Potential Failure Points Research Articles

Research on the Stability of Lining Structures Under Different Fault Moments Based on FDM-DEM

Currently, research on employing finite difference method and discrete element method (FDM-DEM) coupling to assess the stability of tunnel lining structures is limited. This study utilized the FDM-DEM coupling approach, with the F2 fault of the East Tianshan Tunnel as a case study, to develop a numerical model in conjunction with PFC3D 6.0 and FLAC3D 6.0 software. We conducted a comprehensive analysis of the displacement deformation and crack progression of the tunnel lining structure under varying dislocation momentum conditions, unveiling the underlying mechanisms. The findings indicated that as the dislocation increased, the extent of damage to the vault intensified, and the particle contact force within the tunnel lining shifted from compression to tension, significantly contributing to the crack formation. Fault dislocation influenced the gradual expansion of cracks from the vault to the spandrel and arch waist, with the crack width increasing alongside the rising dislocation momentum. In particular, under substantial dislocation momentum, the overall stability of the tunnel lining was markedly diminished. The safety factor at the tunnel section declined progressively as the dislocation momentum escalated, with values of 2.53, 2.49, 2.43, 2.39, and 2.32 corresponding to dislocation momenta of 0.01 m, 0.05 m, 0.1 m, 0.15 m, and 0.2 m, respectively. This research offers valuable insights and a reference framework for investigating the stability of tunnel lining structures in proximity to fault dislocations, pinpointing potential failure points, and bolstering the structural integrity of tunnels.

Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization

The application of Internet of Things technology provides a new opportunity for the fault diagnosis and maintenance of power system. This study aims to explore how to improve the fault diagnosis ability of power system through Internet of Things technology, and realize the efficient utilization of heat energy, so as to achieve the goal of intelligent maintenance. Based on the analysis of current power system operation status, this paper determines the key role of thermal energy management in improving system operation efficiency and reducing energy consumption. It then uses iot sensors and data analytics to monitor heat flow and loss in the power system in real time, identifying potential points of failure through big data. In order to realize intelligent maintenance, this paper designs a fault prediction model based on thermal energy optimization, combined with machine learning algorithm, to further improve the accuracy of fault diagnosis. The experimental results show that the fault diagnosis method combined with the Internet of Things technology can significantly reduce the fault incidence and optimize the efficiency of heat energy use. By applying this model, the overall operating cost of the power system is reduced and the maintenance efficiency is improved.

Distributed Identity Authentication with Lenstra-Lenstra-Lovász Algorithm-Ciphertext Policy Attribute-Based Encryption from Lattices: An Efficient Approach Based on Ring Learning with Errors Problem.

In recent years, research on attribute-based encryption (ABE) has expanded into the quantum domain. Because a traditional single authority can cause the potential single point of failure, an improved lattice-based quantum-resistant identity authentication and policy attribute encryption scheme is proposed, in which the generation of random values is optimized by adjusting parameters in the Gaussian sampling algorithm to improve overall performance. Additionally, in the key generation phase, attributes are processed according to their shared nature, which reduces the computational overhead of the authorization authority. In the decryption phase, the basis transformation of the Lenstra-Lenstra-Lovász (LLL) lattice reduction algorithm is utilized to rapidly convert shared matrices into the shortest vector form, which can reduce the computational cost of linear space checks. The experimental results demonstrate that the proposed method not only improves efficiency but also enhances security compared with related schemes.

The effect of Polyolefin elastomer on the 3D printing properties of Acrylonitrile butadiene styrene, Polyethylene, and Polypropylene

This study investigates the influence of incorporating 30 wt.% polyolefin elastomer (POE) on the physical, mechanical, and microstructural properties of 3D printed three widely used thermoplastic: acrylonitrile butadiene styrene (ABS), low density polyethylene (LDPE), and polypropylene (PP). Three ABS-POE, LDPE-POE, and PP-POE blends were prepared by melt mixing method and printed by direct granule-based material extrusion, and finally the printability, microstructure, thermal, and mechanical properties aiming for potential usage in various applications were investigated. Dynamic Mechanical Thermal Analysis (DMTA) results revealed a notable shift in the glass to rubber phase to a higher temperature range and an increase in the glass transition temperature due to the presence of POE elastomers. Mechanical properties of the 3D printed samples were meticulously examined and compared with prior research. All blend samples containing 30 wt.% POE exhibited significantly enhanced ductility, attributed to aligned polymer chain reactions. ABS-POE samples demonstrated superior mechanical properties compared to PP-POE and LDPE-POE samples, likely attributed to fewer potential failure points, as evidenced by Scanning Electron Microscope (SEM) analysis of fractured cross-sections of 3D-printed samples immersed in liquid nitrogen. Additionally, 3D-printed samples with combined infill orientations (0° and 90°) were generated and subjected to tensile strength testing. Furthermore, samples 3D-printed specimens by honeycomb filling pattern for compression tests were included in the study. Microstructure analyses identified common 3D printing defects responsible for failure modes in the printed samples.

A Q-band frequency tunable Doppler backscattering (DBS) system for pedestal and scrape-off layer density fluctuation and flow measurements in the DIII-D tokamak.

We present the design and laboratory tests for a new Q-band frequency tunable Doppler backscattering (DBS) system suitable for probing poloidal wavenumber kñ = 6-8cm-1 density fluctuations and their flow velocities in the pedestal and scape-off layer (SOL) of the DIII-D tokamak. This system will provide new measurements in the increasingly important and under-diagnosed far pedestal and SOL plasma regions. These results are important for experimental transport studies and necessary for the validation of transport models, both of which are important to fusion energy research. The use of a single tunable frequency reduces the complexity and potential failure points as compared to a multichannel system. This new system utilizes a 33-50GHz tunable source and will be integrated into the current V-band DBS in DIII-D using a broadband Q- and V-band multiplexer. A full-scale mockup of the quasi-optical system was used to test and optimize the performance. These tests include beam profile measurements at different distances (and angles) from a paraboloidal focusing and steering mirror. The measurements cover the full frequency range 33-75GHz of the integrated/combined Q-V band DBS system and target a large radial coverage of the low-field side of the plasma from ρ = 1.1 to ρ = 0.5, where ρ is the normalized flux surface radial coordinate.

A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation

Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible Service Process Creation and Recommendation (SPCR-CA), which incorporates blockchain, Recurrent Neural Networks (RNNs), and a Skip-Gram model holistically to enhance the security, efficiency, and quality of service process creation and recommendation. Specifically, the blockchain establishes a trusted service provision environment, ensuring transparent and secure transactions between services and mitigating the risk of tampering. The RNN trains responsible service processes, contextualizing service components and producing coherent recommendations of linkage components. The Skip-Gram model trains responsible user-service process records, generating semantic vectors that facilitate the recommendation of similar service processes to users. Experiments using the Programmable-Web dataset demonstrate the superiority of the SPCR-CA framework to existing benchmarks in precision and recall. The proposed framework enhances the reliability, efficiency, and quality of service process creation and recommendation, enabling users to create responsible and tailored service processes. The SPCR-CA framework offers promising potential to provide users with secure and user-centric service creation and recommendation capabilities.

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

The present research develops and verifies a simpler numerical approach for analyzing the thermal transient state of rehabilitated concrete beams reinforced with various types of FRP (fiber-reinforced polymer) subjected to high temperatures and specifically built as under-reinforced concrete beams. This approach offers a straightforward, efficient, and exact instrument for numerical analysis. The proposed analytical technique has been validated by load-displacement curves and cross-section temperature data, indicating its dependability and practicality. Subsequently, the validated approach was used to examine the impact of significant variables on the outcome and restoration of FRP-reinforced concrete beams at high temperatures. The methodology gives the Comparing conventional and CFRP, GFRP, AFRP reinforced beams using beam, truss, and shell elements. Thermal and UDL loads were applied, mesh at 25 mm × 25 mm. Transient analysis contrasts performance via displacement and temperature. The temperature versus displacement curve shows the FRP comparisons. Identifying the critical temperature before failure is crucial, emphasizing the curve’s significance in assessing structural performance and potential failure points. Nodal temperatures ranged 939 °C–963 °C (rehabilitated) versus 958 °C (conventional). 200 °C difference affects thermal boundary conditions for structural analysis and Conventional peaks at 320 °C, while AFRP, GFRP, and CFRP reach 358 °C, 385 °C, and 390 °C respectively. CFRP lasts 2400 min. Neural network models demonstrate effective generalizability, enabling satisfactory predictions of RC beam rehabilitation with CFRP laminates within the study’s parameter range.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

Decentralized Identity Authentication Mechanism: Integrating FIDO and Blockchain for Enhanced Security

FIDO (Fast Identity Online) is a set of network identity standards established by the FIDO Alliance. It employs a framework based on public key cryptography to facilitate multi-factor authentication (MFA) and biometric login, ensuring the robust protection of personal data associated with cloud accounts and ensuring the security of server-to-terminal device protocols during the login process. The FIDO Alliance has established three standards: FIDO Universal Second Factor (FIDO U2F), FIDO Universal Authentication Framework (FIDO UAF), and the Client to Authenticator Protocols (CTAP). The newer CTAP, also known as FIDO2, integrates passwordless login and two-factor authentication. Importantly, FIDO2’s support for major browsers enables users to authenticate their identities via FIDO2 across a broader range of platforms and devices, ushering in the era of passwordless authentication. In the FIDO2 framework, if a user’s device is stolen or compromised, then the private key may be compromised, and the public key stored on the FIDO2 server may be tampered with by attackers attempting to impersonate the user for identity authentication, posing a high risk to information security. Recognizing this, this study aims to propose a solution based on the FIDO2 framework, combined with blockchain technology and access control, called the FIDO2 blockchain architecture, to address existing security vulnerabilities in FIDO2. By leveraging the decentralized nature of the blockchain, the study addresses potential single points of failure in FIDO2 server centralized identity management systems, thereby enhancing system security and availability. Furthermore, the immutability of the blockchain ensures the integrity of public keys once securely stored on the chain, effectively reducing the risk of attackers impersonating user identities. Additionally, the study implements an access control mechanism to manage user permissions effectively, ensuring that only authorized users can access corresponding permissions and preventing unauthorized modifications and abuse. In addition to proposing practical solutions and steps, the study explains and addresses security concerns and conducts performance evaluations. Overall, this study brings higher levels of security and trustworthiness to FIDO2, providing a robust identity authentication solution.

Level Process Station Control Using ALE111 Communication Module

A noteworthy example of the technological evolution is the ALE111, a cutting-edge system equipped with advanced functionalities designed to monitor the status of field wireless devices. One of its key features is the implementation of bumpless output, a capability that ensures seamless transitions and adjustments in the control process. Remarkably, the ALE111 enables wireless process control, a capability traditionally associated with wired control systems utilizing 4-20 mA signal communication. This wireless paradigm not only introduces a new level of flexibility but also contributes to the overall enhancement of process control reliability. The ALE111 goes a step further by incorporating redundant configurations, a strategic approach that adds an extra layer of reliability to the system. By doing so, potential points of failure are mitigated, and the overall robustness of the process control system is substantially improved. Key Word: ALE111 Communication Module, Ethernet, Level Process Station, Vnet, Field Control Station ,HIS, Data Acquisition Card

Laboratory Experiments on Remediation of Landfill Leachate Contamination with Permeable Reactive Barriers (PRBs) of Reactive Media Derived from Waste

Groundwater at landfills is often contaminated with heavy metals. Permeable reactive barrier (PRB), a potential in-situ treatment system, was investigated in this study, using 'composite reactive media' (COM), primarily composed of waste materials, to remediate heavy metals in groundwater. The COM consisted of 40% Brick and Mortar Waste (BMW), 40% Biochar (BC), 10% Granular Activated Carbon (GAC), and 10% Zeolite, based on volume. In an up-flow column, the average removal efficiencies of Fe, Pb, Cu, As, Mn, Zn, and Cd were 70.9±2.7, 80.9±3.2, 72.2±1 .6, 76.3±2.5, 81.6±6.3, 88.0±2.0, and 67.3±4.6%, respectively. In a bench-scale COM-filled PRB (0.85 x 0.25 x 0.3 m), the average removal efficiencies of the same species were 71.4±1.3, 75.4±4.9, 67.4±4.3, 72.9±1.3, 83.6±7.6, 89.8±7.4, and 80.9±7.1%, respectively. Adsorption associated with fixation, and ion exchange could be the main treatment mechanisms. COM also has potential as a filtration medium in other treatment unit processes and to treat some other types of wastewater than landfill-leachate. Further, this use of COM could alleviate the burden of disposing of BMW waste, thereby addressing a significant issue in waste management. As the identification of potential failure points in reactive media is crucial, field-scale experiments are suggested to be conducted for further investigation.

THE IMPACT OF TECHNICAL SAFETY AND INTEGRITY VERIFICATION ON PROJECT DELIVERY AND ASSET PERFORMANCE

The oil and gas industry operates within a stringent regulatory framework and faces inherent risks associated with complex operations. Technical safety and integrity verification processes play a critical role in ensuring compliance with regulatory standards, minimizing risks, and enhancing asset performance. This review analyzes the influence of rigorous safety and integrity verification on project timelines, cost efficiency, and overall asset performance in the oil and gas industry. Safety and integrity verification processes encompass a comprehensive assessment of design, construction, and operational phases of oil and gas projects. These processes involve rigorous inspections, testing, and verification activities to ensure that assets meet regulatory requirements and industry standards. By adhering to these processes, operators can mitigate safety risks, prevent accidents, and safeguard human lives, as well as protect the environment. Moreover, rigorous safety and integrity verification contribute to efficient project delivery by identifying potential hazards and deficiencies early in the project lifecycle. Through proactive risk assessment and mitigation, operators can address issues promptly, thereby minimizing delays and cost overruns. Additionally, adherence to safety and integrity standards instills confidence among stakeholders, investors, and regulatory authorities, fostering trust and credibility in project execution. Furthermore, the impact of technical safety and integrity verification extends beyond project delivery to asset performance throughout its operational lifespan. Assets subjected to thorough safety and integrity verification processes exhibit higher reliability, availability, and operational efficiency. By identifying and addressing potential failure points, operators can prolong asset lifespan, optimize maintenance schedules, and reduce unplanned downtime. In conclusion, rigorous safety and integrity verification processes significantly influence project timelines, cost efficiency, and overall asset performance in the oil and gas industry. By prioritizing safety and integrity throughout the project lifecycle, operators can enhance operational excellence, mitigate risks, and achieve sustainable long-term success in the dynamic and challenging oil and gas landscape.
 Keywords: Impact, Technical Safety, Integrity Verification, Project Delivery, Asset Performance.

Method to Enable Reduced Sensor Capacitor Voltage Estimation in Modular Multilevel Converters

Bulk power applications such as shipping increasingly consider multilevel converter topologies such as modular multilevel converters (MMC), which offers the advantages of scalability, good power quality, and reconfigurability. The internal functioning of MMC requires complete knowledge of the capacitor voltages that make up their submodules meaning a large number of sensors are needed and thus a high number of potential points of failure exist. To increase reliability and reduce investment costs, state estimation techniques such as KALMAN filters have been employed to replace the physical sensors. Analytical techniques based on the knowledge of arm current, arm voltage, and submodule states have also been developed. These techniques exploit the fact that at an insertion index of 1, the arm voltage equals the capacitor voltage on the submodule which permits the estimation algorithm to refresh periodically with measured data thereby increasing the accuracy. This method requires a long refresher time, especially when many submodules are used per arm. In this study, we propose an improved analytical estimation by not only using unity insertion indices, but also exploiting transitions between two successive insertion indices. The study was carried out on a 4 submodule per arm MMC system. The estimated capacitor voltages were then compared with sensor-based voltage measurements confirming the validity of the proposed method. It was then integrated into a complete MMC controller including the inner controls such as circulating current and capacitor voltage balancing.

Performing FEM Analysis to Evaluate the Static and Thermal Characteristics of a Piston

This study presents a comprehensive Finite Element Method (FEM) analysis conducted using Fusion 360 software to evaluate the static and thermal characteristics of pistons fabricated from alumina and Aluminum 6061. The research aims to compare and contrast the performance of these two materials under typical engine operating conditions. The static analysis investigates stress distribution, deformation, and overall structural integrity of the pistons when subjected to mechanical forces and pressures experienced during engine operation. Simultaneously, the thermal analysis assesses temperature distribution and thermal stresses, providing insights into the pistons’ ability to withstand high temperatures. Detailed simulations were performed with realistic boundary conditions and material properties, allowing for a direct comparison of the performance of alumina, known for its high hardness and thermal stability, against Aluminum 6061, recognized for its lightweight and good thermal conductivity. The findings reveal distinct differences in stress handling, thermal management, and potential failure points between the two materials. This study offers critical insights into material selection and design optimization for enhancing piston performance and durability in modern engines.

The ATLAS Alarm Helper

The Detector Safety System is the last line of defence to protect the ATLAS detector against abnormal and potentially even unforeseen situations. It is designed to return the detector to a safe state based on predefined actions triggered by alarms which are triggered on their part by specific sets of conditions. Every alarm whether it results in an action taken or not is followed up by the operations team that assesses the criticality, takes countermeasures and identifies the point of failure. From experience abnormal situations can result either from faults or from side effects of planned interventions which were either not properly identified despite the mandatory planning and review or where a mistake during execution occurred. In many cases there are multiple interventions ongoing simultaneously in order to profit from shutdown periods. The rapid analysis of alarms while the incident is ongoing is often complicated due to the complexity of the ATLAS detector and its infrastructure and the large number of responsible groups and experts. A new Alarm Helper tool was designed to assist the operation team, particularly the operator in the control room responsible for infrastructure and safety (SLIMOS – Shift Leader in Matters of Safety), by providing real-time information about ongoing interventions and the possible related causes of failure. The new tool will combine historical events, documentation, and limited knowledge about ongoing interventions. It extends the Expert System which visualizes and simulates infrastructure inter-dependencies and allows to trace faults or alarms to a list of potential points of failure. The new tool also proposes which experts should be contacted in the particular circumstances.

Finite Element Analysis of 3D Printed Block Prepared of Sustainable Acrylonitrile Butadiene Styrene (ABS)

ABS and chain-branched amylopectin exhibit poor processing capabilities, making them unsuitable for 3D printing utilizations. While ABS exhibits excellent mechanical properties with high processing costs, it lacks the practical requirements of PLA, an environment-friendly polymer with poor mechanical performances. Studying the toxicity of 3-D printer emissions and the causes of toxicity both in vivo and in vitro is necessary in light of the rapidly expanding applications of 3-D printing technological advances, the documented emissions, and the possible adverse reactions from exposed to those emissions. Despite these limitations, ABS and PLA continue to be developed for 3D printing applications. Several mechanical behaviors, including tensile strength, creep, and fatigue, are examined in the study to determine the structural integrity and durability of a 3D-printed ABS square block. The results of the safety factor analysis show a minimum value of 0.1823, indicating the presence of potential failure points and the need for design optimization. The material can last long under dynamic loads, as shown by the fatigue study. This study not only improves ABS parts in real-life uses but also helps grasp their strength better. It gives clues for their future design and making. Using experimental and simulation data, the study optimizes 3D printing parameters and improves ABS materials’ structural efficiency by integrating finite element methods with practical manufacturing outcomes.

Probabilistic wind uplift resistance framework for the relative evaluation of wood-frame load paths

Past failure risk analyses of wind-impacted wood-frame structural load paths have tended to consider simplified resistance models that account for a few key load path connections, in which connection capacity distributions are generally based on benchmark experimental results. However, recent post-storm reconnaissance studies have demonstrated that connections in the load path of light wood-frame structures are themselves composed of multiple elements with many configurations and possible failure modes. This study presents a flexible approach for modeling wind uplift resistance in wood-frame load paths that includes a more exhaustive set of potential failure points yet is computationally efficient and readily adaptable to various load paths composed of different assemblages of structural members and connections. In this framework, ultimate capacities of connections and wood members are either based on design equations provided in the National Design Specification for Wood Construction or another applicable standard or computed from a comparable mechanics-based model. Analytical capacity estimates for roof sheathing, roof-to-wall connections, and wall-to-slab-foundation connections accord well with the range of published experimental results for these connections. Capacities of connections that act in parallel are summed to transform the load path into an analogous load chain of series components. System-level wind uplift resistance, defined by the weakest component in series, is evaluated by Monte Carlo simulation. By providing a more complete description of resistance than previous simplified models have done while avoiding the expense of a detailed finite-element or other solid mechanics model, the method proposed here holds promise as a rapid, consistent, and accurate way to quantify wind resistance in any arbitrary wood-frame load path, with applications including insurance risk analysis, hybrid data science frameworks utilizing post-storm reconnaissance data, and estimation of hazard intensity from structural damage observations.

Analysis of Productivity Measurement in CPO Production Using OMAX Method

The primary focus of this research revolves around the measurement of productivity and the factors that impact Crude Palm Oil (CPO) production which currently faces challenges in assessing whether existing productivity falls into the 'satisfactory' category. The contribution of this study is conducting a comprehensive productivity assessment, focusing on metrics and identifying the causes of productivity decline, especially potential points of failure. The measurement indicators use the Objective Matrix (OMAX) method which includes five ratios, including raw material utilization, energy consumption, labor efficiency, optimization of production targets, and production capacity utilization. The Analytical Hierarchy Process (AHP) is used to assign relative weight to factors that contribute to overall productivity. In addition, Failure Modes and Effects Analysis (FMEA) functions as a tool to determine the causes of decreased productivity by considering the potential for failure to occur. The research results show that productivity reached its highest point in March at 219.93% and the lowest in July at -67.33%. Based on the score assessment, this decline was mainly caused by the lack of optimal achievement of production targets, symbolized by a ratio of 4 to a score of 45. Potential causes for this ratio include non-compliance in selecting FFB that meets standards, and production standards that are too ambitious. targets, and mental and physical fatigue and stress. To improve overall performance, proposed improvements include the application of Internet of Things (IoT) technology, such as the use of sensors and automation systems in production processes, as well as investment in agricultural technology as a monitoring system. This increase is aimed at achieving higher production targets and overall efficiency.

Examining indicators of complex network vulnerability across diverse attack scenarios

Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators, such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios. Specifically, we examine how node- and link-based network growth or depletion based on specific attack criteria affect their robustness gauged in terms of the largest connected component (LCC) size and diameter. We employ partial least squares discriminant analysis to quantify the individual contribution of the indicators on LCC preservation while accounting for the collinearity stemming from the possible correlation between indicators. Our analysis of 14 complex network datasets and 5 attack models invariably reveals high modularity and disassortativity to be prime indicators of vulnerability, corroborating prior works that report disassortative modular networks to be particularly susceptible to targeted attacks. We conclude with a discussion as well as an illustrative example of the application of this work in fending off strategic attacks on critical infrastructures through models that adaptively and distributively achieve network robustness.

Fixation of the Proximal Hamstring Tendon Using an All-Suture Tensionable Knotless Technique

Proximal hamstring injuries are a common sports and recreational injury among the active patient population. Surgical fixation of the tendons of the hamstring muscle complex, as opposed to conservative treatment alone, has shown improved patient outcomes, prompting the evolution of the suture anchors utilized in these repairs. Previous studies investigating the biomechanical properties of hamstring repair anchors have focused on double-row knotless techniques, in which the fixation of the overall construct relies on each individual anchor to maintain fixation. While these constructs have demonstrated biomechanical strength and clinical durability, each suture anchor represents a potential point of failure for the entire construct due to the crossed stitch anchor configuration. To address this limitation, recent tensionable knotless all-suture anchor designs have been implemented with success due to their smaller size and biomechanical strength. The aim of this technical note is, thus, to describe a technique for proximal hamstring repair using a tensionable knotless all-suture anchor construct that has 5 independent mattress sutures and, in doing so, employs the biomechanical strength of knotless fixation but eliminates the potential single point of failure seen with current knotless suture anchor designs.