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This comprehensive study provides a detailed review toward ongoing research on emerging non-volatile memory technologies based nano-oscillators, i.e., from the perspective of materials to applications. Depending on the materials used to fabricate them, the whole class of emerging nano-oscillators has been broadly classified into two categories: (i) electronic and (ii) spintronic oscillators. Moreover, various material-compositions explored for fabricating the oscillatory devices, their physical properties responsible for generating the oscillations, and device architectures are thoroughly reviewed. Furthermore, various advanced computing applications (i.e., realized through these oscillatory devices), such as Internet of Things, neuro-inspired computing, and sensing, are also studied and their key attributes are highlighted. [ABSTRACT FROM AUTHOR]

The urgent demand for high-performance emerging memory, propelled by artificial intelligence in internet of things (AIoT) and machine learning advancements, spotlights spin-transfer torque magnetic random-access memory as a prime candidate for practical application. However, magnetic tunnel junctions (MTJs) with a single-crystalline MgO barrier, which are central to magnetic random-access memory (MRAM), suffer from significant drawbacks: insufficient endurance due to breakdown and high writing power requirements. A superlattice barrier-based MTJ (SL-MTJ) is proposed to overcome the limitation. We first fabricated the MTJ using an SL barrier while examining the magnetoresistance and resistance-area product. Lower writing power can be achieved in SL-MTJs compared to MgO-MTJs. Our study may provide a new route to the development of MRAM technologies. [ABSTRACT FROM AUTHOR]

The infusion of emerging technologies (e.g., IoT-enabled algorithmic customer service systems [IACSs]) often brings disruptive changes to customer service. In particular, the agentic nature of these technologies challenges prominent service theories. Among these challenges, recent scholarly calls have pushed for more research on the infusion of emerging technologies into the service-profit chain (SPC) framework, advocating for the importance of extended knowledge to develop a techno-infused version of the SPC. Thus, from an interdisciplinary perspective, we draw upon role theory and propose a technoservice-profit chain (TSPC). Specifically, we contextualize the SPC in the technoservice context with different approaches, including decomposing context-specific constructs and theorizing IACS implementation as a contextual factor that moderates TSPC relationships. Using a sequential mixed methods design combining quantitative and qualitative approaches, we tested our research model by conducting multiwave surveys and follow-up interviews in a large business-to-business service firm with data from employees, supervisors, and customers before and after IACS implementation. This interdisciplinary study contributes to the information systems, service marketing, and management literatures by enriching the compositions of core SPC constructs, theorizing interactions between human agents and technology agents, and scrutinizing the impacts of technology agents on the linkages between internal employee management and external customer service. Our results further reveal the emerging issues of competing bosses (i.e., supervisors and IACSs), competing employees (i.e., employees and IACSs), and the unintended dehumanization effects of IACSs on supervisors and employees. [ABSTRACT FROM AUTHOR]

The deployment of the Industrial Internet of Things (IIoT) in smart container terminals provides a foundation for sensing and recording all operational processes. However, little effort has been devoted to integrating the massive data regarding interoperability challenges, thus limiting the value of data in advancing the intelligent evolution of ports. In this research, we propose a graph-based approach to organise operational records semantically, thereby facilitating data-driven decision-making in container terminals. We first construct a knowledge graph for operational processes in container terminals, employing a tailored procedure for the automatic conversion of operational records into triples. By utilising the graph information, we propose a novel method that integrates reinforcement learning (RL) with a mathematical solver for optimising scheduling problems. The quay crane scheduling problem (QCSP) is illustrated as an example to elaborate on the technical details. Based on a dataset from a real-world container terminal, numerical studies demonstrate the superiority of the proposed framework in terms of information retrieval efficiency and solution quality compared with the traditional data organisation approach. [ABSTRACT FROM AUTHOR]

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies. [ABSTRACT FROM AUTHOR]

GaOx devices have been extensively explored for applications such as power devices and solar blind detectors, based on their wide bandgap. In this study, we investigated the synaptic properties of the amorphous gallium oxide (a-GaOx)- based memristor with a W/WOx/a-GaOx/ITO structure, in which a-GaOx are deposited by RF magnetic sputtering at ambient temperature. The structure and components of a-GaOx are characterized by XRD, XPS, SEM, and EDS. The electrical test indicates that W/WOx/a-GaOx is ohmic due to the thin WOx layer with a high concentration of oxygen vacancies. Consequently, the synaptic characteristics of the W/WOx/a-GaOx/ITO memristor depend on both the a-GaOx layer itself and the a-GaOx/ITO junction. The fitting results indicate that the a-GaOx/ITO junction is Schottky with unidirectional conductive properties. However, the elevated defect density results in a larger current for the reverse-biased a-GaOx/ITO junction. Moreover, adjusting the thickness of a-GaOx allows the device to achieve almost symmetrical forward and reverse currents. We have successfully observed typical synaptic characteristics in W/WOx/a-GaOx/ITO when stimulated by consecutive spike signals. Clearly, through careful design considerations regarding the structure and parameters, we have realized superior synaptic performance in a-GaOx-based memristors. This achievement shows that amorphous GaOx has great potential applications in neuromorphic computation chips for artificial intelligence or the Internet of Things in the future. [ABSTRACT FROM AUTHOR]

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things has received enormous attention. Despite the lucrative potential, this study tries to look beyond the hype to understand how IoT impacts firm performance. Based on the information processing theory, we intend to explore how IoT influences the financial performance of firms. Firms, which realise the urgent need to adopt IoT might encounter a diverse set of challenges, like whether adoption will be profitable, the level of commitment they should devote, and whether to adopt as a first or second-mover. We want to comprehend such adoption challenges on the Indian manufacturing firm's financial performance. This study uses panel data from BSE 500 listed manufacturing firms and conducts multivariate analysis to understand the relationship between the IoT implementation proxies: IoT adoption, first-mover advantage, and firm performance in the light of IoT commitment and experts. The result indicates that IoT implementation has an insignificant effect on manufacturing firm performance. Meanwhile, the consideration of IoT commitment and experts jointly can enable a manufacturing firm to reap significantly positive financial outcomes. In other words, the IoT commitment and expertise positively moderate the relationship. The finding is useful to the decision-makers to address the firm-level adjustment before the mere introduction of IoT. [ABSTRACT FROM AUTHOR]

Efficient thermoelectric energy conversion at the nanoscale could power the Internet of Things and cool nanoelectronic circuits and improve the performance of quantum applications. Width-modulated nanowaveguides are suitable for these purposes because their thermoelectric efficiency can be geometrically tuned and integrated into the nanoelectronics industry processes. They are attracting increasing research interest stimulated by theoretical predictions for exceptional performance. To validate their potential, a better understanding of the effect of width modulation on thermoelectric efficiency is needed. So far, it is considered that (a) the thermoelectric efficiency increases monotonically with increasing width-mismatch due to decreasing phonon thermal conduction taking place without significantly affecting electron transport, (b) width-mismatch dominates the effect of width modulation in transport, and (c) phonons play the main role in increasing the thermoelectric efficiency. Here, we demonstrate counterevidence based on an investigation of the effect of width modulation on electrons so far overlooked. We reveal that (a) the thermoelectric efficiency varies non-monotonically with the modulation mismatch due to quantum effects on electron transport, (b) the modulation mismatch is quantified by the size-mismatch of the modulation rather than by the width-mismatch, and (c) it is electrons rather than phonons that play the main role in optimizing width modulation for maximum thermoelectric efficiency when quantum effects dominate. Our findings indicate that research should reorient from large width-mismatch toward optimal modulation-mismatch width-modulated nanostructures to enhance thermoelectric efficiency due to quantum effects. Our work provides new insight for designing nanowaveguides for efficient thermoelectric energy conversion at the nanoscale. [ABSTRACT FROM AUTHOR]

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

With the rise of Internet of Things (IoT) technology, the seamless connection between the physical and digital worlds has been realized. This review paper aims to conduct a science mapping review of IoT applications in industrial management and to identify mainstream research topics, research gaps, and future research directions. Using VOSviewer as a visualization tool, 142 articles retrieved from the Scopus database were quantitatively discussed using scientometric analysis. Additionally, a follow-up qualitative discussion was focused on mainstream research topics, existing research gaps, and future research directions as the main research goals. The results revealed influential findings for the co-occurrence of keywords, journals, countries, authors, and documents analyses. Moreover, it was found that the existing research mainly focused on four main research topics including (1) application of IoT in manufacturing based on cyber-physical systems, (2) IoT-related technologies on logistics and supply chain management, (3) The impact of IoT on business models, and (4) Industrial IoT (IIoT) in the context of Industry 4.0. On this basis, the existing research gaps and future research directions are proposed. This review paper would help relevant practitioners and researchers to better understand the existing body of knowledge and lay the foundation for further research. [ABSTRACT FROM AUTHOR]

In this study, we present a novel low-frequency magnetic field energy harvester (EH) employing beryllium bronze/Pb(Zr,Ti)O3 ceramic composited dual-beam structures with tip magnets attached to the inner and outer beams. This design incorporates the internal magnetic-coupled (IMC) effect, resulting in significantly enhanced coupling ability and a wide bandwidth. The validity of the IMC mechanism is confirmed through theoretical formulas and numerical simulations. By leveraging the IMC condition, the EH achieves an expanded bandwidth, which increases from 22 to 43 Hz. Moreover, the total output voltages at the inherent resonance and internal resonance are boosted by 15.4% and 32%, respectively. The performance of the IMC-EH can be further improved by increasing the number of the endmost magnets. Experimental investigations reveal that the IMC-EH generates a maximum RMS output power density of 56.25 μW Oe−2 cm−3, surpassing existing magnetically coupled piezoelectric energy harvesters. Remarkably, even under an ambient magnetic field as low as 1 Oe, the proposed IMC-EH still yields a total output power of 185 μW, sufficient to continuously power 26 LEDs in real time. This demonstrates its potential as a promising solution for low-power consumption small electronics. Furthermore, the implications of this work extend beyond its immediate benefits, as it inspires the design of future self-powered wireless sensor networks in the context of the Internet of Things. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

The quantum well diode (QWD) performs a dual role, functioning both as an emitter and a detector due to its unique feature of spectral overlap between emission and detection spectra. This dual functionality positions QWDs as promising candidates in the realm of multifunctional sensors. Furthermore, the well-established maturity of optical fiber communication, grounded in its intrinsic property of total reflection, makes it an ideal transmission medium for QWD sensing signals. Leveraging the coexisting emission and detection capabilities of QWDs, we have constructed a sensing system in this article. This system utilizes a QWD, which is stimulated to emit light, with the emitted light traveling through a specified length of optical fiber. A specialized load-bearing film, featuring an aluminum membrane on its rear to act as a mirror and an object of known weight on its front, induces deformation in the film, thereby altering the characteristics of the reflected light. This modulated light is subsequently captured by the QWD via the optical fiber, enabling the computation of the weight of the object. In this article, the QWD's emission peak is around 522 nm, and its detection range extends from 370 to 530 nm. Furthermore, by employing the appropriate approach, integrating QWD with optical fibers can be extended to sensing and measuring various physical quantities such as temperature, solution concentration, wind speed, and more. The advantages of QWDs include cost-effectiveness, multifunctionality, portability, and environmental friendliness. This technology represents a promising avenue for sensor control in the era of the Internet of Things. [ABSTRACT FROM AUTHOR]