Prior research on abstractive text summarization has predominantly relied on the ROUGE evaluation metric which while effective has limitations in capturing semantic meaning due to its focus on exact word or phrase matching. This deficiency is particularly pronounced in abstractive summarization approaches where the goal is to generate novel summaries by rephrasing and paraphrasing the source text highlighting the need for a more nuanced evaluation metric capable of capturing semantic similarity.

In this study the limitations of existing ROUGE metrics are addressed by proposing a novel variant called ROUGE-SS. Unlike traditional ROUGE metrics ROUGE-SS extends beyond exact word matching to consider synonyms and semantic similarity. Leveraging resources such as the WordNet online dictionary ROUGE-SS identifies matches between source text and summaries based on both exact word overlaps and semantic context. Experiments are conducted to evaluate the performance of ROUGE-SS compared to other ROUGE variants particularly in assessing abstractive summarization models. The algorithm for the synonym features (ROUGE-SS) is also proposed.

The experiments demonstrate the superior performance of ROUGE-SS in evaluating abstractive text summarization models compared to existing ROUGE variants. ROUGE-SS yields higher F1 scores and better overall performance achieving a significant reduction in training loss and impressive accuracy. The proposed ROUGE-SS evaluation technique is evaluated in different datasets like CNN/Daily Mail DUC-2004 Gigawords and Inshorts News datasets. ROUGE-SS gives better results than other ROUGE variant metrics. The F1-score of the proposed ROUGE-SS metric is improved by an average of 8.8%. These findings underscore the effectiveness of ROUGE-SS in capturing semantic similarity and providing a more comprehensive evaluation metric for abstractive summarization.

In conclusion the introduction of ROUGE-SS represents a significant advancement in the field of abstractive text summarization evaluation. By extending beyond exact word matching to incorporate synonyms and semantic context ROUGE-SS offers researchers a more effective tool for assessing summarization quality. This study highlights the importance of considering semantic meaning in evaluation metrics and provides a promising direction for future research on abstractive text summarization.

Mobile devices have become an integral part of our digital lives facilitating various tasks and storing a treasure trove of sensitive information. However as more people utilize mobile devices sophisticated cyber threats are emerging to elude traditional security measures.

The use of evasion techniques by malicious actors presents a significant challenge to mobile security necessitating creative solutions. In this work we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics including runtime manipulation code obfuscation and unauthorized data access.

These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor intercept and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape.

Consequently this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

The consistency of the magnetorheological process insisted that the Inconel^® 718 material should be furnished. This process involves every material from the categories of soft and hard materials.

In this study a cylindrical ferromagnetic work-part was finished using the magnetorheological finishing method.

The strength of the magnetic flux controls the density and forces in processes that are assisted by magnetic fields. The mechanism was studied parametrically in this research work using response surface methodology.

Optimal process parameters were determined using response surface methodology to accurately execute the finishing procedure. Each parameter's percentage consumption to the process's finishing output was also estimated. The finishing of an industrial extrusion punch was carried out using the optimum parameters obtained from the parametric analysis.

The RSM optimisation of process parameters is validated using the batch gradient descent (BGD) algorithm which is the best fit and innovation algorithm for these types of optimization solutions. The mathematical model that BGD provides confirms the RSM mathematical model.

The optimized parameters are useful in controlling the capability of the MR finishing process in various industrial applications.

Data is rapidly expanding in today's digital age. The reason for the expansion of data is due to social media sites. The internet produces an enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions feelings attitudes impressions etc. concerning subjects products and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis.

This study provides a thorough examination of the differences between methods of sentiment analysis as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses examines its challenges and outlines common methods of conducting it.

The objective of the current overview is to better understand the market gauge public opinion and make strategic decisions. In addition enterprises governments and scholars can all benefit from conducting a sentiment analysis.

In this study we review and categorize the most widely applied methods of deep learning and machine learning for analyzing sentiment. From the paper we learn that which sentiment analysis technique is the best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context as well as data volume.

Regardless of the fact that the English language has traditionally been the focus of research in this field other spoken languages have recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases and addressing the problems with Natural language processing that have been stated in the context of sentiment examination.

The difficulties of sentiment analysis are examined as well with the goal of illuminating potential solutions.

Drug-resistant Staphylococcus aureus represents a substantial healthcare challenge worldwide and its range of available therapeutic options continues to diminish progressively. Thus this study aimed to identify potential inhibitors against FemA a crucial protein involved in the cell wall biosynthesis of S. aureus.

The screening process involved a comprehensive structure-based virtual screening on the StreptomDB database to identify ligands with potential inhibitory effects on FemA using AutoDock Vina. The most desirable ligands with the highest binding affinity and pharmacokinetic properties were selected. Two ligands with the highest number of hydrogen bonds and hydrophobic interactions were further analyzed by molecular dynamics (MD) using the GROMACS version 2018 simulation package.

Six H-donor conserved residues were selected as protein active sites including Arg-220 Tyr-38 Gln-154 Asn-73 Arg-74 and Thr-24. Through virtual screening a total of nine compounds with the highest binding affinity to the FemA protein were identified. Frigocyclinone and C21H21N3O4 exhibited the highest binding affinity and demonstrated favorable pharmacokinetic properties. Molecular dynamics analysis of the FemA-ligand complexes further indicated desirable stability and reliability of complexes reinforcing the potential efficacy of these ligands as inhibitors of FemA protein.

Our findings suggest that Frigocyclinone and C21H21N3O4 are promising inhibitors of FemA in S. aureus. To further validate these computational results experimental studies are planned to confirm the inhibitory effects of these compounds on various S. aureus strains. Combining computational screening with experimental validation contributes valuable insights to the field of drug discovery in comparison to the classical drug discovery approaches.

Cinnamic acid (Cinn) is a phenolic acid of Cinnamomum cassia (L.) J. Presl. that can ameliorate diabetic nephropathy (DN). However comprehensive therapeutic targets and underlying mechanisms for Cinn against DN are limited.

In this study a network pharmacology approach and in vivo experiments were adopted to predict the pharmacological effects and mechanisms of Cinn in DN therapy.

The nephroprotective effect of Cinn on DN was investigated by a streptozotocin-induced diabetes mellitus (DM) mouse model. The protein-protein interaction network of Cinn against DN was established by a network pharmacology approach. The core targets were then identified and subjected to molecular docking with Cinn.

Cinn treatment effectively restored body weight ameliorated hyperglycemia and reduced kidney dysfunction markers in DM mice also demonstrating a reduction in tissue injury. Network pharmacology analysis identified 298 DN-Cinn co-target genes involved in various biological processes and pathways. Seventeen core targets were identified eight of which showed significant differential expression in the DN and healthy control groups. Molecular docking analysis revealed a strong interaction between Cinn and PTEN. Cinn treatment downregulated the PTEN protein expression in DM mice.

This study revealed the multi-target and multi-pathway characteristics of Cinn against DN. Cinn improved renal pathological damage of DN which was related to the downregulation of PTEN.

This study aimed to investigate the medicinal properties of SZS before and after processing and provide novel insights into its potential for treating insomnia.

This study employed the network pharmacology platform to gather information on the chemical composition of SZS human targets genes molecular networks and pathways associated with insomnia treatment using SZS. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was utilized to analyze the chemical profiles of crude SZS parched SZS and their combined decoction. The effects of different SZS products on p-chlorophenylalanine-induced insomnia mice were evaluated through pentobarbital-induced sleep tests behavioral analyses examination of brain tissue-related mRNA levels and measurement of plasma neurotransmitters aiming to explore the sedative and hypnotic effects of various SZS products.

SZS was found to contain a total of 47 genes including 22 target genes associated with insomnia. These genes may contribute to the sedative and hypnotic effects through 9 related pathways and 69 biological processes. The active components of SZS remained consistent before and after processing. Jujuboside B was found in higher concentrations in crude SZS while jujuboside A was more abundant in parched SZS. Additionally SZS exhibited reduced locomotor activity in mice enhanced the hypnotic effect of pentobarbital sodium and decreased the levels of acetylcholinesterase α-1B adrenergic receptor and solute carrier family 6 member 4 mRNA in the cortex and hippocampus of mice. The levels of acetylcholine choline acetyltransferase 5-hydroxyindoleacetic acid and glutamate in plasma increased with the hypnotic effect being proportional to the dosage of the drug.

SZS demonstrates sedative and hypnotic effects potentially mediated by its influence on neurotransmitter levels and related receptors within the central nervous system. There was a slight variation in regulatory capabilities before and after SZS processing with the combined decoction of crude and parched SZS exhibiting a more pronounced effect particularly at higher dosages.

Cancer poses a great threat to human health and effective drugs to treat it are always needed. Several compounds containing a 2-aminopyrazine framework have been identified as antitumor agents with SHP2 inhibition activities. This current work aimed to search for more potent novel compounds possessing a 2-aminopyrazine moiety with antitumor activities.

A series of 12 novel 2-aminopyrazine derivatives was synthesized and their structures were confirmed by spectroscopic techniques. The inhibitory activities of all the synthesized compounds against MDA-MB-231 and H1975 cancer cell lines were evaluated by an MTT assay. The most potent compound 3e was analyzed by flow cytometry. Subsequently computational studies were performed to investigate the possible antitumor mechanisms of compound 3e.

The results indicated that compound 3e exhibited potent antitumor activities with IC50 values of 11.84 ± 0.83 μM against H1975 cells and 5.66 ± 2.39 μM against MDA-MB-231 cells which were more potent than the SHP2 inhibitor GS493 (IC50 = 19.08 ± 1.01 μM against H1975 cells and IC50 = 25.02 ± 1.47 μM against MDA-MB-231 cells). Further analysis by flow cytometry demonstrated that compound 3e induced cell apoptosis in H1975 cells. The results of the molecular docking and MD simulations including RMSD RMSF PCA DCCM and binding energy and decomposition analyses revealed that compound 3e probably selectively inhibited SHP2.

A new compound having a 2-aminopyrazine substructure with potent inhibitory activities against the H1975 and MDA-MB-231 cancer cells was obtained meriting further investigation as an antitumor drug.

Methicillin-resistant Staphylococcus aureus (MRSA) is a causative agent for multiple drug-resistant diseases and is a prime health concern. Currently antibiotics like vancomycin daptomycin fluoroquinolones linezolid fifth-generation cephalosporin and others are available in the market for the treatment of MRSA infection.

With the increasing prevalence of drug-resistant cases researchers are actively investigating alternative strategies to combat MRSA including the exploration of peptide therapeutics. This study employed computational methods to prospect for potential Antimicrobial Peptides (AMPs).

A total of One hundred and fifty antimicrobial peptides were explored based on physicochemical properties. The results showed that Clavanin B was the most appropriate candidate. Molecular Docking and Molecular Dynamics Simulation results showed the protein-peptide interaction of the MRSA target proteins Penicillin Binding Protein 2a and Panton-Valentine Leukocidin Toxin with the Antimicrobial Peptide Clavanin B.

Currently the antimicrobial peptide database highlights Clavanin B's role as an anti-HIV peptide. Moreover this investigation proposes Clavanin B as a viable repurposed drug for treating MRSA underscoring its potential deployment in the management of MRSA infections.