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Artykuły w czasopismach na temat „Vulnerability discovery model (VDM)”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Sharma, Ruchi, Ritu Sibal, and A. K. Shrivastava. "Vulnerability Discovery Modeling for Open and Closed Source Software." International Journal of Secure Software Engineering 7, no. 4 (2016): 19–38. http://dx.doi.org/10.4018/ijsse.2016100102.

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With growing concern for security, the researchers began with the quantitative modeling of vulnerabilities termed as vulnerability discovery models (VDM). These models aim at finding the trend of vulnerability discovery with time and facilitate the developers in patch management, optimal resource allocation and assessing associated security risks. Among the existing models for vulnerability discovery, Alhazmi-Malaiya Logistic Model (AML) is considered the best fitted model on all kinds of datasets. But, each of the existing models has a predefined basic shape and can only fit datasets followin

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Wang, Xiajing, Rui Ma, Binbin Li, Donghai Tian, and Xuefei Wang. "E-WBM: An Effort-Based Vulnerability Discovery Model." IEEE Access 7 (2019): 44276–92. http://dx.doi.org/10.1109/access.2019.2907977.

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Taib, Abidah Hj Mat, Wan Nor Ashiqin Wan Ali, and Nurul Sharidah Shaari. "ICMPV6 Vulnerability." International Journal of Mobile Computing and Multimedia Communications 5, no. 2 (2013): 78–100. http://dx.doi.org/10.4018/jmcmc.2013040106.

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Handling Internet Control Message Protocol version 6 (ICMPv6) vulnerabilities is among the challenges in securing the IPv6 deployment. Since ICMPv6 messages are crucial in IPv6 communications, this paper discusses the discovery of ICMPv6 vulnerabilities by producing a threat model of ICMPv6 vulnerabilities and proposing the use of Selective Filtering ICMPv6 Messages (SF-ICMP6) to handle some of the vulnerabilities. An experiment was conducted to verify the threat model by observing and discussing the usage of ICMPv6 threat model together with SF-ICMP6. The findings showed that the attack deriv

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Kansal, Yogita, P. K. Kapur, Uday Kumar, and Deepak Kumar. "User-dependent vulnerability discovery model and its interdisciplinary nature." Life Cycle Reliability and Safety Engineering 6, no. 1 (2017): 23–29. http://dx.doi.org/10.1007/s41872-017-0003-y.

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Johnston, Reuben, Shahryar Sarkani, Thomas Mazzuchi, Thomas Holzer, and Timothy Eveleigh. "Bayesian-model averaging using MCMCBayes for web-browser vulnerability discovery." Reliability Engineering & System Safety 183 (March 2019): 341–59. http://dx.doi.org/10.1016/j.ress.2018.11.030.

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Bhatt, Navneet, Adarsh Anand, V. S. S. Yadavalli, and Vijay Kumar. "Modeling and Characterizing Software Vulnerabilities." International Journal of Mathematical, Engineering and Management Sciences 2, no. 4 (2017): 288–99. http://dx.doi.org/10.33889/ijmems.2017.2.4-022.

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With the association of software security assurance in the development of code based systems; software developers are relying on the Vulnerability discovery models to mitigate the breaches by estimating the total number of vulnerabilities, before they’re exploited by the intruders. Vulnerability Discovery Models (VDMs) provide the quantitative classification of the flaws that exists in a software that will be discovered after a software is released. In this paper, we develop a vulnerability discovery model that accumulate the vulnerabilities due to the influence of previously discovered vulner

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Anand, Adarsh, Navneet Bhatt, and Deepti Aggrawal. "Modeling Software Patch Management Based on Vulnerabilities Discovered." International Journal of Reliability, Quality and Safety Engineering 27, no. 02 (2019): 2040003. http://dx.doi.org/10.1142/s0218539320400033.

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A software system deals with various security implications after its release in the market. Correspondingly, firm releases security patches to counter those flaws discovered in the software system. A vendor releases a patch only if a vulnerability has been discovered in a software. It is an important aspect that encompasses the prediction of potential number of patches to be released to maintain the stability of a software. Vulnerability Discovery Models (VDMs) help a software vendor to acknowledge the security trends, forecast security investments and to plan patches, but very few attempts ha

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Yuan, Xue, Guanjun Lin, Yonghang Tai, and Jun Zhang. "Deep Neural Embedding for Software Vulnerability Discovery: Comparison and Optimization." Security and Communication Networks 2022 (January 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/5203217.

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Due to multitudinous vulnerabilities in sophisticated software programs, the detection performance of existing approaches requires further improvement. Multiple vulnerability detection approaches have been proposed to aid code inspection. Among them, there is a line of approaches that apply deep learning (DL) techniques and achieve promising results. This paper attempts to utilize CodeBERT which is a deep contextualized model as an embedding solution to facilitate the detection of vulnerabilities in C open-source projects. The application of CodeBERT for code analysis allows the rich and laten

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U. Rahane, Kavita, and A. B. Pawar. "A REVIEW ON CYBERSECURITY VULNERABILITY DISCOVERY AND MITIGATION MODEL IN SMART COMPUTING." International Journal of Engineering Applied Sciences and Technology 09, no. 05 (2024): 68–77. http://dx.doi.org/10.33564/ijeast.2024.v09i05.008.

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Smart computing links physical and digital objects which communicate online using sensors, software, and other technologies. It offers many advantages but lacks information security, it could potentially face serious problems. The number of smart computing devices are growing rapidly around the world, making them a target of many attackers, whose aim is to steal confidential data and threaten user's privacy. With features like confidentiality, access control, correctness, completeness, authentication, availability, and privacy. Data and services should be safeguarded in the ecosystem. It has p

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Narang, Swati, P. K. Kapur, D. Damodaran, and A. K. Shrivastava. "Bi-Criterion Problem to Determine Optimal Vulnerability Discovery and Patching Time." International Journal of Reliability, Quality and Safety Engineering 25, no. 01 (2018): 1850002. http://dx.doi.org/10.1142/s021853931850002x.

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In the last decade, we have seen enormous growth in software security related problems. This is due to the presence of bad guys who keep eye on the software vulnerabilities and create the security breach. Because of which software firms face huge loss. The problems of the software firms is two folded. One is to decide the optimal discovery time of the software vulnerability and another one is to determine the optimal patching time of those discovered vulnerability. Optimal discovery time of vulnerability is necessary as not disclosing the vulnerability on time may cause serious loss in the com

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Abraham, Subil, and Suku Nair. "Comparative analysis and patch optimization using the cyber security analytics framework." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 15, no. 2 (2017): 161–80. http://dx.doi.org/10.1177/1548512917705743.

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Dependable metrics are one of the critical elements of an organization’s information security program and are crucial for its long-term success. Current research in the area of enterprise security metrics provides limited insight on understanding the impact that attacks have on the overall security goals of an enterprise as well as predicting the future security state of the network. In this paper we present a novel security analytics framework that takes into account both the inter-relationship between different vulnerabilities and the temporal features that evolve over time, such as the vuln

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Zhang, Liumei, Wei Zhang, Yichuan Wang, Bowen Xia, and Yu Han. "Fine-Grained Modeling of ROP Vulnerability Exploitation Process under Stack Overflow Based on Petri Nets." Electronics 12, no. 23 (2023): 4741. http://dx.doi.org/10.3390/electronics12234741.

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Software vulnerability discovery is currently a hot topic, and buffer overflow remains a prevalent security vulnerability. One of the key issues in vulnerability discovery and analysis is how to quickly analyze buffer overflow vulnerabilities and select critical exploitation paths. Existing modeling methods for vulnerability exploitation cannot accurately reflect the fine-grained execution process of stack overflow exploitation paths. This paper, based on the discussion of buffer overflow exploitation techniques, proposes a fine-grained modeling and analysis method based on Petri nets for the

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Schiele, Miriam A., Katharina Herzog, Leonie Kollert, et al. "Extending the vulnerability–stress model of mental disorders: three-dimensional NPSR1 × environment × coping interaction study in anxiety." British Journal of Psychiatry 217, no. 5 (2020): 645–50. http://dx.doi.org/10.1192/bjp.2020.73.

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BackgroundThe general understanding of the ‘vulnerability–stress model’ of mental disorders neglects the modifying impact of resilience-increasing factors such as coping ability.AimsProbing a conceptual framework integrating both adverse events and coping factors in an extended ‘vulnerability–stress–coping model’ of mental disorders, the effects of functional neuropeptide S receptor gene (NPSR1) variation (G), early adversity (E) and coping factors (C) on anxiety were addressed in a three-dimensional G × E × C model.MethodIn two independent samples of healthy probands (discovery: n = 1403; rep

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Joh, HyunChul. "Software Risk Assessment for Windows Operating Systems with respect to CVSS." European Journal of Engineering Research and Science 4, no. 11 (2019): 41–45. http://dx.doi.org/10.24018/ejers.2019.4.11.1610.

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CVSS is recognized as a de facto standard for categorizing and measuring software vulnerabilities in both how easy for exploitation for the given security bug and how much impact on a system having the vulnerability in a sense of the three security factors. Meanwhile, since the early 2000s, quantitative risk assessments of software systems had been able to be examined thanks to the accumulated enough datasets for a scientific investigation. However, there are still a lot of research attempts not to be taken in a quantitative examination of software risk assessments. In this paper, we are quant

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Joh, HyunChul. "Software Risk Assessment for Windows Operating Systems with respect to CVSS." European Journal of Engineering and Technology Research 4, no. 11 (2019): 41–45. http://dx.doi.org/10.24018/ejeng.2019.4.11.1610.

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CVSS is recognized as a de facto standard for categorizing and measuring software vulnerabilities in both how easy for exploitation for the given security bug and how much impact on a system having the vulnerability in a sense of the three security factors. Meanwhile, since the early 2000s, quantitative risk assessments of software systems had been able to be examined thanks to the accumulated enough datasets for a scientific investigation. However, there are still a lot of research attempts not to be taken in a quantitative examination of software risk assessments. In this paper, we are quant

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Kathiresan, Gopinath. "AI-Augmented Vulnerability Discovery through Static Code Pattern Clustering in Micro services." International Journal of Scientific Research and Management (IJSRM) 13, no. 05 (2025): 2145–54. https://doi.org/10.18535/ijsrm/v13i05.ec01.

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The mushrooming adoption of microservice architecture in modern software engineering certainly resulted in a substantial boost in scalability, deployment flexibility, and system resilience. Nevertheless, it has also given rise to excessively extensive security concerns owing to its inherent distribution of communication among one or more high viscosity services. In many cases, current techniques of anomaly identification have been ineffective in dealing with subtle non-robust vulnerabilities that are specific to the microservice-based systems. To close this exploration and design gap, we propo

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Jarr, Kai-Uwe, Jianqin Ye, Yoko Kojima, et al. "18 F-Fluorodeoxyglucose-Positron Emission Tomography Imaging Detects Response to Therapeutic Intervention and Plaque Vulnerability in a Murine Model of Advanced Atherosclerotic Disease—Brief Report." Arteriosclerosis, Thrombosis, and Vascular Biology 40, no. 12 (2020): 2821–28. http://dx.doi.org/10.1161/atvbaha.120.315239.

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Objective: This study sought to determine whether 18 F-fluorodeoxyglucose-positron emission tomography/computed tomography could be applied to a murine model of advanced atherosclerotic plaque vulnerability to detect response to therapeutic intervention and changes in lesion stability. Approach and Results: To analyze plaques susceptible to rupture, we fed ApoE −/− mice a high-fat diet and induced vulnerable lesions by cast placement over the carotid artery. After 9 weeks of treatment with orthogonal therapeutic agents (including lipid-lowering and proefferocytic therapies), we assessed vascul

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Yang, Pei-Chi, Kevin R. DeMarco, Parya Aghasafari, et al. "A Computational Pipeline to Predict Cardiotoxicity." Circulation Research 126, no. 8 (2020): 947–64. http://dx.doi.org/10.1161/circresaha.119.316404.

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Rationale: Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. Objective: To predict the impact of a drug from the drug chemistry on the cardiac rhythm. Methods and Results: In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure inter

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Buja, Atdhe. "AN ONLINE SQL VULNERABILITY ASSESSMENT TOOL AND IT’S IMPACT ON SMEs." International Journal of Advanced Research in Computer Science 13, no. 5 (2022): 23–28. http://dx.doi.org/10.26483/ijarcs.v13i5.6903.

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Information security has received everyone's attention, especially in the case of the global Covid-19 pandemic. SMEs are looking for solutions that offer greater security and a normal functioning of activities. Our research is aiming to measure the benefits from the usage of an online Vulnerability Assessment SQL tool (VA SQL). In the study, through an experiment of various tools used we can see different results in the findings. We present the best practice and a model of proactive approach to analyze database security using Microsoft technology. Currently, we need to have and use a lot of sc

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Forster, J. I., S. Köglsberger, C. Trefois, et al. "Characterization of Differentiated SH-SY5Y as Neuronal Screening Model Reveals Increased Oxidative Vulnerability." Journal of Biomolecular Screening 21, no. 5 (2016): 496–509. http://dx.doi.org/10.1177/1087057115625190.

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The immortalized and proliferative cell line SH-SY5Y is one of the most commonly used cell lines in neuroscience and neuroblastoma research. However, undifferentiated SH-SY5Y cells share few properties with mature neurons. In this study, we present an optimized neuronal differentiation protocol for SH-SY5Y that requires only two work steps and 6 days. After differentiation, the cells present increased levels of ATP and plasma membrane activity but reduced expression of energetic stress response genes. Differentiation results in reduced mitochondrial membrane potential and decreased robustness

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Li, Qin, Mark E. Robinson, Etienne Leveille, et al. "Computational Discovery and Validation of NAD+ Biosynthesis As Unique Vulnerability in B-Lymphoid Malignancies." Blood 142, Supplement 1 (2023): 418. http://dx.doi.org/10.1182/blood-2023-190269.

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Background and Significance: Glucocorticoids have been a mainstay in virtually all therapy regimens for B-lymphoid malignancies since the 1950s. Glucocorticoids selectively kill B-cell leukemia and lymphoma cells but have very limited effects on myeloid and epithelial cells. Likewise, inhibitors of B-cell signaling (e.g. ibrutinib) and selective depletion of B-cells (e.g. rituximab, CD19 CAR-T cells) have achieved important progress and are well tolerated. Drug discovery tool for selective targeting of B-cell malignancies: For these reasons, we developed an interactive computational tool ( lym

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Ningrum, Rohima Wahyu, Hendra Fauzi, Wiwit Suryanto, Estuning Tyas Wulan Mei, and Risky Nuri Amelia. "Microtremor Data to Strengthen the Students’ Mastery of Materials in the Implementation of Integrated Physics Learning." Jurnal Penelitian Fisika dan Aplikasinya (JPFA) 10, no. 1 (2020): 81. http://dx.doi.org/10.26740/jpfa.v10n1.p81-89.

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A geophysical research has been conducted to determine the level of hazard vulnerability in West Halmahera by using microtremor data. Microtremor data are analyzed using the Horizontal to Vertical Spectral Ratio (HVSR) method and it is processed using Geopsy software to know hazard vulnerability level in the region . Data analysis and processing are used as sources of information in physics learning materials using an integrated learning model. Research objectives are to train the students to understand the materials in the fields of study and to enhance other discovery ideas.This research met

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Madanan, Mukesh, Biju T. Sayed, Nurul Akhmal Mohd Zulkefli, and Nitha C. Velayudhan. "An Artificial Intelligence Approach Based on Hybrid CNN-XGB Model to Achieve High Prediction Accuracy through Feature Extraction, Classification and Regression for Enhancing Drug Discovery in Biomedicine." International Journal of Biology and Biomedical Engineering 15 (June 2, 2021): 190–201. http://dx.doi.org/10.46300/91011.2021.15.22.

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In the field of biomedicine, drug discovery is the cycle by which new and upcoming medicines are tested and invented to cure ailments. Drug discovery and improvement is an extensive, complex, and exorbitant cycle, settled in with a serious extent of vulnerability that a drug will really be successful or not. Developing new drugs have several challenges to enrich the current field of biomedicine. Among these ultimatums, predicting the reaction of the cell line to the injected or consumed drug is a significant point and this can minimize the cost of drug discovery in sophisticated fashion with a

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Matovu, Daniel, and Paul E. Alele. "Seizure vulnerability and anxiety responses following chronic co-administration and acute withdrawal of caffeine and ethanol in a rat model." Journal of Basic and Clinical Physiology and Pharmacology 29, no. 1 (2018): 1–10. http://dx.doi.org/10.1515/jbcpp-2017-0018.

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AbstractBackground:Caffeine antagonizes the intoxicating effects of alcohol. Consequently, there has been a dramatic global increase in the consumption of caffeinated drinks together with alcohol, especially among young adults. We assessed the seizure vulnerability and anxiety responses following the chronic co-administration of, and withdrawal from, caffeine and ethanol in male rats.Methods:The rats were randomly assigned to six groups consisting of 10 animals each: 10 mg/kg of caffeine, 20 mg/kg of caffeine, 4 g/kg of 20% ethanol, combined caffeine (20 mg/kg) and ethanol (4 g/kg of 20%), 4 m

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Aghababazadeh, Masoumeh, Ana Castillo-Orozco, Niusha Khazaei, Wajih Jawhar, Geoffroy Danieu, and Livia Garzia. "Abstract A074 Uncovering chemoresistance mechanisms in CIC-DUX4 sarcoma using a novel xenograft model." Cancer Research 84, no. 17_Supplement (2024): A074. http://dx.doi.org/10.1158/1538-7445.pediatric24-a074.

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Abstract Objective: Capicua-double homeobox 4 (CIC-DUX4)–rearranged sarcomas (CDS) are exceptionally rare and highly aggressive tumors that usually develop in soft tissues in children, adolescents, and adults. These sarcomas exhibit high rates of metastasis and quickly develop resistance to chemotherapy, posing significant treatment challenges. Patients are currently treated using Ewing sarcoma chemotherapy protocols, but those with CDS have a significantly poorer prognosis, with a median survival of less than 2 years. This underscores the urgent need for effective therapeutic strategies for C

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Nidhi Mishra. "Employing Evolutionary Computing and Hybrid Artificial Neural Networks to Improve Drug Discovery." Communications on Applied Nonlinear Analysis 32, no. 2s (2024): 64–75. http://dx.doi.org/10.52783/cana.v32.2251.

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An ultimate relevant translational scientific endeavour that contributes to human vulnerability and happiness might be the creation and advancement of medications. Fast drug discovery procedures necessitate using contemporary computational approaches to tackle pharma data's complexities and high complexity. By combining Evolutionary Computing with Hybrid Artificial Neural Networks (EC-HANNs), this research introduces a novel strategy for optimizing and speeding up the drug development process. To handle various drug-target relations, predict the efficacy of compounds, and discover more accurat

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Totura, Allison L., and Sina Bavari. "Broad-spectrum coronavirus antiviral drug discovery." Expert Opinion on Drug Discovery 14, no. 4 (2019): 397–412. https://doi.org/10.5281/zenodo.13531117.

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(Uploaded by Plazi for the Bat Literature Project) Introduction: The highly pathogenic coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are lethal zoonotic viruses that have emerged into human populations these past 15 years. These coronaviruses are associated with novel respiratory syndromes that spread from person-to-person via close contact, resulting in high morbidity and mortality caused by the progression to Acute Respiratory Distress Syndrome (ARDS).Areas covered: The risks of re-emergence of SARS-CoV from

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Totura, Allison L., and Sina Bavari. "Broad-spectrum coronavirus antiviral drug discovery." Expert Opinion on Drug Discovery 14, no. 4 (2019): 397–412. https://doi.org/10.5281/zenodo.13531117.

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(Uploaded by Plazi for the Bat Literature Project) Introduction: The highly pathogenic coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are lethal zoonotic viruses that have emerged into human populations these past 15 years. These coronaviruses are associated with novel respiratory syndromes that spread from person-to-person via close contact, resulting in high morbidity and mortality caused by the progression to Acute Respiratory Distress Syndrome (ARDS).Areas covered: The risks of re-emergence of SARS-CoV from

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Chen, Xiao, Le-Tian Sha, Fu Xiao, Jia-Ye Pan, and Jian-Kuo Dong. "AB-DHD: An Attention Mechanism and Bi-Directional Gated Recurrent Unit Based Model for Dynamic Link Library Hijacking Vulnerability Discovery." Journal of Computer Science and Technology 40, no. 3 (2025): 887–903. https://doi.org/10.1007/s11390-025-4497-x.

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Meyers, Tatyana A., Jackie A. Heitzman, and DeWayne Townsend. "DMD carrier model with mosaic dystrophin expression in the heart reveals complex vulnerability to myocardial injury." Human Molecular Genetics 29, no. 6 (2020): 944–54. http://dx.doi.org/10.1093/hmg/ddaa015.

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Abstract Duchenne muscular dystrophy (DMD) is a devastating neuromuscular disease that causes progressive muscle wasting and cardiomyopathy. This X-linked disease results from mutations of the DMD allele on the X-chromosome resulting in the loss of expression of the protein dystrophin. Dystrophin loss causes cellular dysfunction that drives the loss of healthy skeletal muscle and cardiomyocytes. As gene therapy strategies strive toward dystrophin restoration through micro-dystrophin delivery or exon skipping, preclinical models have shown that incomplete restoration in the heart results in het

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Koo, Kijong, Daesung Moon, Jun-Ho Huh, Se-Hoon Jung, and Hansung Lee. "Attack Graph Generation with Machine Learning for Network Security." Electronics 11, no. 9 (2022): 1332. http://dx.doi.org/10.3390/electronics11091332.

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Recently, with the discovery of various security threats, diversification of hacking attacks, and changes in the network environment such as the Internet of Things, security threats on the network are increasing. Attack graph is being actively studied to cope with the recent increase in cyber threats. However, the conventional attack graph generation method is costly and time-consuming. In this paper, we propose a cheap and simple method for generating the attack graph. The proposed approach consists of learning and generating stages. First, it learns how to generate an attack path from the at

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Zhou, Xin, Jianmin Pang, Zheng Shan, et al. "TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification." Applied Sciences 13, no. 6 (2023): 3938. http://dx.doi.org/10.3390/app13063938.

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Nowadays, the time lag between vulnerability discovery and the timely remediation of the vulnerability is extremely important to the current state of cybersecurity. Unfortunately, the silent security patch presents a significant challenge. Despite related work having been conducted in this area, the patch identification lacks interpretability. To solve this problem, this paper first proposes a trusted multi-view security patch identification system called TMVDPatch. The system obtains evidence from message commit and code diff views respectively, and models the uncertainty of each view based o

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Pan, Yan, Wei Lin, Liang Jiao, and Yuefei Zhu. "Model-Based Grey-Box Fuzzing of Network Protocols." Security and Communication Networks 2022 (May 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/6880677.

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The widely used network protocols play a crucial role in various systems. However, the protocol vulnerabilities caused by the design of the network protocol or its implementation by programmers lead to multiple security incidents and substantial losses. Hence, it is important to study the protocol fuzzing in order to ensure its correctness. However, the challenges of protocol fuzzing are the mutation of protocol messages and the deep interactivity of the protocol implementation. This paper proposes a model-based grey-box fuzzing approach for protocol implementations, including the server-side

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Zhang, Yifan, Kailong Zhu, Jie Peng, Yuliang Lu, Qian Chen, and Zixiong Li. "StatePre: A Large Language Model-Based State-Handling Method for Network Protocol Fuzzing." Electronics 14, no. 10 (2025): 1931. https://doi.org/10.3390/electronics14101931.

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As essential components for communication, network protocol programs are highly security-critical, making it crucial to identify their vulnerabilities. Fuzzing is one of the most popular software vulnerability discovery techniques, being highly efficient and having low false-positive rates. However, current network protocol fuzzing is hindered by the coarse-grained and missing state annotations in programs. The current solutions primarily rely on the manual modification of programs, which is inefficient and prone to omissions. In this paper, we propose StatePre, a novel state-handling method f

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Phansopkar, Pratik. "FEAR AVOIDANCE MODEL OF KINESIOPHOBIA AND REHABILITATION." Journal of Medical pharmaceutical and allied sciences 10, no. 5 (2021): 3529–33. http://dx.doi.org/10.22270/jmpas.v10i5.1267.

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Kinesiophobia is defined as "an irrational, and debilitating fear of physical movement and activity resulting from a feeling of vulnerability to painful injury or re-injury" by those who coined the term. Pain catastrophizing and kinesiophobia (fear of movement and re-injury) magnify somatosensory perceptions, overstating this perception in terms of a pain-related exaggeration of fearful anticipation of the pain getting worse and worse. Fear of pain has a high prognostic value when it comes to the development of chronic musculoskeletal pain and impairment. Kinesiophobia is most closely linked t

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Zhao, Wendian, Yongjie Wang, Xinli Xiong, and Jiazhen Zhao. "IKN-CF: An Approach to Identify Key Nodes in Inter-Domain Routing Systems Based on Cascading Failures." Entropy 23, no. 11 (2021): 1456. http://dx.doi.org/10.3390/e23111456.

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Inter-domain routing systems is an important complex network in the Internet. Research on the vulnerability of inter-domain routing network nodes is of great support to the stable operation of the Internet. For the problem of node vulnerability, we proposed a method for identifying key nodes in inter-domain routing systems based on cascading failures (IKN-CF). Firstly, we analyzed the topology of inter-domain routing network and proposed an optimal valid path discovery algorithm considering business relationships. Then, the reason and propagation mechanism of cascading failure in the inter-dom

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Kalogeraki, Eleni-Maria, Spyridon Papastergiou, and Themis Panayiotopoulos. "An Attack Simulation and Evidence Chains Generation Model for Critical Information Infrastructures." Electronics 11, no. 3 (2022): 404. http://dx.doi.org/10.3390/electronics11030404.

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Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical Information Infrastructures (CIIs) (e.g., Supervisory Control and Data Acquisition (SCADA) systems) by exploiting a series of vulnerabilities and launching multiple attacks. In this context, industry players need to increase their knowledge on the security of the CIs they

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H., Manjunath, and Saravana Kumar. "Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset." Fusion: Practice and Applications 13, no. 1 (2023): 117–25. http://dx.doi.org/10.54216/fpa.130109.

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Increase in network activity of transferring information online allows network breeches where intruders easily avail the most important information or data. The growth of online functioning and many other governmental data over the internet without security has caused data vulnerability; attackers can easily detect the data and misuse them. Network Intrusion Detection System (NIDS) has allowed this whole process of online data transfer to occur safely and secured transactions. Due to the cloud usage in network the huge amount of traffic is created as well as number of attacks are increased day

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Blanco-Gandía, Maria C., José Miñarro, and Marta Rodríguez-Arias. "Common Neural Mechanisms of Palatable Food Intake and Drug Abuse: Knowledge Obtained with Animal Models." Current Pharmaceutical Design 26, no. 20 (2020): 2372–84. http://dx.doi.org/10.2174/1381612826666200213123608.

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Eating is necessary for survival, but it is also one of the great pleasures enjoyed by human beings. Research to date shows that palatable food can be rewarding in a similar way to drugs of abuse, indicating considerable comorbidity between eating disorders and substance-use disorders. Analysis of the common characteristics of both types of disorder has led to a new wave of studies proposing a Gateway Theory of food as a vulnerability factor that modulates the development of drug addiction. The homeostatic and hedonic mechanisms of feeding overlap with some of the mechanisms implicated in drug

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Marcu, Gabriela Mariana, Ana Maria Radu, Mihaela Dana Bucuță, et al. "Cognitive and Behavioral Factors Predicting the Decision to Vaccinate against COVID-19 in Clinical Psychiatric Population—A Cross-Sectional Survey." Vaccines 11, no. 2 (2023): 441. http://dx.doi.org/10.3390/vaccines11020441.

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The spread of the COVID-19 virus created more than a medical crisis, while it also negatively affected the mental health of the general population. This context increased the vulnerability of the psychiatric population. While research interest highly targeted vaccine hesitancy and acceptance, many studies focused on trust issues—both in vaccine efficacy and in communication with authorities. Less is known about the psychological underpinnings of the COVID vaccination decision, specifically in the high-uncertainty circumstances due to the novelty of the virus. In a cross-sectional study, we inv

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van Dongen, Laura H., Peter P. Harms, Mark Hoogendoorn, et al. "Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project." Open Heart 8, no. 1 (2021): e001554. http://dx.doi.org/10.1136/openhrt-2020-001554.

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IntroductionEarly recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information.AimTo describe the st

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Prabowo, Satria Bagus, Rizki Wulandari, Intan Andriani Putri, Edlyn Yoadan Nathania, Purwaditya Nugraha, and Yogi Y. Ch Sitio. "Assessment of Seismic Vulnerability and Shear Wave Velocity Estimation Using the Horizontal-to-Vertical Spectral Ratio (HVSR) Method in Umbul Niti Geothermal Manifestations, Jatimulyo Village, South Lampung." IOP Conference Series: Earth and Environmental Science 1458, no. 1 (2025): 012024. https://doi.org/10.1088/1755-1315/1458/1/012024.

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Abstract The discovery of geothermal manifestations in 2021 in Umbul Niti, Jatimulyo Village, South Lampung Regency, reveals significant potential for geotourism development, which can help reduce poverty. However, this potential is accompanied by an unstable zone due to a fault, increasing earthquake risks. Additionally, population growth and the presence of the Sumatera Institute of Technology (ITERA) pose development challenges related to population density and potential losses. Therefore, investigating soil characteristics based on natural frequency (f 0), amplification (A 0), and shear wa

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Kolawole, Oyebamiji Abel, Akintelu Sunday Adewale, Simon N. Odoemene, Oyeneyin Oluwatoba Emmanuel, and Semire Banjo. "Theoretical Bioevaluation of 1,2,4-Thiadiazole-1,2,4-triazole Derivatives via Molecular Modelling Approach." Asian Journal of Organic & Medicinal Chemistry 6, no. 1 (2021): 40–46. http://dx.doi.org/10.14233/ajomc.2021.ajomc-p307.

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Breast cancer still remains one of the precarious ailments among humans globally. The vulnerability of this ailment in homeopathic world remains colossal and this has drawn the attention of seasoned researchers to find lasting solution to this hazard. Therefore, 10 novel 1,2,4-thiadiazole-1,2,4-triazole derivatives were studied so as to explore their anti-breast cancer activities. The studied compounds were optimized using Spartan 14 and the QSAR study was executed by using Gretl and MATLAB. Also, docking study was observed using Pymol (for treating downloaded protein), Autodock Tool (for loca

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Richardson, David E., Katrin E. Marancik, Jeffrey R. Guyon, et al. "Discovery of a spawning ground reveals diverse migration strategies in Atlantic bluefin tuna (Thunnus thynnus)." Proceedings of the National Academy of Sciences 113, no. 12 (2016): 3299–304. http://dx.doi.org/10.1073/pnas.1525636113.

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Atlantic bluefin tuna are a symbol of both the conflict between preservationist and utilitarian views of top ocean predators, and the struggle to reach international consensus on the management of migratory species. Currently, Atlantic bluefin tuna are managed as an early-maturing eastern stock, which spawns in the Mediterranean Sea, and a late-maturing western stock, which spawns in the Gulf of Mexico. However, electronic tagging studies show that many bluefin tuna, assumed to be of a mature size, do not visit either spawning ground during the spawning season. Whether these fish are spawning

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Mofleh, Dania, Maha Almohamad, Ikponmwosa Osaghae, et al. "Spatial Patterns of COVID-19 Vaccination Coverage by Social Vulnerability Index and Designated COVID-19 Vaccine Sites in Texas." Vaccines 10, no. 4 (2022): 574. http://dx.doi.org/10.3390/vaccines10040574.

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Equitable access to the COVID-19 vaccine remains a public health priority. This study explores the association between ZIP Code–Tabulation Area level Social Vulnerability Indices (SVI) and COVID-19 vaccine coverage in Texas. A mixed-effects, multivariable, random-intercept negative binomial model was used to explore the association between ZIP Code–Tabulation Area level SVI and COVID-19 vaccination coverage stratified by the availability of a designated vaccine access site. Lower COVID-19 vaccine coverage was observed in ZIP codes with the highest overall SVIs (adjusted mean difference (aMD) =

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Lin, Mingmin, Yingpei Zeng, and Yang Li. "RegFuzz: A Linear Regression-Based Approach for Seed Scheduling in Directed Fuzzing." Electronics 12, no. 17 (2023): 3650. http://dx.doi.org/10.3390/electronics12173650.

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Directed fuzzing aims to focus on fuzzing specific locations within a target program to enhance the efficiency of vulnerability discovery. However, directed fuzzing may yield fewer vulnerabilities and obtain lower code coverage when the specified locations have little to no vulnerabilities. Additionally, the existing directed fuzzing approaches often overlook the differences in variable values when calculating distances between seeds and specific locations. In order to address these issues, this paper introduces RegFuzz, a method that improves seed scheduling in directed fuzzing. RegFuzz utili

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Kieliszek, Agata M., Daniel Mobilio, Blessing I. Bassey-Archibong, et al. "Abstract 2474: Uncovering a new therapeutic vulnerability for preventing brain metastases." Cancer Research 83, no. 7_Supplement (2023): 2474. http://dx.doi.org/10.1158/1538-7445.am2023-2474.

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Abstract INTRO: Patients with brain metastases (BM) face a 90% mortality rate within one year of their diagnosis and they lack targeted therapeutic options, particularly preventative or interceptional ones. METHODS: The Singh lab has generated a large in-house biobank of patient-derived BM cell lines that are established from patient-derived BM from primary lung and breast cancers and melanoma. We use these BM cell lines to generate murine orthotopic xenograft models of BM and interrogate the biological processes that lead to BM. These models have successfully recapitulated all the stages of t

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Syafitri, Faizah, Aris Munandar, and Rayuna Handawati. "PENGARUH MODEL PEMBELAJARAN DISCOVERY LEARNING PADA MATERI PERSEBARAN WILAYAH RAWAN BENCANA ALAM DI INDONESIA TERHADAP KEMAMPUAN BERPIKIR SPASIAL PESERTA DIDIK." GEOGRAPHY : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan 11, no. 2 (2023): 288. http://dx.doi.org/10.31764/geography.v11i2.15748.

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Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh dari penggunaan model pembelajaran discovery learning terhadap kemampuan berpikir spasial peserta didik SMA pada materi persebaran wilayah rawan bencana alam di Indonesia. Penelitian ini menggunakan metode kuantitatif dengan pendekatan quasi eksperimen. Pengumpulan data dalam penelitian ini menggunakan observasi dan tes kemampuan berpikir spasial berdasarkan indikator AAG, 2008. Teknik yang digunakan untuk menganalisis yaitu uji instrumen, uji persyaratan analisis data, dan uji analisis data menggunakan uji persamaan dua rata-rata men

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Nihar, Malali. "THE ROLE OF DEVSECOPS IN FINANCIAL AI MODELS: INTEGRATING SECURITY AT EVERY STAGE OF AI/ML MODEL DEVELOPMENT IN BANKING AND INSURANCE." International Journal of Engineering Technology Research & Management (IJETRM) 06, no. 11 (2022): 218–25. https://doi.org/10.5281/zenodo.15239176.

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Artificial intelligence (AI) and machine learning (ML) technologies have brought revolutionary changes tofinancial institutions such as banks and insurers during their operations. The financial industry relies heavily onAI models for both automated underwriting policies and personalized recommendation services and fraudulentactivity discovery along with credit scoring assessments. The deeper financial institutions incorporate thesemodels into their systems them more vulnerability to cyberattacks. DevSecOps represents a revolutionary methodwhich includes security measures during every stage of

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Naeem, Muhammad Rehan, Rashid Amin, Muhammad Farhan, Faisal S. Alsubaei, Eesa Alsolami, and Muhammad D. Zakaria. "Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective." PLOS One 20, no. 5 (2025): e0324595. https://doi.org/10.1371/journal.pone.0324595.

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A zero-day vulnerability is a critical security weakness of software or hardware that has not yet been found and, for that reason, neither the vendor nor the users are informed about it. These vulnerabilities may be taken advantage of by malicious people to execute cyber-attacks leading to severe effects on organizations and individuals. Given that nobody knows and is aware of these weaknesses, it becomes challenging to detect and prevent them. For the real-time zero-day vulnerabilities detection, we bring out a novel reinforcement learning (RL) methodology with the help of Deep Q-Networks (DQ

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