Search Results (7)

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

The rapid evolution of Internet-enabled smart systems has accelerated the adoption of the Internet of Things (IoT), Cyber–Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Human–Computer Interaction (HCI/AR–VR) across distributed digital ecosystems. Despite these advances, the architectural integration of sensing, information processing, and system-level reasoning remains fragmented, limiting system coherence and accountability. This study derives an architectural foundation through a systematic synthesis of smart system architectures. An activity-mediated socio-technical perspective is employed to analyze diverse paradigms—including IoT-centric frameworks, AI-driven infrastructures, digital twins, Big Data pipelines, and cyber–physical systems—as well as reference architectures such as RAMI 4.0, IIRA, and other representative smart system architectures. Here, activity-mediated denotes an architectural mediation mechanism that coordinates sensing, data-driven reasoning, and human–AI interaction. The synthesis reveals a lack of explicit mechanisms for vertical integration and alignment between bottom-up data flows and top-down goal propagation. In response, this study derives three architectural pillars that integrate interaction, governance, and smart technologies. Their operationalization reveals a structured transformation process in which activity-derived signals are translated into actionable intelligence and adaptive interventions, enabling feedback-driven behavior and cross-layer traceability. Full article

This research characterized a novel Komagataella phaffii strain generated through intraspecific crossing between a wild isolate and a laboratory strain. This segregant, called S467, expressed 2.2-fold more secreted recombinant β-glucosidase than its parental strains in microtiter scale, which suggested that S467 could be an attractive host for bioprocess optimization. S467 was grown alongside the laboratory strain CBS7435 expressing β-glucosidase (CBS_BGL9), as a control, in a 1.5 L bioreactor to determine kinetics parameters, and similar cell growth rate (0.12 h⁻¹) but higher recombinant protein activity, measured as enzymatic activity, was observed in S467. The effect of specific cell growth rate was studied using continuous cultures (chemostat) at different dilution rates, identifying conditions that provided up to a twofold increase in enzymatic activity in S467. RT-qPCR was conducted on key genes associated with the genetic background of S467, in order to clarify differences at the transcriptomic level that render S467 as a potential superior host for recombinant protein production. Overall, this study provides quantitative evidence of the positive effect of the natural isolate IRA1 allele for the generation of recombinant β-glucosidase and highlights the usability of natural genetic diversity in K. phaffii. Full article

The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article

We aimed to determine whether berbamine (BBM) would have an effect on retinyl acetate (RA)-induced cystometric and biochemical parameters, characteristic of bladder overactivity. BBM exhibits anti-inflammatory, anti-oxidant, and muscle-relaxant effects which could counteract pathophysiological mechanisms observed in overactive bladder (OAB) syndrome. The cohort of 60 rats was divided into 4 groups: I—control, II—RA group, III—BBM, and IV—group with the combination of RA + BBM. The cystometry, BBF, cardiovascular parameters and diuresis, the analysis of the cFos, and biochemical biomarker levels were analyzed 48 h after completion of BRB administration. The examined substance turned out to reverse the cystometric changes and c-Fos expression changes induced by RA when compared to the control group. There were no significant changes observed in the analyzed groups of animals MAP, HR, BBF, or UP. Importantly, BBM also turned out to reduce the level of OAB biomarkers present in urine (NGF, BDNF), urothelium (TRPV1, SNAP29, ATP, CGRP, or OCT-3), bladder detrusor muscle (VAChT, Rho kinase) as well as to reduce the exponents of oxidative stress (3-nitrotyrosine, malondialdehyde). The multifactorial explanation of the successful alleviation of the RA-induced detrusor overactivity makes the concept of incorporation of BBM in the OAB treatment promising for the future research. Full article

Due to the growing environmental and geopolitical challenges nowadays, which are causing supply chain complications, industry and society are facing significant new objections. As a complement and extension to the technology-driven premises of Industry 4.0, the value-driven Industry 5.0 focuses on society and the environment. Human centricity, sustainability, and resilience should become a more integral part of both industrial and societal revolutions. One of the enabler technologies for both is the Digital Twin (DT). In order to make DTs intelligent, they must become active, online, goal-seeking, and anticipatory. To meet these requirements, the characteristics of Multi-Agent Systems (MASs) can be employed. This paper contributes to the bilateral emergence of the two industrial paradigms and establishes an approach for the provision of Intelligent Digital Twins (IDTs) within the Internet of Digital Twins (IoDT). Initially, a DT reference model aligned with already established Industry 4.0 reference models enriched with the goals of Industry 5.0 is developed, followed by an outline of how IDTs can be realized with the characteristics of MAS. The work is substantiated by an architectural design for IDTs choreographing marketplace-oriented production processes with a subsequent prototypical implementation, followed by a proof of concept. Full article

Receivers able to track satellites belonging to different GNSSs (Global Navigation Satellite Systems) are available on the market. To compute coordinates and velocities it is necessary to identify all the elements that contribute to interoperability of the different GNSSs. For example the timescales kept by different GNSSs have to be aligned. Receiver-specific biases, or firmware-dependent biases, need to be calibrated. The reference frame used in the representation of the orbits must be unique. In this paper we address the interoperability issues from the standpoint of a Single Point Positioning (SPP) user, i.e., using pseudoranges and broadcast ephemeris. The biases between GNSSs timescales and receiver-dependent biases are analyzed for a set of 31 MGEX (Multi-GNSS Experiment) stations over a time span of more than three years. Time series of biases between timescales of GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), Galileo, BeiDou, QZSS (Quasi-Zenith Satellite System), SBAS (Satellite Based Augmentation System) and NAVIC (Navigation with Indian Constellation) are investigated, in addition to the identification of events like discontinuity of receiver-dependent biases due to firmware updating. The GPS broadcast reference frame is shown to be aligned to the one (IGS14) realized by the precise ephemeris of CODE (Center for Orbit Determination in Europe) to within 0.1 m and 2 milliarcsec, with values dependent on whether IIR-A, IIR-B/M or IIF satellite blocks are considered. Larger offsets are observed for GLONASS, up to 1 m for GLONASS K satellites. For Galileo the alignment of the broadcast orbit to IGS14/CODE is again at the 0.1 m and several milliarcsec level, with the FOC (Full Operational Capability) satellites slightly better than IOV (In Orbit Validation). For BeiDou an alignment of the broadcast frame to IGS14/CODE comparable to GLONASS is observed, regardless of whether IGSO (Inclined Geosynchronous Orbit) or MEO (Medium Earth Orbit) satellites are considered. For all satellites, position differences according to the broadcast ephemeris relative to IGS14/CODE orbits are projected to the radial, along-track and crosstrack triad, with the largest periodic differences affecting mostly the along track component. Sudden discontinuities at the level of up to 1 m and 2–3 ns are observed for the along-track component and the satellite clock, respectively. The time scales of GLONASS, Galileo, QZSS, SBAS and NAVIC are very closely aligned to GPS, with constant offsets depending on receiver type. The offset of the BeiDou time scale to GPS has an oscillatory pattern with peak-to-peak values up to 100 ns. To characterize receiver-dependent biases the average of six Septentrio receivers is taken as reference, and relative offsets of the other receiver types are investigated. These receiver-dependent biases may depend on the individual station, or for the same station on the update of the firmware. A detailed calibration history is presented for each multiGNSS station studied. Full article