 individuals associated with EACVI

Abstract Introduction Women are under-represented in cardiology trial enrolment, specialty training, faculty, scientific authorship and board membership in cardiology journals and societies. An EACVI Task Force on Women in Cardiovascular Imaging aims to promote women’s representation and leadership, but data is lacking in this area. Purpose To characterise the representation of women in cardiovascular imaging within Europe and examine temporal and geographic trends. Methods We scraped publicly available name data from over 100 online sources including EACVI taskforces, committees, research project members, consensus and position paper authors, board membership and past presidents alongside members of affiliated cardiovascular imaging national societies and working groups. We also included names from EACVI or national accreditation records for cardiac imaging. We recorded data on country location and year of affiliation. We defined gender in a binary manner as either male or female. The previously validated artificial intelligence (AI) system Namsor assigned gender based on name morphology and geolocation data. We pre-set a confidence of gender-name matching of 95%. Results The AI was able to assign gender at 95% confidence to 12,643/13,436 (94.1%) names. The overall number of names assigned female gender was 5903/12643 (46.7%), rejecting the null hypothesis that gender was equally distributed (i.e. 50% female) [p<0.0001]. Among the 9712 individuals with year of affiliation we observed female representation to increase over time: 1989-2000 4/18 (22.2%), 2001-2004 107/287 (37.8%) 2005-2009 77/170 (45.3%), 2010-2014 109/203 (53.7%), 2015-2019 1063/2022 (51.3%) and 2020-2024 3352/3064 (52.2%) (Cochran-Armitage trend test 24.196 [p<0.0001]) (Figure 1). Female representation was lower in countries outside of Europe, compared to within Europe; 740/2654 (27.9%) vs. 5163/9989 (51.7%) [p<0.0001]. The lowest rate of female representation was in Saudia Arabia (15.1%), the highest in Lithuania (83.3%). Figure 2 displays a choropleth of gender representation (%) between European countries with n>20. Conclusion(s) There are significant limitations in this form of analysis, including that we have arbritarily defined gender in a binary format, thereby excluding individuals who do not identify with such a social construct. It is also likely that the excluded names based on the 95% confidence of the AI software disproportionately represents individuals from ethnic minorities. However, our data provides a basic understanding of the representation of women in European cardiovascular imaging over time and between countries. We observed a trend of increasing female participation but with substantial variation between countries both inside and outside of Europe. This data may be useful to refine and direct efforts to improve female representation in areas in which it is needed most.

The Role of Fairness for Accepting Stricter Carbon Taxes in Sweden

Carbon taxes are considered to be an efficient method to reduce greenhouse gas emissions; however, such taxes are generally unpopular, partly because they are seen as unfair. To explore if public acceptance of a stricter carbon tax in Sweden can be enhanced, this study investigates the effectiveness of three different policy designs, addressing collective and personal distributional consequences and promoting procedural aspects (democratic influence). A large-scale (n = 5200) survey is applied, combining a traditional multi-category answer format with a binary choice format. The results show that support for higher carbon taxation can be enhanced if tax revenues are redistributed to affected groups. Policies with collective justice framings can change the attitudes of individuals who express antagonistic attitudes to increased carbon taxation and influence groups comparably more affected by carbon taxes, such as rural residents, low-income groups, and people who are driving long distances. Policy designs addressing collective distributional consequences are, however, less effective on individuals expressing right-leaning ideological views and low environmental concern. Policies addressing personal distributional outcomes, or perceptions of procedural injustice, had no significant effect on policy acceptance.

The Heavier the Faster: A Subpopulation of Heavy, Rapidly Spinning and Quickly Evolving Binary Black Holes

Abstract The spins of binary black holes (BBHs) measured from gravitational waves contain valuable information about their formation pathways. In this study, we propose a new quantity, the “dimensionless net spin” (χ N), which relates to the sum of the angular momenta of the component black holes (BHs) in the system, offering a novel perspective for exploring the origins of BBHs. Through hierarchical Bayesian inference on χ N, we find strong evidence that its distribution is better described by two Gaussian components rather than one, and their branching ratios vary with total mass and redshift: there is a narrow peak at χ N ∼ 0.15 and an extended peak at χ N ∼ 0.3–0.6. The rapidly spinning systems likely dominate the high-mass end of the population and evolve with redshift more quickly. These findings present new challenges to the field binary formation scenario and suggest that dynamical processes may play a key role in the formation of high total mass BBHs.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Structures of a DNA Polymerase Caught while Incorporating Responsive Dual-Functional Nucleotide Probes.

Functionalizing nucleic acids using DNA polymerases is essential in biophysical and biotechnology applications. This study focuses on understanding how DNA polymerases recognize and incorporate nucleotides with diverse chemical modifications, aiming to develop advanced nucleotide probes. We present the crystal structures of ternary complexes of Thermus aquaticus DNA polymerase (KlenTaq) with C5-heterocycle-modified environment-sensitive 2'-deoxyuridine-5'-triphosphate (dUTP) probes. These nucleotides include SedUTP, BFdUTP and FBFdUTP, which bear selenophene, benzofuran and fluorobenzofuran, respectively, at the C5 position of uracil, and exhibit high conformational sensitivity. SedUTP and FBFdUTP serve as dual-app probes, combining a fluorophore with X-ray anomalous scattering Se or 19F NMR labels. Our study reveals that the size of the heterocycle influences how DNA polymerase families A and B incorporate these modified nucleotides during single nucleotide incorporation and primer extension reactions. Remarkably, FBFdUTP's responsiveness enabled real-time monitoring of the binary complex formation and polymerase activity through fluorescence and 19F NMR. Comparative analysis of incorporation profiles, fluorescence, 19F NMR data, and crystal structures of ternary complexes highlights the enzyme's plasticity. Key insights are provided into the role of gatekeeper amino acids (Arg660 and Arg587) in accommodating and processing these modified substrates, offering a structural basis for next-generation nucleotide probe development.

Structures of a DNA Polymerase Caught while Incorporating Responsive Dual‐Functional Nucleotide Probes

Functionalizing nucleic acids using DNA polymerases is essential in biophysical and biotechnology applications. This study focuses on understanding how DNA polymerases recognize and incorporate nucleotides with diverse chemical modifications, aiming to develop advanced nucleotide probes. We present the crystal structures of ternary complexes of Thermus aquaticus DNA polymerase (KlenTaq) with C5‐heterocycle‐modified environment‐sensitive 2′‐deoxyuridine‐5′‐triphosphate (dUTP) probes. These nucleotides include SedUTP, BFdUTP and FBFdUTP, which bear selenophene, benzofuran and fluorobenzofuran, respectively, at the C5 position of uracil, and exhibit high conformational sensitivity. SedUTP and FBFdUTP serve as dual‐app probes, combining a fluorophore with X‐ray anomalous scattering Se or 19F NMR labels. Our study reveals that the size of the heterocycle influences how DNA polymerase families A and B incorporate these modified nucleotides during single nucleotide incorporation and primer extension reactions. Remarkably, FBFdUTP's responsiveness enabled real‐time monitoring of the binary complex formation and polymerase activity through fluorescence and 19F NMR. Comparative analysis of incorporation profiles, fluorescence, 19F NMR data, and crystal structures of ternary complexes highlights the enzyme's plasticity. Key insights are provided into the role of gatekeeper amino acids (Arg660 and Arg587) in accommodating and processing these modified substrates, offering a structural basis for next‐generation nucleotide probe development.

EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm

This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm’s binary format is specifically designed to select features that can significantly enhance the accuracy of classification. The proposed algorithm and competing ones, namely, Al-Biruni Earth Radius (BER), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were evaluated using predefined sets of assessment criteria. The statistical analysis employed the ANOVA and Wilcoxon signed-rank tests and assessed the effectiveness and significance of the proposed algorithm compared to the other five algorithms. Furthermore, A series of visual depictions were presented to validate the effectiveness and robustness of the proposed algorithm. Thus, the MBER algorithm outperformed the other optimizers on the majority of the unimodal benchmark functions due to these considerations. Different ML models were used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, and LR. The KNN model achieved the highest values of Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), accuracy (0.9612), Sensitivity (0.970578) and Specificity (0.949711). Thus, KNN serves as a fitness function and is optimized by the utilization of Modified Al-Biruni earth radius (MBER). Finally, the accuracy of eye state classification achieved 96.12% using the proposed algorithm.

Efficient indexing and querying of annotations in a pangenome graph.

The current reference genome is the backbone of diverse and rich annotations. Simple text formats, like VCF or BED, have been widely adopted and helped the critical exchange of genomic information. There is a dire need for tools and formats enabling pangenomic annotation to facilitate such enrichment of pangenomic references. The Graph Alignment Format (GAF) is a text format, tab-delimited like BED/VCF files, which was proposed to represent alignments. GAF could also be used to store paths representing annotations in a pangenome graph, but there are no tools to index and query them efficiently. Here, we present extensions to vg and HTSlib that provide efficient sorting, indexing, and querying for GAF files. With this approach, annotations overlapping a subgraph can be extracted quickly. Paths are sorted based on the IDs of traversed nodes, compressed with BGZIP, and indexed with HTSlib/tabix via our extensions for the GAF format. Compared to the binary GAM format, GAF files are easier to edit or inspect because they are plain text, and we show that they are twice as fast to sort and half as large on disk. In addition, we updated vg annotate , which takes BED or GFF3 annotation files relative to linear sequences and projects them into the pangenome. It can now produce GAF files representing these annotations' paths through the pangenome. We showcase these new tools on several applications. We projected annotations for all Human Pangenome Reference Consortium Year 1 haplotypes, including genes, segmental duplications, tandem repeats and repeats annotations, into the Minigraph-Cactus pangenome (GRCh38-based v1.1). We also projected known variants from the GWAS Catalog and expression QTLs from the GTEx project into the pangenome. Finally, we reanalyzed ATAC-seq data from ENCODE to demonstrate what a coverage track could look like in a pangenome graph. These rich annotations can be quickly queried with vg and visualized using existing tools like the Sequence Tube Map or Bandage.

Research on high voltage circuit breaker multi-sensor signal feature selection method based on GA-Kmeans algorithm

Abstract Currently, in the research on fault information of high-voltage circuit breakers (HVCBs) based on multi-sensor data, issues such as shallow feature assessment and unclear feature selection rules exist, leading to a decrease in fault identification accuracy due to redundant characteristics. To address this, this paper proposes a novel feature selection method based on GA-Kmeans. This method encodes the original feature space into binary format and employs the clustering accuracy of the Kmeans model as the fitness function to obtain a high-quality feature subset that maximally represents and distinguishes faults. By combining different feature selection methods with fault diagnosis models, six typical application scenarios are constructed. Results indicate that compared to traditional methods such as Relief-F, KPCA, GA-SVM for feature selection, and SVM for fault diagnosis, the proposed GA-Kmeans method reduces the dimensionality of the original feature space and employs the Kmeans clustering algorithm as the diagnostic model, achieving a final diagnostic accuracy of 95.29%. This method outperforms others, with a 37.24% higher diagnostic accuracy than SVM under the original feature space, and a decrease of 47.90% in standard deviation. This validates the necessity of feature selection and the superiority of the proposed method, providing a reliable and stable diagnostic basis for subsequent mechanical fault diagnosis of HVCBs.

Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification

Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).

Wasm-R3: Record-Reduce-Replay for Realistic and Standalone WebAssembly Benchmarks

WebAssembly (Wasm for short) brings a new, powerful capability to the web as well as Edge, IoT, and embedded systems. Wasm is a portable, compact binary code format with high performance and robust sandboxing properties. As Wasm applications grow in size and importance, the complex performance characteristics of diverse Wasm engines demand robust, representative benchmarks for proper tuning. Stopgap benchmark suites, such as PolyBenchC and libsodium, continue to be used in the literature, though they are known to be unrepresentative. Porting of more complex suites remains difficult because Wasm lacks many system APIs and extracting real-world Wasm benchmarks from the web is difficult due to complex host interactions. To address this challenge, we introduce Wasm-R3, the first record and replay technique for Wasm. Wasm-R3 transparently injects instrumentation into Wasm modules to record an execution trace from inside the module, then reduces the execution trace via several optimizations, and finally produces a replay module that is executable standalone without any host environment-on any engine. The benchmarks created by our approach are (i) realistic, because the approach records real-world web applications, (ii) faithful to the original execution, because the replay benchmark includes the unmodified original code, only adding emulation of host interactions, and (iii) standalone, because the replay benchmarks run on any engine. Applying Wasm-R3 to web-based Wasm applications in the wild demonstrates the correctness of our approach as well as the effectiveness of our optimizations, which reduce the recorded traces by 99.53% and the size of the replay benchmark by 9.98%. We release the resulting benchmark suite of 27 applications, called Wasm-R3-Bench, to the community, to inspire a new generation of realistic and standalone Wasm benchmarks.

Transfer function adaptation for effective feature selection with the side-blotched lizard algorithm

AbstractFeature selection is a crucial preprocessing step in data mining and machine learning, enhancing model performance and computational efficiency. This paper investigates the effectiveness of the Side-Blotched Lizard Optimization Algorithm (SBLA) for feature selection by developing six novel variants: Sbla-s1, Sbla-s2, Sbla-s3, Sbla-v1, Sbla-v2, and Sbla-v3, each employing distinct S-shaped or V-shaped transfer functions to convert the continuous search space to a binary format. These variants were rigorously evaluated on nineteen benchmark datasets from the UCI repository, comparing their performance based on average classification accuracy, average number of selected features, and average fitness value. The results demonstrated the superiority of Sbla-s3, achieving an average classification accuracy of 92.8% across all datasets, a mean number of selected features of 20, and an average fitness value of 0.08. Furthermore, Sbla-s3 consistently outperformed six other state-of-the-art metaheuristic algorithms, achieving the highest average accuracy on sixteen out of nineteen datasets. These findings establish Sbla-s3 as a promising and effective approach for feature selection, capable of identifying relevant features while maintaining high classification accuracy, potentially leading to improved model performance in various machine learning applications.

Signatures of circumbinary disc dynamics in multimessenger population studies of massive black hole binaries

ABSTRACT We investigate the effect of the cutting-edge circumbinary disc (CBD) evolution models on massive black hole binary (MBHB) populations and the gravitational wave background (GWB). We show that CBD-driven evolution leaves a tell-tale signature in MBHB populations, by driving binaries towards an equilibrium eccentricity that depends on the binary mass ratio. We find high orbital eccentricities ($e_{\rm b} \sim 0.5$) as MBHBs enter multimessenger observable frequency bands. The CBD-induced eccentricity distribution of MBHB populations in observable bands is independent of the initial eccentricity distribution at binary formation, erasing any memory of eccentricities induced in the large-scale dynamics of merging galaxies. Our results suggest that eccentric MBHBs are the rule rather than the exception in upcoming transient surveys, provided that CBDs regularly form in MBHB systems. We show that the GWB amplitude is sensitive to CBD-driven preferential accretion onto the secondary, resulting in an increase in GWB amplitude $A_{\rm yr^{-1}}$ by over 100 per cent with just 10 per cent Eddington accretion. As we self-consistently allow for binary hardening and softening, we show that CBD-driven orbital expansion does not diminish the GWB amplitude, and instead increases the amplitude by a small amount. We further present detection rates and population statistics of MBHBs with $M_{\rm b} \gtrsim 10^6 \, {\rm M}_{\odot }$ in Laser Interferometer Space Antenna, showing that most binaries have equal mass ratios and can retain residual eccentricities up to $e_{\rm b} \sim 10^{-3}$ due to CBD-driven evolution.

The formation and evolution of dark star clusters – II. The impact of primordial mass segregation

ABSTRACT We investigate the impact of primordial mass segregation on the formation and evolution of dark star clusters (DSCs). Considering a wide range of initial conditions, we conducted N-body simulations of globular clusters (GCs) around the Milky Way. In particular, we assume a canonical initial mass function for all GCs without natal kicks for supernova remnants, namely neutron stars or black holes. Our results demonstrate that clusters with larger degrees of primordial mass segregation reach their DSC phase earlier and spend a larger fraction of their dissolution time in such a phase, compared to clusters without mass segregation. In primordially segregated clusters, the maximum Galactocentric distance that the clusters can have to enter the DSC phase is almost twice that of the clusters without primordial mass segregation. Primordially segregated clusters evolve with a higher number of stellar mass black holes, accelerating energy creation in their central regions and consequently increasing evaporation rates and cluster sizes during dark phases. The simulations reveal that aggregating heavy components at the centre doubles the time spent in the dark phase. Additionally, the study identifies potential links between simulated dark clusters and initial conditions of Milky Way GCs, suggesting some may transition to dark phases before dissolution. Higher primordial mass segregation coefficients amplify the average binary black hole formation rate by 2.5 times, raising higher expectations for gravitational-wave emissions.

The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method

Increasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern.

Protostellar spin-up and fast rotator formation through binary star formation

Context. Many fast-rotating stars (rotation periods of < 2 days) are found to be unresolved binaries with separations of tens of AU. This correlation between fast rotators and binarity leads to the question of whether the formation of binary stars inherently produces fast rotators. Aims. Our goal is to understand the spin evolution of protostars and whether the formation of companions plays a role in spinning up stars. Methods. We used magneto-hydrodynamic simulations to study the formation of multiple star systems from turbulent and non-turbulent protostellar cores. We tracked the angular momentum accreted by individual star and inner disc systems by using a sink (star) particle technique. We ran a resolution study to extrapolate protostellar properties. Results. We find in all simulations that the primary star can experience a spin-up event correlated with the formation of companions, namely fragmentation into binaries or higher-order systems. The primary star can spin up by up to 84% of its pre-fragmentation angular momentum and by up to 18% of its pre-fragmentation mass-specific angular momentum. The mechanism for the spin-up is gravitational disc instabilities in the circumstellar disc around the primary star, which leads to the accretion of material with high specific angular momentum. The simulations that experience the strongest disc instabilities fragment to form companions. Simulations with weaker spin-up events experience disc instabilities triggered by a companion flyby, and the disc instability in these cases typically does not produce further fragments (i.e. they remain binary systems). Conclusions. The primary star in multiple star systems can end up with a higher spin than single stars. This is because gravitational instabilities in the circumstellar disc around the primary star can trigger a spin-up event. In the strongest spin-up events, the instability is likely to cause disc fragmentation and the formation of companions. This spin-up mechanism, coupled with shorter disc lifetimes due to truncated circumstellar discs (and thus short spin-down times), may help produce fast rotators.