Search Results (11)

This study examines the adoption of real-time visibility (RTV) technologies in the Australian meat cold supply chain, a sector where sustainability challenges such as food spoilage, energy inefficiency, and waste are acute. RTV technologies offer promising solutions by enhancing traceability, operational efficiency, and decision-making across supply chain stages. However, adoption remains uneven due to a range of contextual, organisational, and perceptual factors. Through a nationally distributed quantitative survey targeting stakeholders across inventory, logistics, and retail operations, we identify key drivers and barriers influencing RTV adoption. We explore how demographic factors (e.g., age, role), perceived usefulness and ease of use, and supply chain characteristics interact to shape adoption outcomes. Importantly, the study investigates how horizontal collaboration and data-sharing practices moderate these relationships, especially within the transport and logistics stages where cold chain vulnerabilities are highest. Spearman and partial correlation analyses, alongside binary logistic regression, reveal that perceived ease of use and usefulness are significant predictors of adoption, while barriers such as cost and technical complexity impede it. However, strong collaboration and data-sharing networks can mitigate these barriers and enhance adoption likelihood. Our findings suggest that targeted digital infrastructure investment, workforce training, and policy support for cross-organisational collaboration are essential for advancing sustainability in meat cold chains. This research contributes to a growing body of knowledge that connects technological innovation with food system resilience and waste minimisation. Full article

This paper presents an intelligent navigation wearable device for visually impaired individuals. The system aims to improve their independent travel capabilities and reduce the negative emotional impacts associated with visible disability indicators in travel tools. It employs an RGB-D camera and an inertial measurement unit (IMU) sensor to facilitate real-time obstacle detection and recognition via advanced point cloud processing and YOLO-based target recognition techniques. An integrated intelligent interaction module identifies the core obstacle from the detected obstacles and translates this information into multidimensional auxiliary guidance. Users receive haptic feedback to navigate obstacles, indicating directional turns and distances, while auditory prompts convey the identity and distance of obstacles, enhancing spatial awareness. The intuitive vibrational guidance significantly enhances safety during obstacle avoidance, and the voice instructions promote a better understanding of the surrounding environment. The device adopts an arm-mounted design, departing from the traditional cane structure that reinforces disability labeling and social stigma. This lightweight mechanical design prioritizes user comfort and mobility, making it more user-friendly than traditional stick-type aids. Experimental results demonstrate that this system outperforms traditional white canes and ultrasonic devices in reducing collision rates, particularly for mid-air obstacles, thereby significantly improving safety in dynamic environments. Furthermore, the system’s ability to vocalize obstacle identities and distances in advance enhances spatial perception and interaction with the environment. By eliminating the cane structure, this innovative wearable design effectively minimizes social stigma, empowering visually impaired individuals to travel independently with increased confidence, ultimately contributing to an improved quality of life. Full article

Due to the rapid expansion of e-commerce and urbanization, Last-Mile Delivery (LMD) faces increasing challenges related to cost, timeliness, and sustainability. Artificial intelligence (AI) techniques are widely used to optimize fleet management, while augmented and mixed reality (AR/MR) technologies are being adopted to enhance warehouse operations. However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. This work proposes the adoption of novel technological solutions integrated in an LMD framework that combines AI-based optimization of shipment allocation and vehicle route planning with a knowledge graph (KG)-driven decision support system. Additionally, the paper discusses the exploitation of relevant recent tools, including large language model (LLM)-powered conversational assistants for managers and operators and MR-based headset interfaces supporting warehouse operators by providing real-time data and enabling direct interaction with the system through virtual contextual UI elements. The framework prioritizes the customizability of AI algorithms and real-time information sharing between stakeholders. An experiment with a system prototype in the Apulia region is presented to evaluate the feasibility of the system in a realistic logistics scenario, highlighting its potential to enhance coordination and efficiency in LMD operations. The results suggest the usefulness of the approach while also identifying benefits and challenges in real-world applications. Full article

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) has evolved from a niche research technique into a powerful global monitoring tool. With the launch of nationwide and continent-wide ground motion services (GMSs), freely available deformation data can now be analyzed on a large scale. However, their applicability for monitoring critical infrastructure, such as dams, has not yet been thoroughly assessed, and several challenges have hindered the integration of MT-InSAR into existing monitoring frameworks. These challenges include technical limitations, difficulties in interpreting deformation results, and the rigidity of existing safety protocols, which often restrict the adoption of remote sensing techniques for operational dam monitoring. This study evaluates the effectiveness of persistent scatterer (PS) data from the German ground motion service (Bodenbewegungsdienst Deutschland, BBD) in complementing time-consuming in situ techniques. By analyzing a gravity dam in Germany, BBD time series were compared with in situ pendulum data. We propose a two-stage assessment procedure: First, we evaluate the dam’s suitability for PS analysis using the CR-Index to identify areas with good radar visibility. Second, we assess the interpretability of BBD data for radial deformations by introducing a novel index that quantifies the radial sensitivity of individual PS points on the dam. This index is universally applicable and can be transferred to other types of infrastructure. The results revealed a fair correlation between PS deformations and pendulum data for many PS points (up to $R^2$ = 0.7). A priori feasibility assessments are essential, as factors such as topography, land cover, and dam type influence the applicability of the PS technique. The dam’s orientation relative to the look direction of the sensor emerged as a key criterion for interpreting radial deformations. For angle differences (${\Delta }_{\mathrm{RAD}}$) of up to 20° between the true north radial angle of a PS point and the satellite’s look direction, the line-of-sight (LOS) sensitivity accounts for approximately 50 to 70% of the true radial deformation, depending on the satellite’s incidence angle. This criterion is best fulfilled by dams aligned in a north–south direction. For the dam investigated in this study, the LOS sensitivity to radial deformations was low due to its east–west orientation, resulting in significantly higher errors (6 mm $\le RMSE\le 43$ mm) compared to in situ pendulum data. Eliminating PS points with an unfavorable alignment with the sensor should be considered before interpreting radial deformations. For implementation into operational monitoring programs, greater effort must be spent on near-real-time updates of BBD datasets. Full article

Background: This review evaluates needle navigation technologies in minimally invasive cardiovascular surgery (MICS), identifying their strengths and limitations and the requirements for an ideal needle navigation system that features optimal guidance and easy adoption in clinical practice. Methods: A systematic search of PubMed, Web of Science, and IEEE databases up until June 2024 identified original studies on needle navigation in MICS. Eligible studies were those published within the past decade and that performed MICS requiring needle navigation technologies in adult patients. Animal studies, case reports, clinical trials, or laboratory experiments were excluded to focus on actively deployed techniques in clinical practice. Extracted data included the study year, modalities used, procedures performed, and the reported strengths and limitations, from which the requirements for an optimal needle navigation system were derived. Results: Of 36 eligible articles, 21 used ultrasound (US) for real-time imaging despite depth and needle visibility challenges. Computer tomography (CT)-guided fluoroscopy, cited in 19 articles, enhanced deep structure visualization but involved radiation risks. Magnetic resonance imaging (MRI), though excellent for soft-tissue contrast, was not used due to metallic tool incompatibility. Multimodal techniques, like US–fluoroscopy fusion, improved accuracy but added cost and workflow complexity. No single technology meets all the criteria for an ideal needle navigation system, which should combine real-time imaging, 3D spatial awareness, and tissue integrity feedback while being cost-effective and easily integrated into existing workflows. Conclusions: This review derived the criteria and obstacles an ideal needle navigation system must address before its clinical adoption, along with novel technological approaches that show potential to overcome those challenges. For instance, fusion technologies overlay information from multiple visual approaches within a single interface to overcome individual limitations. Additionally, emerging diagnostic methods like vibroacoustic sensing or optical fiber needles offer information from complementary sensory channels, augmenting visual approaches with insights into tissue integrity and structure, thereby paving the way for enhanced needle navigation systems in MICS. Full article

Power systems worldwide are experiencing rapid evolvements with a massive increase of renewable generation in order to meet the ambitious decarbonization targets. A significant amount of renewable generation is from Distributed Energy Resources (DERs), upon which the system operators often have limited visibility. This can bring significant challenges as the increasing DERs’ can lead to network constraints being violated, presenting critical risks for network security. Enhancing the visibility of DERs can be achieved via the provision of communication links, but this can be costly, particularly for real time applications. Digital Twin (DT) is an emerging technology that is considered as a promising solution for enhancing the visibility of a physical system, where only a limited set of data is required to be transmitted with the rest data of interest can be estimated via the DT. The development and demonstration of DTs requires realistic testing and validation enviorment in order to accelerate its adoption in the industry. This paper presents a real time simulation and hardware-in-the-loop (HiL) testing platform, specifically designed for prototyping, demonstrating and testing DTs of DERs. Within the proposed platform, a software-based communication emulator is developed, which allows the investigation of the impact of communication latency and jitter on the performance of DTs of the DERs. Case studies are presented to demonstrate the application of the developed DT prototyping process and testing platform to enable frequency control using the DTs, which provide valuable learnings and tools for enabling future DTs-based solutions. Full article

A visibility forecast model called a boosting-based fusion model (BFM) was established in this study. The model uses a fusion machine learning model based on multisource data, including air pollutants, meteorological observations, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and an operational regional atmospheric environmental modeling System for eastern China (RAEMS) outputs. Extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), and a numerical prediction method, i.e., RAEMS were fused to establish this prediction model. Three sets of prediction models, that is, BFM, LightGBM based on multisource data (LGBM), and RAEMS, were used to conduct visibility prediction tasks. The training set was from 1 January 2015 to 31 December 2018 and used several data pre-processing methods, including a synthetic minority over-sampling technique (SMOTE) data resampling, a loss function adjustment, and a 10-fold cross verification. Moreover, apart from the basic features (variables), more spatial and temporal gradient features were considered. The testing set was from 1 January to 31 December 2019 and was adopted to validate the feasibility of the BFM, LGBM, and RAEMS. Statistical indicators confirmed that the machine learning methods improved the RAEMS forecast significantly and consistently. The root mean square error and correlation coefficient of BFM for the next 24/48 h were 5.01/5.47 km and 0.80/0.77, respectively, which were much higher than those of RAEMS. The statistics and binary score analysis for different areas in Shanghai also proved the reliability and accuracy of using BFM, particularly in low-visibility forecasting. Overall, BFM is a suitable tool for predicting the visibility. It provides a more accurate visibility forecast for the next 24 and 48 h in Shanghai than LGBM and RAEMS. The results of this study provide support for real-time operational visibility forecasts. Full article

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets. Full article

In the design phase, building information modeling (BIM) software has been widely employed due to its high efficiency, precision, and synergy among different teams. However, the advantages of BIM have not been fully explored in the manufacturing stage where the progress is not so transparent, and information exchange is not so smooth. To deal with these problems, a BIM platform for the manufacture of steel structures is developed in this article, which aims for the management and visualization of manufacturing progress in a steel structure factory in China. The proposed platform was developed and tested by using practical projects. The requirement is analyzed with different users involved in the manufacturing progress. The platform is web-based, where Node.js is adopted for server-side scripting, Neo4j is used for data storage, hyper text markup language (HTML), cascading style sheets (CSS), and JavaScript are used to compile user interface. Besides, a quick response (QR) code is attached to components for traceability. By parsing the BIM model exported in the design phase, essential information of components is imported into the platform, which are the data that form the basis of the following operation. By introducing the platform as a collaborative tool, the traceability and visibility of real-time manufacturing progress of each steel component are significantly enhanced. As a result, this platform can help managers make decisions, workers check quality problems, and other stakeholders grasp the manufacturing progress. Full article

In order to improve the observation capability in one of the most active volcanic areas in the world, Mt. Etna, we developed a processing method to use the surveillance cameras for a quasi real-time mapping of syn-eruptive processes. Following an evaluation of the current performance of the Etna permanent ground NEtwork of Thermal and Visible Sensors (Etna_NETVIS), its possible implementation and optimization was investigated to determine the locations of additional observation sites to be rapidly set up during emergencies. A tool was then devised to process time series of ground-acquired images and extract a coherent multi-temporal dataset of georeferenced map. The processed datasets can be used to extract 2D features such as evolution maps of active lava flows. The tool was validated on ad-hoc test fields and then adopted to map the evolution of two recent lava flows. The achievable accuracy (about three times the original pixel size) and the short processing time makes the tool suitable for rapidly assessing lava flow evolutions, especially in the case of recurrent eruptions, such as those of the 2011–2015 Etna activity. The tool can be used both in standard monitoring activities and during emergency phases (eventually improving the present network with additional mobile stations) when it is mandatory to carry out a quasi-real-time mapping to support civil protection actions. The developed tool could be integrated in the control room of the Osservatorio Etneo, thus enabling the Etna_NETVIS for mapping purposes and not only for video surveillance. Full article

The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined. Full article