Modern electronic systems increasingly operate in harsh environments where vibration, thermal cycling, and electromagnetic interference (EMI) jointly threaten their long-term reliability. Acoustic metamaterials (AMMs), with their ability to manipulate wave propagation through engineered subwavelength structures, offer a promising pathway toward mitigating these reliability risks. This review provides a comprehensive overview of AMMs from a reliability-oriented design (ROD) perspective. The proposed ROD framework systematically maps environmental stressors to failure mechanisms and corresponding AMM strategies, thereby bridging the gap between material innovation and reliability assurance. Core AMM mechanisms—including local resonance, Bragg scattering, and cavity absorption—are analyzed alongside emerging multifunctional designs that integrate mechanical, thermal, and electromagnetic functions. Representative applications in MEMS, RF resonators, vehicular electronics, and data-center cooling systems demonstrate the practical benefits of AMMs in suppressing failure-inducing stressors, while emerging applications such as voice security and wearable devices are also introduced. The discussion section addresses persistent challenges in multifunctional coupling, scale incompatibility, manufacturing constraints, and the need for reliable integration across multiple physical domains, while also highlighting emerging trends in intelligent design facilitated by artificial intelligent (AI) integration. This review links material-level innovation and system-level reliability, offering a new paradigm for embedding AMMs into next-generation resilient electronic systems.

Peripheral neuropathies are estimated to affect several million patients in the US, with no long-lasting therapy currently available. In humans, the Nav1.7 sodium channel, encoded by the SCN9A gene, is involved in a spectrum of inherited neuropathies and has emerged as a promising target for analgesic drug development. The development of a selective Nav1.7 inhibitor has been challenging, in part because of structural similarities with other Nav channels. Here, we present preclinical studies for a genomic medicine approach using engineered zinc finger repressors (ZFRs) specifically targeting the human/nonhuman primate (NHP) SCN9A gene. Adeno-associated virus (AAV)-mediated delivery of ZFRs in human induced pluripotent stem cell (iPSC)-derived neurons resulted in the reduction of SCN9A with no detectable off-target activity. In the spared nerve injury (SNI) neuropathic pain mouse model, AAV-ZFR administration resulted in ≤70% repression of Scn9a in mouse dorsal root ganglia (DRGs) and was associated with reduction in pain hypersensitivity. AAV9-mediated intrathecal-lumbar (IT-lumbar) delivery of ZFRs in NHPs demonstrated repression of SCN9A in bulk DRG tissue and single-cell levels in nociceptors 1 month after treatment. A lead AAV9-ZFR investigational product, ST-503, was developed and further evaluated in a 6-month study in NHPs. ST-503 administration by IT-lumbar infusion resulted in 50% repression of SCN9A in bulk DRG tissue at 6 months without findings of dose-limiting toxicity or impact on neurological and cardiac safety pharmacology. Together, our results support further development of an AAV-delivered ZFR as a potential therapy for patients with peripheral neuropathies.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

This study uses a wind turbine case study to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real time condition monitoring, predictive analytics, and health management of selected components of Wind turbines in a wind farm.. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by Machine Learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as Condition Monitoring and Predictive Maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real time sensor data, and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Peripheral neuropathies are estimated to affect several million patients in the US with no long-lasting therapy currently available. In humans, the Nav1.7 sodium channel, encoded by the SCN9A gene, is involved in a spectrum of inherited neuropathies, and has emerged as a promising target for analgesic drug development. The development of a selective Nav1.7 inhibitor has been challenging, in part due to structural similarities among other Nav channels. Here we present preclinical studies for the first genomic medicine approach using engineered zinc finger repressors (ZFRs) specifically targeting human/nonhuman primate (NHP) SCN9A . AAV-mediated delivery of ZFRs in human iPSC-derived neurons resulted in 90% reduction of SCN9A with no detectable off-target activity. To establish proof-of-concept, a ZFR targeting the mouse Scn9a was assessed in the SNI neuropathic pain mouse model, which resulted in up to 70% repression of Scn9a in mouse DRGs and was associated with reduction in pain hypersensitivity as measured by increased mechanical- and cold-induced pain thresholds. AAV-mediated intrathecal delivery of ZFR in NHPs demonstrated up to 60% repression of SCN9A in bulk DRG tissue and on single-cell levels in the nociceptors. The treatment was well tolerated in NHPs, and no dose-limiting findings were observed four weeks after a single intrathecal injection. Taken together, our results demonstrate that AAV-delivered ZFR targeting the SCN9A gene is promising and supports further development as a potential therapy for peripheral neuropathies. ### Competing Interest Statement M. Samie and J. Eshleman are inventors on a pending United States patent application related to this work. M. Samie, T. Parman, M. Jalan, J. Lee, P. Dunn, J. Eshleman, Y. Pan, M. Falaleeva, S. Hinkley, T. Chen, S. Bhardwaj, A. Ward, M. Trias, A. Chikere, M. Som, S. Yadav, K. Meyer, B. Zeitler, and A. Pooler are current employees of Sangamo Therapeutics, Inc. D. Baldwin Vidales, J. Holter, B. Jones, and J. Fontenot were employed by Sangamo Therapeutics, Inc. when this work was conducted.

Applications in harsh environments greatly suffer from intermittency faults in their interconnections/wirings. Due to the erratic behavior of intermittency that causes signal irregularities, it is tough to distinguish irregularities from an actual transmitted signal, particularly in the earlier stages where signal abnormalities mainly resemble noise. This paper explores step changes in the resistance of a wire caused by broken strands as a failure parameter. Thus, a test rig was designed to emulate the ageing mechanism of the wire. with results of the study highlighting that resistance step changes could effectively be used to locate intermittency faults in low power cable applications.

This paper proposed a cross-domain self-authentication scheme to address the "information isolated island" problem of users' identities storage in servers and the "redundant registration problem" of users' identities for Autonomous Valet Parking (AVP). This scheme adopts a decentralized anonymous authentication method to relieve the authentication center's service load. Users are segregated into two categories to increase authentication efficiency: inexperienced and regular users. For the former, the paper explores a self-authentication mechanism based on verification parameters. Then, its valid personal information, pseudonym and public key, were stored in a consortium blockchain (PseIDChain) as the transaction records so that they can be securely shared among servers located in different domains. For the latter (regular users), an efficient authentication mechanism, searching users' personal information on PseIDChain by the smart contract, was proposed. Security proof and simulation results show that the designed scheme has superior security to the existing schemes. Its authentication efficiency is 80.29% and 50.45% higher than the traditional anonymous and batch authentication schemes.

Vehicles are equipped with Electronic Control Units (ECUs) to increase their overall system functionality and connectivity. However, the rising connectivity exposes a defenseless internal Controller Area Network (CAN) to cyberattacks. An Intrusion Detection System (IDS) is a supervisory module, proposed for identifying CAN network malicious messages, without modifying legacy ECUs and causing high traffic overhead. The traditional IDS approaches rely on time and frequency thresholding, leading to high false alarm rates, whereas state-of-the-art solutions may suffer from vehicle dependency. This paper presents a wavelet-based approach to locating the behavior change in the CAN traffic by analyzing the CAN network’s transmission pattern. The proposed Wavelet-based Intrusion Detection System (WINDS) is tested on various attack scenarios, using real vehicle traffic from two independent research centers, while being expanded toward more comprehensive attack scenarios using synthetic attacks. The technique is evaluated and compared against the state-of-the-art solutions and the baseline frequency method. Experimental results show that WINDS offers a vehicle-independent solution applicable for various vehicles through a unique approach while generating low false alarms.

3D Stacked Integrated Circuits (SICs) offer a promising way to cope with the technology scaling; however, the test access requirements are highly complicated due to increased transistor density and a limited number of test channels. Moreover, although the vertical interconnects in 3D SIC are capable of high-speed data transfer, the overall test speed is restricted by scan-chains that are not optimized for timing. Reduced Pin-Count Testing (RPCT) has been effectively used under these scenarios. In particular, Time Division Multiplexing (TDM) allows full utilization of interconnect bandwidth while providing low scan frequencies supported by the scan chains. However, these methods rely on Uni-Directional Signaling (UDS), in which a chip terminal (pin or a TSV) can either be used to transmit or receive data at a given time. This requires that at least two chip terminals are available at every die interface (Tester-Die or Die-Die) to form a single test channel. In this paper, we propose Simultaneous Bi-Directional Signaling (SBS), which allows a chip terminal to be used simultaneously to send and receive data, thus forming a test channel using one pin instead of two. We demonstrate how SBS can be used in conjunction with TDM to achieve reduced pin count testing while using only half the number of pins compared to conventional TDM based methods, consuming only 22.6% additional power. Alternatively, the advantage could be manifested as a test time reduction by utilizing all available test channels, allowing more parallelism and test time reduction down to half compared to UDS-based TDM. Experiments using 45nm technology suggest that the proposed method can operate at up to 1.2 GHz test clock for a stack of 3-dies, whereas for higher frequencies, a binary-weighted transmitter is proposed capable of up to 2.46 GHz test clock.

The automobile industry no longer relies on pure mechanical systems; instead, it benefits from many smart features based on advanced embedded electronics. Although the rise in electronics and connectivity has improved comfort, functionality, and safe driving, it has also created new attack surfaces to penetrate the in-vehicle communication network, which was initially designed as a close loop system. For such applications, the Controller Area Network (CAN) is the most-widely used communication protocol, which still suffers from various security issues because of the lack of encryption and authentication. As a result, any malicious/hijacked node can cause catastrophic accidents and financial loss. This paper analyses the CAN bus comprehensively to provide an outlook on security concerns. It also presents the security vulnerabilities of the CAN and a state-of-the-art attack surface with cases of implemented attack scenarios and goes through different solutions that assist in attack prevention, mainly based on an intrusion detection system (IDS).

The ageing phenomenon of negative bias temperature instability (NBTI) continues to challenge the dynamic thermal management of modern FPGAs. Increased transistor density leads to thermal accumulation and propagates higher and non-uniform temperature variations across the FPGA. This aggravates the impact of NBTI on key PMOS transistor parameters such as threshold voltage and drain current. Where it ages the transistors, with a successive reduction in FPGA lifetime and reliability, it also challenges its security. The ingress of threshold voltage-triggered hardware Trojan, a stealthy and malicious electronic circuit, in the modern FPGA, is one such potential threat that could exploit NBTI and severely affect its performance. The development of an effective and efficient countermeasure against it is, therefore, highly critical. Accordingly, we present a comprehensive FPGA security scheme, comprising novel elements of hardware Trojan infection, detection, and mitigation, to protect FPGA applications against the hardware Trojan. Built around the threat model of a naval warship's integrated self-protection system (ISPS), we propose a threshold voltage-triggered hardware Trojan that operates in a threshold voltage region of 0.45V to 0.998V, consuming ultra-low power (10.5nW), and remaining stealthy with an area overhead as low as 1.5% for a 28 nm technology node. The hardware Trojan detection sub-scheme provides a unique lightweight threshold voltage-aware sensor with a detection sensitivity of 0.251mV/nA. With fixed and dynamic ring oscillator-based sensor segments, the precise measurement of frequency and delay variations in response to shifts in the threshold voltage of a PMOS transistor is also proposed. Finally, the FPGA security scheme is reinforced with an online transistor dynamic scaling (OTDS) to mitigate the impact of hardware Trojan through run-time tolerant circuitry capable of identifying critical gates with worst-case drain current degradation.