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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study aims to investigate supply network (SN) resilience capabilities across the organizational, supply chain (SC) and industry levels by drawing on the complex adaptive systems (CASs) theory and the social–ecological perspective of resilience. An empirically grounded framework operationalizes the concept of social–ecological resilience by expounding resilience capabilities across phases of the CAS adaptive cycle.

This research uses a qualitative multiple case study approach. It draws on the case of the Australian Defence Manufacturing SN (ADM SN) during COVID-19 disruptions. A total of 28 interviews with senior decision makers from 17 companies, complemented by 5 interviews with the Australian Defence SC organizations and secondary data analysis, support the findings.

Individual organizations’ SC visibility and flexibility enabled by effective risk management and collaboration enhance the ability of the SN to anticipate and prepare for disruption. At the same time, the strength of SC relationships reduces resilience. SN disruption response velocity is enabled by inventory redundancy, process flexibility at the organizational level and visibility and collaboration at the SC level. Institutional support at the national industry level, development of value-adding capabilities and manufacturing process flexibility at the organizational level enhances the SN’s ability to re-organize. The transition from hierarchical to decentralized collaborative governance enhances SN resilience.

From a practitioner’s perspective, the findings highlight the need to embrace a broader view of SC beyond immediate tiers. Decision-makers in multinational companies must recognize the long-term impact of their procurement decisions on the supplier ecosystem. Developing local supplier capabilities rather than relying on established global SCs will pay off with future resilience. It, however, demands substantial investment and radical changes across all SC tiers. The lesson for smaller firms is not to over-rely on the existing relationships with supply partners. Although trust-based relationships and collaboration are essential, over-commitment can be counterproductive during global disruptions. With a lack of visibility and control over the SC, operational flexibility is critical for small firms to adapt to shifts in supply and demand.

To the best of the authors’ knowledge, this empirical research is one of the first attempts to operationalize the social–ecological perspective of SN resilience. Evidence-based theoretical propositions contribute to the emerging conversation about the CAS nature of resilience by demonstrating the multi-level effects of resilience capabilities.

Solar cells could make textile-based wearable systems energy independent without the need for battery replacement or recharging; however, their laundry resistance, which is prerequisite for the product acceptance of e-textiles, has been rarely examined. This paper aims to report a systematic study of the laundry durability of solar cells embedded in textiles.

This research included small commercial monocrystalline silicon solar cells which were encapsulated with functional synthetic textile materials using an industrially relevant textile lamination process and found them to reliably endure laundry washing (ISO 6330:2012). The energy harvesting capability of eight textile laminated solar cells was measured after 10–50 cycles of laundry at 40 °C and compared with light transmittance spectroscopy and visual inspection.

Five of the eight textile solar cell samples fully maintained their efficiency over the 50 laundry cycles, whereas the other three showed a 20%–27% decrease. The cells did not cause any visual damage to the fabric. The result indicates that the textile encapsulated solar cell module provides sufficient protection for the solar cells against water, washing agents and mechanical stress to endure repetitive domestic laundry.

This study used rigid monocrystalline silicon solar cells. Flexible amorphous silicon cells were excluded because of low durability in preliminary tests. Other types of solar cells were not tested.

A review of literature reveals the tendency of researchers to avoid standardized textile washing resistance testing. This study removes the most critical obstacle of textile integrated solar energy harvesting, the washing resistance.

Waste generated from electrical and electronic equipment, collectively known as E-waste, remains a persistent environmental, economic and social problem. Sustainable E-waste management (EWM) has numerous benefits, such as preventing electronic waste from entering landfills, reducing the need for virgin materials by recovering valuable materials from recycling and lowering greenhouse gas emissions. Circular economy (CE) practices are considered the initial steps toward sustainable EWM, but some hurdles have been reported in the adoption of these practices. Therefore, the current study aims to identify the common CE practices, sustainability of the EWM process and the challenges in EWM, and to develop a conceptual framework for effective EWM.

Very few studies have proposed frameworks that acknowledge the challenges and CE practices of EWM. To fill this gap, a systematic literature review (SLR) was performed, and 169 research articles were explored.

A total of seven challenges in the adoption of effective EWM were identified: rules and policy, infrastructure, consumer behaviour, informal sectors, community culture, technology and economy. Eight common CE practices were also found for effective EWM: reuse, recycle, remanufacturing, refurbishment, repair, reduce, recover and repurpose.

A conceptual framework guiding sustainable EWM was proposed, which includes solutions for the identified challenges, and CE practices with sustainable benefits.

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

This paper aims to discuss the ethical aspects of hardware reverse engineering (HRE) and propose an ethical framework for HRE when used to mitigate cyber risks of the digital supply chain of critical infrastructure operators.

A thorough review and analysis of existing relevant literature was performed to establish the current state of knowledge in the field. Ethical frameworks proposed for other areas/disciplines and identified pertinent ethical principles have been used to inform the proposed framework’s development.

The proposed framework provides actionable guidance to security professionals engaged with such activities to support them in assessing whether an HRE project conforms to ethical principles. Recommendations on action needed to complement the framework are also proposed. According to the proposed framework, reverse engineering is neither unethical nor illegal if performed honourably. Collaboration with vendors and suppliers at an industry-wide level is critical for appropriately endorsing the proposed framework.

To the best of the authors’ knowledge, no ethical framework currently guides cybersecurity research, far less of cybersecurity vulnerability research and reverse engineering.

Induction heating processes need to adapt to complex geometries or variable processes that require a high degree of flexibility in the induction heating setup. This is usually done using complex inductors or adaptable resonant tanks, which leads to costly and constrained implementations. This paper aims to propose a multi-level, versatile power supply able to adapt the output to the required induction heating process.

This paper proposes a versatile multilevel topology able to generate versatile output waveforms. The methodology followed includes simulation of the proposed architecture, design of the power electronics, control and magnetic elements and laboratory tests after building a 10-level prototype.

The proposed converter has been designed and tested using an experimental prototype. The designed generator is able to operate at 1 kVpp and 100 A at 250 kHz, proving the feasibility of the proposed approach.

The proposed converter enables versatile waveform generation, enabling advanced tests and processes on induction heating system. The proposed system allows for multifrequency generation using a single inductor and converter, or advanced tests for inductive and capacitive components used on induction heating systems. Unlike previous multifrequency proposals, the proposed generator enables a significantly improved versatility in terms of operational frequency and amplitude in a single converter.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.