Search Results (508)

The socks, hosiery, bodywear (SHB) industry is a critical segment of the textile sector, characterized by high-volume production and rapid delivery requirements, making efficiency and resource optimization essential. A corporate sustainability system is needed to minimize environmental impact, ensure long-term competitiveness, and align operations with global sustainability standards. Thus, this research aims to propose an integrated Corporate Sustainability System (CSS) framework that synergizes Lean Manufacturing (LM), Digital Transformation (DT), and sustainability transition through a methodological triangulation of (1) a narrative review, (2) in-depth expert interviews, and (3) a comprehensive Turkish case study. The proposed framework integrates foundational lean principles such as 5S, TPM, and Value Stream Mapping with Industry 4.0 technologies, including RFID traceability, real-time ERP integration and machine vision systems. Empirical demonstration through the case study reveals that establishing foundational lean maturity is a critical foundation for successful digital adoption. Furthermore, the study demonstrates that transitioning from manual tracking to integrated digital platforms resolves data silos and enhances the transparency of customer revisions and warehouse accuracy. The framework also incorporates human-centric Lean 5.0 improvements, proving that ergonomic interventions such as rail-mounted cable systems are vital for operational sustainability. Ultimately, the CSS provides a scalable model that aligns SHB production with global mandates like the EU Green Deal and CBAM, positioning the sector for long-term competitive advantage in an increasingly eco-conscious global market. Full article

Warehouses are energy-intensive nodes in a logistics chain and critical hotspots for decarbonization efforts. Digitalization and Industry 4.0 technologies are increasingly promoted as enablers of greener warehousing; however, environmental benefits are often implied rather than empirically quantified. This study examines how digitalization, automation, and robotization support the implementation of green logistics principles in warehousing operations. The research combines a scientific literature review and document content analysis with semi-structured interviews with company managers and logistics professionals. The results indicate that implementing a warehouse management system (Vision Equinox), integrating information systems, and adopting RFID technology reduce paper-based processes, improve picking accuracy and internal routing, shorten loading and unloading times, and may decrease the risk of human error. Consequently, these technologies enable more efficient resource use and can contribute to lower energy consumption and a reduced environmental footprint associated with warehouse activities. The study concludes that digital technologies already serve as a systematic enabler of green logistics within the organization; however, their environmental benefits have not yet been quantified. Future research should therefore focus on measuring changes in energy use and CO₂ emissions under different warehousing scenarios. Full article

By 2050, the global population is expected to reach approximately 10 billion, leading to a projected 50% increase in food demand relative to 2013 levels. If not adequately anticipated, this growing demand will place significant strain on agri-food systems worldwide, with disproportionate impacts on low- and middle-income countries. Moreover, current projections may underestimate the accelerating effects of climate change, political instability, and civil unrest, which continue to disrupt food production and distribution systems. In this context, technological advancements offer a promising pathway to enhance efficiency, improve transparency, and mitigate risks related to food safety, adulteration, and counterfeiting. Emerging innovations can decouple food production from environmental degradation while strengthening monitoring, verification, and accountability across supply chains. This review examines state-of-the-art technologies developed to support traceability and anti-counterfeiting in agri-food supply chains, considering their application across the full spectrum of stakeholders. To provide a system-level perspective, the review adopts a five-layer socio-technical traceability and anti-counterfeiting framework, comprising identity, sensing, intelligence, integrity, and interaction layers, which is used to map enabling technologies and reinterpret the evolution of traceability systems (TS 1.0–TS 4.0) as a progression of functional capabilities rather than isolated technological upgrades. Using this framework, the review analyzes the advantages and limitations of current solutions and clarifies how traceability and anti-counterfeiting functions emerge through technology integration. It further identifies gaps that hinder large-scale and equitable adoption. Finally, future research directions are outlined to address current technical, economic, and governance challenges and to guide the development of more resilient, trustworthy, and sustainable agri-food traceability systems. Full article

With the rapid expansion of the global textile sector and increasing awareness of the environmental pollution caused by textile waste, enhancing the recycling of textile waste has become essential to reduce the volume of materials sent to landfill or incineration. As recycling technologies advance, automated sorting systems that are capable of handling large waste streams and accurately identifying materials for appropriate recycling pathways are increasingly recognised as being critical for efficient textile-waste management. Since 2015, over 20 studies have specifically explored technologies and strategies for automating textile sorting of textile wastes. This mini review introduces various textile fibre identification technologies, including traditional visual and tactile examination; label checking and modern identification technology; and NIR, FT-IR, RFID tags. It summarises the current state of sorting processes, with particular emphasis on the development of AI-assisted, fibre-type-based sorting technologies. Commercial scale automated sorting is not established yet for textile waste recycling, due to the complexity of materials used in textiles, the equipment identification limits and high cost of processing, while machine learning and artificial neural networks provide opportunities for future research advancement and commercialisation. Full article

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study. Full article

Automated production lines are increasingly being expanded with Industrial Internet of Things (IIoT) devices, creating complex Cyber-Physical Systems (CPSs) that connect physical production with control and information infrastructure. However, the convergence of Information Technology (IT) and Operational Technology (OT) layers creates new entry points for attacks targeting communication availability. Most existing studies analyze Distributed Denial of Service (DDoS) attacks primarily in simulation or testbed environments, with limited experimental verification of their impact on real-world production systems. This article presents an experimental evaluation of the impact of DDoS and Distributed Reflection Denial of Service (DRDoS) attacks carried out directly on a physical automated production line with integrated IIoT infrastructure during real operation. Three attack scenarios (TCP SYN flood, TCP ACK flood, and ICMP reflected attack) were implemented, targeting Programmable Logic Controllers (PLCs), Radio-Frequency Identification (RFID) subsystems, and selected IIoT devices. The results showed rapid degradation of deterministic PROFINET communication, disruption of the link between the OT and IT layers, loss of digital product representation, and physical interruption of the production process. Based on the findings, a minimally invasive security solution based on perimeter protection was designed and experimentally verified. The results emphasize the need to design IIoT-based manufacturing systems with an emphasis on network segmentation and architectural separation of the IT and OT layers. Full article

Addressing the pressing need to extend the communication range of RF RFID tag antennas, this paper introduces a novel UHF RFID tag antenna technology based on resonant superstrate regulation using a Split-Ring Resonator (SRR). First, a finite element model of the UHF RFID folded dipole antenna was constructed based on the tag chip’s port impedance. Subsequently, a Two-element SRR resonant superstrate was employed to enhance the dipole antenna’s gain through “resonance and near-field coupling” technology. A folded dipole antenna gain-enhancing SRR resonant superstrate unit was designed, and a multi-parameter joint optimization method was adopted to obtain the optimal SRR resonant superstrate configuration for regulating the dipole antenna. Near-field coupling technology was used to design SRR resonant superstrate elements that enhance the folded dipole antenna’s gain. A multi-parameter joint optimization method was employed to obtain the optimal structural parameter set for the SRR resonant superstrate-controlled dipole antenna. Finally, simulations and experimental measurements of the RFID antenna performance revealed that: within the 920–925 MHz band, the maximum measured forward reading distance enhancement reached 62.1%. The research findings significantly enhance the practical performance of UHF RFID tags in complex environments, enabling more stable and efficient long-range identification in applications such as logistics tracking, asset management, and smart warehousing. Full article

In grain storage and transportation, biological activity, including respiration and metabolism, generates heat, creating temperature gradients that can induce moisture migration and form high-humidity areas. This accelerates fungal and insect activity, leading to quality degradation. Long-term, distributed temperature monitoring inside grain piles is essential for ensuring safe storage and early risk warning. Radio Frequency Identification (RFID) technology has become widely adopted in storage temperature monitoring due to its low cost, maintenance-free operation, and high security. However, traditional RFID systems have limited communication ranges, and the large size of storage facilities necessitates the deployment of multiple readers, which increases costs. Additionally, the high density and fluctuating moisture content of bulk grain lead to significant RF signal absorption and scattering, weakening the accessibility of purely wireless systems to deeper parts of the grain pile. To address these issues, a passive distributed temperature monitoring system based on RFID technology is proposed. The system utilizes RFID readers to harvest RF energy for passive power supply, with an external antenna ensuring stable energy harvesting and data transmission. Single-bus multi-point temperature sensor modules are integrated into the system, enabling distributed temperature measurements across grain piles or warehouses. Experimental results show that the system achieves a temperature collection success rate of 98%, with an accuracy of ±1 °C and a polling cycle of less than 30 s, providing a low-cost, battery-free, and scalable solution for grain storage monitoring, significantly improving storage quality. Full article

In many IIoT-based yard operations, accurately identifying the spatial position of containers is becoming increasingly relevant as operators try to automate stacking and retrieval processes by technologies like Container Spatial Localization (CSL). Despite this automation in IIoT, RTK-GPS–based container stacker positioning frequently lacks precision, which causes disruptions in stacking and reduces efficiency in space utilisation. Though it offers placement precision accurately up to 3 cm, this is still insufficient in high-volume Yard Management Systems (YMS). Consequently, this yields to variable container orientation, waste of usable space, increased man input is required in handling goods, and potential automated system failures. This research proposes a novel methodology that combines conventional RTK-GPS measurements with angular information captured from a BHI-260AP–based spreader sensor, allowing the system to correct container placement errors arising from orientation rather than only from positioning. In addition to the spatial positioning problem, we found that continuous IIoT operation raises concerns regarding energy use, particularly when micro-controllers remain active throughout the task cycle. As a solution, this integrates a dynamic task scheduling approach that puts the device in sleep modes whenever computation is not required. In our experiments, this strategy improved overall power efficiency by 34.44%, which makes long automated operation more practical. Full article

As a non-contact identification technology, RFID (Radio Frequency Identification) is widely used in various Internet of Things applications. However, RFID systems are highly vulnerable to diverse attacks due to the openness of communication links between readers and tags, leading to serious security and privacy concerns. Numerous RFID authentication protocols have been designed that employ hash functions and symmetric cryptography to secure communications. Despite these efforts, such schemes generally exhibit limitations in key management flexibility and scalability, which significantly restricts their applicability in large-scale RFID deployments. Confronted with this challenge, public key cryptography offers an effective solution. Taking into account factors such as parameter size, computational complexity, and resistance to quantum attacks, the NTRU algorithm emerges as one of the most promising choices. Since the NTRU signature algorithm is highly complex and requires large parameters, there are currently only a few NTRU encryption-based RFID authentication protocols available, all of which exhibit significant security flaws—such as supporting only one-way authentication, failing to address public key distribution, and so on. Moreover, performance evaluations of the algorithm in these contexts are often incomplete. This paper proposes a mutual authentication protocol for RFID based on the NTRU encryption algorithm to address security and privacy issues. The security of the protocol is analyzed using the BAN-logic tools and some non-formalized methods, and it is further validated through simulation with the AVISPA tool. With the parameter set (N, p, q) = (443, 3, 2048), the NTRU algorithm can provide 128 bits of post-quantum security strength. This configuration not only demonstrates greater foresight at the theoretical security level but also offers significant advantages in practical energy consumption and computation time when compared to traditional algorithms such as ECC, making it a highly competitive candidate in the field of post-quantum cryptography. Full article

Background/Objectives: Safe delivery and correct identification of newborns are critical aspects of healthcare systems globally. The accreditation of healthcare and standards regulation significantly promotes the adoption of modern technologies to address risks related to infant abduction and misidentification. The effectiveness and extent of these mandates vary across settings and countries. Therefore, this study aims to map and explore modern technologies used for safe newborn delivery and correct identification aligned with healthcare accreditation and regulatory frameworks. Methods: This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guidelines. The Problem, Intervention, Comparison, and Outcome (PICO) framework was employed to facilitate the development of the research question. This study examined studies reporting technologies such as radio frequency identification (RFID), biometric identification, and real-time monitoring across healthcare settings for infant protection through the Normalization Process Theory (NPT). Among three databases and search engines (PubMed, Google Scholar, and Web of Science). The risk of bias for each study was assessed using the AACODS Checklist, SQUIRE 2.0 Checklist, TIDieR Checklist, and JBI tools. Results: Out of 8753 records, only 27 reports were eligible to be included in this review. The most frequently reported technologies were RFID systems (11 studies, 37.9%) and biometric systems such as footprint and facial recognition (6 studies, 20.7%). Despite strong technological potential, many healthcare institutions struggled with the adoption of infant protection technologies. Accreditation systems among the high-resource settings actively mandate advanced technologies and support the integration of staff training and simulation drills. Comparably, middle- and low-income regions usually face challenges related to regulatory enforcement, infrastructure, staff readiness, and limited adoption of modern technologies. Conclusions: Accreditation and standards development are critical catalysts for the adoption of modern infant protection technology. Standards must be comprehensible, adaptable, and supported by investment in human resources and infrastructure. Future regulation must focus on strengthening enforcement, continuous quality improvement, and capacity building to achieve sustainable protection across the world. Full article

Olive oil is a high-value product that is highly exposed to fraud, making robust traceability systems essential to protect authenticity, consumer trust, and competitiveness. This study examines how digital traceability technologies influence fraud mitigation and the sustainable performance of olive oil mills in southern Brazil. A systematic literature review, conducted according to the PRISMA 2020 protocol in Scopus and Web of Science, identified state-of-the-art supply chain and authentication technologies, including blockchain, IoT, RFID, QR codes, cloud computing, Big Data, artificial intelligence, and physicochemical methods. Two structured questionnaires were then applied to managers from nine mills in the main Brazilian olive oil cluster, and the data were analyzed using descriptive statistics, Chi-Square tests, and correlation measures within a framework grounded in Resource-Based View and Institutional Isomorphism theories. The results show that adoption of digital traceability is still incipient, while internal factors such as organizational commitment and marketing strategies play a more decisive role than external pressures in explaining adoption. Although managers do not yet perceive a direct impact on fraud mitigation, adoption is positively associated with economic, environmental, and social sustainability outcomes. Given the exploratory design and the small, non-probabilistic sample (n = 9), the findings should be interpreted as indicative rather than definitive. The proposed framework is intended as a transferable analytical lens that can be adapted and further validated in other agri-food and industrial contexts using larger samples and objective fraud-related indicators. Full article

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

The present study presents long-term monitoring data on the dynamics of bedload transport processes in alpine gravel-bed river systems in Austria (Urslau, Strobler-Weißenbach) using radio frequency identification (RFID) technology. The detection of embedded RFID tracers was facilitated by the use of stationary antennas. This methodology enabled the acquisition of high-resolution data on particle transport velocities, transport distances, and sediment dynamics. Monitoring has been in operation permanently over a period of 8 years, including several intense flood events. In total, 1612 RFID-tagged stones were deployed, and the maximum measured particle velocity was 2.47 m s⁻¹. The measurements at the Urslau stream revealed seasonal variability and long-term trends, while targeted short-term measurements at the Strobler-Weißenbach stream provided valuable insights into the dynamics of flood events. The results underscore the significance of environmental factors, including the grain size, river gradient, and hydraulic parameters, in the dynamics of bedload transport in alpine gravel bed streams. Furthermore, the efficiency of stationary antennas was optimised to ensure uninterrupted monitoring. This study underscores the importance of contemporary monitoring technologies in analysing river processes and addressing challenges, including those brought about by climate change. Full article