Similarity Properties Research Articles

A critical review on utility of Mamsa Rasa in Basti Yogas

Vasthi (Basti) Karma is a single most widely practised panchakarma therapy for diverse diseases especially diseases caused due to vata in Ayurveda. Niruha(term means unimaginable or incomparable effect) type of vasthi is known for its multiple systemic action in management of diseases in Ayurveda. Mamsa rasa is a very commonly used as food as well as medicine. A thorough reading by going into each sentences through the Brihatrayis, other texts and also the related articles was done for this work. Mamsa is good substance to manage vatavyadhi which can be inferred from antagonistic propertyies presented in the table. Selection of perfect mamsa is important inorder to get desired results, different Acharyas have different opinions in this area even in dosage. Aja mamsa is the most commonlly used mamsa, it has got additional property of similarity to human body tissues. Direct reference of preparation methods for vasthi is not available in Brihatrayis. While describing Yapana category of vasthi yogas, the most utilized avapa dravya is mamsa rasa. It is a popular vasthi kalpana which has its own space in treatment of diseases and in restoration of health and homeostasis. For example Rajayapana vasthi is known as the king of vasthi. Thorough observations and logical thinking is needed in future to unreveal the unknown effects of mamsa rasa in vasthi. Here is an attempt to compile and discuss some points related to the above said topic which gives an overall view of mamsa rasa in vasthi yogas.

Phytoconstituent and In silico Screening of Active Compounds from Red Ginger (Zingiber officinale var. rubrum Theilade) Rhizome and Avocado (Persea americana Mill.) Leaves Extracts as Novel Inhibitors of MRSA

Red ginger rhizome (Zingiber officinale var. Rubrum) and avocado leaves (Persea americana Mill.) are empirically known as one of the medicinal plants used in Taro Village, Gianyar Regency, Bali which have great potential in treating infectious diseases caused by antibiotic resistance, such as MRSA. This study aims to analyze the phytoconstituents and anti-MRSA potential contained in red ginger rhizome and avocado leaves extracts by assessing their inhibitory effects on three proteins related to MRSA resistance and virulence (PBAP2a, transglycosylase, and glycosyltransferase). Phytoconstituents of avocado leaf and red ginger extracts were analyzed using GC-MS. Molecular docking was performed in silico to determine the similarity properties of predicted drugs, bioactivity, toxicity, identification of active sites and validation of protein structures, and docking simulations were performed between compounds found in the extract and their target proteins. Phytoconstituent analysis revealed that avocado leaves and red ginger extracts as a whole have 43 types of compounds and 10 bioactive compounds each with beneficial drug-like properties. The compound 6,11-hexadecadien-1-ol from avocado leaves extracts was predicted to have hepatotoxic properties. There were at least 3 compounds, namely beta-bisabolene from avocado leaves extract, zingiberenol and gamma-curcumene from red ginger rhizome extract, showing the lowest binding affinity for the target protein. Red ginger rhizome and avocado leaves extracts showed valuable potential as anti-MRSA agents through the mechanism of inhibition of three resistance-related proteins, as predicted by in silico analysis.

Deep active learning with high structural discriminability for molecular mutagenicity prediction

The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model’s predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.

A new approach to the similarity problem

We say that a C∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C^*$$\\end{document}-algebra A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {A}}$$\\end{document} satisfies the similarity property ((SP)) if every bounded homomorphism u:A→B(H),\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u: {\\mathcal {A}}\\rightarrow {\\mathcal {B}}(H),$$\\end{document} where H is a Hilbert space, is similar to a ∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document}-homomorphism and that a von Neumann algebra M\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {M}}$$\\end{document} satisfies the weak similarity property ((WSP)) if every w∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{w}^*$$\\end{document}-continuous, unital and bounded homomorphism π:M→B(H),\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi : {\\mathcal {M}}\\rightarrow {\\mathcal {B}}(H),$$\\end{document} where H is a Hilbert space, is similar to a ∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document}-homomorphism. The similarity problem is known to be equivalent to the question of whether every von Neumann algebra is hyperreflexive. We improve on that by introducing the following hypothesis (EP): Every separably acting von Neumann algebra with a cyclic vector is hyperreflexive. We prove that under (EP), every separably acting von Neumann algebra satisfies (WSP) and we pass from the case of separably acting von Neumann algebras to all C∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C^*$$\\end{document}-algebras.

A Review on Analysis of Flavonoid and Isoflavonoids Derivatives as Anticancer Agent using Swiss ADME

Cancer, generally characterized by uncontrollable growth of abnormal cells that metastasize to other parts of the body. It is a one of the leading concerned disease that affects the worldwide population. It can originate from almost any organ or tissue and are of various types; leukemia, lung cancer, lymphoma, stomach cancer, cervical cancer. Flavonoids and Isoflavonoids are naturally occurring polyphenolic secondary metabolites that possess numerous medicinal benefits including antioxidant, anti-inflammatory, anticancer and antiviral properties. Potential agents derived from flavonoids and isoflavonoids for its anticancer activity have been studied. Due to recent technological advancement, the detailed study of any moiety and molecules is possible. A virtual screening of flavonoids was carried out using molecular docking, drug similarity, ADMET prediction, drug likeness, chemical and physicochemical properties to determine its potential anticancer activity with the use of Swiss ADME and Chem Draw software. In this review, analysis of drug likeness properties of flavonoids and isoflavonoids was thoroughly performed. Additionally, structures were examined and observed for better interaction of flavonoids scaffold with receptors. Due to their great stability, flavonoids are strongly recommended as anticancer medicines for various cancer stages.

Theoretical spectroscopic electronic elucidation with polar and non-polar solvents (IEFPCM model), molecular docking and molecular dynamic studies on bendiocarb -antiallergic drug agent

The potential antiallergic compound bendiocarb was studied theoretically by polar and non-polar (six) solvents (IEFPCM model) by quantum chemical DFT calculations. Rotational potential barriers were estimated for water, acetone, THF, chloroform, benzene, and toluene are 12.62, 12.45, 12.55, 12.69, 12.74 and 12.57 kJ/mol. Analysis was done on the variations in geometries brought about by the electronegative atom’s replacement as well as the intramolecular interactions caused by the benzodioxol ring. NBO analysis as well as geometrical equilibrium have been performed using DFT employing the B3LYP technique with the 6-311G++(d, p) basis set. In vibrational analysis, CH stretching of bendiocarb was theoretically estimated within 3207–3181 cm−1 (gas), 3207–3184 cm−1 (acetone and water), 3207–3182 cm−1 (benzene and toluene), and 3207–3183 cm−1 (chloroform and THF) due to CN vibrations is challenging because of the overlapping and appearance of multiple bands in similar regions. Energy gaps for Bendiocarb in different solvents for water (5.719 eV), acetone (5.716 eV), THF (5.722 eV), chloroform (5.724 eV), benzene (5.731 eV), toluene (5.730 eV), and gas phase (5.746 eV). Lipinski’s rule of five was exploited to guide research on drug similarity and oral action. Using docking simulation against the antiallergic protein, a binding energy of −5.22 kcal/mol, 4P5M has been examined. The protein 4P5M exhibits variations between 0.12 and 0.155 nm for RMSD in molecular dynamic simulation. One can infer information about the pharmacokinetic features of the target molecule from its drug similarity, absorption, distribution, metabolism, excretion, and toxicity properties.

Dissimilarity functions co-symmetry property: a focus on probability distributions with involutive negation

In today’s data-rich era, there is a growing need for developing effective similarity and dissimilarity measures to compare vast datasets. It is desirable that these measures reflect the intrinsic structure of the domain of these measures. Recently, it was shown that the space of finite probability distributions has a symmetric structure generated by involutive negation mapping probability distributions into their “opposite” probability distributions and back, such that the correlation between opposite distributions equals –1. An important property of similarity and dissimilarity functions reflecting such symmetry of probability distribution space is the co-symmetry of these functions when the similarity between probability distributions is equal to the similarity between their opposite distributions. This article delves into the analysis of five well-known dissimilarity functions, used for creating new co-symmetric dissimilarity functions. To conduct this study, a random dataset of one thousand probability distributions is employed. From these distributions, dissimilarity matrices are generated that are used to determine correlations similarity between different dissimilarity functions. The hierarchical clustering is applied to better understand the relationships between the studied dissimilarity functions. This methodology aims to identify and assess the dissimilarity functions that best match the characteristics of the studied probability distribution space, enhancing our understanding of data relationships and patterns. The study of these new measures offers a valuable perspective for analyzing and interpreting complex data, with the potential to make a significant impact in various fields and applications.

Locality-aware virtual machine placement based on similarity properties in mobile edge computing

Locality-aware virtual machine placement based on similarity properties in mobile edge computing

Maternal mental health monitoring in an online community: a natural language processing approach

ABSTRACT Digital maternity support communities are increasingly popular. The communities are often based on discussion forums called ‘birth clubs’, to which users are assigned according to their estimated due months. Distinguishing between support-seeking and non-support-seeking posts submitted to these ‘birth clubs’ is a crucial first step for monitoring maternal mental health. This study utilised natural language processing (NLP) techniques on 52,558 posts collected from one of the largest online maternity communities in China, employing machine learning algorithms trained for post classification with a randomly selected and manually labelled subset of 3000 posts. The results validated the properties of information similarity and time sensitivity within the post data, and demonstrated the feasibility of employing simple algorithms and small training sets for effective maternal mental health monitoring.

SAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

Computational Evaluation of Bioactive Compounds from Dysphania ambrosioides Leaves.

Dysphania ambrosioides has been reported to have many medicinal properties, due to its possession of a multitude of biologically active molecules contained in its leaves. However, very few studies have been reported to evaluate their pharmacological properties. Consequently, in the present study, many computational tools have been performed to predict drug similarity and ADMET properties. Besides, the inhibitory potential of D.ambrosioides major compounds against Bacterial, Fungal and cardiovascular main receptor targets has been investigated. This study suggests that Carvone oxide, 5-Isopropenyl-2-Methylenecyclohexanol, and Caryophyllene oxide were the most active molecules belonging to D. ambrosioides Leaves, possessing drug-likeness with satisfactory bioactivity scores, having good pharmacokinetic values. Metabolism and toxicities were further studied using FAME3, GLORY, and pred-hERG. Slight cardiotoxicity and cytotoxicity were predicted, respectively, for Caryophyllene oxide and Carvone oxide, 5-Isopropenyl-2-Methylenecyclohexanol. Good inhibitory activities of the three compounds against Bacterial, Fungal, and Cardiovascular receptor targets. Hence, this is a comprehensive in silico approach to evaluate D.ambrosioides Leaves main phytocompounds in the background of its potential in future drug development.

Search and Retrieval in Semantic-Structural Representations of Novel Malware

In this study, we present a novel representation for binary programs which captures semantic similarity and structural properties. This representation enables the search and retrieval of binary executable programs based on their similarity of behavioral properties. The proposed representation is composed in a bottom-up approach: we begin by extracting data dependency graphs (DDG), which are representative of both program structure and operational semantics. We then encode each program as a set of graph hashes representing isomorphic uniqueness, a method we have labeled DDG Fingerprinting. We present experimental results of search using k-Nearest Neighbors in a metric space constructed from a set of binary executables. Searches in the dataset are based on the operational semantics of specific malware examples By quantifying behavioral similarity we show that we can recognize patterns of operation in novel malware with functionality not previously identified. We show in addition that the associated metric space allows an adjustable level of resolution. Resolution of the features may be decreased for breadth of search and retrieval, or as the search space is reduced, the resolution may be increased for accuracy and fine-grained analysis of malware behavior. This allows for explainability in the interpretation of fine-grained analysis.

Latent Diffusion Enhanced Rectangle Transformer for Hyperspectral Image Restoration.

The restoration of hyperspectral image (HSI) plays a pivotal role in subsequent hyperspectral image applications. Despite the remarkable capabilities of deep learning, current HSI restoration methods face challenges in effectively exploring the spatial non-local self-similarity and spectral low-rank property inherently embedded with HSIs. This paper addresses these challenges by introducing a latent diffusion enhanced rectangle Transformer for HSI restoration, tackling the non-local spatial similarity and HSI-specific latent diffusion low-rank property. In order to effectively capture non-local spatial similarity, we propose the multi-shape spatial rectangle self-attention module in both horizontal and vertical directions, enabling the model to utilize informative spatial regions for HSI restoration. Meanwhile, we propose a spectral latent diffusion enhancement module that generates the image-specific latent dictionary based on the content of HSI for low-rank vector extraction and representation. This module utilizes a diffusion model to generatively obtain representations of global low-rank vectors, thereby aligning more closely with the desired HSI. A series of comprehensive experiments were carried out on four common hyperspectral image restoration tasks, including HSI denoising, HSI super-resolution, HSI reconstruction, and HSI inpainting. The results of these experiments highlight the effectiveness of our proposed method, as demonstrated by improvements in both objective metrics and subjective visual quality.

Evaluation of the similarity properties of double-headed streamer propagation

Evaluation of the similarity properties of double-headed streamer propagation

Similarity Property and Slide Attack of Block Cipher FESH

This paper focuses on similarity properties and extension of the classical slide property of block ciphers. Taking FESH, an award-winning block cipher of the National Cryptographic Algorithm Design Competition 2019, as an example, similarity properties of the encryption and key transformation are found, owing to the similar structures that the encryption and key transformation adopted, and the constants generation. Based on the similarity properties, extended slide properties can be constructed for FESH. Slide attacks of FESH are then proposed. The similarity properties and extended slide property are immune to the increasing of iterated rounds, i.e., it cannot be avoided by increasing the round number of FESH. Furthermore, extended slide property helps relaxing the strict requirements of the subkeys in slide attacks. Taking Feistel and SPN structures as examples, frameworks of slide attacks based on the extended slide properties are presented. Slide attack of FESH is exactly a concrete example of SPN structure.

HOMA Index Establishes Similarity to a Reference Molecule.

The article shows that the definition of the HOMA index of geometrical aromaticity satisfies the axioms of a similarity function between the examined and benzene ring. Consequently, for purely mathematical reasons, the index works exceptionally well as an index of aromaticity: it expresses a geometric similarity to the archetypal aromatic benzene. Thus, if the molecule is geometrically similar to benzene, then it is also chemically similar, and therefore, it is aromatic. However, the similarity property legitimizes using the HOMA-like indices to express similarity to molecules other than benzene, whether cyclic or linear and existing or hypothetical. The paper demonstrates an example of HOMA-similarity to cyclohexane, which expresses a (relaxed)-saturicity property not accompanied by strong structural strains or steric hindrances. Further, it is also shown that the HOMA index can evaluate the properties of whole molecules, such as 25 unbranched catacondensed isomers of hexacene. The index exhibits a significant quadratic correlation with the total energy differences of planar isomers from which the nonplanar ones deviate. Moreover, the HOMA index of hexacene isomers significantly correlates with the Kekulé count connected to the resonance energy in the Hückel approximation. As a result, the study shows that the HOMA index can be used not only for aromaticity analyses but also as a general chemical descriptor applicable to rings, chains, composed molecular moieties, or even whole molecules.

In Silico Prediction of ADMET/Drug-likeness Properties of Bioactive Phloroglucinols from Hypericum genus.

Dimeric acylphloroglucinols occurring in species from sections Brathys and Trigynobrathys of the genus Hypericum exhibit acylfilicinic acid and acylphloroglucinol moieties linked by a methylene bridge. However, this chemical feature differs from hyperforin, from H. perforatum (Hypericum section), some dimeric acylphloroglucinols, such as uliginosin B, display similar pharmacological activities, namely antidepressant and antinociceptive. However, there is no knowledge about the pharmacokinetic profile and no toxicity studies of these compounds in intact mammals. to perform an in silico evaluation of the similarity, pharmacokinetics and toxicity (ADMET) properties of dimeric acylphloroglucinols from species native to Central and South America. ADMET prediction of eleven elected phloroglucinols followed by the chemical space evaluation of thirty-five dimeric acylphloroglucinols derivatives labeled according to their prenylation/geranylation pattern through principal component analysis (PCA). The similarity analysis was performed using the Tanimoto similarity index. ADMET properties were predicted with the open-source software SwissADME and pkCSM-pharmacokinetics. Several compounds showed good human intestinal absorption. However, they may present difficulties in crossing the blood-brain barrier, probably due to the high tPSA values. The predicted toxicity parameters indicated that most compounds have low toxicity. Most non-prenylated phloroglucinols were disposed into Lipinski's rule limits. Uliginosin B, isouliginosin B and japonica seem to be druglike compounds. The PCA model explained 77.49% of the total variance, and molecular similarity analyses revealed some expected similarities between isomers and different compounds. dimeric acylphloroglucinols may be promising drug candidates and deserve further pharmacological and medicinal chemistry studies.

ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate.

Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory. We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation. ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.

Gene silencing of Helicobacter pylori through newly designed siRNA convenes the treatment of gastric cancer.

Helicobacter pylori is a gastric pathogen that is responsible for causing chronic inflammation and increasing the risk of gastric cancer development. It is capable of persisting for decades in the harsh gastric environment because of the inability of the host to eradicate the infection. Several treatment strategies have been developed against this bacterium using different antibiotics. But the effectiveness of treating H. pylori has significantly decreased due to widespread antibiotic resistance, including an increased risk of gastric cancer. The small interfering RNAs (siRNA), which is capable of sequence-specific gene-silencing can be used as a new therapeutic approach for the treatment of a variety of such malignancies. In the current study, we rationally designed two siRNA molecules to silence the cytotoxin-associated gene A (CagA) and vacuolating cytotoxin A (VacA) genes of H. pylori for their significant involvement in developing cancer. We selected a common region of all the available transcripts from different countries of CagA and VacA to design the siRNA molecules. The final siRNA candidate was selected based on the results from machine learning algorithms, off-target similarity, and various thermodynamic properties. Further, we utilized molecular docking and all atom molecular dynamics (MD) simulations to assess the binding interactions of the designed siRNAs with the major components of the RNA-induced silencing complex (RISC) and results revealed the ability of the designed siRNAs to interact with the proteins of RISC complex in comparable to those of the experimentally reported siRNAs. These designed siRNAs should effectively silence the CagA and VacA genes of H. pylori during siRNA mediated treatment in gastric cancer.

Adaptive detection of impact signals with two-dimensional piecewise tri-stable stochastic resonance and its application in bearing fault diagnosis

In this paper, we introduce an innovative method known as Adaptive Two-Dimensional Piecewise Tri-Stable Stochastic Resonance (TDPTSR) and discuss its applicability in extracting weak fault features. Initially, addressing the saturation issue of the Standard Tri-Stable Stochastic Resonance (STSR) system, we construct a novel piecewise tri-stable potential function. This function independently adjusts the depth and width of potential wells. We explore the particle output characteristics of the Piecewise Tri-Stable Stochastic Resonance (PTSR) system using the fourth-order Runge-Kutta algorithm. Subsequently, we delve into the performance analysis of the TDPTSR system. Leveraging adiabatic approximation theory, we derive the equivalent potential function, steady-state probability density (SPD), mean first-pass time (MFPT), and output signal-to-noise ratio (SNR) of the TDPTSR. We also investigate the impact of changes in system parameters on these metrics. Additionally, drawing from kurtosis and cosine similarity properties, we introduce a Modified Kurtosis Index (MKC) as a measurement index for impact signal detection. We propose a method for impact signal feature extraction by integrating MKC with the TDPTSR system. Simulation analysis demonstrates the suitability of MKC as an index for quantifying Stochastic Resonance and the superior detection capability of the TDPTSR system. Finally, we apply TDPTSR to the fault diagnosis of two types of bearings, optimizing system parameters using a genetic algorithm (GA). Comparative analysis with STSR and PTSR confirms that our proposed method in this paper yields further improvements in Signal-to-Noise Ratio Improvement (SNRI), higher spectral peak magnitude (Amout) at characteristic frequencies, and increased recognition quantity (Δ).