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As the volume of end-of-life lithium-ion batteries (LIB) used in electric vehicles increases, so does the demand for recycling to recover valuable active materials as secondary resources, reducing the need for primary raw materials and minimizing supply dependencies. In addition to pyro- and hydrometallurgy recycling processes, an increasingly examined approach is the direct recycling route, which involves disassembling battery systems into modules, modules to cells, and ultimately cells to electrodes. To minimize passive materials such as various metals from the housing, joints, arresters, plastics, foams, and adhesives in the subsequent crushed mass, the introduction of module disassembly processes into the existing LIB recycling process chain is promising for the resulting increase in recyclate purity. This study presents the conceptualization of a LIB module disassembly process chain which aims to elevate the depth of disassembly of LIB during early recycling stages. A reverse engineering approach is used to recover individual cells from two battery modules of one specific automotive manufacturer by removing their casing through milling and disconnecting the cells from the arresters. The experimental results are analyzed for influences on the disassembly possibilities for individual components as well as the interactions within the system, enabling the elaboration of a priority graph and a disassembly plan. In order to allow for scalable recycling process chains suitable for very high quantities, a concept for a fully automated and variant-compatible robot application based on the experiment is derived. Furthermore, the depth of disassembly is compared with the corresponding reduction of passive materials in the crushed mass. The findings indicate that several module components can be disassembled, resulting in cell recovery and a reduction of metal and plastic in the crushed mass by about 17 %, highlighting the importance of robotic disassembly in enhancing material separation efficiency during LIB recycling. [ABSTRACT FROM AUTHOR]

This paper explores the reverse engineering of the relationship between a lower tooth and the lower mandible, along with potential finite element analysis methods. The primary focus of the study is to develop a high-quality computer model of the mandible with precise, continuous spline surfaces and to subsequently examine the tooth-bone relationship using this more accurate geometric model. Many of the studies reviewed employ a highly simplified model, where the individual layers of tooth and mandible bone are not distinctly analyzed. In contrast, our research emphasizes the reverse engineering of the tooth's integration into the mandible, enabling us to decompose the model into layers and conduct a comprehensive computer simulation of the entire structure. This approach allows us to assign different material properties to various regions of the model during the finite element analysis. This procedure is done by using scanned 3D (3-Dimensional) data in a mechanical CAD system the PTC Creo 8. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar. [ABSTRACT FROM AUTHOR]

Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques. However, the issues presented by modern malware cannot be met by traditional signature or heuristic-based malware detection methods. Previous research suggests that machine-learning classifiers can be utilised to analyse permissions, making it possible to differentiate between malicious and benign applications on the Android platform. There exist machine-learning methods that utilise permission-based attributes to build models for the detection of malware on Android devices. Nevertheless, the performance of these detection methods is dependent on the raw or feature datasets. Android malware research frequently faces a major obstacle due to the lack of adequate and up-to-date raw malware datasets. In this paper, we put forward a systematic approach to generate an Android permission-based dataset using static analysis. To create the dataset, we collect recent raw malware samples (APK files) and focus on the reverse engineering approach and permission-based features extraction. We also conduct a thorough feature analysis to determine the important Android permissions and present a machine-learning-based Android malware detection mechanism. The experimental result of our study demonstrates that with just 48 features, the random forest classifier-based Android malware detection model obtains the best accuracy of 97.5%. [ABSTRACT FROM AUTHOR]

Replication, heredity, and evolution are characteristic of Life. We and others have postulated that the reconstruction of a synthetic living system in the laboratory will be contingent on the development of a genetic self-replicator capable of undergoing Darwinian evolution. Although DNA-based life dominates, the in vitro reconstitution of an evolving DNA self-replicator has remained challenging. We hereby emulate in liposome compartments the principles according to which life propagates information and evolves. Using two different experimental configurations supporting intermittent or semi-continuous evolution (i.e., with or without DNA extraction, PCR, and re-encapsulation), we demonstrate sustainable replication of a linear DNA template – encoding the DNA polymerase and terminal protein from the Phi29 bacteriophage – expressed in the 'protein synthesis using recombinant elements' (PURE) system. The self-replicator can survive across multiple rounds of replication-coupled transcription-translation reactions in liposomes and, within only ten evolution rounds, accumulates mutations conferring a selection advantage. Combined data from next-generation sequencing with reverse engineering of some of the enriched mutations reveal nontrivial and context-dependent effects of the introduced mutations. The present results are foundational to build up genetic complexity in an evolving synthetic cell, as well as to study evolutionary processes in a minimal cell-free system. The construction of a synthetic cell will be contingent on the development of an evolvable genetic replicator. Here, authors create a Darwinian protocell that supports protein synthesis, DNA self-replication, and adaptive evolution. [ABSTRACT FROM AUTHOR]

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75-90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems. [ABSTRACT FROM AUTHOR]

We envision this work to assist researchers and medical device developers (beside other stakeholders) to better understand biomaterial‐based medical device development and its approval process proposed by the new MDR and IVDR in the European Union, as more complex biomaterials emerge, with the MDR reflecting the progress in biomaterial discoveries. Additionally, insufficient international harmonization in regulatory laws and poor‐quality data reporting contribute to the problem. This review describes the possible reasons for a slowing biomaterials translational trend observed over the past decades, focusing on the European Market, and suggests a feasible approach for biomaterials‐based medical device translation into the clinic. Suitable solutions to upgrade biomaterial translation to the clinic have not yet been provided by the field: no additional hurdles should be imposed for researchers, clinicians, the medical device industry, and insurance companies, which all should collaborate on bringing innovative solutions to patients. The new MDR and IVDR represent a substantial advancement in ensuring patient safety and reflect a major step forward in healthcare. However, they should not constrain innovation in biomaterials‐based medical device development. Incorporating reverse engineering from patient safety and a ‘safe by design’ (SbD) strategy early into medical device development might lead to a smoother and successful approval process. A solid R&D phase, with an emphasis on device safety and performance assessment, is fundamental to ensure an effective transition into the clinic. We offer an overview of the recently implemented regulations on medical devices and in vitro diagnostics across the EU, describing a shifting paradigm in the field of biomaterials discovery. As more complex biomaterials emerge, suitable regulations will be necessary to keep bringing safe and well‐performing medical solutions to patients. [ABSTRACT FROM AUTHOR]

This paper investigates new analytical wave solutions for the fractional Kuralay-IIB equations (FK-IIBE), including a new definition of the fractional derivative. This model works in disciplines such as ferromagnetic materials, optical fibers, wave mixing, and nonlinear optics. The integrable motion of space curves can be determined by our governing model. This research holds great importance in the area of optical fibers, exact and analytical solitons solution. The polynomial method’s complete discrimination system (CDSPM) is used to identify a variety of exact and analytical soliton solutions such as periodic, hyperbolic, rational, Jacobian elliptic (JE), and trigonometric functions. Additionally, we also convert the JE function into a solitary wave (SW) solution. To provide a comprehensive understanding of the dynamic aspects of the system, we also address bifurcation points, quasi-periodic behavior, sensitivity analysis, Poincarè maps, time series profile, and critical solution conditions. The research offers straightforward, efficient algorithms that can consistently and effectively address FK-IIBE. Machine learning technologies are also utilized to satisfy the criteria defined by dynamic analysis. Python regression learner tools are used for reverse engineering to analyze the mathematical model’s equilibrium based on parameter intervals and thresholds determined by dynamical analysis. [ABSTRACT FROM AUTHOR]

With the widespread adoption of 3D scanners, reverse engineering methods for obtaining CAD drawings from physical objects have become increasingly utilized. When converting point data obtained from a 3D scanner into a mesh structure, a smoothing process is typically applied because the point data often contains a lot of noise. However, this can result in the loss of important high-frequency details, or, in cases where significant high-frequency information remains, there are limitations in representing the object with basic geometric shapes. In this paper, a method to address this issue by using environment mapping and supervised learning techniques is proposed. By mapping the object to an environment and then using supervised learning to analyze the data, it can accurately identify the basic geometric shapes. This approach reduces the working time and allows for easier design of complex surfaces that were previously difficult to model. [ABSTRACT FROM AUTHOR]

This study explores the automation enhancement in the assembly process of wiring harnesses for automotive applications, focusing on manually inserting fuses and relays into boxes—a task known for quality and efficiency challenges. This research aimed to address these challenges by implementing a robotic arm integrated with API technology for digital twin. The methods used included the development of a digital twin model to simulate and monitor the assembly process, supported by real-time adjustments and optimizations. The results showed that the robotic system significantly improved the accuracy and speed of fuse insertion, reducing the insertion errors typically seen in manual operations. The conclusions drawn from the research confirm the feasibility of using robotic automation supported by digital twin technology to enhance assembly processes in automotive manufacturing, promising substantial improvements in production efficiency and quality control. [ABSTRACT FROM AUTHOR]

Unmyelinated C-fibers constitute the vast majority of axons in peripheral nerves and play key roles in homeostasis and signaling pain. However, little is known about their ion channel expression, which controls their firing properties. Also, because of their small diameters (~ 1 μm), it has not been possible to characterize their membrane properties using voltage clamp. We developed a novel library of isoform-specific ion channel models to serve as the basis functions of our C-fiber models. We then developed a particle swarm optimization (PSO) framework that used the isoform-specific ion channel models to reverse engineer C-fiber membrane properties from measured autonomic and cutaneous C-fiber conduction responses. Our C-fiber models reproduced experimental conduction velocity, chronaxie, action potential duration, intracellular threshold, and paired pulse recovery cycle. The models also matched experimental activity-dependent slowing, a property not included in model optimization. We found that simple conduction responses, characterizing the action potential, were controlled by similar membrane properties in both the autonomic and cutaneous C-fiber models, but complicated conduction response, characterizing the afterpotenials, were controlled by differential membrane properties. The unmyelinated C-fiber models constitute important tools to study autonomic signaling, assess the mechanisms of pain, and design bioelectronic devices. Additionally, the novel reverse engineering approach can be applied to generate models of other neurons where voltage clamp data are not available. Author summary: Computational models of axons play an important role in studying neural signaling and developing therapeutic electrical stimulation devices. While robust models of large myelinated axons exist, models of unmyelinated C-fibers do not adequately reproduce experimental conduction responses. C-fibers constitute the vast majority of axons in peripheral nerves and play key roles in homeostasis and signaling pain, but because of their small diameters (~ 1 μm), it has not been possible to characterize their membrane properties using voltage clamp. We used particle swarm optimization and a novel library of isoform-specific ion channel models to reverse engineer C-fiber membrane properties from measured autonomic and cutaneous C-fiber conduction responses. Our C-fiber models reproduced experimental conduction velocity, chronaxie, action potential duration, intracellular threshold, paired pulse recovery cycle, and activity dependent slowing. The models constitute important tools to study autonomic signaling, assess the mechanisms of pain, and design bioelectronic devices. Additionally, the novel reverse engineering approach can be applied to generate models of other neurons where voltage clamp data are not available. [ABSTRACT FROM AUTHOR]

This work will review and discuss the use of computerised X-ray tomography (CXT) for analysing ancient, manufactured items, like stone building materials, glass and ceramics. It will consider particular techniques required, and/or of benefit, for CXT of heritage materials, such as special precautions during the experimentation to ensure there is no damage to the materials, special imaging methods such as elemental-specific imaging, and sample-specific image analysis requirements. This study shows how the knowledge of internal features, particularly pores, discerned from CXT can be used to reverse engineer the artefact fabrication process. CXT can be used to obtain information on both the raw materials (such as types and impurities) and fabrication techniques used. These abilities can then be used to establish technological evolution and the incidence of ancient behaviours like recycling and allow the linking of particular items to specific production sites. It will also be seen how CXT can aid the development of effective conservation techniques. This work will also consider how conclusions drawn from CXT data can be amended or augmented by the use of complementary non-destructive characterisation methods, such as gas overcondensation. [ABSTRACT FROM AUTHOR]

Objective: This study investigated how the number of images collected for digital measurements in dentistry affects accuracy compared with traditional methods. Methods: A Frasaco maxillary model was scanned using a SHINING 3D AutoScan-DS-MIX dental 3D scanner to create an STL file. The maxilla was molded 10 times using polyvinyl siloxane (Zhermack Elite HD+) to produce plaster models, which were scanned with the same reference scanner to generate 10 STL files. The Frasaco model was scanned 10 times, capturing images in intervals of 800–1000, 1000–1200, and 1200–1500 using a 3Shape TRIOS 3 intraoral scanner, creating additional STL files. These were analyzed with reverse engineering software. Results: The most accurate measurements were obtained using 1200–1500 images. Conventional impression techniques performed significantly worse. There was a significant difference between the groups Digital 1200–1500 and Plaster (p < 0.001) and between Digital 800–1000 and Plaster (p = 0.007). No significant difference was found when the digital groups were compared among themselves. There was also no significant difference between the Plaster and Digital 1000–1200 groups. To compare precision values that were normally distributed across three or more methods, a one-way ANOVA was used. Trueness values that were not normally distributed with three or more methods were compared employing the Kruskal–Wallis test. Conclusions: Different image counts affect digital measurement accuracy. The most accurate measurements were obtained when collecting 1200–1500 images. Conventional impression techniques were shown to perform significantly worse than digital impression. [ABSTRACT FROM AUTHOR]

The automotive market is very competitive and demands consistently improving the technologies used and reducing the product cost and dimensions with each product model iteration. Hence, it is important to have access to well-defined reference designs of high-quality products to evaluate new ideas and technologies. This paper provides readers with a numerical model of such a high-quality product, i.e., the IPMSM-type traction motor from the fourth generation of the Toyota Prius hybrid transaxle. The presented results also serve for a discussion regarding the design decisions of the Toyota engineers and the applicability of the linearized machine model for the approximated torque calculations. In the introductory section, a brief history of the Prius model and references to the reverse engineering reports are given. Afterward, the machine dimensions, material properties, and winding configuration are described. Then, the model is validated with the torque measurements at constant speed. The simulation results are presented in the next chapters, and the numerical source data are supplied to the reader. Finally, the design philosophy of the Toyota drive is briefly discussed in comparison with the BMWi3 drive and the results are concluded in the last section. [ABSTRACT FROM AUTHOR]

Localizing security-relevant hard blocks on modern System-on-Chips for physical attacks, such as side-channel analysis and fault attacks, has become increasingly time-consuming due to ever-increasing chip-area and -complexity. While this development increases the effort and reverse engineering cost, it is not sufficient to withstand resolute attackers. This paper explores the application of camera-based lock-in thermography, a nondestructive testing method, for identifying and localizing security hard blocks on integrated circuits. We use a synchronous signal to periodically activate security-related functions in the firmware, which causes periodic temperature changes in the activated die areas that we detect and localize via an infrared camera. Using this method, we demonstrate the precise detection and localization of security-related hard blocks at the die level on a modern SoC. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Static self‐assembly resides in thermodynamically stable global minima of the energy landscape, whereas dynamic self‐assembly occupies local minima of the energy profile and remains in the ordered state for a limited time via dissipation of energy to surroundings. This makes the spatiotemporal control over the assembly and disassembly mechanism easily controllable in the case of dynamic self‐assembly. However, due to the higher thermal stability of static self‐assembly, it is very challenging to perform reverse engineering on these types of systems. Herein we report growth reaction‐based reversal of static silver cubes obtained via cucurbit[8]uril (CB[8]) crosslinked self‐assembly of silver nanoparticles (AgNP). The AgNP building units with variable CB[8] surface coverage have been used as seeds onto which deposition of gold via growth reaction has been performed. The disassembly of supracube structure has been controlled by the formation of [AuCl4]−–CB[8] complex and successive reduction of [AuCl4]− to Au0 on the surface of the seed. The resulting monodispersed isotropic nanoparticles, formed from the CB[8] based cubic self‐assembly after growth, exhibit Au−Ag bimetallic nature. Quenching of the fluorogenic response from the hydrophobic dye coumarin‐7, added after growth, suggests direct interaction with the metallic nanoparticle surface after disassembly and thereby confirms successful growth reaction mediated reversal of self‐assembly. [ABSTRACT FROM AUTHOR]

An area of interest in orthopaedics is the development of efficient customized neck orthoses, considered that pathologies which affect the neck area are widespread. Advanced acquisition and modelling approaches combined with Additive Manufacturing (AM) can potentially provide customized orthoses with improved performance and complexity. However, in the design of these devices, besides functional and structural requirements, benefit and comfort of the patient should be a main concern, in particular, at the early stage of design during the acquisition of the body's part, and while using the printed orthosis. In this paper, a scanning system with three sensors was developed which allows a fast, about 5 s, and accurate acquisition of the neck area with minimum discomfort for the patient. A neck orthosis with a ventilation pattern obtained by Topology Optimization (TO), lightened by about 35%, was also established. In fact, a main role for comfort is played by the ventilation pattern which contributes both to lightness and breathability. Its structural and comfort performance was evaluated in comparison with an orthosis with a ventilation pattern configured by Voronoi cells. Structural assessment was carried out by means of finite element analysis under main loading conditions. An evaluation of neck temperatures in relation to wearing 3D printed prototypes, manufactured with Hemp Bio-Plastic® filament, was finally conducted by means of a thermal imaging camera. TO orthosis prototype showed a better performance regarding thermal comfort, with a maximum increase of neck temperature less than 1 °C, which makes the proposed configuration very promising for user's comfort. [ABSTRACT FROM AUTHOR]

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain. [ABSTRACT FROM AUTHOR]

Machining the holes on the widely used deep-drawing products requires significant time or equipment costs. Therefore, it is a feasible way to design the initial sheets by a reverse engineering method to guide the forming of the final products. However, since the combinations of both the highly precise fitting of the complex surfaces and the zoning supported by mold geometric parameters are difficult, the existing reverse engineering models are not available. So a novel reverse engineering method with NURBS considering mathematical zoning for deep-drawing with sand die is provided in this paper. Also based on the lower sand die, upper sand die, and only metal dies, three different groups with complex surfaces are named as LS, US, and R, respectively. To study the influences of complexities of both surfaces and hole shapes on the forming results of the deep-drawing products, the position prediction model established by the method is forward validated by five holes with different symmetries on three complex surfaces. The inverse model is used to design holes for three complex surfaces, and the error rates of the Group LS, the Group R, and the Group US are 1.07%, 1.38%, and 1.68%, respectively, which demonstrate the feasibility of the position prediction model. This method can provide prospective exploration for application of more complex initial sheets with holes in more widespread forming process. [ABSTRACT FROM AUTHOR]

From both economic and environmental perspectives, ethylene glycol, the principal constituent in the degradation of PET, emerges as an optimal feedstock for microbial cell factories. Traditional methods for constructing Escherichia coli chassis cells capable of utilizing ethylene glycol as a non-sugar feedstock typically involve overexpressing the genes fucO and aldA. However, these approaches have not succeeded in enabling the exclusive use of ethylene glycol as the sole source of carbon and energy for growth. Through ultraviolet radiation-induced mutagenesis and subsequent laboratory adaptive evolution, an EG02 strain emerged from E. coli MG1655 capable of utilizing ethylene glycol as its sole carbon and energy source, demonstrating an uptake rate of 8.1 ± 1.3 mmol/gDW h. Comparative transcriptome analysis guided reverse metabolic engineering, successfully enabling four wild-type E. coli strains to metabolize ethylene glycol exclusively. This was achieved through overexpression of the gcl, hyi, glxR, and glxK genes. Notably, the engineered E. coli chassis cells efficiently metabolized the 87 mM ethylene glycol found in PET enzymatic degradation products following 72 h of fermentation. This work presents a practical solution for recycling ethylene glycol from PET waste degradation products, demonstrating that simply adding M9 salts can effectively convert them into viable raw materials for E. coli cell factories. Our findings also emphasize the significant roles of genes associated with the glycolate and glyoxylate degradation I pathway in the metabolic utilization of ethylene glycol, an aspect frequently overlooked in previous research. [ABSTRACT FROM AUTHOR]

Background: Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus. Objective: In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCIDmethod, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ. Results: In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities. [ABSTRACT FROM AUTHOR]

Field-programmable gate arrays (FPGAs) offer the inherent ability to reconfigure at runtime, making them ideal for applications such as data centers, cloud computing, and edge computing. This reconfiguration, often achieved through remote access, enables efficient resource utilization but also introduces critical security vulnerabilities. An adversary could exploit this access to insert a dormant hardware trojan (HT) into the configuration bitstream, bypassing conventional security and verification measures. To address this security threat, we propose a supervised learning approach using deep recurrent neural networks (RNNs) for HT detection within FPGA configuration bitstreams. We explore two RNN architectures: basic RNN and long short-term memory (LSTM) networks. Our proposed method analyzes bitstream patterns, to identify anomalies indicative of malicious modifications. We evaluated the effectiveness on ISCAS 85 benchmark circuits of varying sizes and topologies, implemented on a Xilinx Artix-7 FPGA. The experimental results revealed that the basic RNN model showed lower accuracy in identifying HT-compromised bitstreams for most circuits. In contrast, the LSTM model achieved a significantly higher average accuracy of 93.5%. These results demonstrate that the LSTM model is more successful for HT detection in FPGA bitstreams. This research paves the way for using RNN architectures for HT detection in FPGAs, eliminating the need for time-consuming and resource-intensive reverse engineering or performance-degrading bitstream conversions. [ABSTRACT FROM AUTHOR]

This study reviews methods for detecting fracture closure pressure in both unconventional and conventional reservoirs using mathematical models and fluid flow equations. It evaluates techniques such as the Nolte method, tangent method, and compliance method. The investigation relies on observing changes in fluid flow regimes from preclosure to postclosure using fluid flow equations to examine the postclosure flow regime effect on the G function. Reverse calculations model pressure decline across synthesized flow regimes, facilitating a detailed investigation of the closure process. The analysis reveals that the tangent method is sensitive to postclosure fluid flow, while the compliance method is less effective in reservoirs with significant tortuosity or natural fractures. This paper recommends assessing natural fractures' characteristics and permeability to identify the source of leak-off before selecting a technique. It proposes integrating various methods to comprehensively understand subsurface formations, combining their strengths for accurate fracture closure identification and a better understanding of subsurface formations. The new proposed workflow employs the continuous wavelet transform (CWT) technique for fracture closure detection, avoiding physical model preassumptions or simplifications to confirm the results. This approach offers guidance on selecting appropriate methods by integrating different techniques. [ABSTRACT FROM AUTHOR]

In sheet metal forming, the interaction between virtual models and the real world remains challenging. Process simulations can exhibit significant errors, and reliable measurements are often scarce during early production stages. This study presents a hybrid twin framework that systematically unifies computer-aided design, simulation, and measurement data in an adaptive manner. Central to this framework is a reverse engineering algorithm that reconstructs and transforms the geometry of deep-drawn components from optical scan data into B-spline surfaces. The algorithm demonstrated high precision, indicating its suitability for process control and geometric analysis. The hybrid twin framework integrates virtual data from simulations and real-world data, as evidenced by a sensor concept for inline surface measurement. The framework ensures robust and redundant measurement concepts by estimating complete geometries from a few systematically preselected measuring points. This adaptive approach permits continuous updates and extensions to the database, accommodating both sparse inline signals and offline inspection data. This framework provides a conceptual model for integrating direct feedback interactions between virtual and physical environments, thereby enhancing the precision of analytical and predictive models in sheet metal forming processes. [ABSTRACT FROM AUTHOR]

The article discusses the advancements in brain-machine interfaces (BMIs) and reverse engineering the brain, highlighting the convergence of neuroscience, biotechnology, and artificial intelligence. BMIs facilitate direct interaction between the human brain and external technological equipment, with applications in healthcare, robotics, and personal performance improvement. Reverse engineering the brain aims to decipher its complex pathways for potential interventions in disorders like Alzheimer's and Parkinson's. The ethical implications of these technologies, including privacy concerns and the potential for exploitation, are also addressed in the article. [Extracted from the article]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid adoption of mobile banking applications has raised significant concerns about their security vulnerabilities. This study presents a comparative vulnerability analysis of mobile banking applications from Thai and non-Thai banks, utilising the OWASP Mobile Top 10 framework. Nine mobile banking applications (five Thai and four non-Thai) were assessed using three vulnerability detection tools: AndroBugs, MobSF, and QARK. The results showed that both Thai and non-Thai mobile banking applications had vulnerabilities across multiple OWASP Mobile Top 10 categories, with reverse engineering, code tampering, and insufficient cryptography being the most common. Statistical analysis revealed that Thai banking applications exhibited significantly more vulnerabilities compared to non-Thai banking applications. In the context of vulnerability detection tools, AndroBugs and QARK proved more effective in detecting vulnerabilities compared to MobSF. Additionally, the study highlights critical security challenges in mobile banking applications, particularly for Thai banks, and emphasises the need for enhanced security measures. The findings also show the importance of using multiple assessment tools for comprehensive security evaluation and suggest potential areas for improvement in mobile banking applications. [ABSTRACT FROM AUTHOR]

China sees Iran as a crucial power with significant influence over the security landscape of the Middle East. Despite being sensitive to regional and global dynamics surrounding Iran, China cannot ignore Tehran's strategic significance. Yet, their bilateral relations are significantly influenced by the regional security environment. China has consistently prioritized its ties with the United States over Iran and has generally adhered to UN regulations concerning Iran. China's sensitivity to Arab and regional interests in the Middle East significantly impacts the China–Iran arms relationship. These concerns significantly restricted the flow of Chinese arms to Iran since 2005, with UN sanctions ultimately sealing the cessation of arms flow. [ABSTRACT FROM AUTHOR]

Cowper–Symonds parameters were estimated for the complex-phase high-strength steel with a commercial name of SZBS800. The parameter estimation was based on a series of conventional tensile tests and unconventional high-strain rate experiments. The parameters were estimated using a reverse engineering approach. LS-Dyna was used for numerical simulations, and the material's response was modelled using a piece-wise linear plasticity model with a visco-plastic formulation of the Cowper–Symonds material model. A multi-criteria cost function was defined and applied to obtain a response function for the parameters p and C. The cost function was modelled with a response surface, and the optimal parameters were estimated using a real-valued genetic algorithm. The main novelty and innovation of this article is the definition of a cost function that measures a deviation between the deformed geometry of the flat plate-like specimens and the results of the numerical simulations. The results are compared to the relevant literature. A critical evaluation of our results and references is another novelty of this article. [ABSTRACT FROM AUTHOR]

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To investigate the fabric performance of tight tops and the influence of clothing pressure on the relative slip between clothing and the human body, a simulation model based on finite element analysis is proposed. This model simulates the relative slip distance under dynamic conditions. Initially, a three-dimensional model for numerical simulation was established through reverse engineering. Subsequently, material properties and boundary conditions were defined. Finally, the model was analyzed using ANSYS Workbench, and the obtained results were compared with the stretch ratio of the skin on the human back. The results indicate that, under constant conditions, the Young's modulus of the fabric has a greater influence on the slip and shows a significant positive correlation, with correlation coefficients (R) all exceeding 0.99. The impact of clothing pressure on sliding is relatively small, and the sliding distance decreases with an increase in clothing pressure. In addition, it has been demonstrated that there is a significant correlation (p < 0.05) between the relative sliding distance of clothing and the stretch ratio of the skin. The study could provide a helpful guideline for the design of smart clothing, and it can provide valuable insights for various applications, such as sportswear and medical apparel. [ABSTRACT FROM AUTHOR]

In this work, cam profiles of a camshaft are measured by a roundness measurement machine. First, the measured cam profiles are transformed into their actual sizes, based on the measurement of the bearing locations. The 8 cams of the camshaft are of two types. From the 4–4 cam profiles of each type an approximate profile is created by fitting them to one another using their base circles as reference, and calculating the center of gravity of the cumulative points of these fitted profiles corresponding to an arc segment.

The accurate description of anisotropic plastic deformation is key to accurately predicting the stamping forming of metal sheets. The anisotropic yield criterion, when based on the assumption of isotropic hardening, often leads to significant inaccuracies. To address this issue, this paper observes the anisotropic hardening phenomenon through non-associated flow rules and evaluates the anisotropy of 7075-O aluminum alloy. Through a series of tensile tests, we determined the mechanical properties of 7075-O aluminum alloy in three distinct orientations. To describe the metal hardening behavior, we employed the Swift-Voce hardening criterion. From the hardening curves in three different directions, it was found that AA7075-O exhibits plastic anisotropy. Based on the VUMAT subroutine, finite element simulation of AA7075-O tensile tests was conducted through Abaqus. Compared with the Hill48 model, it was found that the simulated values of the S–Y2009 anisotropic hardening model have a higher degree of agreement with the experimental curves. The S–Y2009 anisotropic hardening model was adopted to predict the earing behavior of AA7075-O during circular cup deep drawing. The root mean square error between the predicted values of the S–Y2009 model and the experimental values was only 0.1795, which is far smaller than that of the Hill48 yield model. Therefore, the SY2009 model has important guiding significance for the stamping forming of metal sheets.

A significant part of the compressor fleet in Russia consists of foreign-made centrifugal and screw compressors, which, with operating time and depreciation, require repair according to regulations or modernization due to changed technological operating conditions. The supply of spare parts needed for repairs has become more complicated due to the rise in cost or lack of logistics. This leads to the problem of maintaining the compressor equipment in working order or its downtime. The manufacture of spare parts is problematic due to the lack of design documentation from operating organizations. The offer of reverse engineering services for spare parts and compressor assemblies by specialists of JSC «NIIturbocompressor n. a. V. B. Shnepp» makes it possible to improve and solve the problem of maintaining the effective condition of foreign equipment. At the same time, the article describes the problems that arise during reverse engineering of rotors of centrifugal and screw compressors, the spatial profile of impellers and fluid film bearings. The main points contributing to the successful completion of reverse engineering tasks are indicated, the accuracy of 3D scanning is determined, and methods for setting nominal dimensions and deviation tolerances are presented.

Abstract Reverse engineering, a process of extracting information or knowledge from existing objects or systems, has gained significant attention in various fields, including medicine. This article presents a comprehensive literature review and a proof of concept on the application of reverse engineering in the medical field. The review particularly focuses on the reverse engineering process, available technologies, and their specific relevance to the medical domain. Various imaging techniques, such as computed tomography and magnetic resonance imaging, are discussed in respect of their integration with reverse engineering methodologies. Furthermore, the article explores the wide range of medical applications facilitated by reverse engineering, including prosthetics, implants, tissue engineering, and surgical planning. The potential of reverse engineering to enhance personalized medicine and patient-specific treatments is highlighted. A detailed proof of concept focusing on femur reconstruction is a significant component of the article. The proof of concept showcases the practical implementation of reverse engineering techniques to assist in designing and manufacturing precise custom-made implants and bone reconstruction. It emphasizes the integration of patient-specific anatomical data obtained through imaging technologies and the subsequent utilization of reverse engineering processes for anatomical reconstruction (solid modeling). Overall, this article provides an extensive overview of reverse engineering in medical applications, incorporating a literature review and a case study. The findings highlight reverse engineering’s potential to advance medical practices, improve patient outcomes, and foster personalized treatments. The review emphasizes the reverse engineering process, available technologies, and their specific relevance to the medical field, as well as their potential and effectiveness in advancing medical practices.

The bar properties (elastic modulus, density, Poisson's ratio, and sound speed) necessary for exploiting bar impact tests were calibrated via two methods called the 1D method (which combines the iterative dispersion correction with 1D approximation) and 3D method (which resorts to the simulation-based reverse engineering of the strain profile in a circular 3D bar). The two methods cross-verified one another for a maraging steel bar with 19.1 mm in diameter. This paper also considers the method of calibrating measured strain and finally presents a method of determining the impact velocity with reference to a reliably measured strain profile for a bar calibrated via the 1D method. [ABSTRACT FROM AUTHOR]

For surgical craniofacial reconstruction, preoperative planning may be limited by missing 3D skeletal geometry. In forensic sciences, 'reconstruction' models the 3D facial structure from skull geometries using soft-tissue depth mapping. This work investigates 'reverse engineering' the forensics' TD Morpheus model to infer the bony shape from 3D facial surfaces by subtracting tissue depths inwards along the normal vectors. This approach using Euclidean tissue depths successfully estimated the upper and outermost skeletal regions (i.e. frontal, zygoma, and nasal bones) in 24 head CT scans, but concave skeletal surfaces were inaccurately evaluated where the face is convex yielding misshapen anatomy around the orbits and zygomatic arches. A perpendicular tissue depth algorithm was developed to probe inwards along the face's normal vectors until contacting bone, demonstrating superior performance to the Euclidean depth approach. Accurate regional tissue depths achievable with this approach may provide a useful bridge to connect the 3D face and underlying skull geometry, with the potential for application in craniofacial reconstruction. [ABSTRACT FROM AUTHOR]

This research article explains the product design and development optimization of multi feature engine carbon cleaning maintenance tools. The implementation of the Reverse Engineering method was integrated with VDI 2222 in the research process. The results of the optimal design and development of this tool obtained a component structure that was able to meet 14 types of consumer needs with a total tool cost of IDR 1.137.900 with a total tool weight of 6.9 kg. The results of the load test simulation concluded that the housing case was able to withstand the overall weight of the components that make up the tool. Depending on the liquid chemical used, the results of tool tests on a limited scale have proven to be able in save fuel consumption by ±12.5% per hour with engine workload which can also be reduced by 3,14%.

Surface reconstruction plays a pivotal role in various fields, including reverse engineering, and oil and gas exploration. However, errors in available data and insufficient surface morphology information often introduce uncertainty into the reconstruction. It is crucial to accurately characterize and visualize the uncertainty in surface reconstruction for risk analysis and planning further data collection. To this end, this paper proposes an uncertainty characterization method based on twin support vector regression. First, various modeling data are effectively integrated and the information contained in the high-confidence sample is efficiently utilized through the uncertainty interval generated by quantiles and upper/lower bound constraints. Second, well-path points are incorporated by imposing inequality constraints on the corresponding prediction points. Finally, in order to reduce computation time, the problem of uncertainty characterization is formulated as two smaller-scale quadratic programming. The results obtained from a real fault dataset and a synthetic dataset validate the effectiveness of the proposed method. When well data are available, the generated uncertainty envelopes are constrained by well data, which can partially mitigate reconstruction uncertainties. [ABSTRACT FROM AUTHOR]

The investigation involves a comparative analysis of linear bearings fabricated by two different manufacturers. One manufacturer is known for producing original, high-quality bearings that come with a higher price tag, while the other provides more affordable alternatives through reverse engineering of the original bearings. The primary aim is to understand the practical behavior of these replicated bearings compared to the original ones and determine whether the cost-effective replicated bearings can be chosen over the more expensive originals for specific applications. This requires assessing their performance under various operational conditions (speeds of 6000, 8000, and 10,000 mm/min, and loads of 500 and 1050 g) and evaluating the impact on functionality in real-world scenarios. Time-domain statistical techniques are incorporated to quantify the differences between these bearings. Statistical scalar indicators, such as root-mean-square value, crest factor, peak, skewness, and kurtosis, are used to characterize the health status of the bearings, with the monitored parameter being the vibration signal. It has been observed that these indicators are consistently higher for the replicated bearings compared to the original ones, the defects are introduced may be during the manufacturing process or utilization low-quality materials for production of replicated bearings. The analysis, therefore, highlights notable deviations and potential issues with the replicated bearings, reinforcing the superior performance and stability of the original bearings despite their higher cost. Thus, original bearings are better suited for applications. [ABSTRACT FROM AUTHOR]

The number of applications of low-power wide-area networks (LPWANs) has been growing quite considerably in the past few years and so has the number of protocol stacks. Despite this fact, there is still no fully open LPWAN protocol stack available to the public, which limits the flexibility and ease of integration of the existing ones. The closest to being fully open is LoRa; however, only its medium access control (MAC) layer, known as LoRaWAN, is open and its physical and logical link control layers, also known as LoRa PHY, are still only partially understood. In this paper, the essential missing aspects of LoRa PHY are not only reverse engineered, but also, a new design of the transceiver and its sub-components are proposed and implemented in a modular and flexible way using GNU Radio. Finally, some examples of applications of both the transceiver and its components, which are made to be run in a simple setup by using cheap and widely available off-the-shelf hardware, are given to show how the library can be used and extended. [ABSTRACT FROM AUTHOR]