Commercial Tools Research Articles

Sociodemographic biases in a commercial AI model for intracranial hemorrhage detection.

To evaluate whether a commercial AI tool for intracranial hemorrhage (ICH) detection on head CT exhibited sociodemographic biases. Our retrospective study reviewed 9736 consecutive, adult non-contrast head CT scans performed between November 2021 and February 2022 in a single healthcare system. Each CT scan was evaluated by a commercial ICH AI tool and a board-certified neuroradiologist; ground truth was defined as final radiologist determination of ICH presence/absence. After evaluating the AI tool's aggregate diagnostic performance, sub-analyses based on sociodemographic groups (age, sex, race, ethnicity, insurance status, and Area of Deprivation Index [ADI] scores) assessed for biases. χ2 test or Fisher's exact tests evaluated for statistical significance with p ≤ 0.05. Our patient population was 50% female (mean age 60 ± 19 years). The AI tool had an aggregate accuracy of 93% [9060/9736], sensitivity of 85% [1140/1338], specificity of 94% [7920/ 8398], positive predictive value (PPV) of 71% [1140/1618] and negative predictive value (NPV) of 98% [7920/8118]. Sociodemographic biases were identified, including lower PPV for patients who were females (67.3% [62,441/656] vs. 72.7% [699/962], p = 0.02), Black (66.7% [454/681] vs. 73.2% [686/937], p = 0.005), non-Hispanic/non-Latino (69.7% [1038/1490] vs. 95.4% [417/437]), p = 0.009), and who had Medicaid/Medicare (69.9% [754/1078]) or Private (66.5% [228/343]) primary insurance (p = 0.003). Lower sensitivity was seen for patients in the third quartile of national (78.8% [241/306], p = 0.001) and state ADI scores (79.0% [22/287], p = 0.001). In our healthcare system, a commercial AI tool had lower performance for ICH detection than previously reported and demonstrated several sociodemographic biases.

Evaluation of knowledge-based planning models for male pelvic CBCT-based online adaptive radiotherapy on conventional linear accelerators.

To assess the practicality of employing a commercial knowledge-based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone-beam computed tomography (iCBCT) datasets. Intact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose-volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan-generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow. Model testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09±02:06 (intact) and 07:12±01:04 (bed). This study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

Shiga Toxin-Producing Escherichia coli Strains from Romania: A Whole Genome-Based Description.

The zoonotic Shiga toxin-producing Escherichia coli (STEC) group is unanimously regarded as exceptionally hazardous for humans. This study aimed to provide a genomic perspective on the STEC recovered sporadically from humans and have a foundation of internationally comparable data. Fifty clinical STEC isolates, representing the culture-confirmed infections reported by the STEC Reference Laboratory between 2016 and 2023, were subjected to whole-genome sequencing (WGS) analysis and sequences were interpreted using both commercial and public free bioinformatics tools. The WGS analysis revealed a genetically diverse population of STEC dominated by non-O157 serogroups commonly reported in human STEC infections in the European Union. The O26:H11 strains of ST21 lineage played a major role in the clinical disease resulting in hospitalisation and cases of paediatric HUS in Romania surpassing the O157:H7 strains. The latter were all clade 7 and mostly ST1804. Notably, among the Romanian isolates was a stx2a-harbouring cryptic clade I strain associated with a HUS case, stx2f- and stx2e-positive strains, and hybrid strains displaying a mixture of intestinal and extraintestinal virulence genes were found. As a clearer picture emerges of the STEC strains responsible for infections in Romania, further surveillance efforts are needed to uncover their prevalence, sources, and reservoirs.

Familiarity analysis and time-advance experimental study of LP-NGD RC-circuit intended to operate with Unity-normalized gain

PurposeThe purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement.Design/methodology/approachThe present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons.FindingsThe RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance.Research limitations/implicationsThe performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components.Practical implicationsThe NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system.Social implicationsAs social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system.Originality/valueThe originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

Biophysical essentials – A full stack open-source software framework for conserved and advanced analysis of patch-clamp recordings

Background and ObjectivesPatch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. MethodsBiophysical Essentials (BPE) was developed as an open-source full stack python software for transparent and reproducible analysis of electrophysiological recordings. For validation, BPE results were compared with manually analyzed single-cell patch-clamp data acquired from a human in vitro nociceptor-model and mouse dorsal root ganglia neurons. ResultsWhile initially designed to improve time consuming and repetitive analysis steps, BPE was further optimized as a technical software solution for entire workflow processing including data acquisition, data preprocessing, normalization and visualization and of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps reproducible review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers' needs in projects involving electrophysiology techniques. ConclusionsBPE extends beyond available patch-clamp specific, open source – and commercial analysis tools in particular because of reproducible and sharable analysis workflows. BPE enables full analysis from raw data acquisition to publication ready result visualizations – all within one single program. Thereby, BPE significantly enhances transparency in the analytical process of patch-clamp data analysis. BPEs function scope is completely accessible through an easy-to-use graphical user interface eliminating the need for programing language proficiency as required by many community patch-clamp analysis frameworks and algorithms.

Automated design and optimization of distributed filter circuits using reinforcement learning

Abstract Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are not only time-consuming and labor-intensive but also rely heavily on the expertise and experience of electronics engineers, making it difficult to adapt to rapidly changing design requirements. Additionally, these commercial tools struggle with precise adjustments when parameters are sensitive to numerical changes, resulting in limited optimization effectiveness. This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Thus, it significantly reduces the subjectivity and constraints associated with circuit design. The experimental findings demonstrate clear improvements in design efficiency and quality when comparing the proposed method with traditional engineer-driven methods. Furthermore, the proposed method achieves superior performance when designing complex or rapidly evolving DFCs, highlighting the substantial potential of RL in circuit design automation. In particular, compared to the existing DFC automation design method CircuitGNN, our method achieves an average performance improvement of 8.72%. Additionally, the execution efficiency of our method is 2000 times higher than CircuitGNN on the CPU and 241 times higher on the GPU.

Towards a practical usage for the Sleuth Kit supporting file system add-ons

Most modern digital devices with storage utilize a file system to manage files and directories. Consequently, when digital forensic investigators derive evidence from such devices, they collect and analyze data stored on them through file system analysis. However, there are numerous types of file systems, with new ones continually being developed. Each file system possesses a distinct metadata structure and file management system. Therefore, investigators must possess prior knowledge of the specific file system being examined. Nevertheless, it is challenging for practitioners to be knowledgeable about all existing file systems. To address this issue, forensic software such as The Sleuth Kit (TSK), an open-source forensic tool, is employed for investigations. However, even these tools may not offer complete support for relatively recent file systems.Hence, we propose a structure for integrating a new file system into the open-source forensic tool TSK. Additionally, to validate this proposed structure, we demonstrate that support for five file systems (Ext4, XFS, Btrfs, F2FS, and Hikvision) can be added following this framework. To achieve this, we conducted an analysis of the metadata and file management scheme for these five file systems. Furthermore, we examined the operational procedures of the TSK framework. Based on these analyses, investigation capabilities for the five file systems have been incorporated into TSK. Moreover, reliability verification experiments were conducted on the developed tools; and performance evaluation was carried out in comparison with other commercial digital forensic tools. The findings of this study can serve as a foundation for future forensic studies based on file systems. Additionally, the TSK developed based on the proposed structure can assist investigators in conducting digital forensics effectively.

Comparison of Transcranial Magnetic Stimulation Dosimetry between Structured and Unstructured Grids Using Different Solvers.

In recent years, the interest in transcranial magnetic stimulation (TMS) has surged, necessitating deeper understanding, development, and use of low-frequency (LF) numerical dosimetry for TMS studies. While various ad hoc dosimetric models exist, commercial software tools like SimNIBS v4.0 and Sim4Life v7.2.4 are preferred for their user-friendliness and versatility. SimNIBS utilizes unstructured tetrahedral mesh models, while Sim4Life employs voxel-based models on a structured grid, both evaluating induced electric fields using the finite element method (FEM) with different numerical solvers. Past studies primarily focused on uniform exposures and voxelized models, lacking realism. Our study compares these LF solvers across simplified and realistic anatomical models to assess their accuracy in evaluating induced electric fields. We examined three scenarios: a single-shell sphere, a sphere with an orthogonal slab, and a MRI-derived head model. The comparison revealed small discrepancies in induced electric fields, mainly in regions of low field intensity. Overall, the differences were contained (below 2% for spherical models and below 12% for the head model), showcasing the potential of computational tools in advancing exposure assessment required for TMS protocols in different bio-medical applications.

Forensic analysis of OpenAI's ChatGPT mobile application

Since its public launch, OpenAI's ChatGPT has achieved significant success, attracting millions of users within the first few months of its release. Although numerous similar applications have emerged, none have yet matched the success of OpenAI's ChatGPT. Last year, OpenAI released the ChatGPT mobile app. This application serves a broad range of uses, some of which may be malicious and, unfortunately, it has not yet been parsed by either commercial or open-source tools. Nevertheless, the data stored by this application, such as JSON files that store a user's conversations with ChatGPT, can be instrumental in attributing user actions, discerning perpetrators' knowledge and motivations, and resolving practical investigations. In this paper, OpenAI's ChatGPT mobile application is examined on both Android and iOS operating systems, focusing on potential evidentiary data within. The cloud-native data associated with the app, which can be retrieved through user data export requests are also investigated. The primary objective of this study is to discover artifacts that investigators can use in real-world cases involving this mobile app. Additionally, the authors have contributed to FOSS to support professionals in this field.

Predicting Software Defect Complexity and Accuracy using Bug Tracking and Clustering

Many open sources, free and commercial bug tracking tools have been developed and are currently under development. There are number of issues are related to software projects are daily increasing and the developers are started to use bug tracking systems in that order to manages the bug reports. The industry needs that the criteria to select the best system tool among the available set of system tools which will helps to fix and track the progressive report of bug fixes. While, collection of useful information from the large and not organized set of these reports is still difficult problem because there are various bug tracking systems are provide the data via many resources like web interfaces. We try to present these comprehensive classification criteria to manage the reviews for available tools and propose a new modified tool for the bug tracking and reporting system. It also helps in reporting the bugs which are founded by that process, assigning the bug to the developer for monitoring and fixing the progress of bug fixing by various graphical/charting facility and status updates. It also providing the reliability of bug prediction and tries to find the bugs for complexity measurements, and allows to distributing.

Secondary cancer risk in six anatomical sites when using PAT, IMPT, and VMAT/IMRT radiotherapy

Background and purposeCompared to intensity modulated proton therapy (IMPT), proton arc therapy (PAT) is expected to improve dose conformality, delivery efficiency, and provide a more favorable LET distribution. Alternatively, the low-dose bath is potentially spread over larger volumes, which could impact the likelihood of developing a radiation-induced, secondary cancer (SC). The goal of this study was to evaluate this risk in several anatomical sites using newly developed commercial tools. Materials and methodsTreatment plans encompassing six anatomical sites, five patients per site, and three techniques per patient were created using RayStation. Techniques included PAT and IMPT for protons, and either volumetrically modulated radiotherapy (VMAT) or intensity modulated radiotherapy (IMRT) for photons. Risk estimates were based on the organ-equivalent dose (OED) concept using both Schneider’s mechanistic dose–response model for carcinoma induction and a linear dose–response model. ResultsWith few exceptions, mean and integral dose were lowest with PAT. For protons, the factor OEDIMPT/OEDPAT ranged from 0.7 to 1.8 with both the mechanistic and linear model, while for photons OEDphoton/OEDPAT ranged from 1.5 to 10 using the mechanistic model and 1.3 to using the linear model. A strong correlation was found between mean dose and OED for organs with significant repopulation/repair (high R value) and less cell death from single hit interactions (low α value). ConclusionBased on results from both mechanistic and linear risk models, the transition from IMPT to PAT should not substantially affect SC risk in patients treated with proton therapy. Additionally, when using Schneider’s model, the shapes of the dose–response curves can be used as a good predictor of how SC risk will respond to shifts from intermediate dose to low dose as anticipated when moving from IMPT to PAT.

FEINT: Automated Framework for Efficient INsertion of Templates/Trojans into FPGAs

Field-Programmable Gate Arrays (FPGAs) play a significant and evolving role in various industries and applications in the current technological landscape. They are widely known for their flexibility, rapid prototyping, reconfigurability, and design development features. FPGA designs are often constructed as compositions of interconnected modules that implement the various features/functionalities required in an application. This work develops a novel tool FEINT, which facilitates this module composition process and automates the design-level modifications required when introducing new modules into an existing design. The proposed methodology is architected as a “template” insertion tool that operates based on a user-provided configuration script to introduce dynamic design features as plugins at different stages of the FPGA design process to facilitate rapid prototyping, composition-based design evolution, and system customization. FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort. For example, FEINT can help insert defensive monitoring, adversarial Trojan, and plugin-based functionality enhancement features. FEINT is scalable, future-proof, and cross-platform without a dependence on vendor-specific file formats, thus ensuring compatibility with FPGA families and tool versions and being integrable with commercial tools. To assess FEINT’s effectiveness, our tests covered the injection of various types of templates/modules into FPGA designs. For example, in the Trojan insertion context, our tests consider diverse Trojan behaviors and triggers, including key leakage and denial of service Trojans. We evaluated FEINT’s applicability to complex designs by creating an FPGA design that features a MicroBlaze soft-core processor connected to an AES-accelerator via an AXI-bus interface. FEINT can successfully and efficiently insert various templates into this design at different FPGA design stages.

Interobserver Agreement and Performance of Concurrent AI Assistance for Radiographic Evaluation of Knee Osteoarthritis.

Background Due to conflicting findings in the literature, there are concerns about a lack of objectivity in grading knee osteoarthritis (KOA) on radiographs. Purpose To examine how artificial intelligence (AI) assistance affects the performance and interobserver agreement of radiologists and orthopedists of various experience levels when evaluating KOA on radiographs according to the established Kellgren-Lawrence (KL) grading system. Materials and Methods In this retrospective observer performance study, consecutive standing knee radiographs from patients with suspected KOA were collected from three participating European centers between April 2019 and May 2022. Each center recruited four readers across radiology and orthopedic surgery at in-training and board-certified experience levels. KL grading (KL-0 = no KOA, KL-4 = severe KOA) on the frontal view was assessed by readers with and without assistance from a commercial AI tool. The majority vote of three musculoskeletal radiology consultants established the reference standard. The ordinal receiver operating characteristic method was used to estimate grading performance. Light kappa was used to estimate interrater agreement, and bootstrapped t statistics were used to compare groups. Results Seventy-five studies were included from each center, totaling 225 studies (mean patient age, 55 years ± 15 [SD]; 113 female patients). The KL grades were KL-0, 24.0% (n = 54); KL-1, 28.0% (n = 63); KL-2, 21.8% (n = 49); KL-3, 18.7% (n = 42); and KL-4, 7.6% (n = 17). Eleven readers completed their readings. Three of the six junior readers showed higher KL grading performance with versus without AI assistance (area under the receiver operating characteristic curve, 0.81 ± 0.017 [SEM] vs 0.88 ± 0.011 [P < .001]; 0.76 ± 0.018 vs 0.86 ± 0.013 [P < .001]; and 0.89 ± 0.011 vs 0.91 ± 0.009 [P = .008]). Interobserver agreement for KL grading among all readers was higher with versus without AI assistance (κ = 0.77 ± 0.018 [SEM] vs 0.85 ± 0.013; P < .001). Board-certified radiologists achieved almost perfect agreement for KL grading when assisted by AI (κ = 0.90 ± 0.01), which was higher than that achieved by the reference readers independently (κ = 0.84 ± 0.017; P = .01). Conclusion AI assistance increased junior readers' radiographic KOA grading performance and increased interobserver agreement for osteoarthritis grading across all readers and experience levels. Published under a CC BY 4.0 license. Supplemental material is available for this article.

A new interactive design method for carbon fibres laminate component

Carbon fibre is the most common reinforcing phase in composite materials. However, it is difficult to determine the performance parameters of a monofilament. This paper provides an efficient method for performing the global layup optimization of composite laminates, considering the relationship between material characteristics and process parameters. In particular, a new method is proposed that, by integrating commercial tools, can support designers in the design and construction of carbon fibre components. The approach involves four functional groups that interact with each other: requirements and specifications, material definition, process implementation, and design and simulation. The idea is to create a continuous process to realize continuous product optimization. The approach was applied to the optimization of the front wing of a Formula 4 vehicle. After the validation method phase, through a comparison between real data and numerical simulations, product optimization was conducted. Different optimized solutions were obtained, and the solution minimizing the mass but ‘allowing the vehicle to bear stress and strain values within the required limits was chosen. This methodology can be applied to support the designer during both the early design phase and the optimization phase of laminated products.

Integration of BIM and GIS Data of a Heritage Building Using FME

BIM and GIS technologies are used in both planning and investment and construction processes. GIS is more often used in the former, where one operates on a macro scale and information about the environment is essential for decision-making. BIM, on the other hand, is increasingly implemented in investment and construction processes (micro scale). The BIM model as a resource of knowledge and information about the construction object is the basis for decision-making, while data from GIS systems are necessary to obtain reliable information about the environment and the prevailing spatial, social or economic conditions. The basic information from GIS systems are attributes related to geographic location (coordinate system, angle to true north, elevation ordinate). Unfortunately, both technologies use different programming paradigms. GIS is mainly a relational database based on multidimensional tables, while BIM uses so-called encapsulation, polyform, hierarchy or instantiation, which enrich semantically stored data. There are many benefits to integrating geospatial data with building object information. The problem of compatibility and interoperability of the two technologies is the subject of many considerations of basic science and the problem of practitioners during application work. Georeferencing of BIM models is conferred in several ways, however, most of them require relatively expensive commercial tools or extensive digital skills or even programming. Rarely, however, are tools such as FME used for data conversion, management and visualization. Thus, the purpose of the present work was to attempt to properly georeference a BIM model of a historic building, located at Constitution Square in Warsaw, in a GIS environment, and then convert the data to shapefile output format using FME software. The results of the experimental work indicate that the BIM data can be embedded quite accurately in the space of a given coordinate system and displayed against various contextual data, but the 3D geometry itself loses its detail and quality. The paper discusses the limitations of the procedure and future research directions.

Breast Cancer Detection using Machine Learning

Breast cancer is one of the worst cancer diseases in the world which affects a very large number of people. Detecting and diagnosing this disease early is vital, since it is crucial for the survival of the patient. The main objective of this study is to investigate the different technologies that are used for identifying and diagnosing breast cancers. Furthermore, it explores different machine learning approaches, computer-aided detection systems (CAD) and common medical treatments used to diagnose and treat these fatal diseases on a higher level. Also, there is a difference between commercial software and tools that are used for identifying and assessing all stages of breast cancer against tools and software that are provided by nonprofit organizations. The conclusion of this report is that there is no method or standardized procedure to identify and diagnose breast cancer using all technologies.

IoT-enabled novel heterostructure FET-based hybrid sensor for real-time arsenic detection

This work presents a novel approach to real-time water quality monitoring using sensor networks based on the Internet of Things (IoT). By integrating sensors made from heterogeneous materials with existing electronic platforms, these systems can outperform homogeneous ones, offering superior sensor performance with higher selectivity and a lower limit of detection (LoD). In this work, a highly sensitive sensor architecture for detecting arsenic (As3+) in water has been developed by integrating a novel heterostructure of dendritic ReS2 with a large surface area and graphene with dithiothreitol (DTT)-functionalized gold nanoparticles (Au-NPs). The sensor can rapidly detect As3+ in less than 2 seconds, with an ultra-low LoD of 10 pM (or 0.0008 ppb). This is the lowest among FET-based sensors and significantly below the EPA’s maximum contaminant level (MCL) of 130 nM for As3+ in drinking water. The proposed sensor platform demonstrates fast, sensitive, selective, and repeatable detection capabilities, indicating its great potential for in-situ monitoring. This can be achieved either autonomously or through sensor-integrated amplifier circuits, which in our work showed a small signal gain of approximately 14. Importantly, all active materials are synthesized using chemical vapor deposition (CVD) based on commercial tools which suggests that these sensors can be produced at a wafer scale, paving the way for large-scale commercial production. The entire system is connected through an IoT-enabled intelligent sensing platform, representing a vision for state-of-the-art sensor network technology.

Hardware Accelerated AES: RTL to GDS

This paper presents a hardware-accelerated Advanced Encryption Standard (AES) implementation featuring an 11-stage pipelined architecture and an optimized subkey generation scheme for enhanced performance. This design is implemented in Verilog and verified through waveform analysis. Operating at a maximum clock frequency of 100 MHz, our implementation achieves significant efficiency. The final design occupies an area of 1.44 mm2 and consists of 32,424 standard cells, with a power consumption of 32 mW at the typical corner. Furthermore, the entire design flow, from synthesis to layout (GDSII), is achieved using OpenLane, an open-source Electronic Design Automation (EDA) tool. This democratization of hardware design empowers a wider range of participants, particularly students and researchers, to efficiently implement their designs and gain hands-on experience without the constraints of expensive commercial tools. This fosters innovation and accelerates progress by allowing a broader community to contribute to technological advancements. Index Terms—OpenLane, Open-source , EDA, Pipelined AES, RTL to GDSII

Forensic analysis of Scientific Linux image using commercial and opensource forensic tools

Depending on their needs and personal preferences, people choose to use different operating systems (OS) such as Windows, Linux, and Mac. The scientific Linux Operating System (SLOS) is designed to provide a stable, secure, and high-performance computing environment for scientific research and education in a steady, scalable, and extensible manner. When criminal activities are committed by suspects involving computers and the internet, it calls for digital forensics which involves the use of scientific procedures and tools to carry out the forensic investigation and analysis of digital evidence for legal and investigative purposes. Forensic investigators use commercial and opensource tools for analysis and gathering inculpatory and exculpatory pieces of evidence. This paper presents a comparative analysis of EnCase, FTK, Autopsy, bulk-extractor, and Scalpel for analyzing the Scientific Linux image. The test scenarios were designed to find out if the selected forensic tools can be appropriately used for investigating crimes committed using the SLOS. The test scenarios include extraction and analysis of operating system details, user accounts, web browsing history, and the recovery of deleted and shredded files and this paper compares and evaluates the capability of the tools in retrieving the evidence designed in the scenarios. This systematic comparison and evaluation results would assist digital forensics practitioners, researchers, and law enforcement agencies in making informed decisions regarding the selection of tools for Scientific Linux image forensics.

Cyber Security Incident Response

In response to the growing cyber-attack threat, incident response teams have become a critical component of an organization's cybersecurity strategy. These teams are responsible for detecting, analyzing, and responding to security incidents promptly and effectively. However, detecting code injection attacks can be particularly challenging, as they can be difficult to detect and often go unnoticed until it is too late. Cybersecurity professionals use detection tools to detect and respond to DLL injection attacks that monitor system activity and detect unusual behavior. A large portion of the related literature focuses on the use of commercial DLL injection tools. In contrast, little attention has been paid to the effectiveness of using open-source DLL injection detection tools. Thus, this research project aims to evaluate the effectiveness of three widely used open-source tools, VirusTotal, Sysinternals, and Yara, in detecting DLL injection incidents. This study's findings highlight each tool's strengths and limitations, which in turn enables cybersecurity professionals to make informed decisions when selecting the most suitable tool for DLL injection detection. Furthermore, the study emphasizes the importance of continuous tool development and updates to keep pace with evolving malware techniques and emerging threats. By highlighting the effectiveness of the tools, this research enhances the overall security posture of organizations and individuals, empowering them to mitigate the risks associated with DLL injection attacks proactively. The outcomes of this research project also underscore the significance of leveraging advanced tools to fortify cybersecurity defenses and safeguard critical systems and data.