Potential Flaws Research Articles

Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM)

Smart contracts are susceptible to various vulnerabilities that can lead to significant financial losses. The usage of tools for vulnerabilities is reducing the threats but presents some limitations related to the approach used by the tool itself. This paper presents a novel approach to smart contract vulnerability detection utilizing Large Language Models (LLMs), as a tool to detect all the vulnerabilities at once. Our proposed tool leverages the advanced natural language processing capabilities of LLMs to analyze smart contract code and identify potential security flaws. By training the LLM on a diverse dataset of known smart contract vulnerabilities and secure coding practices, we enhance its ability to recognize subtle and complex vulnerabilities that traditional static analysis tools might miss. The evaluation of our tool demonstrates its effectiveness in detecting a wide range of vulnerabilities with satisfaction and accuracy, providing developers with a robust mechanism to improve the security of their smart contracts before deployment. This approach signifies a significant advancement in the application of artificial intelligence for blockchain security, highlighting the potential of LLMs to enhance the reliability and safety of decentralized applications.

Javascript And WordPress Are Used In A Structural Engineering Applications

Abstract : In the realm of structural engineering, efficient software tools are essential for designing, analyzing, and managing various types of structures. The integration of WordPress, HTML, CSS, and PHP offers a unique opportunity to develop a versatile structural engineering software platform. This software aims to provide structural engineers with an intuitive and customizable toolset to streamline their workflow and enhance productivity. Structural design software and structural analysis software can significantly increase the effectiveness and precision of engineering projects in several ways. These tools give engineers a powerful platform to model and visualise complex structures in a virtual environment, allowing them to detect potential flaws or weaknesses before building in reality and preventing costly errors. Also, thestructural design software makes it possible for engineers to optimise designs for maximum efficiency and safety by precisely analysing various load scenarios.

Revisiting the success of co‐management in forest protected areas of Bangladesh: Untangling the interplay of power, participation, and community empowerment

AbstractThis narrative review critically assesses the implementation and purported success of co‐management in forest management in Bangladesh. Co‐management has gained global popularity, celebrated for its participatory approach to natural resource conflict resolution, yet critiqued for potential design flaws, susceptibility to elite capture, and perpetuating power imbalances in predominantly top‐down management cultures. Despite two decades of application and reports of positive outcomes in managing forest protected areas of Bangladesh, concerns persist regarding the authenticity of its success and sustainability, particularly in terms of power dynamics and effective community participation in decision‐making processes. The review delves into these aspects, revealing instances where successes might be superficial, overlooking the underlying issues of power disparity among stakeholders. It highlights the crucial role of enhancing social capital within co‐management frameworks to foster institutionalization and sustainability. Recommendations include advancing robust collaboration and communication, alongside the adoption of adaptive management practices to navigate the complex interplay of natural, social, and institutional challenges.

Addressing financial toxicity in cancer treatment-An opportunity for the 340B drug pricing program.

Cancer treatment has become increasingly expensive, partially due to the use of specialty drugs. The costs of these drugs are often passed down to patients, who may face the consequences of paying for more than they can afford, leading to financial toxicity. The 340B drug pricing program is a health care policy that may provide an opportunity to mitigate the financial consequences of cancer care. The 340B program requires manufacturers to sell outpatient drugs at a discount to hospitals caring for a significant number of socioeconomically disadvantaged individuals. The program intended for hospitals to use savings from discounted purchases to expand their safety net to vulnerable patients. Some studies have shown that participating hospitals do this by offering more charity and discounted care, whereas others have demonstrated that hospitals fail to sufficiently expand their safety net. A potential flaw of the program is the lack of guidance from governing bodies on how hospitals should use savings from discounted purchases. There has been growing discussion among stakeholders to reform the 340B program given the mixed findings of its effectiveness. With the rising costs of specialty drugs and associated prevalence of financial toxicity in patients with cancer, there is an opportunity to address these issues through reform that improves the program. Directing hospitals to offer specific safety net opportunities, such as passing along discounted drug prices to vulnerable populations, could help the growing number of patients who are financially burdened by medications at the core of the 340B program.

A novel and secured email classification using deep neural network with bidirectional long short-term memory

Email data has some characteristics that are different from other social media data, such as a large range of answers, formal language, notable length variations, high degrees of anomalies, and indirect relationships. The main goal in this research is to develop a robust and computationally efficient classifier that can distinguish between spam and regular email content. The benchmark Enron dataset, which is accessible to the public, was used for the tests. The six distinct Enron data sets we acquired were combined to generate the final seven Enron data sets. The dataset undergoes early preprocessing to remove superfluous sentences. The proposed model Bidirectional Long Short-Term Memory (BiLSTM) apply spam labels and to examine email documents for spam. On seven Enron datasets, DNN-BiLSTM performs better than other classifiers in the performance comparison in terms of accuracy. DNN-BiLSTM and convolutional neural networks demonstrated that they can classify spam with 96.39 % and 98.69 % accuracy, respectively, in comparison to other machine learning classifiers. The risks associated with cloud data management and potential security flaws are also covered in the paper. This research presents hybrid encryption as a means of protecting cloud data while preserving privacy by using the hybrid AES-Rabit encryption algorithm which is based on symmetric session key exchange.

Making 3D printing of machine parts more efficient

Identifying potential flaws without having to remove the parts from the manufacturing equipment.

Разработка и реализация протокола поведенческого эксперимента для оценки когнитивных способностей и социального поведения мышей после стресса в раннем возрасте

<p style="text-align: justify;">This manuscript presents a protocol designed for the comprehensive investigation of early life stress (ELS) outcomes and a feasibility study conducted with this protocol. ELS alters normal development by interfering at various levels: hormonal changes, brain cellular architecture, epigenome, and chromosomal structural elements. The protocol combines classic behavioral tests with advanced molecular techniques to obtain comprehensive data and thus uncover the underlying mechanisms of ELS. In this protocol, the main source of stress is maternal separation. Briefly, a group of C57Bl/6 mice undergoes maternal separation; then, mice perform the radial maze test and the resident-intruder test. As a control, another group of mice stays undisturbed and performs the same behavioral tests in the same timeframe. After the behavioral tests, biosamples are collected, including urine for corticosterone measurements, peripheral blood, hippocampus, amygdala, and prefrontal cortex tissues for DNA isolation and its downstream analyses (DNA methylation profiling and telomere length measuring), and whole brains for immunohistochemistry analysis of the glucocorticoid receptor density. This protocol was successfully tested as a feasibility study for a large-scale investigation that addresses potential flaws to establish a robust methodology. This paper reports on a comprehensive approach to examining multiple aspects of development that interrogates a holistic analysis of multilayer and multidimensional data and may contribute valuable insights for both animal and human studies.</p>

Assessing System Quality Changes during Software Evolution: The Impact of Design Patterns Explored via Dependency Analysis

Design patterns provide solutions to recurring problems in software design and development, promoting scalability, readability, and maintainability. While past research focused on the utilization of the design patterns and performance, there is limited insight into their impact on program evolution. Dependency signifies relationships between program elements, reflecting a program’s structure and interaction. High dependencies indicate complexity and potential flaws, hampering system quality and maintenance. This paper presents how design patterns influence software evolution by analyzing dependencies using the Abstract Syntax Tree (AST) to examine dependency patterns during evolution. We employed three widely adopted design patterns from the Gang of Four (GoF) as experimental examples. The results show that design patterns effectively reduce dependencies, lowering system complexity and enhancing quality.

EDR (Event Data Recorder) in Vehicles using IOT

The increasing integration of Electronic Control Units (ECUs) in modern vehicles has paved the way for sophisticated in-vehicle monitoring and diagnostics systems, givingrise to the concept of Event-Driven Recording (EDR). EDR in vehicles involves the continuous capture and storage of critical data related to various events, such ascollisions, sudden accelerations, or system malfunctions. This data, often collected from multiple sensors and modules within the vehicle, serves as a crucial tool for post-incident analysis, aiding in the reconstruction of events leading up to and during accidents. EDR not only provides valuable insights for accident investigation but also plays a pivotal role in enhancing vehicle safety by facilitating the identification of potential design flaws and contributing to the development of more robust safety features.

Can You Give Me a Lift?

Static Analysis (SA) is the practice of examining computer program source code for errors or vulnerabilities outside the compiler’s capabilities. To carry out Static Analysis on computer programs, various tools exist that parse and examine each line for known issues. These tools do not compile the program, nor do they run the program. Instead, they analyse the source code directly and infer properties about the program without executing it - thus “static” analysis. Static analysis is not new. Early UNIX contained a program called “lint” for static analysis; a tool that has now existed since the late 1970s (Johnson 1978). Increasingly, however, modern static analysis practices indicate a more focused intent; find cybersecurity weaknesses. At the 22nd European Conference on Cyber Warfare and Security, the authors presented the background of Intermediate Representations (IRs) and described how this “middleware” representation can be utilized for static analysis of source code for cybersecurity weaknesses. By examining an IR, potential flaws in the source code can be located. When utilizing the IR as opposed to the original high-level language, the static analysis process becomes independent of the original source language; if several languages such as C, Rust, and others all compile into the same IR, static analysis of the IR allows the analysis process to no longer be tied to the high-level language grammar or syntax. The previous paper implemented a literature survey of available IR analysis tools to discover prior work; the authors have subsequently advanced the research and are actively using an IR framework, LLVM, for vulnerability analysis. In this research, source code in a high-level language is first compiled to LLVM and the resulting IR is used for analysis. This approach uses a “code-to-IR” SA analysis preparation paradigm. At the same time, there is the potential for binary “lifters” to be used. These tools “lift” an executable program – binary machine instructions – back to LLVM. In this way, the paradigm can also be reversed such that static analysis of LLVM can be performed on source code compiled into LLVM, or on executable programs in the field that are “lifted”. This begs: how effective are these “lifters”? In this work, the authors present experiences in installation and operation of several binary “lifters” available as open-source projects. Some are supported better than others, some operate better than others, and some don’t operate at all. Those that do lead to the follow-up: Is the “lifted” code suitable for static analysis, or is it too obfuscated relative to the original program? This paper describes just that – our efforts and results in locating a binary “lifter” suitable for bringing executable program test cases back to LLVM for analysis by the cybersecurity vulnerability tool concurrently under development.

Enhanced Coalescence Backdoor Attack Against DNN Based on Pixel Gradient

Deep learning has been widely used in many applications such as face recognition, autonomous driving, etc. However, deep learning models are vulnerable to various adversarial attacks, among which backdoor attack is emerging recently. Most of the existing backdoor attacks use the same trigger or the same trigger generation approach to generate the poisoned samples in the training and testing sets, which is also commonly adopted by many backdoor defense strategies. In this paper, we develop an enhanced backdoor attack (EBA) that aims to reveal the potential flaws of existing backdoor defense methods. We use a low-intensity trigger to embed the backdoor, while a high-intensity trigger to activate it. Furthermore, we propose an enhanced coalescence backdoor attack (ECBA) where multiple low-intensity incipient triggers are designed to train the backdoor model, and then, all incipient triggers are gathered on one sample and enhanced to launch the attack. Experiment results on three popular datasets show that our proposed attacks can achieve high attack success rates while maintaining the model classification accuracy of benign samples. Meanwhile, by hiding the incipient poisoned samples and preventing them from activating the backdoor, the proposed attack exhibits significant stealth and the ability to evade mainstream defense methods during the model training phase.

The mismatch between geographical indication protection against evocation and its underlying objectives

The deliberations on the revision of the European Union (EU) system for geographical indications (GIs), proposed in 2022, manifest the contentious nature of protecting GIs against evocation. This article shifts attention to the origins of the anti-evocation protection, allowing for further exploration of how the protection deviates from the objectives behind its establishment. The study shows that the flaws that triggered the prospective reform were foreseen in the 1990s when anti-evocation protection was first introduced in GI law. In the 1990s, legislative discussions surrounding the introduction had already brought to light a lack of clarity regarding the objectives, the over-inclusiveness of the evocation test, and the potential absurdities it might generate. Those once potential flaws have gradually become a reality, and practice has manifested a mismatch between the anti-evocation protection and its underlying purposes. The judicial and legislative authorities’ latest bridging efforts have missed the fundamental solution to the mismatch. The EU GI legislative reform should give due consideration to adopting a harm-based approach, thus bringing the anti-evocation protection closer to its core objective of preventing harm.

TabEE: Tabular Embeddings Explanations

Tabular embedding methods have become increasingly popular due to their effectiveness in improving the results of various tasks, including classic databases tasks and machine learning predictions. However, most current methods treat these embedding models as "black boxes" making it difficult to understand the insights captured by the models. Our research proposes a novel approach to interpret these models, aiming to provide local and global explanations for the original data and detect potential flaws in the embedding models. The proposed solution is appropriate for every tabular embedding algorithm, as it fits the black box view of the embedding model. Furthermore, we propose methods for comparing different embedding models, which can help identify data biases that might impact the models' credibility without the user's knowledge. Our approach is evaluated on multiple datasets and multiple embeddings, demonstrating that our proposed explanations provide valuable insights into the behavior of tabular embedding methods. By making these models more transparent, we believe our research will contribute to the development of more effective and reliable embedding methods for a wide range of applications.

Safety analysis of evaluation and acceptance processes for space systems and operations based on system-theoretic approach

The proposed analysis consists of identifying systemic aspects that can influence safety and mission fulfillment in the Evaluation and Acceptance Processes of Space Systems and Operations for Launch through the application of System-Theoretic Process Analysis, a technique capable of identifying potential hazardous design and operational flaws, including system design errors and unsafe interactions among multiple procedures and system components. This research identifies losses, hazards, system-level safety constraints, the control structure of the general system, unsafe control actions, loss scenarios that could occur and related causal factors, detecting improvement possibilities for future verifications, evaluations, approvals, and acceptances of space systems and launch operations. These findings can promote safety in space system designs and operations, supporting the activities conducted by launch vehicle and payload developers, certification authorities, and launch centers management, enabling means to proactively act in order to mitigate risks, avoiding unsafe actions and undesired system behaviors, or even to mitigating their consequences. The practical applications of this work can result in safety and mission fulfillment improvements for safety management systems, launch approval regulations and standards; launch operation procedures; space systems design; vehicle, payload, and ground support equipment productions; systems testing; and launch authorization processes.

Microstructural characterization and mechanical behaviour of laser powder Bed Fusion stainless steel 316L

Laser Powder Bed Fusion (L-PBF) is a highly precise and customizable additive manufacturing (AM) technique that uses a high-energy laser to selectively melt and fuse powdered material into a three-dimensional object. However, depending on the process parameters, the final components may have potential flaws that can affect their quality and mechanical properties, due to porosity, melting and incomplete fusion of powder particles; and because the process involves local heating and sometimes uneven heat transfer, the processed components may warp or crack due to residual stresses or thermal gradients. The manufacturing process itself reflects in the final component structure having a detrimental effect on the strength, durability, fatigue resistance, and corrosion.In this work, static tensile and fatigue tests were performed on traditional and L-PBF manufactured AISI 316L stainless steel specimens. The energetic release has been evaluated with an infrared camera during the static and fatigue tests aiming to identify material thermal response to the loading and to predict the failure in rapid way adopting Thermographic Methods. Differences were observed comparing the fatigue data of the L-PBF processed specimens with the traditional material. However, analysis of internal structure, porosity, and surface characteristics of the AM material in combination with fractographic analysis helped to explain the differences in the fatigue life. The observed energy release, different for both material types, was discussed based on the structural characteristics. The results show that the crack originates from a defect on the surface or just below the surface, with a transgranular propagation.

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.

Chemical and visual stimuli for conservation biological control of stink bugs in soybean: potential use and flaws on egg parasitoid recruitment

Chemical and visual stimuli for conservation biological control of stink bugs in soybean: potential use and flaws on egg parasitoid recruitment

Panchayat System in Karnataka: Democracy, Representation and Political Parties

Abstract: As far as the Indian sub-continent is concerned local governance/panchayat has an elaborate history. The rural population have for long practiced this system of governing themselves through their representatives. The mode of working and the powers vested in the local bodies, have not been uniform throughout. The federation conferred constitutional status to the local governance bodies in 1993 and this system has progressed in an interesting way. Karnataka, a southern State in India has been ahead of time in enacting legislations dealing with local governance. Even before the amendment inserting provisions to the Constitution of India was passed, Karnataka had and was implementing extravagant laws pertaining to panchayats. Since democracy and decentralisation are closely associated with the panchayats, the idea of representation has a key role to play. The concerns with respect to involvement and influence of political parties in the local body elections are an important aspect as well. This paper ventures out to understand the panchayat system in Karnataka, pre and post the Constitutional amendment. The paper intends to throw light on the essence of the legislations relating to panchayats in Karnataka and understand the idea of democracy, representational factor and the concept of elections on a non-party basis. The researchers have keenly pondered upon the implementation of apolitical elections in Karnataka, the flaws in the proposed phenomenon. The paper also aims to look into hurdles in the way of these local bodies and propose suggestions to get over the same. Purpose: To Analyze the panchayat system in Karnataka, both before and after the 1993 constitutional amendment. Examine the essence of Karnataka's panchayat-related legislation. Explore the concepts of democracy, representation, and non-party elections in the context of panchayats. Evaluate the implementation of non-partisan elections in Karnataka and identify potential flaws. Analyze the challenges faced by panchayats and propose solutions. Theoretical Reference: Mainly Doctrinal research methodology. Relies on primary sources like the Indian Constitution, relevant statutes relating to panchayaths, and relevant data. Draws on secondary sources like articles and research papers. Method: Analyzes relevant legal documents and scholarly works. Analytical Method of Study Comparative Studies in relation to various factors pertaining to the research between the three levels of Panchayat (Gram Panchayat, Taluk Panchayat and Zilla Panchayat) Results and Conclusion: Non-partisan elections in Karnataka are largely theoretical; political parties influence local elections. Political interference hinders effective panchayat functioning. The paper likely proposes recommendations for strengthening panchayats and minimizing political influence. Implications of Research: Provides insights into the challenges and potential of decentralized governance in India. Informs policymakers and stakeholders about improving panchayat effectiveness. May contribute to debates on non-partisan elections and local democracy. Originality/Value: The study offers a focused analysis of Karnataka's panchayat system within a specific theoretical framework. Its findings on the limitations of non-partisan elections could be valuable for other regions considering similar approaches.

The performance of artificial intelligence in medical field

In modern society, there is an increasing prevalence of individuals who are able to avail themselves of medical facilities for the purpose of receiving healthcare services. With the rise in population, there has been a corresponding decrease in the availability of medical resources for some disorders, leading to challenges in accurately diagnosing patients by healthcare professionals. According to specialists, artificial intelligence (AI) is being considered as a potential solution for addressing medical challenges. This paper mainly focuses on discussing the impact of artificial intelligence in the medical field. Through methods of literature review and analysis, this study explores the fundamental idea of AI and its use in the medical field. Besides, the paper also introduces the potential flaws behind AI in the medical field, and how will artificial intelligence help us further in the medical field. The study reveals that artificial intelligence is extensively employed within the medical sector. Through extensive training, AI has the potential to attain a considerable level of accuracy when it comes to diagnosing various ailments. The level of precision exhibited is akin to that observed in medical practitioners diagnoses. Nevertheless, artificial intelligence possesses certain limitations. For instance, in the context of privacy preservation, several patients exhibit a reluctance to divulge their symptoms. In the absence of adequate safeguards for information security, this situation might potentially lead to adverse consequences in the lives of these patients and their interpersonal interactions. Ultimately, AI continues to possess significant potential for advancement within the realm of medicine.

Potential merits and flaws of large language models in epilepsy care: A critical review.

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.