Search Results (81)

The huge computational cost required to test whether a number is prime and the inefficiency of the known sieving algorithms for extremely large inputs have posed significant challenges in computational number theory. Traditional deterministic prime generation methods struggle to maintain performance when the input sizes increase exponentially. In this work, we show that, through multiscale distribution and deterministic prime number generation, it is possible to create a multiscale sieve with drastically better performance than the deterministic algorithms known to date, providing a more efficient solution for large-scale prime number generation, demonstrated by several benchmarks that highlight the potential of our approach. Consequently, we can gain some advantages in cryptography and in info-security, such as in IoT and blockchain environments. Full article

In this article, we study some new properties of twin primes and algorithms for their generation. We find the necessary conditions to generate a pair of twins. These conditions seem to indicate that the conjecture is true, namely, there are infinitely many twin primes. Furthermore, we developed some algorithms that are very useful from a computer science point of view, which can be applied in cryptography and data encryption. Full article

Ramon syndrome (MIM 266270) is an extremely rare genetic syndrome, characterized by gingival fibromatosis, cherubism-like lesions, epilepsy, intellectual disability, hypertrichosis, short stature, juvenile rheumatoid arthritis, and ocular abnormalities. Hereditary or non-syndromic gingival fibromatosis (HGF) is also rare and considered to represent a heterogeneous group of disorders characterized by benign, slowly progressive, non-inflammatory gingival overgrowth. To date, two genes, ELMO2 and TBC1D2B, have been linked to Ramon syndrome. The objective of this study was to further investigate the genetic variants associated with Ramon syndrome as well as HGF. Clinical, radiographic, histological, and immunohistochemical examinations were performed on affected individuals. Exome sequencing identified rare variants in TBC1D2B in both conditions: a novel homozygous variant (c.1879_1880del, p.Glu627LysfsTer61) in a Thai patient with Ramon syndrome and a rare heterozygous variant (c.2471A>G, p.Tyr824Cys) in a Cambodian family with HGF. A novel variant (c.892C>T, p.Arg298Cys) in KREMEN2 was also identified in the individuals with HGF. With support from mutant protein modeling, our data suggest that TBC1D2B variants contribute to both Ramon syndrome and HGF, although variants in additional genes might also contribute to the pathogenesis of HGF. Full article

Rock quality designation (RQD) is a parameter that describes rock mass quality in terms of percentage recovery of core pieces greater than 10 cm. The RQD represents a basic element of several classification systems. This paper studies scale effects for RQD measurements using synthetic rock masses generated using discrete fracture network (DFN) models. RQD measurements are performed for rock masses with varying fracture intensities and by changing the orientation of the simulated boreholes to account for orientation bias. The objective is to demonstrate the existence of a representative elementary length (REL, 1D analogue of a 3D representative elementary volume, or REV) above which RQD measurements would represent an average indicator of rock mass quality. For the synthetic rock masses, RQD measurements were calculated using the relationship proposed by Priest and Hudson and compared to the simulated RQD measurements along the boreholes. DFN models generated for a room-and-pillar mine using mapped field data were then used as an initial validation, and the conclusion of the study was further validated using the RQD calculation results directly obtained from the depth data collected at an iron cap deposit. The relationship between rock mass scale and assumed threshold length used to calculate RQD is also studied. Full article

Malware classification stands as a crucial element in establishing robust computer security protocols, encompassing the segmentation of malware into discrete groupings. Recently, the emergence of machine learning has presented itself as an apt approach for addressing this challenge. Models can undergo training employing diverse malware attributes, such as opcodes and API calls, to distill valuable insights for effective classification. Within the realm of natural language processing, word embeddings assume a pivotal role by representing text in a manner that aligns closely with the proximity of similar words. These embeddings facilitate the quantification of word resemblances. This research embarks on a series of experiments that harness hybrid machine learning methodologies. We derive word vectors from dynamic API call logs associated with malware and integrate them as features in collaboration with diverse classifiers. Our methodology involves the utilization of Hidden Markov Models and Word2Vec to generate embeddings from API call logs. Additionally, we amalgamate renowned models like BERT and ELMo, noted for their capacity to yield contextualized embeddings. The resultant vectors are channeled into our classifiers, namely Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (kNNs), and Convolutional Neural Networks (CNNs). Through two distinct sets of experiments, our objective revolves around the classification of both malware families and categories. The outcomes achieved illuminate the efficacy of API call embeddings as a potent instrument in the domain of malware classification, particularly in the realm of identifying malware families. The best combination was RF and word embeddings generated by Word2Vec, ELMo, and BERT, achieving an accuracy between 0.91 and 0.93. This result underscores the potential of our approach in effectively classifying malware. Full article

This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The case aims to select explanatory variables correlated with the target debt level in Swedish listed companies. The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression tree (CART)-based imputations by multiple imputations chained equations (MICEs) to address this problem. The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel element linked multinomial-ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable that affected the target debt level. Full article

Starting from the dynamics of a bouncing ball in classical and quantum regime, we have suggested in a previous paper to add an arbitrary function of time to the standard expression of the probability current in quantum mechanics. In this paper, we suggest a way to determine this function: imposing a suitable normalization condition. The application of our proposal to the case of the harmonic oscillator is discussed. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that includes autism, Asperger’s syndrome, and pervasive developmental disorder. Individuals with ASD may exhibit difficulties in social interactions, communication challenges, repetitive behaviors, and restricted interests. While genetic mutations in individuals with ASD can either activate or inactivate the activities of the gene product, impacting neuronal morphogenesis and causing symptoms, the underlying mechanism remains to be fully established. Herein, for the first time, we report that genetically conserved Rac1 guanine-nucleotide exchange factor (GEF) Dock5 signalosome molecules control process elongation in the N1E-115 cell line, a model line capable of achieving neuronal morphological changes. The increased elongation phenotypes observed in ASD and intellectual disability (ID)-associated Semaphorin-5A (Sema5A) Arg676-to-Cys [p.R676C] were also mediated by Dock5 signalosome molecules. Indeed, knockdown of Dock5 using clustered regularly interspaced short palindromic repeat (CRISPR)/CasRx-based guide(g)RNA specifically recovered the mutated Sema5A-induced increase in process elongation in cells. Knockdown of Elmo2, an adaptor molecule of Dock5, also exhibited similar recovery. Comparable results were obtained when transfecting the interaction region of Dock5 with Elmo2. The activation of c-Jun N-terminal kinase (JNK), one of the primary signal transduction molecules underlying process elongation, was ameliorated by either their knockdown or transfection. These results suggest that the Dock5 signalosome comprises abnormal signaling involved in the process elongation induced by ASD- and ID-associated Sema5A. These molecules could be added to the list of potential therapeutic target molecules for abnormal neuronal morphogenesis in ASD at the molecular and cellular levels. Full article

Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and time-consuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM. Full article

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security. Full article

Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering’s use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage). Full article

Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning. Full article

Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380–1022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology’s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields. Full article

Understanding public opinion on ChatGPT is crucial for recognizing its strengths and areas of concern. By utilizing natural language processing (NLP), this study delves into tweets regarding ChatGPT to determine temporal patterns, content features, and topic modeling and perform a sentiment analysis. Analyzing a dataset of 500,000 tweets, our research shifts from conventional data science tools like Python and R to exploit Wolfram Mathematica’s robust capabilities. Additionally, with the aim of solving the problem of ignoring semantic information in the LDA model feature extraction, a synergistic methodology entwining LDA, GloVe embeddings, and K-Nearest Neighbors (KNN) clustering is proposed to categorize topics within ChatGPT-related tweets. This comprehensive strategy ensures semantic, syntactic, and topical congruence within classified groups by utilizing the strengths of probabilistic modeling, semantic embeddings, and similarity-based clustering. While built-in sentiment classifiers often fall short in accuracy, we introduce four transfer learning techniques from the Wolfram Neural Net Repository to address this gap. Two of these techniques involve transferring static word embeddings, “GloVe” and “ConceptNet”, which are further processed using an LSTM layer. The remaining techniques center on fine-tuning pre-trained models using scantily annotated data; one refines embeddings from language models (ELMo), while the other fine-tunes bidirectional encoder representations from transformers (BERT). Our experiments on the dataset underscore the effectiveness of the four methods for the sentiment analysis of tweets. This investigation augments our comprehension of user sentiment towards ChatGPT and emphasizes the continued significance of exploration in this domain. Furthermore, this work serves as a pivotal reference for scholars who are accustomed to using Wolfram Mathematica in other research domains, aiding their efforts in text analytics on social media platforms. Full article

Reproductive traits hold considerable economic importance in pig breeding and production. However, candidate genes underpinning the reproductive traits are still poorly identified. In the present study, we executed a genome-wide association study (GWAS) and runs of homozygosity (ROH) analysis using the PorcineSNP50 BeadChip array for 585 Yorkshire pigs. Results from the GWAS identified two genome-wide significant and eighteen suggestive significant single nucleotide polymorphisms (SNPs) associated with seven reproductive traits. Furthermore, we identified candidate genes, including ELMO1, AOAH, INSIG2, NUP205, LYPLAL1, RPL34, LIPH, RNF7, GRK7, ETV5, FYN, and SLC30A5, which were chosen due to adjoining significant SNPs and their functions in immunity, fertilization, embryonic development, and sperm quality. Several genes were found in ROH islands associated with spermatozoa, development of the fetus, mature eggs, and litter size, including INSL6, TAF4B, E2F7, RTL1, CDKN1C, and GDF9. This study will provide insight into the genetic basis for pig reproductive traits, facilitating reproduction improvement using the marker-based selection methods. Full article