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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]

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]

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 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]

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]

This case report presents a virtual treatment simulation of the orthodontic treatment and surgery-first orthognathic surgery employed to treat a patient with a repaired unilateral cleft lip and alveolus with Class III malocclusion and lower third facial asymmetry. The patient exhibited a negative overjet of 9 mm, a missing lower right second premolar, and a 5 mm gap between the upper right central and lateral incisors with midline discrepancy. The three-dimensional virtual planning began with virtual pre-surgical orthodontics, followed by the positioning of the facial bones and teeth in their ideal aesthetic and functional positions. The sequence of steps needed to achieve this outcome was then reverse-engineered and recorded using multiplatform Nemostudio software (Nemotec, Madrid, Spain), which facilitated both surgical and orthodontic planning. The treatment included a two-piece segmental maxillary osteotomy for dental space closure, a LeFort I maxillary advancement, and a mandibular setback with bilateral sagittal split osteotomy to correct the skeletal underbite and asymmetry. A novel approach was employed by pre-treating the patient for orthognathic surgeries at age 11, seven years prior to the surgery. This early phase of orthodontic treatment aligned the patient's teeth and established the dental arch form. The positions of the teeth were maintained with retainers, eliminating the need for pre-surgical orthodontics later. This early phase of treatment significantly reduced the treatment time. The use of software to predict all the necessary steps for surgery and post-surgical orthodontic tooth movements made this approach possible. Multi-step virtual planning can be a powerful tool for analyzing complex craniofacial problems that require multidisciplinary care, such as cleft lip and/or palate. [ABSTRACT FROM AUTHOR]

Algorithmic radicalization is the idea that algorithms used by social media platforms push people down digital "rabbit holes" by framing personal online activity. Algorithms control what people see and when they see it and learn from their past activities. As such, people gradually and subconsciously adopt the ideas presented to them by the rabbit hole down which they have been pushed. In this study, TikTok's role in fostering radicalized ideology is examined to offer a critical analysis of the state of radicalism and extremism on platforms. This study conducted an algorithm audit of the role of radicalizing information in social media by examining how TikTok's algorithms are being used to radicalize, polarize, and spread extremism and societal instability. The results revealed that the pathways through which users access far-right content are manifold and that a large portion of the content can be ascribed to platform recommendations through radicalization pipelines. Algorithms are not simple tools that offer personalized services but rather contributors to radicalism, societal violence, and polarization. Such personalization processes have been instrumental in how artificial intelligence (AI) has been deployed, designed, and used to the detrimental outcomes that it has generated. Thus, the generation and adoption of extreme content on TikTok are, by and large, not only a reflection of user inputs and interactions with the platform but also the platform's ability to slot users into specific categories and reinforce their ideas. [ABSTRACT FROM AUTHOR]

The development of hydroelectric technology and much of the "knowledge" on hydraulic phenomena derive from scale modeling and "bench" tests to improve machinery efficiency. The result of these experimental tests is mapping the so-called "hill chart", representing the "DNA" of a turbine model. Identifying the efficiency values as a function of the specific parameters of the flow and energy coefficient (which both identify the operating point) allows us to represent the complete behavior of a turbine in hydraulic similarity with the original model developed in the laboratory. The present work carries out a "reverse engineering" operation that leads to the definition of "an innovative research model" that is relatively simple to use in every field. Thus, from the experimental survey of the degree of efficiency of several prototypes of machines deriving from the same starting model, the hill chart of the hydraulic profile used is reconstructed. The "mapping" of all the characteristic quantities of the machine, together with the physical parameters of the regulating organs of a four-blade Kaplan turbine model, also made it possible to complete the process, allowing to identify not only the iso-efficiency regions but also the curves relating to the trend of the angle of the impeller blades, the specific opening of the distributor, and the identification of critical areas of cavitation. The development of the hill chart was made possible by investigating the behavior of 33 actual prototypes and 46 characteristic curves derived from the same reference model based on practical experiments for finding the optimal blade distributor "setup curve". To complete this, theoretical characteristic curves of "not physically realized" prototypes were also mapped, allowing us to complete the regions comprising the diagram. The study of the unified hill charts found in previous documentation of the most famous manufacturers was of great help. Finally, the validation of the "proposed procedure" was obtained through the experimental survey of the actual efficiency of the new prototype based on the theoretical values defined in the design phase on the chart obtained with the method described. [ABSTRACT FROM AUTHOR]

Malware attacks are posing a significant threat to national security, cooperate network, and public endpoint security. Identifying the Advanced Persistent Threat (APT) groups behind the attacks and grouping their activities into attack campaigns help security investigators trace their activities thus providing better security protections against future attacks. Existing Cyber Threat Intelligent (CTI) components mainly focus on malware family identification and behavior characterization, which cannot solve the APT tracking problem: while APT tracking needs one to link malware binaries of multiple families to a single threat actor, these behavior or function-based techniques are tightened up to a specific attack technique and would fail on connecting different families. Binary Authorship Attribution (AA) solutions could discriminate against threat actors based on their stylometric traits. However, AA solutions assume that the author of a binary is within a fixed candidate author set. However, real-world malware binaries may be created by a new unknown threat actor. To address this research gap, we propose VeriBin for the Binary Authorship Verification (BAV) problem. VeriBin is a novel adversarial neural network that extracts functionality-agnostic style representations from assembly code for the AV task. The extracted style representations can be visualized and are explainable with VeriBin's multi-head attention mechanism. We benchmark VeriBin with state-of-the-art coding style representations on a standard dataset and a recent malware-APT dataset. Given two anonymous binaries of out-of-sample authors, VeriBin can accurately determine whether they belong to the same author or not. VeriBin is resilient to compiler optimizations and robust against malware family variants. [ABSTRACT FROM AUTHOR]

Prosthetic rehabilitation refers to the process of restoring or improving a patient's physical function, appearance, and quality of life through the use of prosthetic devices. Providing cost-effective prosthetic rehabilitation for transfemoral amputees remains a challenging task. Traditional prosthetic fabrication methods, such as casting and lamination, are expensive and prone to fitting errors. Moreover, these methods are time-consuming, labor-intensive, and lack data-driven precision, heavily relying on artisanal expertise for socket fitting. However, recent advancements in rapid prototyping technologies, in conjunction with Reverse Engineering through 3D scanners, offer a promising alternative. This scoping review explores the current state of knowledge in transfemoral prosthesis fabrication, with a focus on additive manufacturing (AM), RE, and computer-aided manufacturing (CAM) methods. A systematic database search on PubMed and LENS.ORG was conducted, followed by a thorough analysis of selected articles. The review highlights major research areas, including pressure/stress/strain distribution analysis at the socket-stump interface, gait cycle assessment, and developments in 3D printing of knee and ankle joints. Critical appraisals of each article provide valuable insights for future research and advancements in affordable prosthetic rehabilitation. In conclusion, the utilization of AM, RE, and CAM methods for TFP fabrication is in its infancy but holds immense potential for improving cost-effective solutions in this domain. [ABSTRACT FROM AUTHOR]

As technology advances and develops, the need for strong and simple authentication mechanisms that can help protect data intensifies. The contemporary approach to giving access control is through graphical passwords comprising images, patterns, or graphical items. The objective of this review was to determine the documented security risks that are related to the use of graphical passwords, together with the measures that have been taken to prevent them. The review was intended to present an extensive literature review of the subject matter on graphical password protection and to point toward potential future research directions. Many attacks, such as shoulder surfing attacks, SQL injection attacks, and spyware attacks, can easily exploit the graphical password scheme, which is one of the most widely used. To counter these security threats, several measures have been suggested, but none of the security attacks can be completely overcome. Each of the proposed measures has its pros and cons. This study begins by elucidating some of the graphical password schemes studied between 2012 and 2023, delving into potential threats and defense mechanisms associated with these schemes. Following a thorough identification and selection process, five of the reviewed papers explain the threat of shoulder surfing and spyware attacks on graphical password schemes, while two explain the threat of brute force attacks. One paper focuses on dictionary attacks, while four other papers address social engineering, SQL injection attacks, and guessing attacks as potential threats to graphical password schemes. In addition, the papers recognize other forms of attacks, such as video recording attacks, filtering attacks, reverse engineering attacks, multiple observation attacks, key/mouse logger attacks, insider attacks, computer vision attacks, image gallery attacks, sonar attacks, reply attacks, data interception attacks, and histogram manipulation attacks. These attacks are examined in three, three, eight, one, four, one, one, one, one, one, one, and one papers, respectively. Moreover, out of all such countermeasures, most of them are based on three categories—randomization, obfuscation, and password space complexity—which are the most commonly employed strategies for improving graphical password schemes. [ABSTRACT FROM AUTHOR]

Methods have been developed for monitoring and diagnosing the degree of wear and loss of the geometric shape of surfaces. A selection of technological routes was carried out for the complex restoration of working surfaces of large-sized products using electrophysical energy sources during surfacing of powders and wires. It has been shown that the cost of major repairs when restoring a screw shaft is, on average, two times lower than manufacturing a new one. It is important to monitor regularly the degree of wear of shaft turns and their local restoration using surfacing materials and technologies. [ABSTRACT FROM AUTHOR]

Classification is mandatory for all seagoing ships engaged in international trade worldwide, with regulations that require the strength of the hull construction to be confirmed. Upon completion of the ship's construction or during the execution of repair works in shipyards (reconversion works, modification of panels, replacement of equipment, etc.), the strength structure of the ship may be affected and the classification society must certify that it meets the requirements. Replacing old equipment sometimes implies some variations in overall dimensions, which may impose some changes (adjustments) on the layout of structural panel elements. This study applied the Finite Element Method (FEM) considering the von Mises theory in a case study of a beam modification with the support of the ANSYS software. This study used the reverse engineering method to investigate an already implemented solution. [ABSTRACT FROM AUTHOR]

This paper presents the research results on the strength of materials and power of the Toyota 3C engine when changing the structure and number of blades of the compressor wheel in the turbocharger. 3D models of different compressor wheels were created using reverse engineering and then simulated in the ANSYS environment with turbine shaft rotation speeds of 10,000, 15,000, and 20,000 rpm, respectively, to examine the strength of the compression wheel materials. To evaluate engine power, compressor wheels were machined on a 5-axis CNC milling machine. The MP 100S specialized test bed was used to perform experiments and compare engine power when using the original and alternative compressor wheels of the CT9 turbocharger. The compressor wheels were made of aluminum alloy, with a structure and number of blades selected to ensure durability when working. The CT9 turbocharger has a four-pair blade compressor wheel that consistently delivers higher engine power than in other cases. [ABSTRACT FROM AUTHOR]

The widespread use of smartphones worldwide has led to a corresponding rise in the number of mobile applications available for Android devices. These apps offer users convenient ways to perform various daily tasks, but their proliferation has also created an environment in which attackers can steal sensitive information. Insecure options employed by many app developers create vulnerabilities that can be exploited by attackers to gain access to most smartphones. While existing methods can detect malware during app installation, they do not sufficiently address post-installation attacks, such as those resulting from fake apps or Man-in-the-Disk (MitD) attacks. To address this issue, the current study conducted research on post-installation attacks, including data leakage, malware injection, repackaging, reverse engineering, privilege escalation, and UI spoofing. MitD attacks are particularly challenging to counter, so, to mitigate this risk, the Post-Installation App Detection Method is proposed to monitor and regulate sensitive information flow and prevent MitD attacks. [ABSTRACT FROM AUTHOR]

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.

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.

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]

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 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%.

This paper presents the process of digital data flow in RE/CAD/RP/CAI systems to develop models for planning surgical procedures in the craniofacial area. At the first RE modeling stage, digital data processing, segmentation, and the reconstruction of the geometry of the anatomical structures were performed. During the CAD modeling stage, three different concepts were utilized. The first concept was used to create a tool that could mold the geometry of the cranial vault. The second concept was created to prepare a prototype implant that would complement the anterior part of the mandibular geometry. And finally, the third concept was used to design a customized prototype surgical plate that would match the mandibular geometry accurately. Physical models were made using a rapid prototyping technique. A Bambu Lab X1 3D printer was used for this purpose. The process of geometric accuracy evaluation was carried out on manufactured prototypes of surgical plates made of ABS+, CPE, PLA+, and PETG material. In the geometric accuracy evaluation process, the smallest deviation values were obtained for the ABS plus material, within a tolerance of ±0.1 mm, and the largest were obtained for CPE (±0.2 mm) and PLA plus (±0.18 mm). In terms of the surface roughness evaluation, the highest value of the Sa parameter was obtained for the PLA plus material, which was 4.15 µm, and the lowest was obtained for the CPE material, equal to 3.62 µm. The knowledge of the flow of digital data and the identification of factors determining the accuracy of mapping the geometry of anatomical structures allowed for the development of a procedure that improves the modeling and manufacturing of anatomical structures within the craniofacial region.

As a person ages, usually after age 50, knee joint bone damage caused by osteoarthritis can occur. It is to cause pain in the knee joint so that its function moves abnormally. The knee joint bone damage could be replaced with a prosthesis (Total Knee Replacement, TKR) to restore the knee joint's normal movement function. However, orthopaedic doctors sometimes have difficulty choosing standard implant designs available for patients' knees when performing TKR surgery, especially for Asian patients, including Indonesians. It is due to the geometry of the standard implant being different from the patient's knee anatomical shape. As a result, postoperative pain reactions can occur. Therefore, this study is to customize the TKR femoral implant's design according to the knee bone's geometry, especially in Indonesian patients. Reverse Engineering (RE) technology assisted by the CT-Scan and the boundary surface technique was applied for reconstructing the patient's knee bone into a femoral implant. The application of RE is initially, the patient's knee bone sample was scanned with Computer Tomography (CT) Scan. Then, the femur implant model was designed on the surface of the patient's knee bone using the boundary surface method. The 3D model of the TKR femoral implant was successfully designed to match the geometry of the patient's femur bone. It will be a reference for designing other TKR implants, namely the tibia component.